diff --git "a/original/compiled/Unet.mlmodelc/model.mil" "b/original/compiled/Unet.mlmodelc/model.mil" --- "a/original/compiled/Unet.mlmodelc/model.mil" +++ "b/original/compiled/Unet.mlmodelc/model.mil" @@ -1,12327 +1,12305 @@ program(1.0) [buildInfo = dict, tensor>({{"coremlc-component-MIL", "5.33.4"}, {"coremlc-version", "1436.100.10"}, {"coremltools-component-torch", "2.1.0.dev20230718"}, {"coremltools-version", "7.0b1"}})] { - func main(tensor encoder_hidden_states, tensor sample, tensor text_embeds, tensor time_ids, tensor timestep) { - tensor time_embedding_linear_1_bias = const()[name = tensor("time_embedding_linear_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor time_embedding_linear_1_weight = const()[name = tensor("time_embedding_linear_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5248)))]; - tensor time_embedding_linear_2_bias = const()[name = tensor("time_embedding_linear_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1643712)))]; - tensor time_embedding_linear_2_weight = const()[name = tensor("time_embedding_linear_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1648896)))]; - tensor add_embedding_linear_1_bias = const()[name = tensor("add_embedding_linear_1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8202560)))]; - tensor add_embedding_linear_1_weight = const()[name = tensor("add_embedding_linear_1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8207744)))]; - tensor add_embedding_linear_2_bias = const()[name = tensor("add_embedding_linear_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22625728)))]; - tensor add_embedding_linear_2_weight = const()[name = tensor("add_embedding_linear_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22630912)))]; - tensor conv_in_bias = const()[name = tensor("conv_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29184576)))]; - tensor conv_in_weight = const()[name = tensor("conv_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29185920)))]; - tensor down_blocks_0_resnets_0_conv1_bias = const()[name = tensor("down_blocks_0_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29232064)))]; - tensor down_blocks_0_resnets_0_conv1_weight = const()[name = tensor("down_blocks_0_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29233408)))]; - tensor down_blocks_0_resnets_0_time_emb_proj_bias = const()[name = tensor("down_blocks_0_resnets_0_time_emb_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32919872)))]; - tensor down_blocks_0_resnets_0_time_emb_proj_weight = const()[name = tensor("down_blocks_0_resnets_0_time_emb_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32921216)))]; - tensor down_blocks_0_resnets_0_conv2_bias = const()[name = tensor("down_blocks_0_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34559680)))]; - tensor down_blocks_0_resnets_0_conv2_weight = const()[name = tensor("down_blocks_0_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34561024)))]; - tensor down_blocks_0_resnets_1_conv1_bias = const()[name = tensor("down_blocks_0_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38247488)))]; - tensor down_blocks_0_resnets_1_conv1_weight = const()[name = tensor("down_blocks_0_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38248832)))]; - tensor down_blocks_0_resnets_1_time_emb_proj_bias = const()[name = tensor("down_blocks_0_resnets_1_time_emb_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41935296)))]; - tensor down_blocks_0_resnets_1_time_emb_proj_weight = const()[name = tensor("down_blocks_0_resnets_1_time_emb_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41936640)))]; - tensor down_blocks_0_resnets_1_conv2_bias = const()[name = tensor("down_blocks_0_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43575104)))]; - tensor down_blocks_0_resnets_1_conv2_weight = const()[name = tensor("down_blocks_0_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43576448)))]; - tensor down_blocks_0_downsamplers_0_conv_bias = const()[name = tensor("down_blocks_0_downsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47262912)))]; - tensor down_blocks_0_downsamplers_0_conv_weight = const()[name = tensor("down_blocks_0_downsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(47264256)))]; - tensor down_blocks_1_resnets_0_conv1_bias = const()[name = tensor("down_blocks_1_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50950720)))]; - tensor down_blocks_1_resnets_0_conv1_weight = const()[name = tensor("down_blocks_1_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50953344)))]; - tensor down_blocks_1_resnets_0_time_emb_proj_bias = const()[name = tensor("down_blocks_1_resnets_0_time_emb_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58326208)))]; - tensor down_blocks_1_resnets_0_time_emb_proj_weight = const()[name = tensor("down_blocks_1_resnets_0_time_emb_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58328832)))]; - tensor down_blocks_1_resnets_0_conv2_bias = const()[name = tensor("down_blocks_1_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61605696)))]; - tensor down_blocks_1_resnets_0_conv2_weight = const()[name = tensor("down_blocks_1_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61608320)))]; - tensor down_blocks_1_resnets_0_conv_shortcut_bias = const()[name = tensor("down_blocks_1_resnets_0_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76353984)))]; - tensor down_blocks_1_resnets_0_conv_shortcut_weight = const()[name = tensor("down_blocks_1_resnets_0_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76356608)))]; - tensor down_blocks_1_attentions_0_proj_in_bias = const()[name = tensor("down_blocks_1_attentions_0_proj_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77175872)))]; - tensor down_blocks_1_attentions_0_proj_in_weight = const()[name = tensor("down_blocks_1_attentions_0_proj_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77178496)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78816960)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80455424)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82093888)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83732352)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(83734976)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(85373440)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87011904)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92254848)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97497792)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97500416)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99138880)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99159424)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112266688)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112269312)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118822976)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120461440)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122099904)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123738368)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(123740992)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125379456)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127017920)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132260864)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137503808)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137506432)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(139144896)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(139165440)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152272704)))]; - tensor down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(152275328)))]; - tensor down_blocks_1_attentions_0_proj_out_bias = const()[name = tensor("down_blocks_1_attentions_0_proj_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158828992)))]; - tensor down_blocks_1_attentions_0_proj_out_weight = const()[name = tensor("down_blocks_1_attentions_0_proj_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(158831616)))]; - tensor down_blocks_1_resnets_1_conv1_bias = const()[name = tensor("down_blocks_1_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160470080)))]; - tensor down_blocks_1_resnets_1_conv1_weight = const()[name = tensor("down_blocks_1_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160472704)))]; - tensor down_blocks_1_resnets_1_time_emb_proj_bias = const()[name = tensor("down_blocks_1_resnets_1_time_emb_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175218368)))]; - tensor down_blocks_1_resnets_1_time_emb_proj_weight = const()[name = tensor("down_blocks_1_resnets_1_time_emb_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(175220992)))]; - tensor down_blocks_1_resnets_1_conv2_bias = const()[name = tensor("down_blocks_1_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178497856)))]; - tensor down_blocks_1_resnets_1_conv2_weight = const()[name = tensor("down_blocks_1_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178500480)))]; - tensor down_blocks_1_attentions_1_proj_in_bias = const()[name = tensor("down_blocks_1_attentions_1_proj_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193246144)))]; - tensor down_blocks_1_attentions_1_proj_in_weight = const()[name = tensor("down_blocks_1_attentions_1_proj_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193248768)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194887232)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196525696)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198164160)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199802624)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(199805248)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201443712)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(203082176)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208325120)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213568064)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213570688)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215209152)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(215229696)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(228336960)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(228339584)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(234893248)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(236531712)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(238170176)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239808640)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(239811264)))]; - tensor down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(241449728)))]; 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- tensor mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4762633728)))]; - tensor mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4815062592)))]; - tensor mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight = const()[name = tensor("mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4815067776)))]; - tensor mid_block_attentions_0_proj_out_bias = const()[name = tensor("mid_block_attentions_0_proj_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4841282240)))]; 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- tensor up_blocks_0_resnets_0_time_emb_proj_weight = const()[name = tensor("up_blocks_0_resnets_0_time_emb_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5090350464)))]; - tensor up_blocks_0_resnets_0_conv2_bias = const()[name = tensor("up_blocks_0_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5096904128)))]; - tensor up_blocks_0_resnets_0_conv2_weight = const()[name = tensor("up_blocks_0_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5096909312)))]; - tensor up_blocks_0_resnets_0_conv_shortcut_bias = const()[name = tensor("up_blocks_0_resnets_0_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5155891776)))]; - tensor up_blocks_0_resnets_0_conv_shortcut_weight = const()[name = tensor("up_blocks_0_resnets_0_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5155896960)))]; - tensor up_blocks_0_attentions_0_proj_in_bias = const()[name = tensor("up_blocks_0_attentions_0_proj_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5169004224)))]; - tensor up_blocks_0_attentions_0_proj_in_weight = const()[name = tensor("up_blocks_0_attentions_0_proj_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5169009408)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5175563072)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5182116736)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5188670400)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5195224064)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5195229248)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5201782912)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5208336576)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5218822400)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5229308224)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5229313408)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5235867072)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5235908096)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5288336960)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5288342144)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5314556608)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5321110272)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5327663936)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5334217600)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5334222784)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5340776448)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5347330112)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5357815936)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5368301760)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5368306944)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5374860608)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5374901632)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5427330496)))]; 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- tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5496809472)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5507295296)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5507300480)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5513854144)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5513895168)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5566324032)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5566329216)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5592543680)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5599097344)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5605651008)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5612204672)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5612209856)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5618763520)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5625317184)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5635803008)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5646288832)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5646294016)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5652847680)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5652888704)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5705317568)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5705322752)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5731537216)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5738090880)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5744644544)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5751198208)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5751203392)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5757757056)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5764310720)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5774796544)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5785282368)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5785287552)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5791841216)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5791882240)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5844311104)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5844316288)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5870530752)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5877084416)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5883638080)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5890191744)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5890196928)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5896750592)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5903304256)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5913790080)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5924275904)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5924281088)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5930834752)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5930875776)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5983304640)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5983309824)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6009524288)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6016077952)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6022631616)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6029185280)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6029190464)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6035744128)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6042297792)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6052783616)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6063269440)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6063274624)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6069828288)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6069869312)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6122298176)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6122303360)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6148517824)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6155071488)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6161625152)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6168178816)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6168184000)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6174737664)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6181291328)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6191777152)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6202262976)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6202268160)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6208821824)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6208862848)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6261291712)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6261296896)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6287511360)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6294065024)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6300618688)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6307172352)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6307177536)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6313731200)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6320284864)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6330770688)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6341256512)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6341261696)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6347815360)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6347856384)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6400285248)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6400290432)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6426504896)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6433058560)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6439612224)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6446165888)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6446171072)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6452724736)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6459278400)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6469764224)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6480250048)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6480255232)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6486808896)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6486849920)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6539278784)))]; - tensor up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6539283968)))]; - tensor up_blocks_0_attentions_0_proj_out_bias = const()[name = tensor("up_blocks_0_attentions_0_proj_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6565498432)))]; - tensor up_blocks_0_attentions_0_proj_out_weight = const()[name = tensor("up_blocks_0_attentions_0_proj_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6565503616)))]; - tensor up_blocks_0_resnets_1_conv1_bias = const()[name = tensor("up_blocks_0_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6572057280)))]; - tensor up_blocks_0_resnets_1_conv1_weight = const()[name = tensor("up_blocks_0_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6572062464)))]; - tensor up_blocks_0_resnets_1_time_emb_proj_bias = const()[name = tensor("up_blocks_0_resnets_1_time_emb_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6690027328)))]; - tensor up_blocks_0_resnets_1_time_emb_proj_weight = const()[name = tensor("up_blocks_0_resnets_1_time_emb_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6690032512)))]; - tensor up_blocks_0_resnets_1_conv2_bias = const()[name = tensor("up_blocks_0_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6696586176)))]; - tensor up_blocks_0_resnets_1_conv2_weight = const()[name = tensor("up_blocks_0_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6696591360)))]; - tensor up_blocks_0_resnets_1_conv_shortcut_bias = const()[name = tensor("up_blocks_0_resnets_1_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6755573824)))]; - tensor up_blocks_0_resnets_1_conv_shortcut_weight = const()[name = tensor("up_blocks_0_resnets_1_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6755579008)))]; - tensor up_blocks_0_attentions_1_proj_in_bias = const()[name = tensor("up_blocks_0_attentions_1_proj_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6768686272)))]; - tensor up_blocks_0_attentions_1_proj_in_weight = const()[name = tensor("up_blocks_0_attentions_1_proj_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6768691456)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6775245120)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6781798784)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6788352448)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6794906112)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6794911296)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6801464960)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6808018624)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6818504448)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6828990272)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6828995456)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6835549120)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6835590144)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6888019008)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6888024192)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6914238656)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6920792320)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6927345984)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6933899648)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6933904832)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6940458496)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6947012160)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6957497984)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6967983808)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6967988992)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6974542656)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6974583680)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7027012544)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7027017728)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7053232192)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7059785856)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7066339520)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7072893184)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7072898368)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7079452032)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7086005696)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7096491520)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7106977344)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7106982528)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7113536192)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7113577216)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7166006080)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7166011264)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7192225728)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7198779392)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7205333056)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7211886720)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7211891904)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7218445568)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7224999232)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7235485056)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7245970880)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7245976064)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7252529728)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7252570752)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7304999616)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7305004800)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7331219264)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7337772928)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7344326592)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7350880256)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7350885440)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7357439104)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7363992768)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7374478592)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7384964416)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7384969600)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7391523264)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7391564288)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7443993152)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7443998336)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7470212800)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7476766464)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7483320128)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7489873792)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7489878976)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7496432640)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7502986304)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7513472128)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7523957952)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7523963136)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7530516800)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7530557824)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7582986688)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7582991872)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7609206336)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7615760000)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7622313664)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7628867328)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7628872512)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7635426176)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7641979840)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7652465664)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7662951488)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7662956672)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7669510336)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7669551360)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7721980224)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7721985408)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7748199872)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7754753536)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7761307200)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7767860864)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7767866048)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7774419712)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7780973376)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7791459200)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7801945024)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7801950208)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7808503872)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7808544896)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7860973760)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7860978944)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7887193408)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7893747072)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7900300736)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7906854400)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7906859584)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7913413248)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7919966912)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7930452736)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7940938560)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7940943744)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7947497408)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7947538432)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7999967296)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7999972480)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8026186944)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8032740608)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8039294272)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8045847936)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8045853120)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8052406784)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8058960448)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8069446272)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8079932096)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8079937280)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8086490944)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8086531968)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8138960832)))]; - tensor up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8138966016)))]; - tensor up_blocks_0_attentions_1_proj_out_bias = const()[name = tensor("up_blocks_0_attentions_1_proj_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8165180480)))]; - tensor up_blocks_0_attentions_1_proj_out_weight = const()[name = tensor("up_blocks_0_attentions_1_proj_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8165185664)))]; - tensor up_blocks_0_resnets_2_conv1_bias = const()[name = tensor("up_blocks_0_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8171739328)))]; - tensor up_blocks_0_resnets_2_conv1_weight = const()[name = tensor("up_blocks_0_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8171744512)))]; - tensor up_blocks_0_resnets_2_time_emb_proj_bias = const()[name = tensor("up_blocks_0_resnets_2_time_emb_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8260218176)))]; - tensor up_blocks_0_resnets_2_time_emb_proj_weight = const()[name = tensor("up_blocks_0_resnets_2_time_emb_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8260223360)))]; - tensor up_blocks_0_resnets_2_conv2_bias = const()[name = tensor("up_blocks_0_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8266777024)))]; - tensor up_blocks_0_resnets_2_conv2_weight = const()[name = tensor("up_blocks_0_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8266782208)))]; - tensor up_blocks_0_resnets_2_conv_shortcut_bias = const()[name = tensor("up_blocks_0_resnets_2_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8325764672)))]; - tensor up_blocks_0_resnets_2_conv_shortcut_weight = const()[name = tensor("up_blocks_0_resnets_2_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8325769856)))]; - tensor up_blocks_0_attentions_2_proj_in_bias = const()[name = tensor("up_blocks_0_attentions_2_proj_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8335600320)))]; - tensor up_blocks_0_attentions_2_proj_in_weight = const()[name = tensor("up_blocks_0_attentions_2_proj_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8335605504)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8342159168)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8348712832)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8355266496)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8361820160)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8361825344)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8368379008)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8374932672)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8385418496)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8395904320)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8395909504)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8402463168)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8402504192)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8454933056)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8454938240)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8481152704)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8487706368)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8494260032)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8500813696)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8500818880)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8507372544)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8513926208)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8524412032)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8534897856)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8534903040)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8541456704)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8541497728)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8593926592)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8593931776)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8620146240)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8626699904)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8633253568)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8639807232)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8639812416)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8646366080)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8652919744)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8663405568)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8673891392)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8673896576)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8680450240)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8680491264)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8732920128)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8732925312)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8759139776)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8765693440)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8772247104)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8778800768)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8778805952)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8785359616)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8791913280)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8802399104)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8812884928)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8812890112)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8819443776)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8819484800)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8871913664)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8871918848)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8898133312)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8904686976)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8911240640)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8917794304)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8917799488)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8924353152)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8930906816)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8941392640)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8951878464)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8951883648)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8958437312)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8958478336)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9010907200)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9010912384)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9037126848)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9043680512)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9050234176)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9056787840)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9056793024)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9063346688)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9069900352)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9080386176)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9090872000)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9090877184)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9097430848)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9097471872)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9149900736)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9149905920)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9176120384)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9182674048)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9189227712)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9195781376)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9195786560)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9202340224)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9208893888)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9219379712)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9229865536)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9229870720)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9236424384)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9236465408)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9288894272)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9288899456)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9315113920)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9321667584)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9328221248)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9334774912)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9334780096)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9341333760)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9347887424)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9358373248)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9368859072)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9368864256)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9375417920)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9375458944)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9427887808)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9427892992)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9454107456)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9460661120)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9467214784)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9473768448)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9473773632)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9480327296)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9486880960)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9497366784)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9507852608)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9507857792)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9514411456)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9514452480)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9566881344)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9566886528)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9593100992)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9599654656)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9606208320)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9612761984)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9612767168)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9619320832)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9625874496)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9636360320)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9646846144)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9646851328)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9653404992)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9653446016)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9705874880)))]; - tensor up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight = const()[name = tensor("up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9705880064)))]; - tensor up_blocks_0_attentions_2_proj_out_bias = const()[name = tensor("up_blocks_0_attentions_2_proj_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9732094528)))]; - tensor up_blocks_0_attentions_2_proj_out_weight = const()[name = tensor("up_blocks_0_attentions_2_proj_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9732099712)))]; - tensor up_blocks_0_upsamplers_0_conv_bias = const()[name = tensor("up_blocks_0_upsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9738653376)))]; - tensor up_blocks_0_upsamplers_0_conv_weight = const()[name = tensor("up_blocks_0_upsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9738658560)))]; - tensor up_blocks_1_resnets_0_conv1_bias = const()[name = tensor("up_blocks_1_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9797641024)))]; - tensor up_blocks_1_resnets_0_conv1_weight = const()[name = tensor("up_blocks_1_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9797643648)))]; - tensor up_blocks_1_resnets_0_time_emb_proj_bias = const()[name = tensor("up_blocks_1_resnets_0_time_emb_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9841880512)))]; - tensor up_blocks_1_resnets_0_time_emb_proj_weight = const()[name = tensor("up_blocks_1_resnets_0_time_emb_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9841883136)))]; - tensor up_blocks_1_resnets_0_conv2_bias = const()[name = tensor("up_blocks_1_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9845160000)))]; - tensor up_blocks_1_resnets_0_conv2_weight = const()[name = tensor("up_blocks_1_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9845162624)))]; - tensor up_blocks_1_resnets_0_conv_shortcut_bias = const()[name = tensor("up_blocks_1_resnets_0_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9859908288)))]; - tensor up_blocks_1_resnets_0_conv_shortcut_weight = const()[name = tensor("up_blocks_1_resnets_0_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9859910912)))]; - tensor up_blocks_1_attentions_0_proj_in_bias = const()[name = tensor("up_blocks_1_attentions_0_proj_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9864826176)))]; - tensor up_blocks_1_attentions_0_proj_in_weight = const()[name = tensor("up_blocks_1_attentions_0_proj_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9864828800)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9866467264)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9868105728)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9869744192)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9871382656)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9871385280)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9873023744)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9874662208)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9879905152)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9885148096)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9885150720)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9886789184)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9886809728)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9899916992)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9899919616)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9906473280)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9908111744)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9909750208)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9911388672)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9911391296)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9913029760)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9914668224)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9919911168)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9925154112)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9925156736)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9926795200)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9926815744)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9939923008)))]; - tensor up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9939925632)))]; - tensor up_blocks_1_attentions_0_proj_out_bias = const()[name = tensor("up_blocks_1_attentions_0_proj_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9946479296)))]; - tensor up_blocks_1_attentions_0_proj_out_weight = const()[name = tensor("up_blocks_1_attentions_0_proj_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9946481920)))]; - tensor up_blocks_1_resnets_1_conv1_bias = const()[name = tensor("up_blocks_1_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9948120384)))]; - tensor up_blocks_1_resnets_1_conv1_weight = const()[name = tensor("up_blocks_1_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9948123008)))]; - tensor up_blocks_1_resnets_1_time_emb_proj_bias = const()[name = tensor("up_blocks_1_resnets_1_time_emb_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9977614272)))]; - tensor up_blocks_1_resnets_1_time_emb_proj_weight = const()[name = tensor("up_blocks_1_resnets_1_time_emb_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9977616896)))]; - tensor up_blocks_1_resnets_1_conv2_bias = const()[name = tensor("up_blocks_1_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9980893760)))]; - tensor up_blocks_1_resnets_1_conv2_weight = const()[name = tensor("up_blocks_1_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9980896384)))]; - tensor up_blocks_1_resnets_1_conv_shortcut_bias = const()[name = tensor("up_blocks_1_resnets_1_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9995642048)))]; - tensor up_blocks_1_resnets_1_conv_shortcut_weight = const()[name = tensor("up_blocks_1_resnets_1_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9995644672)))]; - tensor up_blocks_1_attentions_1_proj_in_bias = const()[name = tensor("up_blocks_1_attentions_1_proj_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9998921536)))]; - tensor up_blocks_1_attentions_1_proj_in_weight = const()[name = tensor("up_blocks_1_attentions_1_proj_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9998924160)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10000562624)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10002201088)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10003839552)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10005478016)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10005480640)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10007119104)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10008757568)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10014000512)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10019243456)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10019246080)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10020884544)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10020905088)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10034012352)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10034014976)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10040568640)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10042207104)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10043845568)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10045484032)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10045486656)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10047125120)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10048763584)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10054006528)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10059249472)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10059252096)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10060890560)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10060911104)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10074018368)))]; - tensor up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10074020992)))]; - tensor up_blocks_1_attentions_1_proj_out_bias = const()[name = tensor("up_blocks_1_attentions_1_proj_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10080574656)))]; - tensor up_blocks_1_attentions_1_proj_out_weight = const()[name = tensor("up_blocks_1_attentions_1_proj_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10080577280)))]; - tensor up_blocks_1_resnets_2_conv1_bias = const()[name = tensor("up_blocks_1_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10082215744)))]; - tensor up_blocks_1_resnets_2_conv1_weight = const()[name = tensor("up_blocks_1_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10082218368)))]; - tensor up_blocks_1_resnets_2_time_emb_proj_bias = const()[name = tensor("up_blocks_1_resnets_2_time_emb_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10104336832)))]; - tensor up_blocks_1_resnets_2_time_emb_proj_weight = const()[name = tensor("up_blocks_1_resnets_2_time_emb_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10104339456)))]; - tensor up_blocks_1_resnets_2_conv2_bias = const()[name = tensor("up_blocks_1_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10107616320)))]; - tensor up_blocks_1_resnets_2_conv2_weight = const()[name = tensor("up_blocks_1_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10107618944)))]; - tensor up_blocks_1_resnets_2_conv_shortcut_bias = const()[name = tensor("up_blocks_1_resnets_2_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10122364608)))]; - tensor up_blocks_1_resnets_2_conv_shortcut_weight = const()[name = tensor("up_blocks_1_resnets_2_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10122367232)))]; - tensor up_blocks_1_attentions_2_proj_in_bias = const()[name = tensor("up_blocks_1_attentions_2_proj_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10124824896)))]; - tensor up_blocks_1_attentions_2_proj_in_weight = const()[name = tensor("up_blocks_1_attentions_2_proj_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10124827520)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10126465984)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10128104448)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10129742912)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10131381376)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10131384000)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10133022464)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10134660928)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10139903872)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10145146816)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10145149440)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10146787904)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10146808448)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10159915712)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10159918336)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10166472000)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10168110464)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10169748928)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10171387392)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10171390016)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10173028480)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10174666944)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10179909888)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10185152832)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10185155456)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10186793920)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10186814464)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10199921728)))]; - tensor up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10199924352)))]; - tensor up_blocks_1_attentions_2_proj_out_bias = const()[name = tensor("up_blocks_1_attentions_2_proj_out_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10206478016)))]; - tensor up_blocks_1_attentions_2_proj_out_weight = const()[name = tensor("up_blocks_1_attentions_2_proj_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10206480640)))]; - tensor up_blocks_1_upsamplers_0_conv_bias = const()[name = tensor("up_blocks_1_upsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10208119104)))]; - tensor up_blocks_1_upsamplers_0_conv_weight = const()[name = tensor("up_blocks_1_upsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10208121728)))]; - tensor up_blocks_2_resnets_0_conv1_bias = const()[name = tensor("up_blocks_2_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10222867392)))]; - tensor up_blocks_2_resnets_0_conv1_weight = const()[name = tensor("up_blocks_2_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10222868736)))]; - tensor up_blocks_2_resnets_0_time_emb_proj_bias = const()[name = tensor("up_blocks_2_resnets_0_time_emb_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10233928000)))]; - tensor up_blocks_2_resnets_0_time_emb_proj_weight = const()[name = tensor("up_blocks_2_resnets_0_time_emb_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10233929344)))]; - tensor up_blocks_2_resnets_0_conv2_bias = const()[name = tensor("up_blocks_2_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10235567808)))]; - tensor up_blocks_2_resnets_0_conv2_weight = const()[name = tensor("up_blocks_2_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10235569152)))]; - tensor up_blocks_2_resnets_0_conv_shortcut_bias = const()[name = tensor("up_blocks_2_resnets_0_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10239255616)))]; - tensor up_blocks_2_resnets_0_conv_shortcut_weight = const()[name = tensor("up_blocks_2_resnets_0_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10239256960)))]; - tensor up_blocks_2_resnets_1_conv1_bias = const()[name = tensor("up_blocks_2_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10240485824)))]; - tensor up_blocks_2_resnets_1_conv1_weight = const()[name = tensor("up_blocks_2_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10240487168)))]; - tensor up_blocks_2_resnets_1_time_emb_proj_bias = const()[name = tensor("up_blocks_2_resnets_1_time_emb_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10247860032)))]; - tensor up_blocks_2_resnets_1_time_emb_proj_weight = const()[name = tensor("up_blocks_2_resnets_1_time_emb_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10247861376)))]; - tensor up_blocks_2_resnets_1_conv2_bias = const()[name = tensor("up_blocks_2_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10249499840)))]; - tensor up_blocks_2_resnets_1_conv2_weight = const()[name = tensor("up_blocks_2_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10249501184)))]; - tensor up_blocks_2_resnets_1_conv_shortcut_bias = const()[name = tensor("up_blocks_2_resnets_1_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10253187648)))]; - tensor up_blocks_2_resnets_1_conv_shortcut_weight = const()[name = tensor("up_blocks_2_resnets_1_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10253188992)))]; - tensor up_blocks_2_resnets_2_conv1_bias = const()[name = tensor("up_blocks_2_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10254008256)))]; - tensor up_blocks_2_resnets_2_conv1_weight = const()[name = tensor("up_blocks_2_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10254009600)))]; - tensor up_blocks_2_resnets_2_time_emb_proj_bias = const()[name = tensor("up_blocks_2_resnets_2_time_emb_proj_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10261382464)))]; - tensor up_blocks_2_resnets_2_time_emb_proj_weight = const()[name = tensor("up_blocks_2_resnets_2_time_emb_proj_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10261383808)))]; - tensor up_blocks_2_resnets_2_conv2_bias = const()[name = tensor("up_blocks_2_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10263022272)))]; - tensor up_blocks_2_resnets_2_conv2_weight = const()[name = tensor("up_blocks_2_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10263023616)))]; - tensor up_blocks_2_resnets_2_conv_shortcut_bias = const()[name = tensor("up_blocks_2_resnets_2_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10266710080)))]; - tensor up_blocks_2_resnets_2_conv_shortcut_weight = const()[name = tensor("up_blocks_2_resnets_2_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10266711424)))]; - tensor conv_out_bias = const()[name = tensor("conv_out_bias"), val = tensor([0x1.6e7a7ep-9, -0x1.6ca83cp-10, 0x1.ff648cp-10, -0x1.9ce23ep-9])]; - tensor conv_out_weight = const()[name = tensor("conv_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267530688)))]; - tensor var_24 = const()[name = tensor("op_24"), val = tensor(-1)]; - tensor var_42_axes_0 = const()[name = tensor("op_42_axes_0"), val = tensor([1])]; - tensor var_42 = expand_dims(axes = var_42_axes_0, x = timestep)[name = tensor("op_42")]; - tensor var_44 = const()[name = tensor("op_44"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267576832)))]; - tensor emb_3 = mul(x = var_42, y = var_44)[name = tensor("emb_3")]; - tensor var_49 = sin(x = emb_3)[name = tensor("op_49")]; - tensor var_50 = cos(x = emb_3)[name = tensor("op_50")]; + func main(tensor encoder_hidden_states, tensor sample, tensor text_embeds, tensor time_ids, tensor timestep) { + tensor var_18 = const()[name = tensor("op_18"), val = tensor(3)]; + tensor var_23 = const()[name = tensor("op_23"), val = tensor(true)]; + tensor var_31 = const()[name = tensor("op_31"), val = tensor(1)]; + tensor var_32 = const()[name = tensor("op_32"), val = tensor(-1)]; + tensor var_59_axes_0 = const()[name = tensor("op_59_axes_0"), val = tensor([1])]; + tensor var_59_cast = expand_dims(axes = var_59_axes_0, x = timestep)[name = tensor("op_59_cast")]; + tensor var_61_to_fp16 = const()[name = tensor("op_61_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor emb_3_cast = mul(x = var_59_cast, y = var_61_to_fp16)[name = tensor("emb_3_cast")]; + tensor var_66_cast = sin(x = emb_3_cast)[name = tensor("op_66_cast")]; + tensor var_67_cast = cos(x = emb_3_cast)[name = tensor("op_67_cast")]; tensor emb_7_interleave_0 = const()[name = tensor("emb_7_interleave_0"), val = tensor(false)]; - tensor emb_7 = concat(axis = var_24, interleave = emb_7_interleave_0, values = (var_49, var_50))[name = tensor("emb_7")]; - tensor var_54_begin_0 = const()[name = tensor("op_54_begin_0"), val = tensor([0, 160])]; - tensor var_54_end_0 = const()[name = tensor("op_54_end_0"), val = tensor([2, 320])]; - tensor var_54_end_mask_0 = const()[name = tensor("op_54_end_mask_0"), val = tensor([true, true])]; - tensor var_54 = slice_by_index(begin = var_54_begin_0, end = var_54_end_0, end_mask = var_54_end_mask_0, x = emb_7)[name = tensor("op_54")]; - tensor var_56_begin_0 = const()[name = tensor("op_56_begin_0"), val = tensor([0, 0])]; - tensor var_56_end_0 = const()[name = tensor("op_56_end_0"), val = tensor([2, 160])]; - tensor var_56_end_mask_0 = const()[name = tensor("op_56_end_mask_0"), val = tensor([true, false])]; - tensor var_56 = slice_by_index(begin = var_56_begin_0, end = var_56_end_0, end_mask = var_56_end_mask_0, x = emb_7)[name = tensor("op_56")]; + tensor emb_7_cast = concat(axis = var_32, interleave = emb_7_interleave_0, values = (var_66_cast, var_67_cast))[name = tensor("emb_7_cast")]; + tensor var_71_begin_0 = const()[name = tensor("op_71_begin_0"), val = tensor([0, 160])]; + tensor var_71_end_0 = const()[name = tensor("op_71_end_0"), val = tensor([2, 320])]; + tensor var_71_end_mask_0 = const()[name = tensor("op_71_end_mask_0"), val = tensor([true, true])]; + tensor var_71_cast = slice_by_index(begin = var_71_begin_0, end = var_71_end_0, end_mask = var_71_end_mask_0, x = emb_7_cast)[name = tensor("op_71_cast")]; + tensor var_73_begin_0 = const()[name = tensor("op_73_begin_0"), val = tensor([0, 0])]; + tensor var_73_end_0 = const()[name = tensor("op_73_end_0"), val = tensor([2, 160])]; + tensor var_73_end_mask_0 = const()[name = tensor("op_73_end_mask_0"), val = tensor([true, false])]; + tensor var_73_cast = slice_by_index(begin = var_73_begin_0, end = var_73_end_0, end_mask = var_73_end_mask_0, x = emb_7_cast)[name = tensor("op_73_cast")]; tensor sample_3_interleave_0 = const()[name = tensor("sample_3_interleave_0"), val = tensor(false)]; - tensor sample_3 = concat(axis = var_24, interleave = sample_3_interleave_0, values = (var_54, var_56))[name = tensor("sample_3")]; - tensor var_59 = const()[name = tensor("op_59"), val = tensor(1)]; - tensor var_66_axes_0 = const()[name = tensor("op_66_axes_0"), val = tensor([-1])]; - tensor var_66 = expand_dims(axes = var_66_axes_0, x = sample_3)[name = tensor("op_66")]; + tensor sample_3_cast = concat(axis = var_32, interleave = sample_3_interleave_0, values = (var_71_cast, var_73_cast))[name = tensor("sample_3_cast")]; + tensor var_78_axes_0 = const()[name = tensor("op_78_axes_0"), val = tensor([-1])]; + tensor var_78_cast = expand_dims(axes = var_78_axes_0, x = sample_3_cast)[name = tensor("op_78_cast")]; tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([-1])]; - tensor input_1 = expand_dims(axes = input_1_axes_0, x = var_66)[name = tensor("input_1")]; - tensor var_70 = const()[name = tensor("op_70"), val = tensor([1, 1])]; - tensor var_72 = const()[name = tensor("op_72"), val = tensor([1, 1])]; + tensor input_1_cast = expand_dims(axes = input_1_axes_0, x = var_78_cast)[name = tensor("input_1_cast")]; + tensor var_82 = const()[name = tensor("op_82"), val = tensor([1, 1])]; + tensor var_84 = const()[name = tensor("op_84"), val = tensor([1, 1])]; tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor input_3 = conv(bias = time_embedding_linear_1_bias, dilations = var_72, groups = var_59, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_70, weight = time_embedding_linear_1_weight, x = input_1)[name = tensor("input_3")]; - tensor input_5 = silu(x = input_3)[name = tensor("input_5")]; - tensor var_78 = const()[name = tensor("op_78"), val = tensor([1, 1])]; - tensor var_80 = const()[name = tensor("op_80"), val = tensor([1, 1])]; + tensor unet_time_embedding_linear_1_weight_to_fp16 = const()[name = tensor("unet_time_embedding_linear_1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448)))]; + tensor unet_time_embedding_linear_1_bias_to_fp16 = const()[name = tensor("unet_time_embedding_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(819712)))]; + tensor input_3_cast = conv(bias = unet_time_embedding_linear_1_bias_to_fp16, dilations = var_84, groups = var_31, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_82, weight = unet_time_embedding_linear_1_weight_to_fp16, x = input_1_cast)[name = tensor("input_3_cast")]; + tensor input_5_cast = silu(x = input_3_cast)[name = tensor("input_5_cast")]; + tensor var_90 = const()[name = tensor("op_90"), val = tensor([1, 1])]; + tensor var_92 = const()[name = tensor("op_92"), val = tensor([1, 1])]; tensor emb_pad_type_0 = const()[name = tensor("emb_pad_type_0"), val = tensor("custom")]; tensor emb_pad_0 = const()[name = tensor("emb_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor emb = conv(bias = time_embedding_linear_2_bias, dilations = var_80, groups = var_59, pad = emb_pad_0, pad_type = emb_pad_type_0, strides = var_78, weight = time_embedding_linear_2_weight, x = input_5)[name = tensor("emb")]; + tensor unet_time_embedding_linear_2_weight_to_fp16 = const()[name = tensor("unet_time_embedding_linear_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(822336)))]; + tensor unet_time_embedding_linear_2_bias_to_fp16 = const()[name = tensor("unet_time_embedding_linear_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4099200)))]; + tensor emb_cast = conv(bias = unet_time_embedding_linear_2_bias_to_fp16, dilations = var_92, groups = var_31, pad = emb_pad_0, pad_type = emb_pad_type_0, strides = var_90, weight = unet_time_embedding_linear_2_weight_to_fp16, x = input_5_cast)[name = tensor("emb_cast")]; tensor concat_0 = const()[name = tensor("concat_0"), val = tensor([12])]; - tensor timesteps = reshape(shape = concat_0, x = time_ids)[name = tensor("timesteps")]; - tensor var_86 = const()[name = tensor("op_86"), val = tensor(-1)]; + tensor timesteps_cast = reshape(shape = concat_0, x = time_ids)[name = tensor("timesteps_cast")]; tensor var_104_axes_0 = const()[name = tensor("op_104_axes_0"), val = tensor([1])]; - tensor var_104 = expand_dims(axes = var_104_axes_0, x = timesteps)[name = tensor("op_104")]; - tensor var_106 = const()[name = tensor("op_106"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267577536)))]; - tensor emb_11 = mul(x = var_104, y = var_106)[name = tensor("emb_11")]; - tensor var_111 = sin(x = emb_11)[name = tensor("op_111")]; - tensor var_112 = cos(x = emb_11)[name = tensor("op_112")]; + tensor var_104_cast = expand_dims(axes = var_104_axes_0, x = timesteps_cast)[name = tensor("op_104_cast")]; + tensor var_106_to_fp16 = const()[name = tensor("op_106_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4101824)))]; + tensor emb_11_cast = mul(x = var_104_cast, y = var_106_to_fp16)[name = tensor("emb_11_cast")]; + tensor var_111_cast = sin(x = emb_11_cast)[name = tensor("op_111_cast")]; + tensor var_112_cast = cos(x = emb_11_cast)[name = tensor("op_112_cast")]; tensor emb_15_interleave_0 = const()[name = tensor("emb_15_interleave_0"), val = tensor(false)]; - tensor emb_15 = concat(axis = var_86, interleave = emb_15_interleave_0, values = (var_111, var_112))[name = tensor("emb_15")]; + tensor emb_15_cast = concat(axis = var_32, interleave = emb_15_interleave_0, values = (var_111_cast, var_112_cast))[name = tensor("emb_15_cast")]; tensor var_116_begin_0 = const()[name = tensor("op_116_begin_0"), val = tensor([0, 128])]; tensor var_116_end_0 = const()[name = tensor("op_116_end_0"), val = tensor([12, 256])]; tensor var_116_end_mask_0 = const()[name = tensor("op_116_end_mask_0"), val = tensor([true, true])]; - tensor var_116 = slice_by_index(begin = var_116_begin_0, end = var_116_end_0, end_mask = var_116_end_mask_0, x = emb_15)[name = tensor("op_116")]; + tensor var_116_cast = slice_by_index(begin = var_116_begin_0, end = var_116_end_0, end_mask = var_116_end_mask_0, x = emb_15_cast)[name = tensor("op_116_cast")]; tensor var_118_begin_0 = const()[name = tensor("op_118_begin_0"), val = tensor([0, 0])]; tensor var_118_end_0 = const()[name = tensor("op_118_end_0"), val = tensor([12, 128])]; tensor var_118_end_mask_0 = const()[name = tensor("op_118_end_mask_0"), val = tensor([true, false])]; - tensor var_118 = slice_by_index(begin = var_118_begin_0, end = var_118_end_0, end_mask = var_118_end_mask_0, x = emb_15)[name = tensor("op_118")]; + tensor var_118_cast = slice_by_index(begin = var_118_begin_0, end = var_118_end_0, end_mask = var_118_end_mask_0, x = emb_15_cast)[name = tensor("op_118_cast")]; tensor time_embeds_1_interleave_0 = const()[name = tensor("time_embeds_1_interleave_0"), val = tensor(false)]; - tensor time_embeds_1 = concat(axis = var_86, interleave = time_embeds_1_interleave_0, values = (var_116, var_118))[name = tensor("time_embeds_1")]; - tensor var_126 = const()[name = tensor("op_126"), val = tensor([2, -1])]; - tensor time_embeds = reshape(shape = var_126, x = time_embeds_1)[name = tensor("time_embeds")]; - tensor var_129 = const()[name = tensor("op_129"), val = tensor(-1)]; + tensor time_embeds_1_cast = concat(axis = var_32, interleave = time_embeds_1_interleave_0, values = (var_116_cast, var_118_cast))[name = tensor("time_embeds_1_cast")]; + tensor var_122 = const()[name = tensor("op_122"), val = tensor([2, -1])]; + tensor time_embeds_cast = reshape(shape = var_122, x = time_embeds_1_cast)[name = tensor("time_embeds_cast")]; tensor add_embeds_interleave_0 = const()[name = tensor("add_embeds_interleave_0"), val = tensor(false)]; - tensor add_embeds = concat(axis = var_129, interleave = add_embeds_interleave_0, values = (text_embeds, time_embeds))[name = tensor("add_embeds")]; - tensor var_136 = const()[name = tensor("op_136"), val = tensor(1)]; - tensor var_143_axes_0 = const()[name = tensor("op_143_axes_0"), val = tensor([-1])]; - tensor var_143 = expand_dims(axes = var_143_axes_0, x = add_embeds)[name = tensor("op_143")]; + tensor add_embeds_cast = concat(axis = var_32, interleave = add_embeds_interleave_0, values = (text_embeds, time_embeds_cast))[name = tensor("add_embeds_cast")]; + tensor var_129_axes_0 = const()[name = tensor("op_129_axes_0"), val = tensor([-1])]; + tensor var_129_cast = expand_dims(axes = var_129_axes_0, x = add_embeds_cast)[name = tensor("op_129_cast")]; tensor input_7_axes_0 = const()[name = tensor("input_7_axes_0"), val = tensor([-1])]; - tensor input_7 = expand_dims(axes = input_7_axes_0, x = var_143)[name = tensor("input_7")]; - tensor var_147 = const()[name = tensor("op_147"), val = tensor([1, 1])]; - tensor var_149 = const()[name = tensor("op_149"), val = tensor([1, 1])]; + tensor input_7_cast = expand_dims(axes = input_7_axes_0, x = var_129_cast)[name = tensor("input_7_cast")]; + tensor var_133 = const()[name = tensor("op_133"), val = tensor([1, 1])]; + tensor var_135 = const()[name = tensor("op_135"), val = tensor([1, 1])]; tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("custom")]; tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor input_9 = conv(bias = add_embedding_linear_1_bias, dilations = var_149, groups = var_136, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = var_147, weight = add_embedding_linear_1_weight, x = input_7)[name = tensor("input_9")]; - tensor input_11 = silu(x = input_9)[name = tensor("input_11")]; - tensor var_155 = const()[name = tensor("op_155"), val = tensor([1, 1])]; - tensor var_157 = const()[name = tensor("op_157"), val = tensor([1, 1])]; + tensor unet_add_embedding_linear_1_weight_to_fp16 = const()[name = tensor("unet_add_embedding_linear_1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4102144)))]; + tensor unet_add_embedding_linear_1_bias_to_fp16 = const()[name = tensor("unet_add_embedding_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11311168)))]; + tensor input_9_cast = conv(bias = unet_add_embedding_linear_1_bias_to_fp16, dilations = var_135, groups = var_31, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = var_133, weight = unet_add_embedding_linear_1_weight_to_fp16, x = input_7_cast)[name = tensor("input_9_cast")]; + tensor input_11_cast = silu(x = input_9_cast)[name = tensor("input_11_cast")]; + tensor var_141 = const()[name = tensor("op_141"), val = tensor([1, 1])]; + tensor var_143 = const()[name = tensor("op_143"), val = tensor([1, 1])]; tensor aug_emb_pad_type_0 = const()[name = tensor("aug_emb_pad_type_0"), val = tensor("custom")]; tensor aug_emb_pad_0 = const()[name = tensor("aug_emb_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor aug_emb = conv(bias = add_embedding_linear_2_bias, dilations = var_157, groups = var_136, pad = aug_emb_pad_0, pad_type = aug_emb_pad_type_0, strides = var_155, weight = add_embedding_linear_2_weight, x = input_11)[name = tensor("aug_emb")]; - tensor input_19 = add(x = emb, y = aug_emb)[name = tensor("input_19")]; - tensor var_165 = const()[name = tensor("op_165"), val = tensor(1)]; - tensor var_168 = const()[name = tensor("op_168"), val = tensor([1, 1])]; - tensor var_170 = const()[name = tensor("op_170"), val = tensor([1, 1])]; + tensor unet_add_embedding_linear_2_weight_to_fp16 = const()[name = tensor("unet_add_embedding_linear_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11313792)))]; + tensor unet_add_embedding_linear_2_bias_to_fp16 = const()[name = tensor("unet_add_embedding_linear_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14590656)))]; + tensor aug_emb_cast = conv(bias = unet_add_embedding_linear_2_bias_to_fp16, dilations = var_143, groups = var_31, pad = aug_emb_pad_0, pad_type = aug_emb_pad_type_0, strides = var_141, weight = unet_add_embedding_linear_2_weight_to_fp16, x = input_11_cast)[name = tensor("aug_emb_cast")]; + tensor input_19_cast = add(x = emb_cast, y = aug_emb_cast)[name = tensor("input_19_cast")]; + tensor var_149 = const()[name = tensor("op_149"), val = tensor([1, 1])]; + tensor var_151 = const()[name = tensor("op_151"), val = tensor([1, 1])]; tensor input_13_pad_type_0 = const()[name = tensor("input_13_pad_type_0"), val = tensor("custom")]; tensor input_13_pad_0 = const()[name = tensor("input_13_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_13 = conv(bias = conv_in_bias, dilations = var_170, groups = var_165, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = var_168, weight = conv_in_weight, x = sample)[name = tensor("input_13")]; - tensor var_179 = const()[name = tensor("op_179"), val = tensor(1)]; + tensor unet_conv_in_weight_to_fp16 = const()[name = tensor("unet_conv_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14593280)))]; + tensor unet_conv_in_bias_to_fp16 = const()[name = tensor("unet_conv_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14616384)))]; + tensor input_13_cast = conv(bias = unet_conv_in_bias_to_fp16, dilations = var_151, groups = var_31, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = var_149, weight = unet_conv_in_weight_to_fp16, x = sample)[name = tensor("input_13_cast")]; tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([2, 32, 10, 128, 128])]; - tensor reshape_0 = reshape(shape = reshape_0_shape_0, x = input_13)[name = tensor("reshape_0")]; + tensor reshape_0_cast = reshape(shape = reshape_0_shape_0, x = input_13_cast)[name = tensor("reshape_0_cast")]; tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_0 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0)[name = tensor("reduce_mean_0")]; - tensor sub_0 = sub(x = reshape_0, y = reduce_mean_0)[name = tensor("sub_0")]; - tensor square_0 = square(x = sub_0)[name = tensor("square_0")]; + tensor reduce_mean_0_cast = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast)[name = tensor("reduce_mean_0_cast")]; + tensor sub_0_cast = sub(x = reshape_0_cast, y = reduce_mean_0_cast)[name = tensor("sub_0_cast")]; + tensor square_0_cast = square(x = sub_0_cast)[name = tensor("square_0_cast")]; tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_2 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0)[name = tensor("reduce_mean_2")]; - tensor add_0_y_0 = const()[name = tensor("add_0_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_0 = add(x = reduce_mean_2, y = add_0_y_0)[name = tensor("add_0")]; - tensor sqrt_0 = sqrt(x = add_0)[name = tensor("sqrt_0")]; - tensor real_div_0 = real_div(x = sub_0, y = sqrt_0)[name = tensor("real_div_0")]; + tensor reduce_mean_2_cast = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast)[name = tensor("reduce_mean_2_cast")]; + tensor add_0_y_0_to_fp16 = const()[name = tensor("add_0_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_0_cast = add(x = reduce_mean_2_cast, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast")]; + tensor sqrt_0_cast = sqrt(x = add_0_cast)[name = tensor("sqrt_0_cast")]; + tensor real_div_0_cast = real_div(x = sub_0_cast, y = sqrt_0_cast)[name = tensor("real_div_0_cast")]; tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([2, 320, 128, 128])]; - tensor reshape_1 = reshape(shape = reshape_1_shape_0, x = real_div_0)[name = tensor("reshape_1")]; - tensor add_1_mean_0 = const()[name = tensor("add_1_mean_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267578112)))]; - tensor add_1_variance_0 = const()[name = tensor("add_1_variance_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267579456)))]; - tensor add_1_gamma_0 = const()[name = tensor("add_1_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267580800)))]; - tensor add_1_beta_0 = const()[name = tensor("add_1_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267582144)))]; - tensor add_1_epsilon_0 = const()[name = tensor("add_1_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_1 = batch_norm(beta = add_1_beta_0, epsilon = add_1_epsilon_0, gamma = add_1_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_1)[name = tensor("add_1")]; - tensor input_17 = silu(x = add_1)[name = tensor("input_17")]; - tensor var_197 = const()[name = tensor("op_197"), val = tensor([1, 1])]; - tensor var_199 = const()[name = tensor("op_199"), val = tensor([1, 1])]; + tensor reshape_1_cast = reshape(shape = reshape_1_shape_0, x = real_div_0_cast)[name = tensor("reshape_1_cast")]; + tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14617088)))]; + tensor add_1_variance_0_to_fp16 = const()[name = tensor("add_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14617792)))]; + tensor add_1_gamma_0_to_fp16 = const()[name = tensor("add_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14618496)))]; + tensor add_1_beta_0_to_fp16 = const()[name = tensor("add_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14619200)))]; + tensor add_1_epsilon_0_to_fp16 = const()[name = tensor("add_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_1_cast = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast)[name = tensor("add_1_cast")]; + tensor input_17_cast = silu(x = add_1_cast)[name = tensor("input_17_cast")]; + tensor var_171 = const()[name = tensor("op_171"), val = tensor([1, 1])]; + tensor var_173 = const()[name = tensor("op_173"), val = tensor([1, 1])]; tensor hidden_states_1_pad_type_0 = const()[name = tensor("hidden_states_1_pad_type_0"), val = tensor("custom")]; tensor hidden_states_1_pad_0 = const()[name = tensor("hidden_states_1_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_1 = conv(bias = down_blocks_0_resnets_0_conv1_bias, dilations = var_199, groups = var_179, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_197, weight = down_blocks_0_resnets_0_conv1_weight, x = input_17)[name = tensor("hidden_states_1")]; - tensor input_21 = silu(x = input_19)[name = tensor("input_21")]; - tensor var_205 = const()[name = tensor("op_205"), val = tensor([1, 1])]; - tensor var_207 = const()[name = tensor("op_207"), val = tensor([1, 1])]; + tensor unet_down_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("unet_down_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14619904)))]; + tensor unet_down_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("unet_down_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16463168)))]; + tensor hidden_states_1_cast = conv(bias = unet_down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_173, groups = var_31, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_171, weight = unet_down_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_17_cast)[name = tensor("hidden_states_1_cast")]; + tensor input_21_cast = silu(x = input_19_cast)[name = tensor("input_21_cast")]; + tensor var_179 = const()[name = tensor("op_179"), val = tensor([1, 1])]; + tensor var_181 = const()[name = tensor("op_181"), val = tensor([1, 1])]; tensor temb_1_pad_type_0 = const()[name = tensor("temb_1_pad_type_0"), val = tensor("custom")]; tensor temb_1_pad_0 = const()[name = tensor("temb_1_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb_1 = conv(bias = down_blocks_0_resnets_0_time_emb_proj_bias, dilations = var_207, groups = var_179, pad = temb_1_pad_0, pad_type = temb_1_pad_type_0, strides = var_205, weight = down_blocks_0_resnets_0_time_emb_proj_weight, x = input_21)[name = tensor("temb_1")]; - tensor input_23 = add(x = hidden_states_1, y = temb_1)[name = tensor("input_23")]; + tensor unet_down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16463872)))]; + tensor unet_down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17283136)))]; + tensor temb_1_cast = conv(bias = unet_down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_181, groups = var_31, pad = temb_1_pad_0, pad_type = temb_1_pad_type_0, strides = var_179, weight = unet_down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_1_cast")]; + tensor input_23_cast = add(x = hidden_states_1_cast, y = temb_1_cast)[name = tensor("input_23_cast")]; tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([2, 32, 10, 128, 128])]; - tensor reshape_4 = reshape(shape = reshape_4_shape_0, x = input_23)[name = tensor("reshape_4")]; + tensor reshape_4_cast = reshape(shape = reshape_4_shape_0, x = input_23_cast)[name = tensor("reshape_4_cast")]; tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_3 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4)[name = tensor("reduce_mean_3")]; - tensor sub_2 = sub(x = reshape_4, y = reduce_mean_3)[name = tensor("sub_2")]; - tensor square_1 = square(x = sub_2)[name = tensor("square_1")]; + tensor reduce_mean_3_cast = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast)[name = tensor("reduce_mean_3_cast")]; + tensor sub_2_cast = sub(x = reshape_4_cast, y = reduce_mean_3_cast)[name = tensor("sub_2_cast")]; + tensor square_1_cast = square(x = sub_2_cast)[name = tensor("square_1_cast")]; tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_5 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1)[name = tensor("reduce_mean_5")]; - tensor add_2_y_0 = const()[name = tensor("add_2_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_2 = add(x = reduce_mean_5, y = add_2_y_0)[name = tensor("add_2")]; - tensor sqrt_1 = sqrt(x = add_2)[name = tensor("sqrt_1")]; - tensor real_div_1 = real_div(x = sub_2, y = sqrt_1)[name = tensor("real_div_1")]; + tensor reduce_mean_5_cast = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast)[name = tensor("reduce_mean_5_cast")]; + tensor add_2_y_0_to_fp16 = const()[name = tensor("add_2_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_2_cast = add(x = reduce_mean_5_cast, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast")]; + tensor sqrt_1_cast = sqrt(x = add_2_cast)[name = tensor("sqrt_1_cast")]; + tensor real_div_1_cast = real_div(x = sub_2_cast, y = sqrt_1_cast)[name = tensor("real_div_1_cast")]; tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([2, 320, 128, 128])]; - tensor reshape_5 = reshape(shape = reshape_5_shape_0, x = real_div_1)[name = tensor("reshape_5")]; - tensor add_3_gamma_0 = const()[name = tensor("add_3_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267583488)))]; - tensor add_3_beta_0 = const()[name = tensor("add_3_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267584832)))]; - tensor add_3_epsilon_0 = const()[name = tensor("add_3_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_3 = batch_norm(beta = add_3_beta_0, epsilon = add_3_epsilon_0, gamma = add_3_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_5)[name = tensor("add_3")]; - tensor input_27 = silu(x = add_3)[name = tensor("input_27")]; - tensor var_217 = const()[name = tensor("op_217"), val = tensor([1, 1])]; - tensor var_219 = const()[name = tensor("op_219"), val = tensor([1, 1])]; + tensor reshape_5_cast = reshape(shape = reshape_5_shape_0, x = real_div_1_cast)[name = tensor("reshape_5_cast")]; + tensor add_3_gamma_0_to_fp16 = const()[name = tensor("add_3_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17283840)))]; + tensor add_3_beta_0_to_fp16 = const()[name = tensor("add_3_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17284544)))]; + tensor add_3_epsilon_0_to_fp16 = const()[name = tensor("add_3_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_3_cast = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast)[name = tensor("add_3_cast")]; + tensor input_27_cast = silu(x = add_3_cast)[name = tensor("input_27_cast")]; + tensor var_191 = const()[name = tensor("op_191"), val = tensor([1, 1])]; + tensor var_193 = const()[name = tensor("op_193"), val = tensor([1, 1])]; tensor hidden_states_3_pad_type_0 = const()[name = tensor("hidden_states_3_pad_type_0"), val = tensor("custom")]; tensor hidden_states_3_pad_0 = const()[name = tensor("hidden_states_3_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_3 = conv(bias = down_blocks_0_resnets_0_conv2_bias, dilations = var_219, groups = var_179, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_217, weight = down_blocks_0_resnets_0_conv2_weight, x = input_27)[name = tensor("hidden_states_3")]; - tensor input_29 = add(x = input_13, y = hidden_states_3)[name = tensor("input_29")]; + tensor unet_down_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17285248)))]; + tensor unet_down_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19128512)))]; + tensor hidden_states_3_cast = conv(bias = unet_down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_193, groups = var_31, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_191, weight = unet_down_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_27_cast)[name = tensor("hidden_states_3_cast")]; + tensor input_29_cast = add(x = input_13_cast, y = hidden_states_3_cast)[name = tensor("input_29_cast")]; tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([2, 32, 10, 128, 128])]; - tensor reshape_8 = reshape(shape = reshape_8_shape_0, x = input_29)[name = tensor("reshape_8")]; + tensor reshape_8_cast = reshape(shape = reshape_8_shape_0, x = input_29_cast)[name = tensor("reshape_8_cast")]; tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_6 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8)[name = tensor("reduce_mean_6")]; - tensor sub_4 = sub(x = reshape_8, y = reduce_mean_6)[name = tensor("sub_4")]; - tensor square_2 = square(x = sub_4)[name = tensor("square_2")]; + tensor reduce_mean_6_cast = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast)[name = tensor("reduce_mean_6_cast")]; + tensor sub_4_cast = sub(x = reshape_8_cast, y = reduce_mean_6_cast)[name = tensor("sub_4_cast")]; + tensor square_2_cast = square(x = sub_4_cast)[name = tensor("square_2_cast")]; tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_8 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2)[name = tensor("reduce_mean_8")]; - tensor add_4_y_0 = const()[name = tensor("add_4_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_4 = add(x = reduce_mean_8, y = add_4_y_0)[name = tensor("add_4")]; - tensor sqrt_2 = sqrt(x = add_4)[name = tensor("sqrt_2")]; - tensor real_div_2 = real_div(x = sub_4, y = sqrt_2)[name = tensor("real_div_2")]; + tensor reduce_mean_8_cast = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast)[name = tensor("reduce_mean_8_cast")]; + tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_4_cast = add(x = reduce_mean_8_cast, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast")]; + tensor sqrt_2_cast = sqrt(x = add_4_cast)[name = tensor("sqrt_2_cast")]; + tensor real_div_2_cast = real_div(x = sub_4_cast, y = sqrt_2_cast)[name = tensor("real_div_2_cast")]; tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([2, 320, 128, 128])]; - tensor reshape_9 = reshape(shape = reshape_9_shape_0, x = real_div_2)[name = tensor("reshape_9")]; - tensor add_5_gamma_0 = const()[name = tensor("add_5_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267586176)))]; - tensor add_5_beta_0 = const()[name = tensor("add_5_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267587520)))]; - tensor add_5_epsilon_0 = const()[name = tensor("add_5_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_5 = batch_norm(beta = add_5_beta_0, epsilon = add_5_epsilon_0, gamma = add_5_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_9)[name = tensor("add_5")]; - tensor input_33 = silu(x = add_5)[name = tensor("input_33")]; - tensor var_234 = const()[name = tensor("op_234"), val = tensor([1, 1])]; - tensor var_236 = const()[name = tensor("op_236"), val = tensor([1, 1])]; + tensor reshape_9_cast = reshape(shape = reshape_9_shape_0, x = real_div_2_cast)[name = tensor("reshape_9_cast")]; + tensor add_5_gamma_0_to_fp16 = const()[name = tensor("add_5_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19129216)))]; + tensor add_5_beta_0_to_fp16 = const()[name = tensor("add_5_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19129920)))]; + tensor add_5_epsilon_0_to_fp16 = const()[name = tensor("add_5_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_5_cast = batch_norm(beta = add_5_beta_0_to_fp16, epsilon = add_5_epsilon_0_to_fp16, gamma = add_5_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_9_cast)[name = tensor("add_5_cast")]; + tensor input_33_cast = silu(x = add_5_cast)[name = tensor("input_33_cast")]; + tensor var_208 = const()[name = tensor("op_208"), val = tensor([1, 1])]; + tensor var_210 = const()[name = tensor("op_210"), val = tensor([1, 1])]; tensor hidden_states_5_pad_type_0 = const()[name = tensor("hidden_states_5_pad_type_0"), val = tensor("custom")]; tensor hidden_states_5_pad_0 = const()[name = tensor("hidden_states_5_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_5 = conv(bias = down_blocks_0_resnets_1_conv1_bias, dilations = var_236, groups = var_179, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = var_234, weight = down_blocks_0_resnets_1_conv1_weight, x = input_33)[name = tensor("hidden_states_5")]; - tensor var_242 = const()[name = tensor("op_242"), val = tensor([1, 1])]; - tensor var_244 = const()[name = tensor("op_244"), val = tensor([1, 1])]; + tensor unet_down_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("unet_down_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19130624)))]; + tensor unet_down_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("unet_down_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20973888)))]; + tensor hidden_states_5_cast = conv(bias = unet_down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_210, groups = var_31, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = var_208, weight = unet_down_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_33_cast)[name = tensor("hidden_states_5_cast")]; + tensor var_216 = const()[name = tensor("op_216"), val = tensor([1, 1])]; + tensor var_218 = const()[name = tensor("op_218"), val = tensor([1, 1])]; tensor temb_3_pad_type_0 = const()[name = tensor("temb_3_pad_type_0"), val = tensor("custom")]; tensor temb_3_pad_0 = const()[name = tensor("temb_3_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb_3 = conv(bias = down_blocks_0_resnets_1_time_emb_proj_bias, dilations = var_244, groups = var_179, pad = temb_3_pad_0, pad_type = temb_3_pad_type_0, strides = var_242, weight = down_blocks_0_resnets_1_time_emb_proj_weight, x = input_21)[name = tensor("temb_3")]; - tensor input_37 = add(x = hidden_states_5, y = temb_3)[name = tensor("input_37")]; + tensor unet_down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20974592)))]; + tensor unet_down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21793856)))]; + tensor temb_3_cast = conv(bias = unet_down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_218, groups = var_31, pad = temb_3_pad_0, pad_type = temb_3_pad_type_0, strides = var_216, weight = unet_down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_3_cast")]; + tensor input_37_cast = add(x = hidden_states_5_cast, y = temb_3_cast)[name = tensor("input_37_cast")]; tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([2, 32, 10, 128, 128])]; - tensor reshape_12 = reshape(shape = reshape_12_shape_0, x = input_37)[name = tensor("reshape_12")]; + tensor reshape_12_cast = reshape(shape = reshape_12_shape_0, x = input_37_cast)[name = tensor("reshape_12_cast")]; tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_9 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12)[name = tensor("reduce_mean_9")]; - tensor sub_6 = sub(x = reshape_12, y = reduce_mean_9)[name = tensor("sub_6")]; - tensor square_3 = square(x = sub_6)[name = tensor("square_3")]; + tensor reduce_mean_9_cast = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast)[name = tensor("reduce_mean_9_cast")]; + tensor sub_6_cast = sub(x = reshape_12_cast, y = reduce_mean_9_cast)[name = tensor("sub_6_cast")]; + tensor square_3_cast = square(x = sub_6_cast)[name = tensor("square_3_cast")]; tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_11 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3)[name = tensor("reduce_mean_11")]; - tensor add_6_y_0 = const()[name = tensor("add_6_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_6 = add(x = reduce_mean_11, y = add_6_y_0)[name = tensor("add_6")]; - tensor sqrt_3 = sqrt(x = add_6)[name = tensor("sqrt_3")]; - tensor real_div_3 = real_div(x = sub_6, y = sqrt_3)[name = tensor("real_div_3")]; + tensor reduce_mean_11_cast = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast)[name = tensor("reduce_mean_11_cast")]; + tensor add_6_y_0_to_fp16 = const()[name = tensor("add_6_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_6_cast = add(x = reduce_mean_11_cast, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast")]; + tensor sqrt_3_cast = sqrt(x = add_6_cast)[name = tensor("sqrt_3_cast")]; + tensor real_div_3_cast = real_div(x = sub_6_cast, y = sqrt_3_cast)[name = tensor("real_div_3_cast")]; tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([2, 320, 128, 128])]; - tensor reshape_13 = reshape(shape = reshape_13_shape_0, x = real_div_3)[name = tensor("reshape_13")]; - tensor add_7_gamma_0 = const()[name = tensor("add_7_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267588864)))]; - tensor add_7_beta_0 = const()[name = tensor("add_7_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267590208)))]; - tensor add_7_epsilon_0 = const()[name = tensor("add_7_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_7 = batch_norm(beta = add_7_beta_0, epsilon = add_7_epsilon_0, gamma = add_7_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_13)[name = tensor("add_7")]; - tensor input_41 = silu(x = add_7)[name = tensor("input_41")]; - tensor var_254 = const()[name = tensor("op_254"), val = tensor([1, 1])]; - tensor var_256 = const()[name = tensor("op_256"), val = tensor([1, 1])]; + tensor reshape_13_cast = reshape(shape = reshape_13_shape_0, x = real_div_3_cast)[name = tensor("reshape_13_cast")]; + tensor add_7_gamma_0_to_fp16 = const()[name = tensor("add_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21794560)))]; + tensor add_7_beta_0_to_fp16 = const()[name = tensor("add_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21795264)))]; + tensor add_7_epsilon_0_to_fp16 = const()[name = tensor("add_7_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_7_cast = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast)[name = tensor("add_7_cast")]; + tensor input_41_cast = silu(x = add_7_cast)[name = tensor("input_41_cast")]; + tensor var_228 = const()[name = tensor("op_228"), val = tensor([1, 1])]; + tensor var_230 = const()[name = tensor("op_230"), val = tensor([1, 1])]; tensor hidden_states_7_pad_type_0 = const()[name = tensor("hidden_states_7_pad_type_0"), val = tensor("custom")]; tensor hidden_states_7_pad_0 = const()[name = tensor("hidden_states_7_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_7 = conv(bias = down_blocks_0_resnets_1_conv2_bias, dilations = var_256, groups = var_179, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = var_254, weight = down_blocks_0_resnets_1_conv2_weight, x = input_41)[name = tensor("hidden_states_7")]; - tensor input_43 = add(x = input_29, y = hidden_states_7)[name = tensor("input_43")]; - tensor var_263 = const()[name = tensor("op_263"), val = tensor([2, 2])]; - tensor var_265 = const()[name = tensor("op_265"), val = tensor([1, 1])]; + tensor unet_down_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21795968)))]; + tensor unet_down_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23639232)))]; + tensor hidden_states_7_cast = conv(bias = unet_down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_230, groups = var_31, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = var_228, weight = unet_down_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_41_cast)[name = tensor("hidden_states_7_cast")]; + tensor input_43_cast = add(x = input_29_cast, y = hidden_states_7_cast)[name = tensor("input_43_cast")]; + tensor var_237 = const()[name = tensor("op_237"), val = tensor([2, 2])]; + tensor var_239 = const()[name = tensor("op_239"), val = tensor([1, 1])]; tensor input_45_pad_type_0 = const()[name = tensor("input_45_pad_type_0"), val = tensor("custom")]; tensor input_45_pad_0 = const()[name = tensor("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_45 = conv(bias = down_blocks_0_downsamplers_0_conv_bias, dilations = var_265, groups = var_179, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_263, weight = down_blocks_0_downsamplers_0_conv_weight, x = input_43)[name = tensor("input_45")]; - tensor var_273 = const()[name = tensor("op_273"), val = tensor(3)]; - tensor var_280 = const()[name = tensor("op_280"), val = tensor(0x1p-3)]; - tensor var_284 = const()[name = tensor("op_284"), val = tensor(true)]; - tensor var_289 = const()[name = tensor("op_289"), val = tensor(1)]; + tensor unet_down_blocks_0_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("unet_down_blocks_0_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23639936)))]; + tensor unet_down_blocks_0_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("unet_down_blocks_0_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25483200)))]; + tensor input_45_cast = conv(bias = unet_down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_239, groups = var_31, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_237, weight = unet_down_blocks_0_downsamplers_0_conv_weight_to_fp16, x = input_43_cast)[name = tensor("input_45_cast")]; tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([2, 32, 10, 64, 64])]; - tensor reshape_16 = reshape(shape = reshape_16_shape_0, x = input_45)[name = tensor("reshape_16")]; + tensor reshape_16_cast = reshape(shape = reshape_16_shape_0, x = input_45_cast)[name = tensor("reshape_16_cast")]; tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_12_keep_dims_0 = const()[name = tensor("reduce_mean_12_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_12 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16)[name = tensor("reduce_mean_12")]; - tensor sub_8 = sub(x = reshape_16, y = reduce_mean_12)[name = tensor("sub_8")]; - tensor square_4 = square(x = sub_8)[name = tensor("square_4")]; + tensor reduce_mean_12_cast = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast)[name = tensor("reduce_mean_12_cast")]; + tensor sub_8_cast = sub(x = reshape_16_cast, y = reduce_mean_12_cast)[name = tensor("sub_8_cast")]; + tensor square_4_cast = square(x = sub_8_cast)[name = tensor("square_4_cast")]; tensor reduce_mean_14_axes_0 = const()[name = tensor("reduce_mean_14_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_14_keep_dims_0 = const()[name = tensor("reduce_mean_14_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_14 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4)[name = tensor("reduce_mean_14")]; - tensor add_8_y_0 = const()[name = tensor("add_8_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_8 = add(x = reduce_mean_14, y = add_8_y_0)[name = tensor("add_8")]; - tensor sqrt_4 = sqrt(x = add_8)[name = tensor("sqrt_4")]; - tensor real_div_4 = real_div(x = sub_8, y = sqrt_4)[name = tensor("real_div_4")]; + tensor reduce_mean_14_cast = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast)[name = tensor("reduce_mean_14_cast")]; + tensor add_8_y_0_to_fp16 = const()[name = tensor("add_8_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_8_cast = add(x = reduce_mean_14_cast, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast")]; + tensor sqrt_4_cast = sqrt(x = add_8_cast)[name = tensor("sqrt_4_cast")]; + tensor real_div_4_cast = real_div(x = sub_8_cast, y = sqrt_4_cast)[name = tensor("real_div_4_cast")]; tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([2, 320, 64, 64])]; - tensor reshape_17 = reshape(shape = reshape_17_shape_0, x = real_div_4)[name = tensor("reshape_17")]; - tensor add_9_gamma_0 = const()[name = tensor("add_9_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267591552)))]; - tensor add_9_beta_0 = const()[name = tensor("add_9_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267592896)))]; - tensor add_9_epsilon_0 = const()[name = tensor("add_9_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_9 = batch_norm(beta = add_9_beta_0, epsilon = add_9_epsilon_0, gamma = add_9_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_17)[name = tensor("add_9")]; - tensor input_49 = silu(x = add_9)[name = tensor("input_49")]; - tensor var_312 = const()[name = tensor("op_312"), val = tensor([1, 1])]; - tensor var_314 = const()[name = tensor("op_314"), val = tensor([1, 1])]; + tensor reshape_17_cast = reshape(shape = reshape_17_shape_0, x = real_div_4_cast)[name = tensor("reshape_17_cast")]; + tensor add_9_gamma_0_to_fp16 = const()[name = tensor("add_9_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25483904)))]; + tensor add_9_beta_0_to_fp16 = const()[name = tensor("add_9_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25484608)))]; + tensor add_9_epsilon_0_to_fp16 = const()[name = tensor("add_9_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_9_cast = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast)[name = tensor("add_9_cast")]; + tensor input_49_cast = silu(x = add_9_cast)[name = tensor("input_49_cast")]; + tensor var_268 = const()[name = tensor("op_268"), val = tensor([1, 1])]; + tensor var_270 = const()[name = tensor("op_270"), val = tensor([1, 1])]; tensor hidden_states_9_pad_type_0 = const()[name = tensor("hidden_states_9_pad_type_0"), val = tensor("custom")]; tensor hidden_states_9_pad_0 = const()[name = tensor("hidden_states_9_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_9 = conv(bias = down_blocks_1_resnets_0_conv1_bias, dilations = var_314, groups = var_289, pad = hidden_states_9_pad_0, pad_type = hidden_states_9_pad_type_0, strides = var_312, weight = down_blocks_1_resnets_0_conv1_weight, x = input_49)[name = tensor("hidden_states_9")]; - tensor var_320 = const()[name = tensor("op_320"), val = tensor([1, 1])]; - tensor var_322 = const()[name = tensor("op_322"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25485312)))]; + tensor unet_down_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29171776)))]; + tensor hidden_states_9_cast = conv(bias = unet_down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_270, groups = var_31, pad = hidden_states_9_pad_0, pad_type = hidden_states_9_pad_type_0, strides = var_268, weight = unet_down_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_49_cast)[name = tensor("hidden_states_9_cast")]; + tensor var_276 = const()[name = tensor("op_276"), val = tensor([1, 1])]; + tensor var_278 = const()[name = tensor("op_278"), val = tensor([1, 1])]; tensor temb_5_pad_type_0 = const()[name = tensor("temb_5_pad_type_0"), val = tensor("custom")]; tensor temb_5_pad_0 = const()[name = tensor("temb_5_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb_5 = conv(bias = down_blocks_1_resnets_0_time_emb_proj_bias, dilations = var_322, groups = var_289, pad = temb_5_pad_0, pad_type = temb_5_pad_type_0, strides = var_320, weight = down_blocks_1_resnets_0_time_emb_proj_weight, x = input_21)[name = tensor("temb_5")]; - tensor input_53 = add(x = hidden_states_9, y = temb_5)[name = tensor("input_53")]; + tensor unet_down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29173120)))]; + tensor unet_down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30811584)))]; + tensor temb_5_cast = conv(bias = unet_down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_278, groups = var_31, pad = temb_5_pad_0, pad_type = temb_5_pad_type_0, strides = var_276, weight = unet_down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_5_cast")]; + tensor input_53_cast = add(x = hidden_states_9_cast, y = temb_5_cast)[name = tensor("input_53_cast")]; tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([2, 32, 20, 64, 64])]; - tensor reshape_20 = reshape(shape = reshape_20_shape_0, x = input_53)[name = tensor("reshape_20")]; + tensor reshape_20_cast = reshape(shape = reshape_20_shape_0, x = input_53_cast)[name = tensor("reshape_20_cast")]; tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_15 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20)[name = tensor("reduce_mean_15")]; - tensor sub_10 = sub(x = reshape_20, y = reduce_mean_15)[name = tensor("sub_10")]; - tensor square_5 = square(x = sub_10)[name = tensor("square_5")]; + tensor reduce_mean_15_cast = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast)[name = tensor("reduce_mean_15_cast")]; + tensor sub_10_cast = sub(x = reshape_20_cast, y = reduce_mean_15_cast)[name = tensor("sub_10_cast")]; + tensor square_5_cast = square(x = sub_10_cast)[name = tensor("square_5_cast")]; tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_17 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5)[name = tensor("reduce_mean_17")]; - tensor add_10_y_0 = const()[name = tensor("add_10_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_10 = add(x = reduce_mean_17, y = add_10_y_0)[name = tensor("add_10")]; - tensor sqrt_5 = sqrt(x = add_10)[name = tensor("sqrt_5")]; - tensor real_div_5 = real_div(x = sub_10, y = sqrt_5)[name = tensor("real_div_5")]; + tensor reduce_mean_17_cast = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast)[name = tensor("reduce_mean_17_cast")]; + tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_10_cast = add(x = reduce_mean_17_cast, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast")]; + tensor sqrt_5_cast = sqrt(x = add_10_cast)[name = tensor("sqrt_5_cast")]; + tensor real_div_5_cast = real_div(x = sub_10_cast, y = sqrt_5_cast)[name = tensor("real_div_5_cast")]; tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([2, 640, 64, 64])]; - tensor reshape_21 = reshape(shape = reshape_21_shape_0, x = real_div_5)[name = tensor("reshape_21")]; - tensor add_11_mean_0 = const()[name = tensor("add_11_mean_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267594240)))]; - tensor add_11_variance_0 = const()[name = tensor("add_11_variance_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267596864)))]; - tensor add_11_gamma_0 = const()[name = tensor("add_11_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267599488)))]; - tensor add_11_beta_0 = const()[name = tensor("add_11_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267602112)))]; - tensor add_11_epsilon_0 = const()[name = tensor("add_11_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_11 = batch_norm(beta = add_11_beta_0, epsilon = add_11_epsilon_0, gamma = add_11_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_21)[name = tensor("add_11")]; - tensor input_57 = silu(x = add_11)[name = tensor("input_57")]; - tensor var_332 = const()[name = tensor("op_332"), val = tensor([1, 1])]; - tensor var_334 = const()[name = tensor("op_334"), val = tensor([1, 1])]; + tensor reshape_21_cast = reshape(shape = reshape_21_shape_0, x = real_div_5_cast)[name = tensor("reshape_21_cast")]; + tensor add_11_mean_0_to_fp16 = const()[name = tensor("add_11_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30812928)))]; + tensor add_11_variance_0_to_fp16 = const()[name = tensor("add_11_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30814272)))]; + tensor add_11_gamma_0_to_fp16 = const()[name = tensor("add_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30815616)))]; + tensor add_11_beta_0_to_fp16 = const()[name = tensor("add_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30816960)))]; + tensor add_11_epsilon_0_to_fp16 = const()[name = tensor("add_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_11_cast = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_21_cast)[name = tensor("add_11_cast")]; + tensor input_57_cast = silu(x = add_11_cast)[name = tensor("input_57_cast")]; + tensor var_288 = const()[name = tensor("op_288"), val = tensor([1, 1])]; + tensor var_290 = const()[name = tensor("op_290"), val = tensor([1, 1])]; tensor hidden_states_11_pad_type_0 = const()[name = tensor("hidden_states_11_pad_type_0"), val = tensor("custom")]; tensor hidden_states_11_pad_0 = const()[name = tensor("hidden_states_11_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_11 = conv(bias = down_blocks_1_resnets_0_conv2_bias, dilations = var_334, groups = var_289, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = var_332, weight = down_blocks_1_resnets_0_conv2_weight, x = input_57)[name = tensor("hidden_states_11")]; - tensor var_339 = const()[name = tensor("op_339"), val = tensor([1, 1])]; - tensor var_341 = const()[name = tensor("op_341"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30818304)))]; + tensor unet_down_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38191168)))]; + tensor hidden_states_11_cast = conv(bias = unet_down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_290, groups = var_31, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = var_288, weight = unet_down_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_57_cast)[name = tensor("hidden_states_11_cast")]; + tensor var_295 = const()[name = tensor("op_295"), val = tensor([1, 1])]; + tensor var_297 = const()[name = tensor("op_297"), val = tensor([1, 1])]; tensor x_1_pad_type_0 = const()[name = tensor("x_1_pad_type_0"), val = tensor("custom")]; tensor x_1_pad_0 = const()[name = tensor("x_1_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor x_1 = conv(bias = down_blocks_1_resnets_0_conv_shortcut_bias, dilations = var_341, groups = var_289, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = var_339, weight = down_blocks_1_resnets_0_conv_shortcut_weight, x = input_45)[name = tensor("x_1")]; - tensor hidden_states_13 = add(x = x_1, y = hidden_states_11)[name = tensor("hidden_states_13")]; + tensor unet_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38192512)))]; + tensor unet_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38602176)))]; + tensor x_1_cast = conv(bias = unet_down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_297, groups = var_31, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = var_295, weight = unet_down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_45_cast)[name = tensor("x_1_cast")]; + tensor hidden_states_13_cast = add(x = x_1_cast, y = hidden_states_11_cast)[name = tensor("hidden_states_13_cast")]; tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([2, 32, 20, 64, 64])]; - tensor reshape_24 = reshape(shape = reshape_24_shape_0, x = hidden_states_13)[name = tensor("reshape_24")]; + tensor reshape_24_cast = reshape(shape = reshape_24_shape_0, x = hidden_states_13_cast)[name = tensor("reshape_24_cast")]; tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_18_keep_dims_0 = const()[name = tensor("reduce_mean_18_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_18 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24)[name = tensor("reduce_mean_18")]; - tensor sub_12 = sub(x = reshape_24, y = reduce_mean_18)[name = tensor("sub_12")]; - tensor square_6 = square(x = sub_12)[name = tensor("square_6")]; + tensor reduce_mean_18_cast = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast)[name = tensor("reduce_mean_18_cast")]; + tensor sub_12_cast = sub(x = reshape_24_cast, y = reduce_mean_18_cast)[name = tensor("sub_12_cast")]; + tensor square_6_cast = square(x = sub_12_cast)[name = tensor("square_6_cast")]; tensor reduce_mean_20_axes_0 = const()[name = tensor("reduce_mean_20_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_20_keep_dims_0 = const()[name = tensor("reduce_mean_20_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_20 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6)[name = tensor("reduce_mean_20")]; - tensor add_12_y_0 = const()[name = tensor("add_12_y_0"), val = tensor(0x1.0c6f7ap-20)]; - tensor add_12 = add(x = reduce_mean_20, y = add_12_y_0)[name = tensor("add_12")]; - tensor sqrt_6 = sqrt(x = add_12)[name = tensor("sqrt_6")]; - tensor real_div_6 = real_div(x = sub_12, y = sqrt_6)[name = tensor("real_div_6")]; + tensor reduce_mean_20_cast = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast)[name = tensor("reduce_mean_20_cast")]; + tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_12_cast = add(x = reduce_mean_20_cast, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast")]; + tensor sqrt_6_cast = sqrt(x = add_12_cast)[name = tensor("sqrt_6_cast")]; + tensor real_div_6_cast = real_div(x = sub_12_cast, y = sqrt_6_cast)[name = tensor("real_div_6_cast")]; tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([2, 640, 64, 64])]; - tensor reshape_25 = reshape(shape = reshape_25_shape_0, x = real_div_6)[name = tensor("reshape_25")]; - tensor add_13_gamma_0 = const()[name = tensor("add_13_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267604736)))]; - tensor add_13_beta_0 = const()[name = tensor("add_13_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267607360)))]; - tensor add_13_epsilon_0 = const()[name = tensor("add_13_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_13 = batch_norm(beta = add_13_beta_0, epsilon = add_13_epsilon_0, gamma = add_13_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_25)[name = tensor("add_13")]; - tensor var_363 = const()[name = tensor("op_363"), val = tensor([1, 1])]; - tensor var_365 = const()[name = tensor("op_365"), val = tensor([1, 1])]; + tensor reshape_25_cast = reshape(shape = reshape_25_shape_0, x = real_div_6_cast)[name = tensor("reshape_25_cast")]; + tensor add_13_gamma_0_to_fp16 = const()[name = tensor("add_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38603520)))]; + tensor add_13_beta_0_to_fp16 = const()[name = tensor("add_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38604864)))]; + tensor add_13_epsilon_0_to_fp16 = const()[name = tensor("add_13_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_13_cast = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_25_cast)[name = tensor("add_13_cast")]; + tensor var_319 = const()[name = tensor("op_319"), val = tensor([1, 1])]; + tensor var_321 = const()[name = tensor("op_321"), val = tensor([1, 1])]; tensor hidden_states_15_pad_type_0 = const()[name = tensor("hidden_states_15_pad_type_0"), val = tensor("custom")]; tensor hidden_states_15_pad_0 = const()[name = tensor("hidden_states_15_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_15 = conv(bias = down_blocks_1_attentions_0_proj_in_bias, dilations = var_365, groups = var_289, pad = hidden_states_15_pad_0, pad_type = hidden_states_15_pad_type_0, strides = var_363, weight = down_blocks_1_attentions_0_proj_in_weight, x = add_13)[name = tensor("hidden_states_15")]; - tensor var_370 = const()[name = tensor("op_370"), val = tensor([2, 640, 1, 4096])]; - tensor inputs_1 = reshape(shape = var_370, x = hidden_states_15)[name = tensor("inputs_1")]; - tensor var_380 = const()[name = tensor("op_380"), val = tensor([1])]; - tensor channels_mean_1 = reduce_mean(axes = var_380, keep_dims = var_284, x = inputs_1)[name = tensor("channels_mean_1")]; - tensor zero_mean_1 = sub(x = inputs_1, y = channels_mean_1)[name = tensor("zero_mean_1")]; - tensor zero_mean_sq_1 = mul(x = zero_mean_1, y = zero_mean_1)[name = tensor("zero_mean_sq_1")]; - tensor var_384 = const()[name = tensor("op_384"), val = tensor([1])]; - tensor var_385 = reduce_mean(axes = var_384, keep_dims = var_284, x = zero_mean_sq_1)[name = tensor("op_385")]; - tensor var_386 = const()[name = tensor("op_386"), val = tensor(0x1.4f8b58p-17)]; - tensor var_387 = add(x = var_385, y = var_386)[name = tensor("op_387")]; - tensor denom_1_epsilon_0 = const()[name = tensor("denom_1_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_1 = rsqrt(epsilon = denom_1_epsilon_0, x = var_387)[name = tensor("denom_1")]; - tensor out_1 = mul(x = zero_mean_1, y = denom_1)[name = tensor("out_1")]; - tensor var_391 = const()[name = tensor("op_391"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267609984)))]; - tensor var_392 = add(x = out_1, y = var_391)[name = tensor("op_392")]; - tensor var_394 = const()[name = tensor("op_394"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267612608)))]; - tensor hidden_states_17 = mul(x = var_392, y = var_394)[name = tensor("hidden_states_17")]; - tensor var_401 = const()[name = tensor("op_401"), val = tensor([1, 1])]; - tensor var_403 = const()[name = tensor("op_403"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38606208)))]; + tensor unet_down_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39425472)))]; + tensor hidden_states_15_cast = conv(bias = unet_down_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_321, groups = var_31, pad = hidden_states_15_pad_0, pad_type = hidden_states_15_pad_type_0, strides = var_319, weight = unet_down_blocks_1_attentions_0_proj_in_weight_to_fp16, x = add_13_cast)[name = tensor("hidden_states_15_cast")]; + tensor var_326 = const()[name = tensor("op_326"), val = tensor([2, 640, 1, 4096])]; + tensor inputs_1_cast = reshape(shape = var_326, x = hidden_states_15_cast)[name = tensor("inputs_1_cast")]; + tensor var_336 = const()[name = tensor("op_336"), val = tensor([1])]; + tensor channels_mean_1_cast = reduce_mean(axes = var_336, keep_dims = var_23, x = inputs_1_cast)[name = tensor("channels_mean_1_cast")]; + tensor zero_mean_1_cast = sub(x = inputs_1_cast, y = channels_mean_1_cast)[name = tensor("zero_mean_1_cast")]; + tensor zero_mean_sq_1_cast = mul(x = zero_mean_1_cast, y = zero_mean_1_cast)[name = tensor("zero_mean_sq_1_cast")]; + tensor var_340 = const()[name = tensor("op_340"), val = tensor([1])]; + tensor var_341_cast = reduce_mean(axes = var_340, keep_dims = var_23, x = zero_mean_sq_1_cast)[name = tensor("op_341_cast")]; + tensor var_342_to_fp16 = const()[name = tensor("op_342_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_343_cast = add(x = var_341_cast, y = var_342_to_fp16)[name = tensor("op_343_cast")]; + tensor denom_1_epsilon_0_to_fp16 = const()[name = tensor("denom_1_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_1_cast = rsqrt(epsilon = denom_1_epsilon_0_to_fp16, x = var_343_cast)[name = tensor("denom_1_cast")]; + tensor out_1_cast = mul(x = zero_mean_1_cast, y = denom_1_cast)[name = tensor("out_1_cast")]; + tensor var_347_to_fp16 = const()[name = tensor("op_347_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39426816)))]; + tensor var_348_cast = add(x = out_1_cast, y = var_347_to_fp16)[name = tensor("op_348_cast")]; + tensor var_350_to_fp16 = const()[name = tensor("op_350_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39428160)))]; + tensor hidden_states_17_cast = mul(x = var_348_cast, y = var_350_to_fp16)[name = tensor("hidden_states_17_cast")]; + tensor var_357 = const()[name = tensor("op_357"), val = tensor([1, 1])]; + tensor var_359 = const()[name = tensor("op_359"), val = tensor([1, 1])]; tensor q_1_pad_type_0 = const()[name = tensor("q_1_pad_type_0"), val = tensor("custom")]; tensor q_1_pad_0 = const()[name = tensor("q_1_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_1 = conv(dilations = var_403, groups = var_289, pad = q_1_pad_0, pad_type = q_1_pad_type_0, strides = var_401, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight, x = hidden_states_17)[name = tensor("q_1")]; - tensor var_407 = const()[name = tensor("op_407"), val = tensor([1, 1])]; - tensor var_409 = const()[name = tensor("op_409"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39429504)))]; + tensor q_1_cast = conv(dilations = var_359, groups = var_31, pad = q_1_pad_0, pad_type = q_1_pad_type_0, strides = var_357, weight = unet_down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_17_cast)[name = tensor("q_1_cast")]; + tensor var_363 = const()[name = tensor("op_363"), val = tensor([1, 1])]; + tensor var_365 = const()[name = tensor("op_365"), val = tensor([1, 1])]; tensor k_1_pad_type_0 = const()[name = tensor("k_1_pad_type_0"), val = tensor("custom")]; tensor k_1_pad_0 = const()[name = tensor("k_1_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_1 = conv(dilations = var_409, groups = var_289, pad = k_1_pad_0, pad_type = k_1_pad_type_0, strides = var_407, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight, x = hidden_states_17)[name = tensor("k_1")]; - tensor var_413 = const()[name = tensor("op_413"), val = tensor([1, 1])]; - tensor var_415 = const()[name = tensor("op_415"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40248768)))]; + tensor k_1_cast = conv(dilations = var_365, groups = var_31, pad = k_1_pad_0, pad_type = k_1_pad_type_0, strides = var_363, weight = unet_down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_17_cast)[name = tensor("k_1_cast")]; + tensor var_369 = const()[name = tensor("op_369"), val = tensor([1, 1])]; + tensor var_371 = const()[name = tensor("op_371"), val = tensor([1, 1])]; tensor v_1_pad_type_0 = const()[name = tensor("v_1_pad_type_0"), val = tensor("custom")]; tensor v_1_pad_0 = const()[name = tensor("v_1_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_1 = conv(dilations = var_415, groups = var_289, pad = v_1_pad_0, pad_type = v_1_pad_type_0, strides = var_413, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight, x = hidden_states_17)[name = tensor("v_1")]; - tensor var_419 = const()[name = tensor("op_419"), val = tensor([2, 10, 64, -1])]; - tensor var_420 = reshape(shape = var_419, x = q_1)[name = tensor("op_420")]; - tensor var_421 = const()[name = tensor("op_421"), val = tensor([2, 10, 64, -1])]; - tensor var_422 = reshape(shape = var_421, x = k_1)[name = tensor("op_422")]; - tensor var_423 = const()[name = tensor("op_423"), val = tensor([2, 10, 64, -1])]; - tensor var_424 = reshape(shape = var_423, x = v_1)[name = tensor("op_424")]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41068032)))]; + tensor v_1_cast = conv(dilations = var_371, groups = var_31, pad = v_1_pad_0, pad_type = v_1_pad_type_0, strides = var_369, weight = unet_down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_17_cast)[name = tensor("v_1_cast")]; + tensor var_375 = const()[name = tensor("op_375"), val = tensor([2, 10, 64, -1])]; + tensor var_376_cast = reshape(shape = var_375, x = q_1_cast)[name = tensor("op_376_cast")]; + tensor var_377 = const()[name = tensor("op_377"), val = tensor([2, 10, 64, -1])]; + tensor var_378_cast = reshape(shape = var_377, x = k_1_cast)[name = tensor("op_378_cast")]; + tensor var_379 = const()[name = tensor("op_379"), val = tensor([2, 10, 64, -1])]; + tensor var_380_cast = reshape(shape = var_379, x = v_1_cast)[name = tensor("op_380_cast")]; tensor attn_weights_1_transpose_x_0 = const()[name = tensor("attn_weights_1_transpose_x_0"), val = tensor(true)]; tensor attn_weights_1_transpose_y_0 = const()[name = tensor("attn_weights_1_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_1 = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_420, y = var_422)[name = tensor("attn_weights_1")]; - tensor attn_weights_3 = mul(x = attn_weights_1, y = var_280)[name = tensor("attn_weights_3")]; - tensor var_428 = softmax(axis = var_273, x = attn_weights_3)[name = tensor("op_428")]; + tensor attn_weights_1_cast = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_376_cast, y = var_378_cast)[name = tensor("attn_weights_1_cast")]; + tensor var_12_to_fp16 = const()[name = tensor("op_12_to_fp16"), val = tensor(0x1p-3)]; + tensor attn_weights_3_cast = mul(x = attn_weights_1_cast, y = var_12_to_fp16)[name = tensor("attn_weights_3_cast")]; + tensor var_384_cast = softmax(axis = var_18, x = attn_weights_3_cast)[name = tensor("op_384_cast")]; tensor attn_1_transpose_x_0 = const()[name = tensor("attn_1_transpose_x_0"), val = tensor(false)]; tensor attn_1_transpose_y_0 = const()[name = tensor("attn_1_transpose_y_0"), val = tensor(true)]; - tensor attn_1 = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_424, y = var_428)[name = tensor("attn_1")]; - tensor var_432 = const()[name = tensor("op_432"), val = tensor([2, 640, 1, -1])]; - tensor input_61 = reshape(shape = var_432, x = attn_1)[name = tensor("input_61")]; - tensor var_437 = const()[name = tensor("op_437"), val = tensor([1, 1])]; - tensor var_439 = const()[name = tensor("op_439"), val = tensor([1, 1])]; - tensor var_441_pad_type_0 = const()[name = tensor("op_441_pad_type_0"), val = tensor("custom")]; - tensor var_441_pad_0 = const()[name = tensor("op_441_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_441 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias, dilations = var_439, groups = var_289, pad = var_441_pad_0, pad_type = var_441_pad_type_0, strides = var_437, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight, x = input_61)[name = tensor("op_441")]; - tensor inputs_3 = add(x = var_441, y = inputs_1)[name = tensor("inputs_3")]; - tensor var_445 = const()[name = tensor("op_445"), val = tensor([1])]; - tensor channels_mean_3 = reduce_mean(axes = var_445, keep_dims = var_284, x = inputs_3)[name = tensor("channels_mean_3")]; - tensor zero_mean_3 = sub(x = inputs_3, y = channels_mean_3)[name = tensor("zero_mean_3")]; - tensor zero_mean_sq_3 = mul(x = zero_mean_3, y = zero_mean_3)[name = tensor("zero_mean_sq_3")]; - tensor var_449 = const()[name = tensor("op_449"), val = tensor([1])]; - tensor var_450 = reduce_mean(axes = var_449, keep_dims = var_284, x = zero_mean_sq_3)[name = tensor("op_450")]; - tensor var_451 = const()[name = tensor("op_451"), val = tensor(0x1.4f8b58p-17)]; - tensor var_452 = add(x = var_450, y = var_451)[name = tensor("op_452")]; - tensor denom_3_epsilon_0 = const()[name = tensor("denom_3_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_3 = rsqrt(epsilon = denom_3_epsilon_0, x = var_452)[name = tensor("denom_3")]; - tensor out_3 = mul(x = zero_mean_3, y = denom_3)[name = tensor("out_3")]; - tensor var_456 = const()[name = tensor("op_456"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267615232)))]; - tensor var_457 = add(x = out_3, y = var_456)[name = tensor("op_457")]; - tensor var_459 = const()[name = tensor("op_459"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267617856)))]; - tensor hidden_states_19 = mul(x = var_457, y = var_459)[name = tensor("hidden_states_19")]; - tensor var_466 = const()[name = tensor("op_466"), val = tensor([1, 1])]; - tensor var_468 = const()[name = tensor("op_468"), val = tensor([1, 1])]; + tensor attn_1_cast = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_380_cast, y = var_384_cast)[name = tensor("attn_1_cast")]; + tensor var_388 = const()[name = tensor("op_388"), val = tensor([2, 640, 1, -1])]; + tensor input_61_cast = reshape(shape = var_388, x = attn_1_cast)[name = tensor("input_61_cast")]; + tensor var_393 = const()[name = tensor("op_393"), val = tensor([1, 1])]; + tensor var_395 = const()[name = tensor("op_395"), val = tensor([1, 1])]; + tensor var_397_pad_type_0 = const()[name = tensor("op_397_pad_type_0"), val = tensor("custom")]; + tensor var_397_pad_0 = const()[name = tensor("op_397_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41887296)))]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42706560)))]; + tensor var_397_cast = conv(bias = unet_down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_395, groups = var_31, pad = var_397_pad_0, pad_type = var_397_pad_type_0, strides = var_393, weight = unet_down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_61_cast)[name = tensor("op_397_cast")]; + tensor inputs_3_cast = add(x = var_397_cast, y = inputs_1_cast)[name = tensor("inputs_3_cast")]; + tensor var_401 = const()[name = tensor("op_401"), val = tensor([1])]; + tensor channels_mean_3_cast = reduce_mean(axes = var_401, keep_dims = var_23, x = inputs_3_cast)[name = tensor("channels_mean_3_cast")]; + tensor zero_mean_3_cast = sub(x = inputs_3_cast, y = channels_mean_3_cast)[name = tensor("zero_mean_3_cast")]; + tensor zero_mean_sq_3_cast = mul(x = zero_mean_3_cast, y = zero_mean_3_cast)[name = tensor("zero_mean_sq_3_cast")]; + tensor var_405 = const()[name = tensor("op_405"), val = tensor([1])]; + tensor var_406_cast = reduce_mean(axes = var_405, keep_dims = var_23, x = zero_mean_sq_3_cast)[name = tensor("op_406_cast")]; + tensor var_407_to_fp16 = const()[name = tensor("op_407_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_408_cast = add(x = var_406_cast, y = var_407_to_fp16)[name = tensor("op_408_cast")]; + tensor denom_3_epsilon_0_to_fp16 = const()[name = tensor("denom_3_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_3_cast = rsqrt(epsilon = denom_3_epsilon_0_to_fp16, x = var_408_cast)[name = tensor("denom_3_cast")]; + tensor out_3_cast = mul(x = zero_mean_3_cast, y = denom_3_cast)[name = tensor("out_3_cast")]; + tensor var_412_to_fp16 = const()[name = tensor("op_412_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42707904)))]; + tensor var_413_cast = add(x = out_3_cast, y = var_412_to_fp16)[name = tensor("op_413_cast")]; + tensor var_415_to_fp16 = const()[name = tensor("op_415_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42709248)))]; + tensor hidden_states_19_cast = mul(x = var_413_cast, y = var_415_to_fp16)[name = tensor("hidden_states_19_cast")]; + tensor var_422 = const()[name = tensor("op_422"), val = tensor([1, 1])]; + tensor var_424 = const()[name = tensor("op_424"), val = tensor([1, 1])]; tensor q_3_pad_type_0 = const()[name = tensor("q_3_pad_type_0"), val = tensor("custom")]; tensor q_3_pad_0 = const()[name = tensor("q_3_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_3 = conv(dilations = var_468, groups = var_289, pad = q_3_pad_0, pad_type = q_3_pad_type_0, strides = var_466, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight, x = hidden_states_19)[name = tensor("q_3")]; - tensor var_472 = const()[name = tensor("op_472"), val = tensor([1, 1])]; - tensor var_474 = const()[name = tensor("op_474"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42710592)))]; + tensor q_3_cast = conv(dilations = var_424, groups = var_31, pad = q_3_pad_0, pad_type = q_3_pad_type_0, strides = var_422, weight = unet_down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_19_cast)[name = tensor("q_3_cast")]; + tensor var_428 = const()[name = tensor("op_428"), val = tensor([1, 1])]; + tensor var_430 = const()[name = tensor("op_430"), val = tensor([1, 1])]; tensor k_3_pad_type_0 = const()[name = tensor("k_3_pad_type_0"), val = tensor("custom")]; tensor k_3_pad_0 = const()[name = tensor("k_3_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_3 = conv(dilations = var_474, groups = var_289, pad = k_3_pad_0, pad_type = k_3_pad_type_0, strides = var_472, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_3")]; - tensor var_478 = const()[name = tensor("op_478"), val = tensor([1, 1])]; - tensor var_480 = const()[name = tensor("op_480"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43529856)))]; + tensor k_3_cast = conv(dilations = var_430, groups = var_31, pad = k_3_pad_0, pad_type = k_3_pad_type_0, strides = var_428, weight = unet_down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_3_cast")]; + tensor var_434 = const()[name = tensor("op_434"), val = tensor([1, 1])]; + tensor var_436 = const()[name = tensor("op_436"), val = tensor([1, 1])]; tensor v_3_pad_type_0 = const()[name = tensor("v_3_pad_type_0"), val = tensor("custom")]; tensor v_3_pad_0 = const()[name = tensor("v_3_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_3 = conv(dilations = var_480, groups = var_289, pad = v_3_pad_0, pad_type = v_3_pad_type_0, strides = var_478, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_3")]; - tensor var_484 = const()[name = tensor("op_484"), val = tensor([2, 10, 64, -1])]; - tensor var_485 = reshape(shape = var_484, x = q_3)[name = tensor("op_485")]; - tensor var_486 = const()[name = tensor("op_486"), val = tensor([2, 10, 64, -1])]; - tensor var_487 = reshape(shape = var_486, x = k_3)[name = tensor("op_487")]; - tensor var_488 = const()[name = tensor("op_488"), val = tensor([2, 10, 64, -1])]; - tensor var_489 = reshape(shape = var_488, x = v_3)[name = tensor("op_489")]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46151360)))]; + tensor v_3_cast = conv(dilations = var_436, groups = var_31, pad = v_3_pad_0, pad_type = v_3_pad_type_0, strides = var_434, weight = unet_down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_3_cast")]; + tensor var_440 = const()[name = tensor("op_440"), val = tensor([2, 10, 64, -1])]; + tensor var_441_cast = reshape(shape = var_440, x = q_3_cast)[name = tensor("op_441_cast")]; + tensor var_442 = const()[name = tensor("op_442"), val = tensor([2, 10, 64, -1])]; + tensor var_443_cast = reshape(shape = var_442, x = k_3_cast)[name = tensor("op_443_cast")]; + tensor var_444 = const()[name = tensor("op_444"), val = tensor([2, 10, 64, -1])]; + tensor var_445_cast = reshape(shape = var_444, x = v_3_cast)[name = tensor("op_445_cast")]; tensor attn_weights_5_transpose_x_0 = const()[name = tensor("attn_weights_5_transpose_x_0"), val = tensor(true)]; tensor attn_weights_5_transpose_y_0 = const()[name = tensor("attn_weights_5_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_5 = matmul(transpose_x = attn_weights_5_transpose_x_0, transpose_y = attn_weights_5_transpose_y_0, x = var_485, y = var_487)[name = tensor("attn_weights_5")]; - tensor attn_weights_7 = mul(x = attn_weights_5, y = var_280)[name = tensor("attn_weights_7")]; - tensor var_493 = softmax(axis = var_273, x = attn_weights_7)[name = tensor("op_493")]; + tensor attn_weights_5_cast = matmul(transpose_x = attn_weights_5_transpose_x_0, transpose_y = attn_weights_5_transpose_y_0, x = var_441_cast, y = var_443_cast)[name = tensor("attn_weights_5_cast")]; + tensor attn_weights_7_cast = mul(x = attn_weights_5_cast, y = var_12_to_fp16)[name = tensor("attn_weights_7_cast")]; + tensor var_449_cast = softmax(axis = var_18, x = attn_weights_7_cast)[name = tensor("op_449_cast")]; tensor attn_3_transpose_x_0 = const()[name = tensor("attn_3_transpose_x_0"), val = tensor(false)]; tensor attn_3_transpose_y_0 = const()[name = tensor("attn_3_transpose_y_0"), val = tensor(true)]; - tensor attn_3 = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_489, y = var_493)[name = tensor("attn_3")]; - tensor var_497 = const()[name = tensor("op_497"), val = tensor([2, 640, 1, -1])]; - tensor input_63 = reshape(shape = var_497, x = attn_3)[name = tensor("input_63")]; - tensor var_502 = const()[name = tensor("op_502"), val = tensor([1, 1])]; - tensor var_504 = const()[name = tensor("op_504"), val = tensor([1, 1])]; - tensor var_506_pad_type_0 = const()[name = tensor("op_506_pad_type_0"), val = tensor("custom")]; - tensor var_506_pad_0 = const()[name = tensor("op_506_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_506 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias, dilations = var_504, groups = var_289, pad = var_506_pad_0, pad_type = var_506_pad_type_0, strides = var_502, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight, x = input_63)[name = tensor("op_506")]; - tensor inputs_5 = add(x = var_506, y = inputs_3)[name = tensor("inputs_5")]; - tensor var_510 = const()[name = tensor("op_510"), val = tensor([1])]; - tensor channels_mean_5 = reduce_mean(axes = var_510, keep_dims = var_284, x = inputs_5)[name = tensor("channels_mean_5")]; - tensor zero_mean_5 = sub(x = inputs_5, y = channels_mean_5)[name = tensor("zero_mean_5")]; - tensor zero_mean_sq_5 = mul(x = zero_mean_5, y = zero_mean_5)[name = tensor("zero_mean_sq_5")]; - tensor var_514 = const()[name = tensor("op_514"), val = tensor([1])]; - tensor var_515 = reduce_mean(axes = var_514, keep_dims = var_284, x = zero_mean_sq_5)[name = tensor("op_515")]; - tensor var_516 = const()[name = tensor("op_516"), val = tensor(0x1.4f8b58p-17)]; - tensor var_517 = add(x = var_515, y = var_516)[name = tensor("op_517")]; - tensor denom_5_epsilon_0 = const()[name = tensor("denom_5_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_5 = rsqrt(epsilon = denom_5_epsilon_0, x = var_517)[name = tensor("denom_5")]; - tensor out_5 = mul(x = zero_mean_5, y = denom_5)[name = tensor("out_5")]; - tensor var_521 = const()[name = tensor("op_521"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267620480)))]; - tensor var_522 = add(x = out_5, y = var_521)[name = tensor("op_522")]; - tensor var_524 = const()[name = tensor("op_524"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267623104)))]; - tensor input_65 = mul(x = var_522, y = var_524)[name = tensor("input_65")]; - tensor var_532 = const()[name = tensor("op_532"), val = tensor([1, 1])]; + tensor attn_3_cast = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_445_cast, y = var_449_cast)[name = tensor("attn_3_cast")]; + tensor var_453 = const()[name = tensor("op_453"), val = tensor([2, 640, 1, -1])]; + tensor input_63_cast = reshape(shape = var_453, x = attn_3_cast)[name = tensor("input_63_cast")]; + tensor var_458 = const()[name = tensor("op_458"), val = tensor([1, 1])]; + tensor var_460 = const()[name = tensor("op_460"), val = tensor([1, 1])]; + tensor var_462_pad_type_0 = const()[name = tensor("op_462_pad_type_0"), val = tensor("custom")]; + tensor var_462_pad_0 = const()[name = tensor("op_462_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48772864)))]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49592128)))]; + tensor var_462_cast = conv(bias = unet_down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_460, groups = var_31, pad = var_462_pad_0, pad_type = var_462_pad_type_0, strides = var_458, weight = unet_down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_63_cast)[name = tensor("op_462_cast")]; + tensor inputs_5_cast = add(x = var_462_cast, y = inputs_3_cast)[name = tensor("inputs_5_cast")]; + tensor var_466 = const()[name = tensor("op_466"), val = tensor([1])]; + tensor channels_mean_5_cast = reduce_mean(axes = var_466, keep_dims = var_23, x = inputs_5_cast)[name = tensor("channels_mean_5_cast")]; + tensor zero_mean_5_cast = sub(x = inputs_5_cast, y = channels_mean_5_cast)[name = tensor("zero_mean_5_cast")]; + tensor zero_mean_sq_5_cast = mul(x = zero_mean_5_cast, y = zero_mean_5_cast)[name = tensor("zero_mean_sq_5_cast")]; + tensor var_470 = const()[name = tensor("op_470"), val = tensor([1])]; + tensor var_471_cast = reduce_mean(axes = var_470, keep_dims = var_23, x = zero_mean_sq_5_cast)[name = tensor("op_471_cast")]; + tensor var_472_to_fp16 = const()[name = tensor("op_472_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_473_cast = add(x = var_471_cast, y = var_472_to_fp16)[name = tensor("op_473_cast")]; + tensor denom_5_epsilon_0_to_fp16 = const()[name = tensor("denom_5_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_5_cast = rsqrt(epsilon = denom_5_epsilon_0_to_fp16, x = var_473_cast)[name = tensor("denom_5_cast")]; + tensor out_5_cast = mul(x = zero_mean_5_cast, y = denom_5_cast)[name = tensor("out_5_cast")]; + tensor var_477_to_fp16 = const()[name = tensor("op_477_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49593472)))]; + tensor var_478_cast = add(x = out_5_cast, y = var_477_to_fp16)[name = tensor("op_478_cast")]; + tensor var_480_to_fp16 = const()[name = tensor("op_480_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49594816)))]; + tensor input_65_cast = mul(x = var_478_cast, y = var_480_to_fp16)[name = tensor("input_65_cast")]; + tensor var_488 = const()[name = tensor("op_488"), val = tensor([1, 1])]; + tensor var_490 = const()[name = tensor("op_490"), val = tensor([1, 1])]; + tensor var_492_pad_type_0 = const()[name = tensor("op_492_pad_type_0"), val = tensor("custom")]; + tensor var_492_pad_0 = const()[name = tensor("op_492_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49596160)))]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56149824)))]; + tensor var_492_cast = conv(bias = unet_down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_490, groups = var_31, pad = var_492_pad_0, pad_type = var_492_pad_type_0, strides = var_488, weight = unet_down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_65_cast)[name = tensor("op_492_cast")]; + tensor var_493_split_sizes_0 = const()[name = tensor("op_493_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_493_axis_0 = const()[name = tensor("op_493_axis_0"), val = tensor(1)]; + tensor var_493_cast_0, tensor var_493_cast_1 = split(axis = var_493_axis_0, split_sizes = var_493_split_sizes_0, x = var_492_cast)[name = tensor("op_493_cast")]; + tensor var_495_mode_0 = const()[name = tensor("op_495_mode_0"), val = tensor("EXACT")]; + tensor var_495_cast = gelu(mode = var_495_mode_0, x = var_493_cast_1)[name = tensor("op_495_cast")]; + tensor input_67_cast = mul(x = var_493_cast_0, y = var_495_cast)[name = tensor("input_67_cast")]; + tensor var_499 = const()[name = tensor("op_499"), val = tensor([1, 1])]; + tensor var_501 = const()[name = tensor("op_501"), val = tensor([1, 1])]; + tensor var_503_pad_type_0 = const()[name = tensor("op_503_pad_type_0"), val = tensor("custom")]; + tensor var_503_pad_0 = const()[name = tensor("op_503_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56160128)))]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59436992)))]; + tensor var_503_cast = conv(bias = unet_down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_501, groups = var_31, pad = var_503_pad_0, pad_type = var_503_pad_type_0, strides = var_499, weight = unet_down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_67_cast)[name = tensor("op_503_cast")]; + tensor inputs_7_cast = add(x = var_503_cast, y = inputs_5_cast)[name = tensor("inputs_7_cast")]; + tensor var_513 = const()[name = tensor("op_513"), val = tensor([1])]; + tensor channels_mean_7_cast = reduce_mean(axes = var_513, keep_dims = var_23, x = inputs_7_cast)[name = tensor("channels_mean_7_cast")]; + tensor zero_mean_7_cast = sub(x = inputs_7_cast, y = channels_mean_7_cast)[name = tensor("zero_mean_7_cast")]; + tensor zero_mean_sq_7_cast = mul(x = zero_mean_7_cast, y = zero_mean_7_cast)[name = tensor("zero_mean_sq_7_cast")]; + tensor var_517 = const()[name = tensor("op_517"), val = tensor([1])]; + tensor var_518_cast = reduce_mean(axes = var_517, keep_dims = var_23, x = zero_mean_sq_7_cast)[name = tensor("op_518_cast")]; + tensor var_519_to_fp16 = const()[name = tensor("op_519_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_520_cast = add(x = var_518_cast, y = var_519_to_fp16)[name = tensor("op_520_cast")]; + tensor denom_7_epsilon_0_to_fp16 = const()[name = tensor("denom_7_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_7_cast = rsqrt(epsilon = denom_7_epsilon_0_to_fp16, x = var_520_cast)[name = tensor("denom_7_cast")]; + tensor out_7_cast = mul(x = zero_mean_7_cast, y = denom_7_cast)[name = tensor("out_7_cast")]; + tensor var_524_to_fp16 = const()[name = tensor("op_524_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59438336)))]; + tensor var_525_cast = add(x = out_7_cast, y = var_524_to_fp16)[name = tensor("op_525_cast")]; + tensor var_527_to_fp16 = const()[name = tensor("op_527_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59439680)))]; + tensor hidden_states_23_cast = mul(x = var_525_cast, y = var_527_to_fp16)[name = tensor("hidden_states_23_cast")]; tensor var_534 = const()[name = tensor("op_534"), val = tensor([1, 1])]; - tensor var_536_pad_type_0 = const()[name = tensor("op_536_pad_type_0"), val = tensor("custom")]; - tensor var_536_pad_0 = const()[name = tensor("op_536_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_536 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias, dilations = var_534, groups = var_289, pad = var_536_pad_0, pad_type = var_536_pad_type_0, strides = var_532, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight, x = input_65)[name = tensor("op_536")]; - tensor var_537_split_sizes_0 = const()[name = tensor("op_537_split_sizes_0"), val = tensor([2560, 2560])]; - tensor var_537_axis_0 = const()[name = tensor("op_537_axis_0"), val = tensor(1)]; - tensor var_537_0, tensor var_537_1 = split(axis = var_537_axis_0, split_sizes = var_537_split_sizes_0, x = var_536)[name = tensor("op_537")]; - tensor var_539_mode_0 = const()[name = tensor("op_539_mode_0"), val = tensor("EXACT")]; - tensor var_539 = gelu(mode = var_539_mode_0, x = var_537_1)[name = tensor("op_539")]; - tensor input_67 = mul(x = var_537_0, y = var_539)[name = tensor("input_67")]; - tensor var_543 = const()[name = tensor("op_543"), val = tensor([1, 1])]; - tensor var_545 = const()[name = tensor("op_545"), val = tensor([1, 1])]; - tensor var_547_pad_type_0 = const()[name = tensor("op_547_pad_type_0"), val = tensor("custom")]; - tensor var_547_pad_0 = const()[name = tensor("op_547_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_547 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias, dilations = var_545, groups = var_289, pad = var_547_pad_0, pad_type = var_547_pad_type_0, strides = var_543, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight, x = input_67)[name = tensor("op_547")]; - tensor inputs_7 = add(x = var_547, y = inputs_5)[name = tensor("inputs_7")]; - tensor var_557 = const()[name = tensor("op_557"), val = tensor([1])]; - tensor channels_mean_7 = reduce_mean(axes = var_557, keep_dims = var_284, x = inputs_7)[name = tensor("channels_mean_7")]; - tensor zero_mean_7 = sub(x = inputs_7, y = channels_mean_7)[name = tensor("zero_mean_7")]; - tensor zero_mean_sq_7 = mul(x = zero_mean_7, y = zero_mean_7)[name = tensor("zero_mean_sq_7")]; - tensor var_561 = const()[name = tensor("op_561"), val = tensor([1])]; - tensor var_562 = reduce_mean(axes = var_561, keep_dims = var_284, x = zero_mean_sq_7)[name = tensor("op_562")]; - tensor var_563 = const()[name = tensor("op_563"), val = tensor(0x1.4f8b58p-17)]; - tensor var_564 = add(x = var_562, y = var_563)[name = tensor("op_564")]; - tensor denom_7_epsilon_0 = const()[name = tensor("denom_7_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_7 = rsqrt(epsilon = denom_7_epsilon_0, x = var_564)[name = tensor("denom_7")]; - tensor out_7 = mul(x = zero_mean_7, y = denom_7)[name = tensor("out_7")]; - tensor var_568 = const()[name = tensor("op_568"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267625728)))]; - tensor var_569 = add(x = out_7, y = var_568)[name = tensor("op_569")]; - tensor var_571 = const()[name = tensor("op_571"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267628352)))]; - tensor hidden_states_23 = mul(x = var_569, y = var_571)[name = tensor("hidden_states_23")]; - tensor var_578 = const()[name = tensor("op_578"), val = tensor([1, 1])]; - tensor var_580 = const()[name = tensor("op_580"), val = tensor([1, 1])]; + tensor var_536 = const()[name = tensor("op_536"), val = tensor([1, 1])]; tensor q_5_pad_type_0 = const()[name = tensor("q_5_pad_type_0"), val = tensor("custom")]; tensor q_5_pad_0 = const()[name = tensor("q_5_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_5 = conv(dilations = var_580, groups = var_289, pad = q_5_pad_0, pad_type = q_5_pad_type_0, strides = var_578, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight, x = hidden_states_23)[name = tensor("q_5")]; - tensor var_584 = const()[name = tensor("op_584"), val = tensor([1, 1])]; - tensor var_586 = const()[name = tensor("op_586"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59441024)))]; + tensor q_5_cast = conv(dilations = var_536, groups = var_31, pad = q_5_pad_0, pad_type = q_5_pad_type_0, strides = var_534, weight = unet_down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_23_cast)[name = tensor("q_5_cast")]; + tensor var_540 = const()[name = tensor("op_540"), val = tensor([1, 1])]; + tensor var_542 = const()[name = tensor("op_542"), val = tensor([1, 1])]; tensor k_5_pad_type_0 = const()[name = tensor("k_5_pad_type_0"), val = tensor("custom")]; tensor k_5_pad_0 = const()[name = tensor("k_5_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_5 = conv(dilations = var_586, groups = var_289, pad = k_5_pad_0, pad_type = k_5_pad_type_0, strides = var_584, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight, x = hidden_states_23)[name = tensor("k_5")]; - tensor var_590 = const()[name = tensor("op_590"), val = tensor([1, 1])]; - tensor var_592 = const()[name = tensor("op_592"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60260288)))]; + tensor k_5_cast = conv(dilations = var_542, groups = var_31, pad = k_5_pad_0, pad_type = k_5_pad_type_0, strides = var_540, weight = unet_down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_23_cast)[name = tensor("k_5_cast")]; + tensor var_546 = const()[name = tensor("op_546"), val = tensor([1, 1])]; + tensor var_548 = const()[name = tensor("op_548"), val = tensor([1, 1])]; tensor v_5_pad_type_0 = const()[name = tensor("v_5_pad_type_0"), val = tensor("custom")]; tensor v_5_pad_0 = const()[name = tensor("v_5_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_5 = conv(dilations = var_592, groups = var_289, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = var_590, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight, x = hidden_states_23)[name = tensor("v_5")]; - tensor var_596 = const()[name = tensor("op_596"), val = tensor([2, 10, 64, -1])]; - tensor var_597 = reshape(shape = var_596, x = q_5)[name = tensor("op_597")]; - tensor var_598 = const()[name = tensor("op_598"), val = tensor([2, 10, 64, -1])]; - tensor var_599 = reshape(shape = var_598, x = k_5)[name = tensor("op_599")]; - tensor var_600 = const()[name = tensor("op_600"), val = tensor([2, 10, 64, -1])]; - tensor var_601 = reshape(shape = var_600, x = v_5)[name = tensor("op_601")]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61079552)))]; + tensor v_5_cast = conv(dilations = var_548, groups = var_31, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = var_546, weight = unet_down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_23_cast)[name = tensor("v_5_cast")]; + tensor var_552 = const()[name = tensor("op_552"), val = tensor([2, 10, 64, -1])]; + tensor var_553_cast = reshape(shape = var_552, x = q_5_cast)[name = tensor("op_553_cast")]; + tensor var_554 = const()[name = tensor("op_554"), val = tensor([2, 10, 64, -1])]; + tensor var_555_cast = reshape(shape = var_554, x = k_5_cast)[name = tensor("op_555_cast")]; + tensor var_556 = const()[name = tensor("op_556"), val = tensor([2, 10, 64, -1])]; + tensor var_557_cast = reshape(shape = var_556, x = v_5_cast)[name = tensor("op_557_cast")]; tensor attn_weights_9_transpose_x_0 = const()[name = tensor("attn_weights_9_transpose_x_0"), val = tensor(true)]; tensor attn_weights_9_transpose_y_0 = const()[name = tensor("attn_weights_9_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_9 = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_597, y = var_599)[name = tensor("attn_weights_9")]; - tensor attn_weights_11 = mul(x = attn_weights_9, y = var_280)[name = tensor("attn_weights_11")]; - tensor var_605 = softmax(axis = var_273, x = attn_weights_11)[name = tensor("op_605")]; + tensor attn_weights_9_cast = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_553_cast, y = var_555_cast)[name = tensor("attn_weights_9_cast")]; + tensor attn_weights_11_cast = mul(x = attn_weights_9_cast, y = var_12_to_fp16)[name = tensor("attn_weights_11_cast")]; + tensor var_561_cast = softmax(axis = var_18, x = attn_weights_11_cast)[name = tensor("op_561_cast")]; tensor attn_5_transpose_x_0 = const()[name = tensor("attn_5_transpose_x_0"), val = tensor(false)]; tensor attn_5_transpose_y_0 = const()[name = tensor("attn_5_transpose_y_0"), val = tensor(true)]; - tensor attn_5 = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_601, y = var_605)[name = tensor("attn_5")]; - tensor var_609 = const()[name = tensor("op_609"), val = tensor([2, 640, 1, -1])]; - tensor input_69 = reshape(shape = var_609, x = attn_5)[name = tensor("input_69")]; - tensor var_614 = const()[name = tensor("op_614"), val = tensor([1, 1])]; - tensor var_616 = const()[name = tensor("op_616"), val = tensor([1, 1])]; - tensor var_618_pad_type_0 = const()[name = tensor("op_618_pad_type_0"), val = tensor("custom")]; - tensor var_618_pad_0 = const()[name = tensor("op_618_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_618 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias, dilations = var_616, groups = var_289, pad = var_618_pad_0, pad_type = var_618_pad_type_0, strides = var_614, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight, x = input_69)[name = tensor("op_618")]; - tensor inputs_9 = add(x = var_618, y = inputs_7)[name = tensor("inputs_9")]; - tensor var_622 = const()[name = tensor("op_622"), val = tensor([1])]; - tensor channels_mean_9 = reduce_mean(axes = var_622, keep_dims = var_284, x = inputs_9)[name = tensor("channels_mean_9")]; - tensor zero_mean_9 = sub(x = inputs_9, y = channels_mean_9)[name = tensor("zero_mean_9")]; - tensor zero_mean_sq_9 = mul(x = zero_mean_9, y = zero_mean_9)[name = tensor("zero_mean_sq_9")]; - tensor var_626 = const()[name = tensor("op_626"), val = tensor([1])]; - tensor var_627 = reduce_mean(axes = var_626, keep_dims = var_284, x = zero_mean_sq_9)[name = tensor("op_627")]; - tensor var_628 = const()[name = tensor("op_628"), val = tensor(0x1.4f8b58p-17)]; - tensor var_629 = add(x = var_627, y = var_628)[name = tensor("op_629")]; - tensor denom_9_epsilon_0 = const()[name = tensor("denom_9_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_9 = rsqrt(epsilon = denom_9_epsilon_0, x = var_629)[name = tensor("denom_9")]; - tensor out_9 = mul(x = zero_mean_9, y = denom_9)[name = tensor("out_9")]; - tensor var_633 = const()[name = tensor("op_633"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267630976)))]; - tensor var_634 = add(x = out_9, y = var_633)[name = tensor("op_634")]; - tensor var_636 = const()[name = tensor("op_636"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267633600)))]; - tensor hidden_states_25 = mul(x = var_634, y = var_636)[name = tensor("hidden_states_25")]; - tensor var_643 = const()[name = tensor("op_643"), val = tensor([1, 1])]; - tensor var_645 = const()[name = tensor("op_645"), val = tensor([1, 1])]; + tensor attn_5_cast = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_557_cast, y = var_561_cast)[name = tensor("attn_5_cast")]; + tensor var_565 = const()[name = tensor("op_565"), val = tensor([2, 640, 1, -1])]; + tensor input_69_cast = reshape(shape = var_565, x = attn_5_cast)[name = tensor("input_69_cast")]; + tensor var_570 = const()[name = tensor("op_570"), val = tensor([1, 1])]; + tensor var_572 = const()[name = tensor("op_572"), val = tensor([1, 1])]; + tensor var_574_pad_type_0 = const()[name = tensor("op_574_pad_type_0"), val = tensor("custom")]; + tensor var_574_pad_0 = const()[name = tensor("op_574_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61898816)))]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62718080)))]; + tensor var_574_cast = conv(bias = unet_down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_572, groups = var_31, pad = var_574_pad_0, pad_type = var_574_pad_type_0, strides = var_570, weight = unet_down_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_69_cast)[name = tensor("op_574_cast")]; + tensor inputs_9_cast = add(x = var_574_cast, y = inputs_7_cast)[name = tensor("inputs_9_cast")]; + tensor var_578 = const()[name = tensor("op_578"), val = tensor([1])]; + tensor channels_mean_9_cast = reduce_mean(axes = var_578, keep_dims = var_23, x = inputs_9_cast)[name = tensor("channels_mean_9_cast")]; + tensor zero_mean_9_cast = sub(x = inputs_9_cast, y = channels_mean_9_cast)[name = tensor("zero_mean_9_cast")]; + tensor zero_mean_sq_9_cast = mul(x = zero_mean_9_cast, y = zero_mean_9_cast)[name = tensor("zero_mean_sq_9_cast")]; + tensor var_582 = const()[name = tensor("op_582"), val = tensor([1])]; + tensor var_583_cast = reduce_mean(axes = var_582, keep_dims = var_23, x = zero_mean_sq_9_cast)[name = tensor("op_583_cast")]; + tensor var_584_to_fp16 = const()[name = tensor("op_584_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_585_cast = add(x = var_583_cast, y = var_584_to_fp16)[name = tensor("op_585_cast")]; + tensor denom_9_epsilon_0_to_fp16 = const()[name = tensor("denom_9_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_9_cast = rsqrt(epsilon = denom_9_epsilon_0_to_fp16, x = var_585_cast)[name = tensor("denom_9_cast")]; + tensor out_9_cast = mul(x = zero_mean_9_cast, y = denom_9_cast)[name = tensor("out_9_cast")]; + tensor var_589_to_fp16 = const()[name = tensor("op_589_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62719424)))]; + tensor var_590_cast = add(x = out_9_cast, y = var_589_to_fp16)[name = tensor("op_590_cast")]; + tensor var_592_to_fp16 = const()[name = tensor("op_592_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62720768)))]; + tensor hidden_states_25_cast = mul(x = var_590_cast, y = var_592_to_fp16)[name = tensor("hidden_states_25_cast")]; + tensor var_599 = const()[name = tensor("op_599"), val = tensor([1, 1])]; + tensor var_601 = const()[name = tensor("op_601"), val = tensor([1, 1])]; tensor q_7_pad_type_0 = const()[name = tensor("q_7_pad_type_0"), val = tensor("custom")]; tensor q_7_pad_0 = const()[name = tensor("q_7_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_7 = conv(dilations = var_645, groups = var_289, pad = q_7_pad_0, pad_type = q_7_pad_type_0, strides = var_643, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight, x = hidden_states_25)[name = tensor("q_7")]; - tensor var_649 = const()[name = tensor("op_649"), val = tensor([1, 1])]; - tensor var_651 = const()[name = tensor("op_651"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62722112)))]; + tensor q_7_cast = conv(dilations = var_601, groups = var_31, pad = q_7_pad_0, pad_type = q_7_pad_type_0, strides = var_599, weight = unet_down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_25_cast)[name = tensor("q_7_cast")]; + tensor var_605 = const()[name = tensor("op_605"), val = tensor([1, 1])]; + tensor var_607 = const()[name = tensor("op_607"), val = tensor([1, 1])]; tensor k_7_pad_type_0 = const()[name = tensor("k_7_pad_type_0"), val = tensor("custom")]; tensor k_7_pad_0 = const()[name = tensor("k_7_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_7 = conv(dilations = var_651, groups = var_289, pad = k_7_pad_0, pad_type = k_7_pad_type_0, strides = var_649, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_7")]; - tensor var_655 = const()[name = tensor("op_655"), val = tensor([1, 1])]; - tensor var_657 = const()[name = tensor("op_657"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63541376)))]; + tensor k_7_cast = conv(dilations = var_607, groups = var_31, pad = k_7_pad_0, pad_type = k_7_pad_type_0, strides = var_605, weight = unet_down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_7_cast")]; + tensor var_611 = const()[name = tensor("op_611"), val = tensor([1, 1])]; + tensor var_613 = const()[name = tensor("op_613"), val = tensor([1, 1])]; tensor v_7_pad_type_0 = const()[name = tensor("v_7_pad_type_0"), val = tensor("custom")]; tensor v_7_pad_0 = const()[name = tensor("v_7_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_7 = conv(dilations = var_657, groups = var_289, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = var_655, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_7")]; - tensor var_661 = const()[name = tensor("op_661"), val = tensor([2, 10, 64, -1])]; - tensor var_662 = reshape(shape = var_661, x = q_7)[name = tensor("op_662")]; - tensor var_663 = const()[name = tensor("op_663"), val = tensor([2, 10, 64, -1])]; - tensor var_664 = reshape(shape = var_663, x = k_7)[name = tensor("op_664")]; - tensor var_665 = const()[name = tensor("op_665"), val = tensor([2, 10, 64, -1])]; - tensor var_666 = reshape(shape = var_665, x = v_7)[name = tensor("op_666")]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(66162880)))]; + tensor v_7_cast = conv(dilations = var_613, groups = var_31, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = var_611, weight = unet_down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_7_cast")]; + tensor var_617 = const()[name = tensor("op_617"), val = tensor([2, 10, 64, -1])]; + tensor var_618_cast = reshape(shape = var_617, x = q_7_cast)[name = tensor("op_618_cast")]; + tensor var_619 = const()[name = tensor("op_619"), val = tensor([2, 10, 64, -1])]; + tensor var_620_cast = reshape(shape = var_619, x = k_7_cast)[name = tensor("op_620_cast")]; + tensor var_621 = const()[name = tensor("op_621"), val = tensor([2, 10, 64, -1])]; + tensor var_622_cast = reshape(shape = var_621, x = v_7_cast)[name = tensor("op_622_cast")]; tensor attn_weights_13_transpose_x_0 = const()[name = tensor("attn_weights_13_transpose_x_0"), val = tensor(true)]; tensor attn_weights_13_transpose_y_0 = const()[name = tensor("attn_weights_13_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_13 = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = var_662, y = var_664)[name = tensor("attn_weights_13")]; - tensor attn_weights_15 = mul(x = attn_weights_13, y = var_280)[name = tensor("attn_weights_15")]; - tensor var_670 = softmax(axis = var_273, x = attn_weights_15)[name = tensor("op_670")]; + tensor attn_weights_13_cast = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = var_618_cast, y = var_620_cast)[name = tensor("attn_weights_13_cast")]; + tensor attn_weights_15_cast = mul(x = attn_weights_13_cast, y = var_12_to_fp16)[name = tensor("attn_weights_15_cast")]; + tensor var_626_cast = softmax(axis = var_18, x = attn_weights_15_cast)[name = tensor("op_626_cast")]; tensor attn_7_transpose_x_0 = const()[name = tensor("attn_7_transpose_x_0"), val = tensor(false)]; tensor attn_7_transpose_y_0 = const()[name = tensor("attn_7_transpose_y_0"), val = tensor(true)]; - tensor attn_7 = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_666, y = var_670)[name = tensor("attn_7")]; - tensor var_674 = const()[name = tensor("op_674"), val = tensor([2, 640, 1, -1])]; - tensor input_71 = reshape(shape = var_674, x = attn_7)[name = tensor("input_71")]; - tensor var_679 = const()[name = tensor("op_679"), val = tensor([1, 1])]; - tensor var_681 = const()[name = tensor("op_681"), val = tensor([1, 1])]; - tensor var_683_pad_type_0 = const()[name = tensor("op_683_pad_type_0"), val = tensor("custom")]; - tensor var_683_pad_0 = const()[name = tensor("op_683_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_683 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias, dilations = var_681, groups = var_289, pad = var_683_pad_0, pad_type = var_683_pad_type_0, strides = var_679, weight = down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight, x = input_71)[name = tensor("op_683")]; - tensor inputs_11 = add(x = var_683, y = inputs_9)[name = tensor("inputs_11")]; - tensor var_687 = const()[name = tensor("op_687"), val = tensor([1])]; - tensor channels_mean_11 = reduce_mean(axes = var_687, keep_dims = var_284, x = inputs_11)[name = tensor("channels_mean_11")]; - tensor zero_mean_11 = sub(x = inputs_11, y = channels_mean_11)[name = tensor("zero_mean_11")]; - tensor zero_mean_sq_11 = mul(x = zero_mean_11, y = zero_mean_11)[name = tensor("zero_mean_sq_11")]; - tensor var_691 = const()[name = tensor("op_691"), val = tensor([1])]; - tensor var_692 = reduce_mean(axes = var_691, keep_dims = var_284, x = zero_mean_sq_11)[name = tensor("op_692")]; - tensor var_693 = const()[name = tensor("op_693"), val = tensor(0x1.4f8b58p-17)]; - tensor var_694 = add(x = var_692, y = var_693)[name = tensor("op_694")]; - tensor denom_11_epsilon_0 = const()[name = tensor("denom_11_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_11 = rsqrt(epsilon = denom_11_epsilon_0, x = var_694)[name = tensor("denom_11")]; - tensor out_11 = mul(x = zero_mean_11, y = denom_11)[name = tensor("out_11")]; - tensor var_698 = const()[name = tensor("op_698"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267636224)))]; - tensor var_699 = add(x = out_11, y = var_698)[name = tensor("op_699")]; - tensor var_701 = const()[name = tensor("op_701"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267638848)))]; - tensor input_73 = mul(x = var_699, y = var_701)[name = tensor("input_73")]; - tensor var_709 = const()[name = tensor("op_709"), val = tensor([1, 1])]; - tensor var_711 = const()[name = tensor("op_711"), val = tensor([1, 1])]; - tensor var_713_pad_type_0 = const()[name = tensor("op_713_pad_type_0"), val = tensor("custom")]; - tensor var_713_pad_0 = const()[name = tensor("op_713_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_713 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias, dilations = var_711, groups = var_289, pad = var_713_pad_0, pad_type = var_713_pad_type_0, strides = var_709, weight = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight, x = input_73)[name = tensor("op_713")]; - tensor var_714_split_sizes_0 = const()[name = tensor("op_714_split_sizes_0"), val = tensor([2560, 2560])]; - tensor var_714_axis_0 = const()[name = tensor("op_714_axis_0"), val = tensor(1)]; - tensor var_714_0, tensor var_714_1 = split(axis = var_714_axis_0, split_sizes = var_714_split_sizes_0, x = var_713)[name = tensor("op_714")]; - tensor var_716_mode_0 = const()[name = tensor("op_716_mode_0"), val = tensor("EXACT")]; - tensor var_716 = gelu(mode = var_716_mode_0, x = var_714_1)[name = tensor("op_716")]; - tensor input_75 = mul(x = var_714_0, y = var_716)[name = tensor("input_75")]; - tensor var_720 = const()[name = tensor("op_720"), val = tensor([1, 1])]; - tensor var_722 = const()[name = tensor("op_722"), val = tensor([1, 1])]; - tensor var_724_pad_type_0 = const()[name = tensor("op_724_pad_type_0"), val = tensor("custom")]; - tensor var_724_pad_0 = const()[name = tensor("op_724_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_724 = conv(bias = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias, dilations = var_722, groups = var_289, pad = var_724_pad_0, pad_type = var_724_pad_type_0, strides = var_720, weight = down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight, x = input_75)[name = tensor("op_724")]; - tensor hidden_states_29 = add(x = var_724, y = inputs_11)[name = tensor("hidden_states_29")]; - tensor var_726 = const()[name = tensor("op_726"), val = tensor([2, 640, 64, 64])]; - tensor input_77 = reshape(shape = var_726, x = hidden_states_29)[name = tensor("input_77")]; - tensor var_730 = const()[name = tensor("op_730"), val = tensor([1, 1])]; - tensor var_732 = const()[name = tensor("op_732"), val = tensor([1, 1])]; + tensor attn_7_cast = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_622_cast, y = var_626_cast)[name = tensor("attn_7_cast")]; + tensor var_630 = const()[name = tensor("op_630"), val = tensor([2, 640, 1, -1])]; + tensor input_71_cast = reshape(shape = var_630, x = attn_7_cast)[name = tensor("input_71_cast")]; + tensor var_635 = const()[name = tensor("op_635"), val = tensor([1, 1])]; + tensor var_637 = const()[name = tensor("op_637"), val = tensor([1, 1])]; + tensor var_639_pad_type_0 = const()[name = tensor("op_639_pad_type_0"), val = tensor("custom")]; + tensor var_639_pad_0 = const()[name = tensor("op_639_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68784384)))]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69603648)))]; + tensor var_639_cast = conv(bias = unet_down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_637, groups = var_31, pad = var_639_pad_0, pad_type = var_639_pad_type_0, strides = var_635, weight = unet_down_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_71_cast)[name = tensor("op_639_cast")]; + tensor inputs_11_cast = add(x = var_639_cast, y = inputs_9_cast)[name = tensor("inputs_11_cast")]; + tensor var_643 = const()[name = tensor("op_643"), val = tensor([1])]; + tensor channels_mean_11_cast = reduce_mean(axes = var_643, keep_dims = var_23, x = inputs_11_cast)[name = tensor("channels_mean_11_cast")]; + tensor zero_mean_11_cast = sub(x = inputs_11_cast, y = channels_mean_11_cast)[name = tensor("zero_mean_11_cast")]; + tensor zero_mean_sq_11_cast = mul(x = zero_mean_11_cast, y = zero_mean_11_cast)[name = tensor("zero_mean_sq_11_cast")]; + tensor var_647 = const()[name = tensor("op_647"), val = tensor([1])]; + tensor var_648_cast = reduce_mean(axes = var_647, keep_dims = var_23, x = zero_mean_sq_11_cast)[name = tensor("op_648_cast")]; + tensor var_649_to_fp16 = const()[name = tensor("op_649_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_650_cast = add(x = var_648_cast, y = var_649_to_fp16)[name = tensor("op_650_cast")]; + tensor denom_11_epsilon_0_to_fp16 = const()[name = tensor("denom_11_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_11_cast = rsqrt(epsilon = denom_11_epsilon_0_to_fp16, x = var_650_cast)[name = tensor("denom_11_cast")]; + tensor out_11_cast = mul(x = zero_mean_11_cast, y = denom_11_cast)[name = tensor("out_11_cast")]; + tensor var_654_to_fp16 = const()[name = tensor("op_654_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69604992)))]; + tensor var_655_cast = add(x = out_11_cast, y = var_654_to_fp16)[name = tensor("op_655_cast")]; + tensor var_657_to_fp16 = const()[name = tensor("op_657_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69606336)))]; + tensor input_73_cast = mul(x = var_655_cast, y = var_657_to_fp16)[name = tensor("input_73_cast")]; + tensor var_665 = const()[name = tensor("op_665"), val = tensor([1, 1])]; + tensor var_667 = const()[name = tensor("op_667"), val = tensor([1, 1])]; + tensor var_669_pad_type_0 = const()[name = tensor("op_669_pad_type_0"), val = tensor("custom")]; + tensor var_669_pad_0 = const()[name = tensor("op_669_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(69607680)))]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76161344)))]; + tensor var_669_cast = conv(bias = unet_down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_667, groups = var_31, pad = var_669_pad_0, pad_type = var_669_pad_type_0, strides = var_665, weight = unet_down_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_73_cast)[name = tensor("op_669_cast")]; + tensor var_670_split_sizes_0 = const()[name = tensor("op_670_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_670_axis_0 = const()[name = tensor("op_670_axis_0"), val = tensor(1)]; + tensor var_670_cast_0, tensor var_670_cast_1 = split(axis = var_670_axis_0, split_sizes = var_670_split_sizes_0, x = var_669_cast)[name = tensor("op_670_cast")]; + tensor var_672_mode_0 = const()[name = tensor("op_672_mode_0"), val = tensor("EXACT")]; + tensor var_672_cast = gelu(mode = var_672_mode_0, x = var_670_cast_1)[name = tensor("op_672_cast")]; + tensor input_75_cast = mul(x = var_670_cast_0, y = var_672_cast)[name = tensor("input_75_cast")]; + tensor var_676 = const()[name = tensor("op_676"), val = tensor([1, 1])]; + tensor var_678 = const()[name = tensor("op_678"), val = tensor([1, 1])]; + tensor var_680_pad_type_0 = const()[name = tensor("op_680_pad_type_0"), val = tensor("custom")]; + tensor var_680_pad_0 = const()[name = tensor("op_680_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76171648)))]; + tensor unet_down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79448512)))]; + tensor var_680_cast = conv(bias = unet_down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_678, groups = var_31, pad = var_680_pad_0, pad_type = var_680_pad_type_0, strides = var_676, weight = unet_down_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_75_cast)[name = tensor("op_680_cast")]; + tensor hidden_states_29_cast = add(x = var_680_cast, y = inputs_11_cast)[name = tensor("hidden_states_29_cast")]; + tensor var_682 = const()[name = tensor("op_682"), val = tensor([2, 640, 64, 64])]; + tensor input_77_cast = reshape(shape = var_682, x = hidden_states_29_cast)[name = tensor("input_77_cast")]; + tensor var_686 = const()[name = tensor("op_686"), val = tensor([1, 1])]; + tensor var_688 = const()[name = tensor("op_688"), val = tensor([1, 1])]; tensor hidden_states_31_pad_type_0 = const()[name = tensor("hidden_states_31_pad_type_0"), val = tensor("custom")]; tensor hidden_states_31_pad_0 = const()[name = tensor("hidden_states_31_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_31 = conv(bias = down_blocks_1_attentions_0_proj_out_bias, dilations = var_732, groups = var_289, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = var_730, weight = down_blocks_1_attentions_0_proj_out_weight, x = input_77)[name = tensor("hidden_states_31")]; - tensor input_79 = add(x = hidden_states_31, y = hidden_states_13)[name = tensor("input_79")]; + tensor unet_down_blocks_1_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79449856)))]; + tensor unet_down_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80269120)))]; + tensor hidden_states_31_cast = conv(bias = unet_down_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_688, groups = var_31, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = var_686, weight = unet_down_blocks_1_attentions_0_proj_out_weight_to_fp16, x = input_77_cast)[name = tensor("hidden_states_31_cast")]; + tensor input_79_cast = add(x = hidden_states_31_cast, y = hidden_states_13_cast)[name = tensor("input_79_cast")]; tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([2, 32, 20, 64, 64])]; - tensor reshape_28 = reshape(shape = reshape_28_shape_0, x = input_79)[name = tensor("reshape_28")]; + tensor reshape_28_cast = reshape(shape = reshape_28_shape_0, x = input_79_cast)[name = tensor("reshape_28_cast")]; tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_21 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28)[name = tensor("reduce_mean_21")]; - tensor sub_14 = sub(x = reshape_28, y = reduce_mean_21)[name = tensor("sub_14")]; - tensor square_7 = square(x = sub_14)[name = tensor("square_7")]; + tensor reduce_mean_21_cast = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast)[name = tensor("reduce_mean_21_cast")]; + tensor sub_14_cast = sub(x = reshape_28_cast, y = reduce_mean_21_cast)[name = tensor("sub_14_cast")]; + tensor square_7_cast = square(x = sub_14_cast)[name = tensor("square_7_cast")]; tensor reduce_mean_23_axes_0 = const()[name = tensor("reduce_mean_23_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_23_keep_dims_0 = const()[name = tensor("reduce_mean_23_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_23 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7)[name = tensor("reduce_mean_23")]; - tensor add_14_y_0 = const()[name = tensor("add_14_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_14 = add(x = reduce_mean_23, y = add_14_y_0)[name = tensor("add_14")]; - tensor sqrt_7 = sqrt(x = add_14)[name = tensor("sqrt_7")]; - tensor real_div_7 = real_div(x = sub_14, y = sqrt_7)[name = tensor("real_div_7")]; + tensor reduce_mean_23_cast = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast)[name = tensor("reduce_mean_23_cast")]; + tensor add_14_y_0_to_fp16 = const()[name = tensor("add_14_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_14_cast = add(x = reduce_mean_23_cast, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast")]; + tensor sqrt_7_cast = sqrt(x = add_14_cast)[name = tensor("sqrt_7_cast")]; + tensor real_div_7_cast = real_div(x = sub_14_cast, y = sqrt_7_cast)[name = tensor("real_div_7_cast")]; tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([2, 640, 64, 64])]; - tensor reshape_29 = reshape(shape = reshape_29_shape_0, x = real_div_7)[name = tensor("reshape_29")]; - tensor add_15_gamma_0 = const()[name = tensor("add_15_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267641472)))]; - tensor add_15_beta_0 = const()[name = tensor("add_15_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267644096)))]; - tensor add_15_epsilon_0 = const()[name = tensor("add_15_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_15 = batch_norm(beta = add_15_beta_0, epsilon = add_15_epsilon_0, gamma = add_15_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_29)[name = tensor("add_15")]; - tensor input_83 = silu(x = add_15)[name = tensor("input_83")]; - tensor var_747 = const()[name = tensor("op_747"), val = tensor([1, 1])]; - tensor var_749 = const()[name = tensor("op_749"), val = tensor([1, 1])]; + tensor reshape_29_cast = reshape(shape = reshape_29_shape_0, x = real_div_7_cast)[name = tensor("reshape_29_cast")]; + tensor add_15_gamma_0_to_fp16 = const()[name = tensor("add_15_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80270464)))]; + tensor add_15_beta_0_to_fp16 = const()[name = tensor("add_15_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80271808)))]; + tensor add_15_epsilon_0_to_fp16 = const()[name = tensor("add_15_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_15_cast = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_29_cast)[name = tensor("add_15_cast")]; + tensor input_83_cast = silu(x = add_15_cast)[name = tensor("input_83_cast")]; + tensor var_703 = const()[name = tensor("op_703"), val = tensor([1, 1])]; + tensor var_705 = const()[name = tensor("op_705"), val = tensor([1, 1])]; tensor hidden_states_33_pad_type_0 = const()[name = tensor("hidden_states_33_pad_type_0"), val = tensor("custom")]; tensor hidden_states_33_pad_0 = const()[name = tensor("hidden_states_33_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_33 = conv(bias = down_blocks_1_resnets_1_conv1_bias, dilations = var_749, groups = var_289, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = var_747, weight = down_blocks_1_resnets_1_conv1_weight, x = input_83)[name = tensor("hidden_states_33")]; - tensor var_755 = const()[name = tensor("op_755"), val = tensor([1, 1])]; - tensor var_757 = const()[name = tensor("op_757"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80273152)))]; + tensor unet_down_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87646016)))]; + tensor hidden_states_33_cast = conv(bias = unet_down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_705, groups = var_31, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = var_703, weight = unet_down_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_83_cast)[name = tensor("hidden_states_33_cast")]; + tensor var_711 = const()[name = tensor("op_711"), val = tensor([1, 1])]; + tensor var_713 = const()[name = tensor("op_713"), val = tensor([1, 1])]; tensor temb_7_pad_type_0 = const()[name = tensor("temb_7_pad_type_0"), val = tensor("custom")]; tensor temb_7_pad_0 = const()[name = tensor("temb_7_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb_7 = conv(bias = down_blocks_1_resnets_1_time_emb_proj_bias, dilations = var_757, groups = var_289, pad = temb_7_pad_0, pad_type = temb_7_pad_type_0, strides = var_755, weight = down_blocks_1_resnets_1_time_emb_proj_weight, x = input_21)[name = tensor("temb_7")]; - tensor input_87 = add(x = hidden_states_33, y = temb_7)[name = tensor("input_87")]; + tensor unet_down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87647360)))]; + tensor unet_down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89285824)))]; + tensor temb_7_cast = conv(bias = unet_down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_713, groups = var_31, pad = temb_7_pad_0, pad_type = temb_7_pad_type_0, strides = var_711, weight = unet_down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_7_cast")]; + tensor input_87_cast = add(x = hidden_states_33_cast, y = temb_7_cast)[name = tensor("input_87_cast")]; tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([2, 32, 20, 64, 64])]; - tensor reshape_32 = reshape(shape = reshape_32_shape_0, x = input_87)[name = tensor("reshape_32")]; + tensor reshape_32_cast = reshape(shape = reshape_32_shape_0, x = input_87_cast)[name = tensor("reshape_32_cast")]; tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_24_keep_dims_0 = const()[name = tensor("reduce_mean_24_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_24 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32)[name = tensor("reduce_mean_24")]; - tensor sub_16 = sub(x = reshape_32, y = reduce_mean_24)[name = tensor("sub_16")]; - tensor square_8 = square(x = sub_16)[name = tensor("square_8")]; + tensor reduce_mean_24_cast = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast)[name = tensor("reduce_mean_24_cast")]; + tensor sub_16_cast = sub(x = reshape_32_cast, y = reduce_mean_24_cast)[name = tensor("sub_16_cast")]; + tensor square_8_cast = square(x = sub_16_cast)[name = tensor("square_8_cast")]; tensor reduce_mean_26_axes_0 = const()[name = tensor("reduce_mean_26_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_26_keep_dims_0 = const()[name = tensor("reduce_mean_26_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_26 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8)[name = tensor("reduce_mean_26")]; - tensor add_16_y_0 = const()[name = tensor("add_16_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_16 = add(x = reduce_mean_26, y = add_16_y_0)[name = tensor("add_16")]; - tensor sqrt_8 = sqrt(x = add_16)[name = tensor("sqrt_8")]; - tensor real_div_8 = real_div(x = sub_16, y = sqrt_8)[name = tensor("real_div_8")]; + tensor reduce_mean_26_cast = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast)[name = tensor("reduce_mean_26_cast")]; + tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_16_cast = add(x = reduce_mean_26_cast, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast")]; + tensor sqrt_8_cast = sqrt(x = add_16_cast)[name = tensor("sqrt_8_cast")]; + tensor real_div_8_cast = real_div(x = sub_16_cast, y = sqrt_8_cast)[name = tensor("real_div_8_cast")]; tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([2, 640, 64, 64])]; - tensor reshape_33 = reshape(shape = reshape_33_shape_0, x = real_div_8)[name = tensor("reshape_33")]; - tensor add_17_gamma_0 = const()[name = tensor("add_17_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267646720)))]; - tensor add_17_beta_0 = const()[name = tensor("add_17_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267649344)))]; - tensor add_17_epsilon_0 = const()[name = tensor("add_17_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_17 = batch_norm(beta = add_17_beta_0, epsilon = add_17_epsilon_0, gamma = add_17_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_33)[name = tensor("add_17")]; - tensor input_91 = silu(x = add_17)[name = tensor("input_91")]; - tensor var_767 = const()[name = tensor("op_767"), val = tensor([1, 1])]; - tensor var_769 = const()[name = tensor("op_769"), val = tensor([1, 1])]; + tensor reshape_33_cast = reshape(shape = reshape_33_shape_0, x = real_div_8_cast)[name = tensor("reshape_33_cast")]; + tensor add_17_gamma_0_to_fp16 = const()[name = tensor("add_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89287168)))]; + tensor add_17_beta_0_to_fp16 = const()[name = tensor("add_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89288512)))]; + tensor add_17_epsilon_0_to_fp16 = const()[name = tensor("add_17_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_17_cast = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_33_cast)[name = tensor("add_17_cast")]; + tensor input_91_cast = silu(x = add_17_cast)[name = tensor("input_91_cast")]; + tensor var_723 = const()[name = tensor("op_723"), val = tensor([1, 1])]; + tensor var_725 = const()[name = tensor("op_725"), val = tensor([1, 1])]; tensor hidden_states_35_pad_type_0 = const()[name = tensor("hidden_states_35_pad_type_0"), val = tensor("custom")]; tensor hidden_states_35_pad_0 = const()[name = tensor("hidden_states_35_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_35 = conv(bias = down_blocks_1_resnets_1_conv2_bias, dilations = var_769, groups = var_289, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_767, weight = down_blocks_1_resnets_1_conv2_weight, x = input_91)[name = tensor("hidden_states_35")]; - tensor hidden_states_37 = add(x = input_79, y = hidden_states_35)[name = tensor("hidden_states_37")]; + tensor unet_down_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89289856)))]; + tensor unet_down_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96662720)))]; + tensor hidden_states_35_cast = conv(bias = unet_down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_725, groups = var_31, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_723, weight = unet_down_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_91_cast)[name = tensor("hidden_states_35_cast")]; + tensor hidden_states_37_cast = add(x = input_79_cast, y = hidden_states_35_cast)[name = tensor("hidden_states_37_cast")]; tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([2, 32, 20, 64, 64])]; - tensor reshape_36 = reshape(shape = reshape_36_shape_0, x = hidden_states_37)[name = tensor("reshape_36")]; + tensor reshape_36_cast = reshape(shape = reshape_36_shape_0, x = hidden_states_37_cast)[name = tensor("reshape_36_cast")]; tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_27 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36)[name = tensor("reduce_mean_27")]; - tensor sub_18 = sub(x = reshape_36, y = reduce_mean_27)[name = tensor("sub_18")]; - tensor square_9 = square(x = sub_18)[name = tensor("square_9")]; + tensor reduce_mean_27_cast = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast)[name = tensor("reduce_mean_27_cast")]; + tensor sub_18_cast = sub(x = reshape_36_cast, y = reduce_mean_27_cast)[name = tensor("sub_18_cast")]; + tensor square_9_cast = square(x = sub_18_cast)[name = tensor("square_9_cast")]; tensor reduce_mean_29_axes_0 = const()[name = tensor("reduce_mean_29_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_29_keep_dims_0 = const()[name = tensor("reduce_mean_29_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_29 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9)[name = tensor("reduce_mean_29")]; - tensor add_18_y_0 = const()[name = tensor("add_18_y_0"), val = tensor(0x1.0c6f7ap-20)]; - tensor add_18 = add(x = reduce_mean_29, y = add_18_y_0)[name = tensor("add_18")]; - tensor sqrt_9 = sqrt(x = add_18)[name = tensor("sqrt_9")]; - tensor real_div_9 = real_div(x = sub_18, y = sqrt_9)[name = tensor("real_div_9")]; + tensor reduce_mean_29_cast = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast)[name = tensor("reduce_mean_29_cast")]; + tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_18_cast = add(x = reduce_mean_29_cast, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast")]; + tensor sqrt_9_cast = sqrt(x = add_18_cast)[name = tensor("sqrt_9_cast")]; + tensor real_div_9_cast = real_div(x = sub_18_cast, y = sqrt_9_cast)[name = tensor("real_div_9_cast")]; tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([2, 640, 64, 64])]; - tensor reshape_37 = reshape(shape = reshape_37_shape_0, x = real_div_9)[name = tensor("reshape_37")]; - tensor add_19_gamma_0 = const()[name = tensor("add_19_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267651968)))]; - tensor add_19_beta_0 = const()[name = tensor("add_19_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267654592)))]; - tensor add_19_epsilon_0 = const()[name = tensor("add_19_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_19 = batch_norm(beta = add_19_beta_0, epsilon = add_19_epsilon_0, gamma = add_19_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_37)[name = tensor("add_19")]; - tensor var_791 = const()[name = tensor("op_791"), val = tensor([1, 1])]; - tensor var_793 = const()[name = tensor("op_793"), val = tensor([1, 1])]; + tensor reshape_37_cast = reshape(shape = reshape_37_shape_0, x = real_div_9_cast)[name = tensor("reshape_37_cast")]; + tensor add_19_gamma_0_to_fp16 = const()[name = tensor("add_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96664064)))]; + tensor add_19_beta_0_to_fp16 = const()[name = tensor("add_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96665408)))]; + tensor add_19_epsilon_0_to_fp16 = const()[name = tensor("add_19_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_19_cast = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_37_cast)[name = tensor("add_19_cast")]; + tensor var_747 = const()[name = tensor("op_747"), val = tensor([1, 1])]; + tensor var_749 = const()[name = tensor("op_749"), val = tensor([1, 1])]; tensor hidden_states_39_pad_type_0 = const()[name = tensor("hidden_states_39_pad_type_0"), val = tensor("custom")]; tensor hidden_states_39_pad_0 = const()[name = tensor("hidden_states_39_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_39 = conv(bias = down_blocks_1_attentions_1_proj_in_bias, dilations = var_793, groups = var_289, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_791, weight = down_blocks_1_attentions_1_proj_in_weight, x = add_19)[name = tensor("hidden_states_39")]; - tensor var_798 = const()[name = tensor("op_798"), val = tensor([2, 640, 1, 4096])]; - tensor inputs_13 = reshape(shape = var_798, x = hidden_states_39)[name = tensor("inputs_13")]; - tensor var_808 = const()[name = tensor("op_808"), val = tensor([1])]; - tensor channels_mean_13 = reduce_mean(axes = var_808, keep_dims = var_284, x = inputs_13)[name = tensor("channels_mean_13")]; - tensor zero_mean_13 = sub(x = inputs_13, y = channels_mean_13)[name = tensor("zero_mean_13")]; - tensor zero_mean_sq_13 = mul(x = zero_mean_13, y = zero_mean_13)[name = tensor("zero_mean_sq_13")]; - tensor var_812 = const()[name = tensor("op_812"), val = tensor([1])]; - tensor var_813 = reduce_mean(axes = var_812, keep_dims = var_284, x = zero_mean_sq_13)[name = tensor("op_813")]; - tensor var_814 = const()[name = tensor("op_814"), val = tensor(0x1.4f8b58p-17)]; - tensor var_815 = add(x = var_813, y = var_814)[name = tensor("op_815")]; - tensor denom_13_epsilon_0 = const()[name = tensor("denom_13_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_13 = rsqrt(epsilon = denom_13_epsilon_0, x = var_815)[name = tensor("denom_13")]; - tensor out_13 = mul(x = zero_mean_13, y = denom_13)[name = tensor("out_13")]; - tensor var_819 = const()[name = tensor("op_819"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267657216)))]; - tensor var_820 = add(x = out_13, y = var_819)[name = tensor("op_820")]; - tensor var_822 = const()[name = tensor("op_822"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267659840)))]; - tensor hidden_states_41 = mul(x = var_820, y = var_822)[name = tensor("hidden_states_41")]; - tensor var_829 = const()[name = tensor("op_829"), val = tensor([1, 1])]; - tensor var_831 = const()[name = tensor("op_831"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96666752)))]; + tensor unet_down_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97486016)))]; + tensor hidden_states_39_cast = conv(bias = unet_down_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_749, groups = var_31, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_747, weight = unet_down_blocks_1_attentions_1_proj_in_weight_to_fp16, x = add_19_cast)[name = tensor("hidden_states_39_cast")]; + tensor var_754 = const()[name = tensor("op_754"), val = tensor([2, 640, 1, 4096])]; + tensor inputs_13_cast = reshape(shape = var_754, x = hidden_states_39_cast)[name = tensor("inputs_13_cast")]; + tensor var_764 = const()[name = tensor("op_764"), val = tensor([1])]; + tensor channels_mean_13_cast = reduce_mean(axes = var_764, keep_dims = var_23, x = inputs_13_cast)[name = tensor("channels_mean_13_cast")]; + tensor zero_mean_13_cast = sub(x = inputs_13_cast, y = channels_mean_13_cast)[name = tensor("zero_mean_13_cast")]; + tensor zero_mean_sq_13_cast = mul(x = zero_mean_13_cast, y = zero_mean_13_cast)[name = tensor("zero_mean_sq_13_cast")]; + tensor var_768 = const()[name = tensor("op_768"), val = tensor([1])]; + tensor var_769_cast = reduce_mean(axes = var_768, keep_dims = var_23, x = zero_mean_sq_13_cast)[name = tensor("op_769_cast")]; + tensor var_770_to_fp16 = const()[name = tensor("op_770_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_771_cast = add(x = var_769_cast, y = var_770_to_fp16)[name = tensor("op_771_cast")]; + tensor denom_13_epsilon_0_to_fp16 = const()[name = tensor("denom_13_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_13_cast = rsqrt(epsilon = denom_13_epsilon_0_to_fp16, x = var_771_cast)[name = tensor("denom_13_cast")]; + tensor out_13_cast = mul(x = zero_mean_13_cast, y = denom_13_cast)[name = tensor("out_13_cast")]; + tensor var_775_to_fp16 = const()[name = tensor("op_775_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97487360)))]; + tensor var_776_cast = add(x = out_13_cast, y = var_775_to_fp16)[name = tensor("op_776_cast")]; + tensor var_778_to_fp16 = const()[name = tensor("op_778_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97488704)))]; + tensor hidden_states_41_cast = mul(x = var_776_cast, y = var_778_to_fp16)[name = tensor("hidden_states_41_cast")]; + tensor var_785 = const()[name = tensor("op_785"), val = tensor([1, 1])]; + tensor var_787 = const()[name = tensor("op_787"), val = tensor([1, 1])]; tensor q_9_pad_type_0 = const()[name = tensor("q_9_pad_type_0"), val = tensor("custom")]; tensor q_9_pad_0 = const()[name = tensor("q_9_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_9 = conv(dilations = var_831, groups = var_289, pad = q_9_pad_0, pad_type = q_9_pad_type_0, strides = var_829, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight, x = hidden_states_41)[name = tensor("q_9")]; - tensor var_835 = const()[name = tensor("op_835"), val = tensor([1, 1])]; - tensor var_837 = const()[name = tensor("op_837"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97490048)))]; + tensor q_9_cast = conv(dilations = var_787, groups = var_31, pad = q_9_pad_0, pad_type = q_9_pad_type_0, strides = var_785, weight = unet_down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_41_cast)[name = tensor("q_9_cast")]; + tensor var_791 = const()[name = tensor("op_791"), val = tensor([1, 1])]; + tensor var_793 = const()[name = tensor("op_793"), val = tensor([1, 1])]; tensor k_9_pad_type_0 = const()[name = tensor("k_9_pad_type_0"), val = tensor("custom")]; tensor k_9_pad_0 = const()[name = tensor("k_9_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_9 = conv(dilations = var_837, groups = var_289, pad = k_9_pad_0, pad_type = k_9_pad_type_0, strides = var_835, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight, x = hidden_states_41)[name = tensor("k_9")]; - tensor var_841 = const()[name = tensor("op_841"), val = tensor([1, 1])]; - tensor var_843 = const()[name = tensor("op_843"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98309312)))]; + tensor k_9_cast = conv(dilations = var_793, groups = var_31, pad = k_9_pad_0, pad_type = k_9_pad_type_0, strides = var_791, weight = unet_down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_41_cast)[name = tensor("k_9_cast")]; + tensor var_797 = const()[name = tensor("op_797"), val = tensor([1, 1])]; + tensor var_799 = const()[name = tensor("op_799"), val = tensor([1, 1])]; tensor v_9_pad_type_0 = const()[name = tensor("v_9_pad_type_0"), val = tensor("custom")]; tensor v_9_pad_0 = const()[name = tensor("v_9_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_9 = conv(dilations = var_843, groups = var_289, pad = v_9_pad_0, pad_type = v_9_pad_type_0, strides = var_841, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight, x = hidden_states_41)[name = tensor("v_9")]; - tensor var_847 = const()[name = tensor("op_847"), val = tensor([2, 10, 64, -1])]; - tensor var_848 = reshape(shape = var_847, x = q_9)[name = tensor("op_848")]; - tensor var_849 = const()[name = tensor("op_849"), val = tensor([2, 10, 64, -1])]; - tensor var_850 = reshape(shape = var_849, x = k_9)[name = tensor("op_850")]; - tensor var_851 = const()[name = tensor("op_851"), val = tensor([2, 10, 64, -1])]; - tensor var_852 = reshape(shape = var_851, x = v_9)[name = tensor("op_852")]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99128576)))]; + tensor v_9_cast = conv(dilations = var_799, groups = var_31, pad = v_9_pad_0, pad_type = v_9_pad_type_0, strides = var_797, weight = unet_down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_41_cast)[name = tensor("v_9_cast")]; + tensor var_803 = const()[name = tensor("op_803"), val = tensor([2, 10, 64, -1])]; + tensor var_804_cast = reshape(shape = var_803, x = q_9_cast)[name = tensor("op_804_cast")]; + tensor var_805 = const()[name = tensor("op_805"), val = tensor([2, 10, 64, -1])]; + tensor var_806_cast = reshape(shape = var_805, x = k_9_cast)[name = tensor("op_806_cast")]; + tensor var_807 = const()[name = tensor("op_807"), val = tensor([2, 10, 64, -1])]; + tensor var_808_cast = reshape(shape = var_807, x = v_9_cast)[name = tensor("op_808_cast")]; tensor attn_weights_17_transpose_x_0 = const()[name = tensor("attn_weights_17_transpose_x_0"), val = tensor(true)]; tensor attn_weights_17_transpose_y_0 = const()[name = tensor("attn_weights_17_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_17 = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_848, y = var_850)[name = tensor("attn_weights_17")]; - tensor attn_weights_19 = mul(x = attn_weights_17, y = var_280)[name = tensor("attn_weights_19")]; - tensor var_856 = softmax(axis = var_273, x = attn_weights_19)[name = tensor("op_856")]; + tensor attn_weights_17_cast = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_804_cast, y = var_806_cast)[name = tensor("attn_weights_17_cast")]; + tensor attn_weights_19_cast = mul(x = attn_weights_17_cast, y = var_12_to_fp16)[name = tensor("attn_weights_19_cast")]; + tensor var_812_cast = softmax(axis = var_18, x = attn_weights_19_cast)[name = tensor("op_812_cast")]; tensor attn_9_transpose_x_0 = const()[name = tensor("attn_9_transpose_x_0"), val = tensor(false)]; tensor attn_9_transpose_y_0 = const()[name = tensor("attn_9_transpose_y_0"), val = tensor(true)]; - tensor attn_9 = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_852, y = var_856)[name = tensor("attn_9")]; - tensor var_860 = const()[name = tensor("op_860"), val = tensor([2, 640, 1, -1])]; - tensor input_95 = reshape(shape = var_860, x = attn_9)[name = tensor("input_95")]; - tensor var_865 = const()[name = tensor("op_865"), val = tensor([1, 1])]; - tensor var_867 = const()[name = tensor("op_867"), val = tensor([1, 1])]; - tensor var_869_pad_type_0 = const()[name = tensor("op_869_pad_type_0"), val = tensor("custom")]; - tensor var_869_pad_0 = const()[name = tensor("op_869_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_869 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias, dilations = var_867, groups = var_289, pad = var_869_pad_0, pad_type = var_869_pad_type_0, strides = var_865, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight, x = input_95)[name = tensor("op_869")]; - tensor inputs_15 = add(x = var_869, y = inputs_13)[name = tensor("inputs_15")]; - tensor var_873 = const()[name = tensor("op_873"), val = tensor([1])]; - tensor channels_mean_15 = reduce_mean(axes = var_873, keep_dims = var_284, x = inputs_15)[name = tensor("channels_mean_15")]; - tensor zero_mean_15 = sub(x = inputs_15, y = channels_mean_15)[name = tensor("zero_mean_15")]; - tensor zero_mean_sq_15 = mul(x = zero_mean_15, y = zero_mean_15)[name = tensor("zero_mean_sq_15")]; - tensor var_877 = const()[name = tensor("op_877"), val = tensor([1])]; - tensor var_878 = reduce_mean(axes = var_877, keep_dims = var_284, x = zero_mean_sq_15)[name = tensor("op_878")]; - tensor var_879 = const()[name = tensor("op_879"), val = tensor(0x1.4f8b58p-17)]; - tensor var_880 = add(x = var_878, y = var_879)[name = tensor("op_880")]; - tensor denom_15_epsilon_0 = const()[name = tensor("denom_15_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_15 = rsqrt(epsilon = denom_15_epsilon_0, x = var_880)[name = tensor("denom_15")]; - tensor out_15 = mul(x = zero_mean_15, y = denom_15)[name = tensor("out_15")]; - tensor var_884 = const()[name = tensor("op_884"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267662464)))]; - tensor var_885 = add(x = out_15, y = var_884)[name = tensor("op_885")]; - tensor var_887 = const()[name = tensor("op_887"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267665088)))]; - tensor hidden_states_43 = mul(x = var_885, y = var_887)[name = tensor("hidden_states_43")]; - tensor var_894 = const()[name = tensor("op_894"), val = tensor([1, 1])]; - tensor var_896 = const()[name = tensor("op_896"), val = tensor([1, 1])]; + tensor attn_9_cast = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_808_cast, y = var_812_cast)[name = tensor("attn_9_cast")]; + tensor var_816 = const()[name = tensor("op_816"), val = tensor([2, 640, 1, -1])]; + tensor input_95_cast = reshape(shape = var_816, x = attn_9_cast)[name = tensor("input_95_cast")]; + tensor var_821 = const()[name = tensor("op_821"), val = tensor([1, 1])]; + tensor var_823 = const()[name = tensor("op_823"), val = tensor([1, 1])]; + tensor var_825_pad_type_0 = const()[name = tensor("op_825_pad_type_0"), val = tensor("custom")]; + tensor var_825_pad_0 = const()[name = tensor("op_825_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99947840)))]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100767104)))]; + tensor var_825_cast = conv(bias = unet_down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_823, groups = var_31, pad = var_825_pad_0, pad_type = var_825_pad_type_0, strides = var_821, weight = unet_down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_95_cast)[name = tensor("op_825_cast")]; + tensor inputs_15_cast = add(x = var_825_cast, y = inputs_13_cast)[name = tensor("inputs_15_cast")]; + tensor var_829 = const()[name = tensor("op_829"), val = tensor([1])]; + tensor channels_mean_15_cast = reduce_mean(axes = var_829, keep_dims = var_23, x = inputs_15_cast)[name = tensor("channels_mean_15_cast")]; + tensor zero_mean_15_cast = sub(x = inputs_15_cast, y = channels_mean_15_cast)[name = tensor("zero_mean_15_cast")]; + tensor zero_mean_sq_15_cast = mul(x = zero_mean_15_cast, y = zero_mean_15_cast)[name = tensor("zero_mean_sq_15_cast")]; + tensor var_833 = const()[name = tensor("op_833"), val = tensor([1])]; + tensor var_834_cast = reduce_mean(axes = var_833, keep_dims = var_23, x = zero_mean_sq_15_cast)[name = tensor("op_834_cast")]; + tensor var_835_to_fp16 = const()[name = tensor("op_835_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_836_cast = add(x = var_834_cast, y = var_835_to_fp16)[name = tensor("op_836_cast")]; + tensor denom_15_epsilon_0_to_fp16 = const()[name = tensor("denom_15_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_15_cast = rsqrt(epsilon = denom_15_epsilon_0_to_fp16, x = var_836_cast)[name = tensor("denom_15_cast")]; + tensor out_15_cast = mul(x = zero_mean_15_cast, y = denom_15_cast)[name = tensor("out_15_cast")]; + tensor var_840_to_fp16 = const()[name = tensor("op_840_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100768448)))]; + tensor var_841_cast = add(x = out_15_cast, y = var_840_to_fp16)[name = tensor("op_841_cast")]; + tensor var_843_to_fp16 = const()[name = tensor("op_843_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100769792)))]; + tensor hidden_states_43_cast = mul(x = var_841_cast, y = var_843_to_fp16)[name = tensor("hidden_states_43_cast")]; + tensor var_850 = const()[name = tensor("op_850"), val = tensor([1, 1])]; + tensor var_852 = const()[name = tensor("op_852"), val = tensor([1, 1])]; tensor q_11_pad_type_0 = const()[name = tensor("q_11_pad_type_0"), val = tensor("custom")]; tensor q_11_pad_0 = const()[name = tensor("q_11_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_11 = conv(dilations = var_896, groups = var_289, pad = q_11_pad_0, pad_type = q_11_pad_type_0, strides = var_894, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight, x = hidden_states_43)[name = tensor("q_11")]; - tensor var_900 = const()[name = tensor("op_900"), val = tensor([1, 1])]; - tensor var_902 = const()[name = tensor("op_902"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(100771136)))]; + tensor q_11_cast = conv(dilations = var_852, groups = var_31, pad = q_11_pad_0, pad_type = q_11_pad_type_0, strides = var_850, weight = unet_down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_43_cast)[name = tensor("q_11_cast")]; + tensor var_856 = const()[name = tensor("op_856"), val = tensor([1, 1])]; + tensor var_858 = const()[name = tensor("op_858"), val = tensor([1, 1])]; tensor k_11_pad_type_0 = const()[name = tensor("k_11_pad_type_0"), val = tensor("custom")]; tensor k_11_pad_0 = const()[name = tensor("k_11_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_11 = conv(dilations = var_902, groups = var_289, pad = k_11_pad_0, pad_type = k_11_pad_type_0, strides = var_900, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_11")]; - tensor var_906 = const()[name = tensor("op_906"), val = tensor([1, 1])]; - tensor var_908 = const()[name = tensor("op_908"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(101590400)))]; + tensor k_11_cast = conv(dilations = var_858, groups = var_31, pad = k_11_pad_0, pad_type = k_11_pad_type_0, strides = var_856, weight = unet_down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_11_cast")]; + tensor var_862 = const()[name = tensor("op_862"), val = tensor([1, 1])]; + tensor var_864 = const()[name = tensor("op_864"), val = tensor([1, 1])]; tensor v_11_pad_type_0 = const()[name = tensor("v_11_pad_type_0"), val = tensor("custom")]; tensor v_11_pad_0 = const()[name = tensor("v_11_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_11 = conv(dilations = var_908, groups = var_289, pad = v_11_pad_0, pad_type = v_11_pad_type_0, strides = var_906, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_11")]; - tensor var_912 = const()[name = tensor("op_912"), val = tensor([2, 10, 64, -1])]; - tensor var_913 = reshape(shape = var_912, x = q_11)[name = tensor("op_913")]; - tensor var_914 = const()[name = tensor("op_914"), val = tensor([2, 10, 64, -1])]; - tensor var_915 = reshape(shape = var_914, x = k_11)[name = tensor("op_915")]; - tensor var_916 = const()[name = tensor("op_916"), val = tensor([2, 10, 64, -1])]; - tensor var_917 = reshape(shape = var_916, x = v_11)[name = tensor("op_917")]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(104211904)))]; + tensor v_11_cast = conv(dilations = var_864, groups = var_31, pad = v_11_pad_0, pad_type = v_11_pad_type_0, strides = var_862, weight = unet_down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_11_cast")]; + tensor var_868 = const()[name = tensor("op_868"), val = tensor([2, 10, 64, -1])]; + tensor var_869_cast = reshape(shape = var_868, x = q_11_cast)[name = tensor("op_869_cast")]; + tensor var_870 = const()[name = tensor("op_870"), val = tensor([2, 10, 64, -1])]; + tensor var_871_cast = reshape(shape = var_870, x = k_11_cast)[name = tensor("op_871_cast")]; + tensor var_872 = const()[name = tensor("op_872"), val = tensor([2, 10, 64, -1])]; + tensor var_873_cast = reshape(shape = var_872, x = v_11_cast)[name = tensor("op_873_cast")]; tensor attn_weights_21_transpose_x_0 = const()[name = tensor("attn_weights_21_transpose_x_0"), val = tensor(true)]; tensor attn_weights_21_transpose_y_0 = const()[name = tensor("attn_weights_21_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_21 = matmul(transpose_x = attn_weights_21_transpose_x_0, transpose_y = attn_weights_21_transpose_y_0, x = var_913, y = var_915)[name = tensor("attn_weights_21")]; - tensor attn_weights_23 = mul(x = attn_weights_21, y = var_280)[name = tensor("attn_weights_23")]; - tensor var_921 = softmax(axis = var_273, x = attn_weights_23)[name = tensor("op_921")]; + tensor attn_weights_21_cast = matmul(transpose_x = attn_weights_21_transpose_x_0, transpose_y = attn_weights_21_transpose_y_0, x = var_869_cast, y = var_871_cast)[name = tensor("attn_weights_21_cast")]; + tensor attn_weights_23_cast = mul(x = attn_weights_21_cast, y = var_12_to_fp16)[name = tensor("attn_weights_23_cast")]; + tensor var_877_cast = softmax(axis = var_18, x = attn_weights_23_cast)[name = tensor("op_877_cast")]; tensor attn_11_transpose_x_0 = const()[name = tensor("attn_11_transpose_x_0"), val = tensor(false)]; tensor attn_11_transpose_y_0 = const()[name = tensor("attn_11_transpose_y_0"), val = tensor(true)]; - tensor attn_11 = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_917, y = var_921)[name = tensor("attn_11")]; - tensor var_925 = const()[name = tensor("op_925"), val = tensor([2, 640, 1, -1])]; - tensor input_97 = reshape(shape = var_925, x = attn_11)[name = tensor("input_97")]; - tensor var_930 = const()[name = tensor("op_930"), val = tensor([1, 1])]; - tensor var_932 = const()[name = tensor("op_932"), val = tensor([1, 1])]; - tensor var_934_pad_type_0 = const()[name = tensor("op_934_pad_type_0"), val = tensor("custom")]; - tensor var_934_pad_0 = const()[name = tensor("op_934_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_934 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias, dilations = var_932, groups = var_289, pad = var_934_pad_0, pad_type = var_934_pad_type_0, strides = var_930, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight, x = input_97)[name = tensor("op_934")]; - tensor inputs_17 = add(x = var_934, y = inputs_15)[name = tensor("inputs_17")]; - tensor var_938 = const()[name = tensor("op_938"), val = tensor([1])]; - tensor channels_mean_17 = reduce_mean(axes = var_938, keep_dims = var_284, x = inputs_17)[name = tensor("channels_mean_17")]; - tensor zero_mean_17 = sub(x = inputs_17, y = channels_mean_17)[name = tensor("zero_mean_17")]; - tensor zero_mean_sq_17 = mul(x = zero_mean_17, y = zero_mean_17)[name = tensor("zero_mean_sq_17")]; - tensor var_942 = const()[name = tensor("op_942"), val = tensor([1])]; - tensor var_943 = reduce_mean(axes = var_942, keep_dims = var_284, x = zero_mean_sq_17)[name = tensor("op_943")]; - tensor var_944 = const()[name = tensor("op_944"), val = tensor(0x1.4f8b58p-17)]; - tensor var_945 = add(x = var_943, y = var_944)[name = tensor("op_945")]; - tensor denom_17_epsilon_0 = const()[name = tensor("denom_17_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_17 = rsqrt(epsilon = denom_17_epsilon_0, x = var_945)[name = tensor("denom_17")]; - tensor out_17 = mul(x = zero_mean_17, y = denom_17)[name = tensor("out_17")]; - tensor var_949 = const()[name = tensor("op_949"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267667712)))]; - tensor var_950 = add(x = out_17, y = var_949)[name = tensor("op_950")]; - tensor var_952 = const()[name = tensor("op_952"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267670336)))]; - tensor input_99 = mul(x = var_950, y = var_952)[name = tensor("input_99")]; - tensor var_960 = const()[name = tensor("op_960"), val = tensor([1, 1])]; + tensor attn_11_cast = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_873_cast, y = var_877_cast)[name = tensor("attn_11_cast")]; + tensor var_881 = const()[name = tensor("op_881"), val = tensor([2, 640, 1, -1])]; + tensor input_97_cast = reshape(shape = var_881, x = attn_11_cast)[name = tensor("input_97_cast")]; + tensor var_886 = const()[name = tensor("op_886"), val = tensor([1, 1])]; + tensor var_888 = const()[name = tensor("op_888"), val = tensor([1, 1])]; + tensor var_890_pad_type_0 = const()[name = tensor("op_890_pad_type_0"), val = tensor("custom")]; + tensor var_890_pad_0 = const()[name = tensor("op_890_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106833408)))]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107652672)))]; + tensor var_890_cast = conv(bias = unet_down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_888, groups = var_31, pad = var_890_pad_0, pad_type = var_890_pad_type_0, strides = var_886, weight = unet_down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_97_cast)[name = tensor("op_890_cast")]; + tensor inputs_17_cast = add(x = var_890_cast, y = inputs_15_cast)[name = tensor("inputs_17_cast")]; + tensor var_894 = const()[name = tensor("op_894"), val = tensor([1])]; + tensor channels_mean_17_cast = reduce_mean(axes = var_894, keep_dims = var_23, x = inputs_17_cast)[name = tensor("channels_mean_17_cast")]; + tensor zero_mean_17_cast = sub(x = inputs_17_cast, y = channels_mean_17_cast)[name = tensor("zero_mean_17_cast")]; + tensor zero_mean_sq_17_cast = mul(x = zero_mean_17_cast, y = zero_mean_17_cast)[name = tensor("zero_mean_sq_17_cast")]; + tensor var_898 = const()[name = tensor("op_898"), val = tensor([1])]; + tensor var_899_cast = reduce_mean(axes = var_898, keep_dims = var_23, x = zero_mean_sq_17_cast)[name = tensor("op_899_cast")]; + tensor var_900_to_fp16 = const()[name = tensor("op_900_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_901_cast = add(x = var_899_cast, y = var_900_to_fp16)[name = tensor("op_901_cast")]; + tensor denom_17_epsilon_0_to_fp16 = const()[name = tensor("denom_17_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_17_cast = rsqrt(epsilon = denom_17_epsilon_0_to_fp16, x = var_901_cast)[name = tensor("denom_17_cast")]; + tensor out_17_cast = mul(x = zero_mean_17_cast, y = denom_17_cast)[name = tensor("out_17_cast")]; + tensor var_905_to_fp16 = const()[name = tensor("op_905_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107654016)))]; + tensor var_906_cast = add(x = out_17_cast, y = var_905_to_fp16)[name = tensor("op_906_cast")]; + tensor var_908_to_fp16 = const()[name = tensor("op_908_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107655360)))]; + tensor input_99_cast = mul(x = var_906_cast, y = var_908_to_fp16)[name = tensor("input_99_cast")]; + tensor var_916 = const()[name = tensor("op_916"), val = tensor([1, 1])]; + tensor var_918 = const()[name = tensor("op_918"), val = tensor([1, 1])]; + tensor var_920_pad_type_0 = const()[name = tensor("op_920_pad_type_0"), val = tensor("custom")]; + tensor var_920_pad_0 = const()[name = tensor("op_920_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107656704)))]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(114210368)))]; + tensor var_920_cast = conv(bias = unet_down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_918, groups = var_31, pad = var_920_pad_0, pad_type = var_920_pad_type_0, strides = var_916, weight = unet_down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_99_cast)[name = tensor("op_920_cast")]; + tensor var_921_split_sizes_0 = const()[name = tensor("op_921_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_921_axis_0 = const()[name = tensor("op_921_axis_0"), val = tensor(1)]; + tensor var_921_cast_0, tensor var_921_cast_1 = split(axis = var_921_axis_0, split_sizes = var_921_split_sizes_0, x = var_920_cast)[name = tensor("op_921_cast")]; + tensor var_923_mode_0 = const()[name = tensor("op_923_mode_0"), val = tensor("EXACT")]; + tensor var_923_cast = gelu(mode = var_923_mode_0, x = var_921_cast_1)[name = tensor("op_923_cast")]; + tensor input_101_cast = mul(x = var_921_cast_0, y = var_923_cast)[name = tensor("input_101_cast")]; + tensor var_927 = const()[name = tensor("op_927"), val = tensor([1, 1])]; + tensor var_929 = const()[name = tensor("op_929"), val = tensor([1, 1])]; + tensor var_931_pad_type_0 = const()[name = tensor("op_931_pad_type_0"), val = tensor("custom")]; + tensor var_931_pad_0 = const()[name = tensor("op_931_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(114220672)))]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117497536)))]; + tensor var_931_cast = conv(bias = unet_down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_929, groups = var_31, pad = var_931_pad_0, pad_type = var_931_pad_type_0, strides = var_927, weight = unet_down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_101_cast)[name = tensor("op_931_cast")]; + tensor inputs_19_cast = add(x = var_931_cast, y = inputs_17_cast)[name = tensor("inputs_19_cast")]; + tensor var_941 = const()[name = tensor("op_941"), val = tensor([1])]; + tensor channels_mean_19_cast = reduce_mean(axes = var_941, keep_dims = var_23, x = inputs_19_cast)[name = tensor("channels_mean_19_cast")]; + tensor zero_mean_19_cast = sub(x = inputs_19_cast, y = channels_mean_19_cast)[name = tensor("zero_mean_19_cast")]; + tensor zero_mean_sq_19_cast = mul(x = zero_mean_19_cast, y = zero_mean_19_cast)[name = tensor("zero_mean_sq_19_cast")]; + tensor var_945 = const()[name = tensor("op_945"), val = tensor([1])]; + tensor var_946_cast = reduce_mean(axes = var_945, keep_dims = var_23, x = zero_mean_sq_19_cast)[name = tensor("op_946_cast")]; + tensor var_947_to_fp16 = const()[name = tensor("op_947_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_948_cast = add(x = var_946_cast, y = var_947_to_fp16)[name = tensor("op_948_cast")]; + tensor denom_19_epsilon_0_to_fp16 = const()[name = tensor("denom_19_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_19_cast = rsqrt(epsilon = denom_19_epsilon_0_to_fp16, x = var_948_cast)[name = tensor("denom_19_cast")]; + tensor out_19_cast = mul(x = zero_mean_19_cast, y = denom_19_cast)[name = tensor("out_19_cast")]; + tensor var_952_to_fp16 = const()[name = tensor("op_952_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117498880)))]; + tensor var_953_cast = add(x = out_19_cast, y = var_952_to_fp16)[name = tensor("op_953_cast")]; + tensor var_955_to_fp16 = const()[name = tensor("op_955_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117500224)))]; + tensor hidden_states_47_cast = mul(x = var_953_cast, y = var_955_to_fp16)[name = tensor("hidden_states_47_cast")]; tensor var_962 = const()[name = tensor("op_962"), val = tensor([1, 1])]; - tensor var_964_pad_type_0 = const()[name = tensor("op_964_pad_type_0"), val = tensor("custom")]; - tensor var_964_pad_0 = const()[name = tensor("op_964_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_964 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias, dilations = var_962, groups = var_289, pad = var_964_pad_0, pad_type = var_964_pad_type_0, strides = var_960, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight, x = input_99)[name = tensor("op_964")]; - tensor var_965_split_sizes_0 = const()[name = tensor("op_965_split_sizes_0"), val = tensor([2560, 2560])]; - tensor var_965_axis_0 = const()[name = tensor("op_965_axis_0"), val = tensor(1)]; - tensor var_965_0, tensor var_965_1 = split(axis = var_965_axis_0, split_sizes = var_965_split_sizes_0, x = var_964)[name = tensor("op_965")]; - tensor var_967_mode_0 = const()[name = tensor("op_967_mode_0"), val = tensor("EXACT")]; - tensor var_967 = gelu(mode = var_967_mode_0, x = var_965_1)[name = tensor("op_967")]; - tensor input_101 = mul(x = var_965_0, y = var_967)[name = tensor("input_101")]; - tensor var_971 = const()[name = tensor("op_971"), val = tensor([1, 1])]; - tensor var_973 = const()[name = tensor("op_973"), val = tensor([1, 1])]; - tensor var_975_pad_type_0 = const()[name = tensor("op_975_pad_type_0"), val = tensor("custom")]; - tensor var_975_pad_0 = const()[name = tensor("op_975_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_975 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias, dilations = var_973, groups = var_289, pad = var_975_pad_0, pad_type = var_975_pad_type_0, strides = var_971, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight, x = input_101)[name = tensor("op_975")]; - tensor inputs_19 = add(x = var_975, y = inputs_17)[name = tensor("inputs_19")]; - tensor var_985 = const()[name = tensor("op_985"), val = tensor([1])]; - tensor channels_mean_19 = reduce_mean(axes = var_985, keep_dims = var_284, x = inputs_19)[name = tensor("channels_mean_19")]; - tensor zero_mean_19 = sub(x = inputs_19, y = channels_mean_19)[name = tensor("zero_mean_19")]; - tensor zero_mean_sq_19 = mul(x = zero_mean_19, y = zero_mean_19)[name = tensor("zero_mean_sq_19")]; - tensor var_989 = const()[name = tensor("op_989"), val = tensor([1])]; - tensor var_990 = reduce_mean(axes = var_989, keep_dims = var_284, x = zero_mean_sq_19)[name = tensor("op_990")]; - tensor var_991 = const()[name = tensor("op_991"), val = tensor(0x1.4f8b58p-17)]; - tensor var_992 = add(x = var_990, y = var_991)[name = tensor("op_992")]; - tensor denom_19_epsilon_0 = const()[name = tensor("denom_19_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_19 = rsqrt(epsilon = denom_19_epsilon_0, x = var_992)[name = tensor("denom_19")]; - tensor out_19 = mul(x = zero_mean_19, y = denom_19)[name = tensor("out_19")]; - tensor var_996 = const()[name = tensor("op_996"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267672960)))]; - tensor var_997 = add(x = out_19, y = var_996)[name = tensor("op_997")]; - tensor var_999 = const()[name = tensor("op_999"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267675584)))]; - tensor hidden_states_47 = mul(x = var_997, y = var_999)[name = tensor("hidden_states_47")]; - tensor var_1006 = const()[name = tensor("op_1006"), val = tensor([1, 1])]; - tensor var_1008 = const()[name = tensor("op_1008"), val = tensor([1, 1])]; + tensor var_964 = const()[name = tensor("op_964"), val = tensor([1, 1])]; tensor q_13_pad_type_0 = const()[name = tensor("q_13_pad_type_0"), val = tensor("custom")]; tensor q_13_pad_0 = const()[name = tensor("q_13_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_13 = conv(dilations = var_1008, groups = var_289, pad = q_13_pad_0, pad_type = q_13_pad_type_0, strides = var_1006, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight, x = hidden_states_47)[name = tensor("q_13")]; - tensor var_1012 = const()[name = tensor("op_1012"), val = tensor([1, 1])]; - tensor var_1014 = const()[name = tensor("op_1014"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117501568)))]; + tensor q_13_cast = conv(dilations = var_964, groups = var_31, pad = q_13_pad_0, pad_type = q_13_pad_type_0, strides = var_962, weight = unet_down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_47_cast)[name = tensor("q_13_cast")]; + tensor var_968 = const()[name = tensor("op_968"), val = tensor([1, 1])]; + tensor var_970 = const()[name = tensor("op_970"), val = tensor([1, 1])]; tensor k_13_pad_type_0 = const()[name = tensor("k_13_pad_type_0"), val = tensor("custom")]; tensor k_13_pad_0 = const()[name = tensor("k_13_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_13 = conv(dilations = var_1014, groups = var_289, pad = k_13_pad_0, pad_type = k_13_pad_type_0, strides = var_1012, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight, x = hidden_states_47)[name = tensor("k_13")]; - tensor var_1018 = const()[name = tensor("op_1018"), val = tensor([1, 1])]; - tensor var_1020 = const()[name = tensor("op_1020"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118320832)))]; + tensor k_13_cast = conv(dilations = var_970, groups = var_31, pad = k_13_pad_0, pad_type = k_13_pad_type_0, strides = var_968, weight = unet_down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_47_cast)[name = tensor("k_13_cast")]; + tensor var_974 = const()[name = tensor("op_974"), val = tensor([1, 1])]; + tensor var_976 = const()[name = tensor("op_976"), val = tensor([1, 1])]; tensor v_13_pad_type_0 = const()[name = tensor("v_13_pad_type_0"), val = tensor("custom")]; tensor v_13_pad_0 = const()[name = tensor("v_13_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_13 = conv(dilations = var_1020, groups = var_289, pad = v_13_pad_0, pad_type = v_13_pad_type_0, strides = var_1018, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight, x = hidden_states_47)[name = tensor("v_13")]; - tensor var_1024 = const()[name = tensor("op_1024"), val = tensor([2, 10, 64, -1])]; - tensor var_1025 = reshape(shape = var_1024, x = q_13)[name = tensor("op_1025")]; - tensor var_1026 = const()[name = tensor("op_1026"), val = tensor([2, 10, 64, -1])]; - tensor var_1027 = reshape(shape = var_1026, x = k_13)[name = tensor("op_1027")]; - tensor var_1028 = const()[name = tensor("op_1028"), val = tensor([2, 10, 64, -1])]; - tensor var_1029 = reshape(shape = var_1028, x = v_13)[name = tensor("op_1029")]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119140096)))]; + tensor v_13_cast = conv(dilations = var_976, groups = var_31, pad = v_13_pad_0, pad_type = v_13_pad_type_0, strides = var_974, weight = unet_down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_47_cast)[name = tensor("v_13_cast")]; + tensor var_980 = const()[name = tensor("op_980"), val = tensor([2, 10, 64, -1])]; + tensor var_981_cast = reshape(shape = var_980, x = q_13_cast)[name = tensor("op_981_cast")]; + tensor var_982 = const()[name = tensor("op_982"), val = tensor([2, 10, 64, -1])]; + tensor var_983_cast = reshape(shape = var_982, x = k_13_cast)[name = tensor("op_983_cast")]; + tensor var_984 = const()[name = tensor("op_984"), val = tensor([2, 10, 64, -1])]; + tensor var_985_cast = reshape(shape = var_984, x = v_13_cast)[name = tensor("op_985_cast")]; tensor attn_weights_25_transpose_x_0 = const()[name = tensor("attn_weights_25_transpose_x_0"), val = tensor(true)]; tensor attn_weights_25_transpose_y_0 = const()[name = tensor("attn_weights_25_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_25 = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1025, y = var_1027)[name = tensor("attn_weights_25")]; - tensor attn_weights_27 = mul(x = attn_weights_25, y = var_280)[name = tensor("attn_weights_27")]; - tensor var_1033 = softmax(axis = var_273, x = attn_weights_27)[name = tensor("op_1033")]; + tensor attn_weights_25_cast = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_981_cast, y = var_983_cast)[name = tensor("attn_weights_25_cast")]; + tensor attn_weights_27_cast = mul(x = attn_weights_25_cast, y = var_12_to_fp16)[name = tensor("attn_weights_27_cast")]; + tensor var_989_cast = softmax(axis = var_18, x = attn_weights_27_cast)[name = tensor("op_989_cast")]; tensor attn_13_transpose_x_0 = const()[name = tensor("attn_13_transpose_x_0"), val = tensor(false)]; tensor attn_13_transpose_y_0 = const()[name = tensor("attn_13_transpose_y_0"), val = tensor(true)]; - tensor attn_13 = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1029, y = var_1033)[name = tensor("attn_13")]; - tensor var_1037 = const()[name = tensor("op_1037"), val = tensor([2, 640, 1, -1])]; - tensor input_103 = reshape(shape = var_1037, x = attn_13)[name = tensor("input_103")]; - tensor var_1042 = const()[name = tensor("op_1042"), val = tensor([1, 1])]; - tensor var_1044 = const()[name = tensor("op_1044"), val = tensor([1, 1])]; - tensor var_1046_pad_type_0 = const()[name = tensor("op_1046_pad_type_0"), val = tensor("custom")]; - tensor var_1046_pad_0 = const()[name = tensor("op_1046_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1046 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias, dilations = var_1044, groups = var_289, pad = var_1046_pad_0, pad_type = var_1046_pad_type_0, strides = var_1042, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight, x = input_103)[name = tensor("op_1046")]; - tensor inputs_21 = add(x = var_1046, y = inputs_19)[name = tensor("inputs_21")]; - tensor var_1050 = const()[name = tensor("op_1050"), val = tensor([1])]; - tensor channels_mean_21 = reduce_mean(axes = var_1050, keep_dims = var_284, x = inputs_21)[name = tensor("channels_mean_21")]; - tensor zero_mean_21 = sub(x = inputs_21, y = channels_mean_21)[name = tensor("zero_mean_21")]; - tensor zero_mean_sq_21 = mul(x = zero_mean_21, y = zero_mean_21)[name = tensor("zero_mean_sq_21")]; - tensor var_1054 = const()[name = tensor("op_1054"), val = tensor([1])]; - tensor var_1055 = reduce_mean(axes = var_1054, keep_dims = var_284, x = zero_mean_sq_21)[name = tensor("op_1055")]; - tensor var_1056 = const()[name = tensor("op_1056"), val = tensor(0x1.4f8b58p-17)]; - tensor var_1057 = add(x = var_1055, y = var_1056)[name = tensor("op_1057")]; - tensor denom_21_epsilon_0 = const()[name = tensor("denom_21_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_21 = rsqrt(epsilon = denom_21_epsilon_0, x = var_1057)[name = tensor("denom_21")]; - tensor out_21 = mul(x = zero_mean_21, y = denom_21)[name = tensor("out_21")]; - tensor var_1061 = const()[name = tensor("op_1061"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267678208)))]; - tensor var_1062 = add(x = out_21, y = var_1061)[name = tensor("op_1062")]; - tensor var_1064 = const()[name = tensor("op_1064"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267680832)))]; - tensor hidden_states_49 = mul(x = var_1062, y = var_1064)[name = tensor("hidden_states_49")]; - tensor var_1071 = const()[name = tensor("op_1071"), val = tensor([1, 1])]; - tensor var_1073 = const()[name = tensor("op_1073"), val = tensor([1, 1])]; + tensor attn_13_cast = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_985_cast, y = var_989_cast)[name = tensor("attn_13_cast")]; + tensor var_993 = const()[name = tensor("op_993"), val = tensor([2, 640, 1, -1])]; + tensor input_103_cast = reshape(shape = var_993, x = attn_13_cast)[name = tensor("input_103_cast")]; + tensor var_998 = const()[name = tensor("op_998"), val = tensor([1, 1])]; + tensor var_1000 = const()[name = tensor("op_1000"), val = tensor([1, 1])]; + tensor var_1002_pad_type_0 = const()[name = tensor("op_1002_pad_type_0"), val = tensor("custom")]; + tensor var_1002_pad_0 = const()[name = tensor("op_1002_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(119959360)))]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120778624)))]; + tensor var_1002_cast = conv(bias = unet_down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_1000, groups = var_31, pad = var_1002_pad_0, pad_type = var_1002_pad_type_0, strides = var_998, weight = unet_down_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_103_cast)[name = tensor("op_1002_cast")]; + tensor inputs_21_cast = add(x = var_1002_cast, y = inputs_19_cast)[name = tensor("inputs_21_cast")]; + tensor var_1006 = const()[name = tensor("op_1006"), val = tensor([1])]; + tensor channels_mean_21_cast = reduce_mean(axes = var_1006, keep_dims = var_23, x = inputs_21_cast)[name = tensor("channels_mean_21_cast")]; + tensor zero_mean_21_cast = sub(x = inputs_21_cast, y = channels_mean_21_cast)[name = tensor("zero_mean_21_cast")]; + tensor zero_mean_sq_21_cast = mul(x = zero_mean_21_cast, y = zero_mean_21_cast)[name = tensor("zero_mean_sq_21_cast")]; + tensor var_1010 = const()[name = tensor("op_1010"), val = tensor([1])]; + tensor var_1011_cast = reduce_mean(axes = var_1010, keep_dims = var_23, x = zero_mean_sq_21_cast)[name = tensor("op_1011_cast")]; + tensor var_1012_to_fp16 = const()[name = tensor("op_1012_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1013_cast = add(x = var_1011_cast, y = var_1012_to_fp16)[name = tensor("op_1013_cast")]; + tensor denom_21_epsilon_0_to_fp16 = const()[name = tensor("denom_21_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_21_cast = rsqrt(epsilon = denom_21_epsilon_0_to_fp16, x = var_1013_cast)[name = tensor("denom_21_cast")]; + tensor out_21_cast = mul(x = zero_mean_21_cast, y = denom_21_cast)[name = tensor("out_21_cast")]; + tensor var_1017_to_fp16 = const()[name = tensor("op_1017_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120779968)))]; + tensor var_1018_cast = add(x = out_21_cast, y = var_1017_to_fp16)[name = tensor("op_1018_cast")]; + tensor var_1020_to_fp16 = const()[name = tensor("op_1020_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120781312)))]; + tensor hidden_states_49_cast = mul(x = var_1018_cast, y = var_1020_to_fp16)[name = tensor("hidden_states_49_cast")]; + tensor var_1027 = const()[name = tensor("op_1027"), val = tensor([1, 1])]; + tensor var_1029 = const()[name = tensor("op_1029"), val = tensor([1, 1])]; tensor q_15_pad_type_0 = const()[name = tensor("q_15_pad_type_0"), val = tensor("custom")]; tensor q_15_pad_0 = const()[name = tensor("q_15_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_15 = conv(dilations = var_1073, groups = var_289, pad = q_15_pad_0, pad_type = q_15_pad_type_0, strides = var_1071, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight, x = hidden_states_49)[name = tensor("q_15")]; - tensor var_1077 = const()[name = tensor("op_1077"), val = tensor([1, 1])]; - tensor var_1079 = const()[name = tensor("op_1079"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(120782656)))]; + tensor q_15_cast = conv(dilations = var_1029, groups = var_31, pad = q_15_pad_0, pad_type = q_15_pad_type_0, strides = var_1027, weight = unet_down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_49_cast)[name = tensor("q_15_cast")]; + tensor var_1033 = const()[name = tensor("op_1033"), val = tensor([1, 1])]; + tensor var_1035 = const()[name = tensor("op_1035"), val = tensor([1, 1])]; tensor k_15_pad_type_0 = const()[name = tensor("k_15_pad_type_0"), val = tensor("custom")]; tensor k_15_pad_0 = const()[name = tensor("k_15_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_15 = conv(dilations = var_1079, groups = var_289, pad = k_15_pad_0, pad_type = k_15_pad_type_0, strides = var_1077, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_15")]; - tensor var_1083 = const()[name = tensor("op_1083"), val = tensor([1, 1])]; - tensor var_1085 = const()[name = tensor("op_1085"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(121601920)))]; + tensor k_15_cast = conv(dilations = var_1035, groups = var_31, pad = k_15_pad_0, pad_type = k_15_pad_type_0, strides = var_1033, weight = unet_down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_15_cast")]; + tensor var_1039 = const()[name = tensor("op_1039"), val = tensor([1, 1])]; + tensor var_1041 = const()[name = tensor("op_1041"), val = tensor([1, 1])]; tensor v_15_pad_type_0 = const()[name = tensor("v_15_pad_type_0"), val = tensor("custom")]; tensor v_15_pad_0 = const()[name = tensor("v_15_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_15 = conv(dilations = var_1085, groups = var_289, pad = v_15_pad_0, pad_type = v_15_pad_type_0, strides = var_1083, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_15")]; - tensor var_1089 = const()[name = tensor("op_1089"), val = tensor([2, 10, 64, -1])]; - tensor var_1090 = reshape(shape = var_1089, x = q_15)[name = tensor("op_1090")]; - tensor var_1091 = const()[name = tensor("op_1091"), val = tensor([2, 10, 64, -1])]; - tensor var_1092 = reshape(shape = var_1091, x = k_15)[name = tensor("op_1092")]; - tensor var_1093 = const()[name = tensor("op_1093"), val = tensor([2, 10, 64, -1])]; - tensor var_1094 = reshape(shape = var_1093, x = v_15)[name = tensor("op_1094")]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124223424)))]; + tensor v_15_cast = conv(dilations = var_1041, groups = var_31, pad = v_15_pad_0, pad_type = v_15_pad_type_0, strides = var_1039, weight = unet_down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_15_cast")]; + tensor var_1045 = const()[name = tensor("op_1045"), val = tensor([2, 10, 64, -1])]; + tensor var_1046_cast = reshape(shape = var_1045, x = q_15_cast)[name = tensor("op_1046_cast")]; + tensor var_1047 = const()[name = tensor("op_1047"), val = tensor([2, 10, 64, -1])]; + tensor var_1048_cast = reshape(shape = var_1047, x = k_15_cast)[name = tensor("op_1048_cast")]; + tensor var_1049 = const()[name = tensor("op_1049"), val = tensor([2, 10, 64, -1])]; + tensor var_1050_cast = reshape(shape = var_1049, x = v_15_cast)[name = tensor("op_1050_cast")]; tensor attn_weights_29_transpose_x_0 = const()[name = tensor("attn_weights_29_transpose_x_0"), val = tensor(true)]; tensor attn_weights_29_transpose_y_0 = const()[name = tensor("attn_weights_29_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_29 = matmul(transpose_x = attn_weights_29_transpose_x_0, transpose_y = attn_weights_29_transpose_y_0, x = var_1090, y = var_1092)[name = tensor("attn_weights_29")]; - tensor attn_weights_31 = mul(x = attn_weights_29, y = var_280)[name = tensor("attn_weights_31")]; - tensor var_1098 = softmax(axis = var_273, x = attn_weights_31)[name = tensor("op_1098")]; + tensor attn_weights_29_cast = matmul(transpose_x = attn_weights_29_transpose_x_0, transpose_y = attn_weights_29_transpose_y_0, x = var_1046_cast, y = var_1048_cast)[name = tensor("attn_weights_29_cast")]; + tensor attn_weights_31_cast = mul(x = attn_weights_29_cast, y = var_12_to_fp16)[name = tensor("attn_weights_31_cast")]; + tensor var_1054_cast = softmax(axis = var_18, x = attn_weights_31_cast)[name = tensor("op_1054_cast")]; tensor attn_15_transpose_x_0 = const()[name = tensor("attn_15_transpose_x_0"), val = tensor(false)]; tensor attn_15_transpose_y_0 = const()[name = tensor("attn_15_transpose_y_0"), val = tensor(true)]; - tensor attn_15 = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_1094, y = var_1098)[name = tensor("attn_15")]; - tensor var_1102 = const()[name = tensor("op_1102"), val = tensor([2, 640, 1, -1])]; - tensor input_105 = reshape(shape = var_1102, x = attn_15)[name = tensor("input_105")]; - tensor var_1107 = const()[name = tensor("op_1107"), val = tensor([1, 1])]; - tensor var_1109 = const()[name = tensor("op_1109"), val = tensor([1, 1])]; - tensor var_1111_pad_type_0 = const()[name = tensor("op_1111_pad_type_0"), val = tensor("custom")]; - tensor var_1111_pad_0 = const()[name = tensor("op_1111_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1111 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias, dilations = var_1109, groups = var_289, pad = var_1111_pad_0, pad_type = var_1111_pad_type_0, strides = var_1107, weight = down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight, x = input_105)[name = tensor("op_1111")]; - tensor inputs_23 = add(x = var_1111, y = inputs_21)[name = tensor("inputs_23")]; - tensor var_1115 = const()[name = tensor("op_1115"), val = tensor([1])]; - tensor channels_mean_23 = reduce_mean(axes = var_1115, keep_dims = var_284, x = inputs_23)[name = tensor("channels_mean_23")]; - tensor zero_mean_23 = sub(x = inputs_23, y = channels_mean_23)[name = tensor("zero_mean_23")]; - tensor zero_mean_sq_23 = mul(x = zero_mean_23, y = zero_mean_23)[name = tensor("zero_mean_sq_23")]; - tensor var_1119 = const()[name = tensor("op_1119"), val = tensor([1])]; - tensor var_1120 = reduce_mean(axes = var_1119, keep_dims = var_284, x = zero_mean_sq_23)[name = tensor("op_1120")]; - tensor var_1121 = const()[name = tensor("op_1121"), val = tensor(0x1.4f8b58p-17)]; - tensor var_1122 = add(x = var_1120, y = var_1121)[name = tensor("op_1122")]; - tensor denom_23_epsilon_0 = const()[name = tensor("denom_23_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_23 = rsqrt(epsilon = denom_23_epsilon_0, x = var_1122)[name = tensor("denom_23")]; - tensor out_23 = mul(x = zero_mean_23, y = denom_23)[name = tensor("out_23")]; - tensor var_1126 = const()[name = tensor("op_1126"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267683456)))]; - tensor var_1127 = add(x = out_23, y = var_1126)[name = tensor("op_1127")]; - tensor var_1129 = const()[name = tensor("op_1129"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267686080)))]; - tensor input_107 = mul(x = var_1127, y = var_1129)[name = tensor("input_107")]; - tensor var_1137 = const()[name = tensor("op_1137"), val = tensor([1, 1])]; - tensor var_1139 = const()[name = tensor("op_1139"), val = tensor([1, 1])]; - tensor var_1141_pad_type_0 = const()[name = tensor("op_1141_pad_type_0"), val = tensor("custom")]; - tensor var_1141_pad_0 = const()[name = tensor("op_1141_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1141 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias, dilations = var_1139, groups = var_289, pad = var_1141_pad_0, pad_type = var_1141_pad_type_0, strides = var_1137, weight = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight, x = input_107)[name = tensor("op_1141")]; - tensor var_1142_split_sizes_0 = const()[name = tensor("op_1142_split_sizes_0"), val = tensor([2560, 2560])]; - tensor var_1142_axis_0 = const()[name = tensor("op_1142_axis_0"), val = tensor(1)]; - tensor var_1142_0, tensor var_1142_1 = split(axis = var_1142_axis_0, split_sizes = var_1142_split_sizes_0, x = var_1141)[name = tensor("op_1142")]; - tensor var_1144_mode_0 = const()[name = tensor("op_1144_mode_0"), val = tensor("EXACT")]; - tensor var_1144 = gelu(mode = var_1144_mode_0, x = var_1142_1)[name = tensor("op_1144")]; - tensor input_109 = mul(x = var_1142_0, y = var_1144)[name = tensor("input_109")]; - tensor var_1148 = const()[name = tensor("op_1148"), val = tensor([1, 1])]; - tensor var_1150 = const()[name = tensor("op_1150"), val = tensor([1, 1])]; - tensor var_1152_pad_type_0 = const()[name = tensor("op_1152_pad_type_0"), val = tensor("custom")]; - tensor var_1152_pad_0 = const()[name = tensor("op_1152_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1152 = conv(bias = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias, dilations = var_1150, groups = var_289, pad = var_1152_pad_0, pad_type = var_1152_pad_type_0, strides = var_1148, weight = down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight, x = input_109)[name = tensor("op_1152")]; - tensor hidden_states_53 = add(x = var_1152, y = inputs_23)[name = tensor("hidden_states_53")]; - tensor var_1154 = const()[name = tensor("op_1154"), val = tensor([2, 640, 64, 64])]; - tensor input_111 = reshape(shape = var_1154, x = hidden_states_53)[name = tensor("input_111")]; - tensor var_1158 = const()[name = tensor("op_1158"), val = tensor([1, 1])]; - tensor var_1160 = const()[name = tensor("op_1160"), val = tensor([1, 1])]; + tensor attn_15_cast = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_1050_cast, y = var_1054_cast)[name = tensor("attn_15_cast")]; + tensor var_1058 = const()[name = tensor("op_1058"), val = tensor([2, 640, 1, -1])]; + tensor input_105_cast = reshape(shape = var_1058, x = attn_15_cast)[name = tensor("input_105_cast")]; + tensor var_1063 = const()[name = tensor("op_1063"), val = tensor([1, 1])]; + tensor var_1065 = const()[name = tensor("op_1065"), val = tensor([1, 1])]; + tensor var_1067_pad_type_0 = const()[name = tensor("op_1067_pad_type_0"), val = tensor("custom")]; + tensor var_1067_pad_0 = const()[name = tensor("op_1067_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126844928)))]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127664192)))]; + tensor var_1067_cast = conv(bias = unet_down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_1065, groups = var_31, pad = var_1067_pad_0, pad_type = var_1067_pad_type_0, strides = var_1063, weight = unet_down_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_105_cast)[name = tensor("op_1067_cast")]; + tensor inputs_23_cast = add(x = var_1067_cast, y = inputs_21_cast)[name = tensor("inputs_23_cast")]; + tensor var_1071 = const()[name = tensor("op_1071"), val = tensor([1])]; + tensor channels_mean_23_cast = reduce_mean(axes = var_1071, keep_dims = var_23, x = inputs_23_cast)[name = tensor("channels_mean_23_cast")]; + tensor zero_mean_23_cast = sub(x = inputs_23_cast, y = channels_mean_23_cast)[name = tensor("zero_mean_23_cast")]; + tensor zero_mean_sq_23_cast = mul(x = zero_mean_23_cast, y = zero_mean_23_cast)[name = tensor("zero_mean_sq_23_cast")]; + tensor var_1075 = const()[name = tensor("op_1075"), val = tensor([1])]; + tensor var_1076_cast = reduce_mean(axes = var_1075, keep_dims = var_23, x = zero_mean_sq_23_cast)[name = tensor("op_1076_cast")]; + tensor var_1077_to_fp16 = const()[name = tensor("op_1077_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1078_cast = add(x = var_1076_cast, y = var_1077_to_fp16)[name = tensor("op_1078_cast")]; + tensor denom_23_epsilon_0_to_fp16 = const()[name = tensor("denom_23_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_23_cast = rsqrt(epsilon = denom_23_epsilon_0_to_fp16, x = var_1078_cast)[name = tensor("denom_23_cast")]; + tensor out_23_cast = mul(x = zero_mean_23_cast, y = denom_23_cast)[name = tensor("out_23_cast")]; + tensor var_1082_to_fp16 = const()[name = tensor("op_1082_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127665536)))]; + tensor var_1083_cast = add(x = out_23_cast, y = var_1082_to_fp16)[name = tensor("op_1083_cast")]; + tensor var_1085_to_fp16 = const()[name = tensor("op_1085_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127666880)))]; + tensor input_107_cast = mul(x = var_1083_cast, y = var_1085_to_fp16)[name = tensor("input_107_cast")]; + tensor var_1093 = const()[name = tensor("op_1093"), val = tensor([1, 1])]; + tensor var_1095 = const()[name = tensor("op_1095"), val = tensor([1, 1])]; + tensor var_1097_pad_type_0 = const()[name = tensor("op_1097_pad_type_0"), val = tensor("custom")]; + tensor var_1097_pad_0 = const()[name = tensor("op_1097_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127668224)))]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134221888)))]; + tensor var_1097_cast = conv(bias = unet_down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_1095, groups = var_31, pad = var_1097_pad_0, pad_type = var_1097_pad_type_0, strides = var_1093, weight = unet_down_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_107_cast)[name = tensor("op_1097_cast")]; + tensor var_1098_split_sizes_0 = const()[name = tensor("op_1098_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_1098_axis_0 = const()[name = tensor("op_1098_axis_0"), val = tensor(1)]; + tensor var_1098_cast_0, tensor var_1098_cast_1 = split(axis = var_1098_axis_0, split_sizes = var_1098_split_sizes_0, x = var_1097_cast)[name = tensor("op_1098_cast")]; + tensor var_1100_mode_0 = const()[name = tensor("op_1100_mode_0"), val = tensor("EXACT")]; + tensor var_1100_cast = gelu(mode = var_1100_mode_0, x = var_1098_cast_1)[name = tensor("op_1100_cast")]; + tensor input_109_cast = mul(x = var_1098_cast_0, y = var_1100_cast)[name = tensor("input_109_cast")]; + tensor var_1104 = const()[name = tensor("op_1104"), val = tensor([1, 1])]; + tensor var_1106 = const()[name = tensor("op_1106"), val = tensor([1, 1])]; + tensor var_1108_pad_type_0 = const()[name = tensor("op_1108_pad_type_0"), val = tensor("custom")]; + tensor var_1108_pad_0 = const()[name = tensor("op_1108_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134232192)))]; + tensor unet_down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137509056)))]; + tensor var_1108_cast = conv(bias = unet_down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_1106, groups = var_31, pad = var_1108_pad_0, pad_type = var_1108_pad_type_0, strides = var_1104, weight = unet_down_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_109_cast)[name = tensor("op_1108_cast")]; + tensor hidden_states_53_cast = add(x = var_1108_cast, y = inputs_23_cast)[name = tensor("hidden_states_53_cast")]; + tensor var_1110 = const()[name = tensor("op_1110"), val = tensor([2, 640, 64, 64])]; + tensor input_111_cast = reshape(shape = var_1110, x = hidden_states_53_cast)[name = tensor("input_111_cast")]; + tensor var_1114 = const()[name = tensor("op_1114"), val = tensor([1, 1])]; + tensor var_1116 = const()[name = tensor("op_1116"), val = tensor([1, 1])]; tensor hidden_states_55_pad_type_0 = const()[name = tensor("hidden_states_55_pad_type_0"), val = tensor("custom")]; tensor hidden_states_55_pad_0 = const()[name = tensor("hidden_states_55_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_55 = conv(bias = down_blocks_1_attentions_1_proj_out_bias, dilations = var_1160, groups = var_289, pad = hidden_states_55_pad_0, pad_type = hidden_states_55_pad_type_0, strides = var_1158, weight = down_blocks_1_attentions_1_proj_out_weight, x = input_111)[name = tensor("hidden_states_55")]; - tensor input_113 = add(x = hidden_states_55, y = hidden_states_37)[name = tensor("input_113")]; - tensor var_1167 = const()[name = tensor("op_1167"), val = tensor([2, 2])]; - tensor var_1169 = const()[name = tensor("op_1169"), val = tensor([1, 1])]; + tensor unet_down_blocks_1_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(137510400)))]; + tensor unet_down_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138329664)))]; + tensor hidden_states_55_cast = conv(bias = unet_down_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_1116, groups = var_31, pad = hidden_states_55_pad_0, pad_type = hidden_states_55_pad_type_0, strides = var_1114, weight = unet_down_blocks_1_attentions_1_proj_out_weight_to_fp16, x = input_111_cast)[name = tensor("hidden_states_55_cast")]; + tensor input_113_cast = add(x = hidden_states_55_cast, y = hidden_states_37_cast)[name = tensor("input_113_cast")]; + tensor var_1123 = const()[name = tensor("op_1123"), val = tensor([2, 2])]; + tensor var_1125 = const()[name = tensor("op_1125"), val = tensor([1, 1])]; tensor input_115_pad_type_0 = const()[name = tensor("input_115_pad_type_0"), val = tensor("custom")]; tensor input_115_pad_0 = const()[name = tensor("input_115_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_115 = conv(bias = down_blocks_1_downsamplers_0_conv_bias, dilations = var_1169, groups = var_289, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = var_1167, weight = down_blocks_1_downsamplers_0_conv_weight, x = input_113)[name = tensor("input_115")]; - tensor var_1177 = const()[name = tensor("op_1177"), val = tensor(3)]; - tensor var_1184 = const()[name = tensor("op_1184"), val = tensor(0x1p-3)]; - tensor var_1188 = const()[name = tensor("op_1188"), val = tensor(true)]; - tensor var_1193 = const()[name = tensor("op_1193"), val = tensor(1)]; + tensor unet_down_blocks_1_downsamplers_0_conv_weight_to_fp16 = const()[name = tensor("unet_down_blocks_1_downsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(138331008)))]; + tensor unet_down_blocks_1_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("unet_down_blocks_1_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145703872)))]; + tensor input_115_cast = conv(bias = unet_down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_1125, groups = var_31, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = var_1123, weight = unet_down_blocks_1_downsamplers_0_conv_weight_to_fp16, x = input_113_cast)[name = tensor("input_115_cast")]; tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([2, 32, 20, 32, 32])]; - tensor reshape_40 = reshape(shape = reshape_40_shape_0, x = input_115)[name = tensor("reshape_40")]; + tensor reshape_40_cast = reshape(shape = reshape_40_shape_0, x = input_115_cast)[name = tensor("reshape_40_cast")]; tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_30_keep_dims_0 = const()[name = tensor("reduce_mean_30_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_30 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40)[name = tensor("reduce_mean_30")]; - tensor sub_20 = sub(x = reshape_40, y = reduce_mean_30)[name = tensor("sub_20")]; - tensor square_10 = square(x = sub_20)[name = tensor("square_10")]; + tensor reduce_mean_30_cast = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast)[name = tensor("reduce_mean_30_cast")]; + tensor sub_20_cast = sub(x = reshape_40_cast, y = reduce_mean_30_cast)[name = tensor("sub_20_cast")]; + tensor square_10_cast = square(x = sub_20_cast)[name = tensor("square_10_cast")]; tensor reduce_mean_32_axes_0 = const()[name = tensor("reduce_mean_32_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_32_keep_dims_0 = const()[name = tensor("reduce_mean_32_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_32 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10)[name = tensor("reduce_mean_32")]; - tensor add_20_y_0 = const()[name = tensor("add_20_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_20 = add(x = reduce_mean_32, y = add_20_y_0)[name = tensor("add_20")]; - tensor sqrt_10 = sqrt(x = add_20)[name = tensor("sqrt_10")]; - tensor real_div_10 = real_div(x = sub_20, y = sqrt_10)[name = tensor("real_div_10")]; + tensor reduce_mean_32_cast = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast)[name = tensor("reduce_mean_32_cast")]; + tensor add_20_y_0_to_fp16 = const()[name = tensor("add_20_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_20_cast = add(x = reduce_mean_32_cast, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast")]; + tensor sqrt_10_cast = sqrt(x = add_20_cast)[name = tensor("sqrt_10_cast")]; + tensor real_div_10_cast = real_div(x = sub_20_cast, y = sqrt_10_cast)[name = tensor("real_div_10_cast")]; tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([2, 640, 32, 32])]; - tensor reshape_41 = reshape(shape = reshape_41_shape_0, x = real_div_10)[name = tensor("reshape_41")]; - tensor add_21_gamma_0 = const()[name = tensor("add_21_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267688704)))]; - tensor add_21_beta_0 = const()[name = tensor("add_21_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267691328)))]; - tensor add_21_epsilon_0 = const()[name = tensor("add_21_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_21 = batch_norm(beta = add_21_beta_0, epsilon = add_21_epsilon_0, gamma = add_21_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_41)[name = tensor("add_21")]; - tensor input_119 = silu(x = add_21)[name = tensor("input_119")]; - tensor var_1214 = const()[name = tensor("op_1214"), val = tensor([1, 1])]; - tensor var_1216 = const()[name = tensor("op_1216"), val = tensor([1, 1])]; + tensor reshape_41_cast = reshape(shape = reshape_41_shape_0, x = real_div_10_cast)[name = tensor("reshape_41_cast")]; + tensor add_21_gamma_0_to_fp16 = const()[name = tensor("add_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145705216)))]; + tensor add_21_beta_0_to_fp16 = const()[name = tensor("add_21_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145706560)))]; + tensor add_21_epsilon_0_to_fp16 = const()[name = tensor("add_21_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_21_cast = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_41_cast)[name = tensor("add_21_cast")]; + tensor input_119_cast = silu(x = add_21_cast)[name = tensor("input_119_cast")]; + tensor var_1152 = const()[name = tensor("op_1152"), val = tensor([1, 1])]; + tensor var_1154 = const()[name = tensor("op_1154"), val = tensor([1, 1])]; tensor hidden_states_57_pad_type_0 = const()[name = tensor("hidden_states_57_pad_type_0"), val = tensor("custom")]; tensor hidden_states_57_pad_0 = const()[name = tensor("hidden_states_57_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_57 = conv(bias = down_blocks_2_resnets_0_conv1_bias, dilations = var_1216, groups = var_1193, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = var_1214, weight = down_blocks_2_resnets_0_conv1_weight, x = input_119)[name = tensor("hidden_states_57")]; - tensor var_1222 = const()[name = tensor("op_1222"), val = tensor([1, 1])]; - tensor var_1224 = const()[name = tensor("op_1224"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145707904)))]; + tensor unet_down_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160453568)))]; + tensor hidden_states_57_cast = conv(bias = unet_down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_1154, groups = var_31, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = var_1152, weight = unet_down_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_119_cast)[name = tensor("hidden_states_57_cast")]; + tensor var_1160 = const()[name = tensor("op_1160"), val = tensor([1, 1])]; + tensor var_1162 = const()[name = tensor("op_1162"), val = tensor([1, 1])]; tensor temb_9_pad_type_0 = const()[name = tensor("temb_9_pad_type_0"), val = tensor("custom")]; tensor temb_9_pad_0 = const()[name = tensor("temb_9_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb_9 = conv(bias = down_blocks_2_resnets_0_time_emb_proj_bias, dilations = var_1224, groups = var_1193, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = var_1222, weight = down_blocks_2_resnets_0_time_emb_proj_weight, x = input_21)[name = tensor("temb_9")]; - tensor input_123 = add(x = hidden_states_57, y = temb_9)[name = tensor("input_123")]; + tensor unet_down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160456192)))]; + tensor unet_down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163733056)))]; + tensor temb_9_cast = conv(bias = unet_down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1162, groups = var_31, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = var_1160, weight = unet_down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_9_cast")]; + tensor input_123_cast = add(x = hidden_states_57_cast, y = temb_9_cast)[name = tensor("input_123_cast")]; tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([2, 32, 40, 32, 32])]; - tensor reshape_44 = reshape(shape = reshape_44_shape_0, x = input_123)[name = tensor("reshape_44")]; + tensor reshape_44_cast = reshape(shape = reshape_44_shape_0, x = input_123_cast)[name = tensor("reshape_44_cast")]; tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_33_keep_dims_0 = const()[name = tensor("reduce_mean_33_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_33 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44)[name = tensor("reduce_mean_33")]; - tensor sub_22 = sub(x = reshape_44, y = reduce_mean_33)[name = tensor("sub_22")]; - tensor square_11 = square(x = sub_22)[name = tensor("square_11")]; + tensor reduce_mean_33_cast = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast)[name = tensor("reduce_mean_33_cast")]; + tensor sub_22_cast = sub(x = reshape_44_cast, y = reduce_mean_33_cast)[name = tensor("sub_22_cast")]; + tensor square_11_cast = square(x = sub_22_cast)[name = tensor("square_11_cast")]; tensor reduce_mean_35_axes_0 = const()[name = tensor("reduce_mean_35_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_35_keep_dims_0 = const()[name = tensor("reduce_mean_35_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_35 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11)[name = tensor("reduce_mean_35")]; - tensor add_22_y_0 = const()[name = tensor("add_22_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_22 = add(x = reduce_mean_35, y = add_22_y_0)[name = tensor("add_22")]; - tensor sqrt_11 = sqrt(x = add_22)[name = tensor("sqrt_11")]; - tensor real_div_11 = real_div(x = sub_22, y = sqrt_11)[name = tensor("real_div_11")]; + tensor reduce_mean_35_cast = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast)[name = tensor("reduce_mean_35_cast")]; + tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_22_cast = add(x = reduce_mean_35_cast, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast")]; + tensor sqrt_11_cast = sqrt(x = add_22_cast)[name = tensor("sqrt_11_cast")]; + tensor real_div_11_cast = real_div(x = sub_22_cast, y = sqrt_11_cast)[name = tensor("real_div_11_cast")]; tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([2, 1280, 32, 32])]; - tensor reshape_45 = reshape(shape = reshape_45_shape_0, x = real_div_11)[name = tensor("reshape_45")]; - tensor add_23_mean_0 = const()[name = tensor("add_23_mean_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267693952)))]; - tensor add_23_variance_0 = const()[name = tensor("add_23_variance_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267699136)))]; - tensor add_23_gamma_0 = const()[name = tensor("add_23_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267704320)))]; - tensor add_23_beta_0 = const()[name = tensor("add_23_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267709504)))]; - tensor add_23_epsilon_0 = const()[name = tensor("add_23_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_23 = batch_norm(beta = add_23_beta_0, epsilon = add_23_epsilon_0, gamma = add_23_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_45)[name = tensor("add_23")]; - tensor input_127 = silu(x = add_23)[name = tensor("input_127")]; - tensor var_1234 = const()[name = tensor("op_1234"), val = tensor([1, 1])]; - tensor var_1236 = const()[name = tensor("op_1236"), val = tensor([1, 1])]; + tensor reshape_45_cast = reshape(shape = reshape_45_shape_0, x = real_div_11_cast)[name = tensor("reshape_45_cast")]; + tensor add_23_mean_0_to_fp16 = const()[name = tensor("add_23_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163735680)))]; + tensor add_23_variance_0_to_fp16 = const()[name = tensor("add_23_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163738304)))]; + tensor add_23_gamma_0_to_fp16 = const()[name = tensor("add_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163740928)))]; + tensor add_23_beta_0_to_fp16 = const()[name = tensor("add_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163743552)))]; + tensor add_23_epsilon_0_to_fp16 = const()[name = tensor("add_23_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_23_cast = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_45_cast)[name = tensor("add_23_cast")]; + tensor input_127_cast = silu(x = add_23_cast)[name = tensor("input_127_cast")]; + tensor var_1172 = const()[name = tensor("op_1172"), val = tensor([1, 1])]; + tensor var_1174 = const()[name = tensor("op_1174"), val = tensor([1, 1])]; tensor hidden_states_59_pad_type_0 = const()[name = tensor("hidden_states_59_pad_type_0"), val = tensor("custom")]; tensor hidden_states_59_pad_0 = const()[name = tensor("hidden_states_59_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_59 = conv(bias = down_blocks_2_resnets_0_conv2_bias, dilations = var_1236, groups = var_1193, pad = hidden_states_59_pad_0, pad_type = hidden_states_59_pad_type_0, strides = var_1234, weight = down_blocks_2_resnets_0_conv2_weight, x = input_127)[name = tensor("hidden_states_59")]; - tensor var_1241 = const()[name = tensor("op_1241"), val = tensor([1, 1])]; - tensor var_1243 = const()[name = tensor("op_1243"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(163746176)))]; + tensor unet_down_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193237440)))]; + tensor hidden_states_59_cast = conv(bias = unet_down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_1174, groups = var_31, pad = hidden_states_59_pad_0, pad_type = hidden_states_59_pad_type_0, strides = var_1172, weight = unet_down_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_127_cast)[name = tensor("hidden_states_59_cast")]; + tensor var_1179 = const()[name = tensor("op_1179"), val = tensor([1, 1])]; + tensor var_1181 = const()[name = tensor("op_1181"), val = tensor([1, 1])]; tensor x_3_pad_type_0 = const()[name = tensor("x_3_pad_type_0"), val = tensor("custom")]; tensor x_3_pad_0 = const()[name = tensor("x_3_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor x_3 = conv(bias = down_blocks_2_resnets_0_conv_shortcut_bias, dilations = var_1243, groups = var_1193, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = var_1241, weight = down_blocks_2_resnets_0_conv_shortcut_weight, x = input_115)[name = tensor("x_3")]; - tensor hidden_states_61 = add(x = x_3, y = hidden_states_59)[name = tensor("hidden_states_61")]; + tensor unet_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193240064)))]; + tensor unet_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194878528)))]; + tensor x_3_cast = conv(bias = unet_down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_1181, groups = var_31, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = var_1179, weight = unet_down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_115_cast)[name = tensor("x_3_cast")]; + tensor hidden_states_61_cast = add(x = x_3_cast, y = hidden_states_59_cast)[name = tensor("hidden_states_61_cast")]; tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([2, 32, 40, 32, 32])]; - tensor reshape_48 = reshape(shape = reshape_48_shape_0, x = hidden_states_61)[name = tensor("reshape_48")]; + tensor reshape_48_cast = reshape(shape = reshape_48_shape_0, x = hidden_states_61_cast)[name = tensor("reshape_48_cast")]; tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_36_keep_dims_0 = const()[name = tensor("reduce_mean_36_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_36 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48)[name = tensor("reduce_mean_36")]; - tensor sub_24 = sub(x = reshape_48, y = reduce_mean_36)[name = tensor("sub_24")]; - tensor square_12 = square(x = sub_24)[name = tensor("square_12")]; + tensor reduce_mean_36_cast = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast)[name = tensor("reduce_mean_36_cast")]; + tensor sub_24_cast = sub(x = reshape_48_cast, y = reduce_mean_36_cast)[name = tensor("sub_24_cast")]; + tensor square_12_cast = square(x = sub_24_cast)[name = tensor("square_12_cast")]; tensor reduce_mean_38_axes_0 = const()[name = tensor("reduce_mean_38_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_38_keep_dims_0 = const()[name = tensor("reduce_mean_38_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_38 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12)[name = tensor("reduce_mean_38")]; - tensor add_24_y_0 = const()[name = tensor("add_24_y_0"), val = tensor(0x1.0c6f7ap-20)]; - tensor add_24 = add(x = reduce_mean_38, y = add_24_y_0)[name = tensor("add_24")]; - tensor sqrt_12 = sqrt(x = add_24)[name = tensor("sqrt_12")]; - tensor real_div_12 = real_div(x = sub_24, y = sqrt_12)[name = tensor("real_div_12")]; + tensor reduce_mean_38_cast = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast)[name = tensor("reduce_mean_38_cast")]; + tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_24_cast = add(x = reduce_mean_38_cast, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast")]; + tensor sqrt_12_cast = sqrt(x = add_24_cast)[name = tensor("sqrt_12_cast")]; + tensor real_div_12_cast = real_div(x = sub_24_cast, y = sqrt_12_cast)[name = tensor("real_div_12_cast")]; tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([2, 1280, 32, 32])]; - tensor reshape_49 = reshape(shape = reshape_49_shape_0, x = real_div_12)[name = tensor("reshape_49")]; - tensor add_25_gamma_0 = const()[name = tensor("add_25_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267714688)))]; - tensor add_25_beta_0 = const()[name = tensor("add_25_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267719872)))]; - tensor add_25_epsilon_0 = const()[name = tensor("add_25_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_25 = batch_norm(beta = add_25_beta_0, epsilon = add_25_epsilon_0, gamma = add_25_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_49)[name = tensor("add_25")]; - tensor var_1281 = const()[name = tensor("op_1281"), val = tensor([1, 1])]; - tensor var_1283 = const()[name = tensor("op_1283"), val = tensor([1, 1])]; + tensor reshape_49_cast = reshape(shape = reshape_49_shape_0, x = real_div_12_cast)[name = tensor("reshape_49_cast")]; + tensor add_25_gamma_0_to_fp16 = const()[name = tensor("add_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194881152)))]; + tensor add_25_beta_0_to_fp16 = const()[name = tensor("add_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194883776)))]; + tensor add_25_epsilon_0_to_fp16 = const()[name = tensor("add_25_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_25_cast = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_49_cast)[name = tensor("add_25_cast")]; + tensor var_1219 = const()[name = tensor("op_1219"), val = tensor([1, 1])]; + tensor var_1221 = const()[name = tensor("op_1221"), val = tensor([1, 1])]; tensor hidden_states_63_pad_type_0 = const()[name = tensor("hidden_states_63_pad_type_0"), val = tensor("custom")]; tensor hidden_states_63_pad_0 = const()[name = tensor("hidden_states_63_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_63 = conv(bias = down_blocks_2_attentions_0_proj_in_bias, dilations = var_1283, groups = var_1193, pad = hidden_states_63_pad_0, pad_type = hidden_states_63_pad_type_0, strides = var_1281, weight = down_blocks_2_attentions_0_proj_in_weight, x = add_25)[name = tensor("hidden_states_63")]; - tensor var_1288 = const()[name = tensor("op_1288"), val = tensor([2, 1280, 1, 1024])]; - tensor inputs_25 = reshape(shape = var_1288, x = hidden_states_63)[name = tensor("inputs_25")]; - tensor var_1298 = const()[name = tensor("op_1298"), val = tensor([1])]; - tensor channels_mean_25 = reduce_mean(axes = var_1298, keep_dims = var_1188, x = inputs_25)[name = tensor("channels_mean_25")]; - tensor zero_mean_25 = sub(x = inputs_25, y = channels_mean_25)[name = tensor("zero_mean_25")]; - tensor zero_mean_sq_25 = mul(x = zero_mean_25, y = zero_mean_25)[name = tensor("zero_mean_sq_25")]; - tensor var_1302 = const()[name = tensor("op_1302"), val = tensor([1])]; - tensor var_1303 = reduce_mean(axes = var_1302, keep_dims = var_1188, x = zero_mean_sq_25)[name = tensor("op_1303")]; - tensor var_1304 = const()[name = tensor("op_1304"), val = tensor(0x1.4f8b58p-17)]; - tensor var_1305 = add(x = var_1303, y = var_1304)[name = tensor("op_1305")]; - tensor denom_25_epsilon_0 = const()[name = tensor("denom_25_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_25 = rsqrt(epsilon = denom_25_epsilon_0, x = var_1305)[name = tensor("denom_25")]; - tensor out_25 = mul(x = zero_mean_25, y = denom_25)[name = tensor("out_25")]; - tensor var_1309 = const()[name = tensor("op_1309"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267725056)))]; - tensor var_1310 = add(x = out_25, y = var_1309)[name = tensor("op_1310")]; - tensor var_1312 = const()[name = tensor("op_1312"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267730240)))]; - tensor hidden_states_65 = mul(x = var_1310, y = var_1312)[name = tensor("hidden_states_65")]; - tensor var_1319 = const()[name = tensor("op_1319"), val = tensor([1, 1])]; - tensor var_1321 = const()[name = tensor("op_1321"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194886400)))]; + tensor unet_down_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198163264)))]; + tensor hidden_states_63_cast = conv(bias = unet_down_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_1221, groups = var_31, pad = hidden_states_63_pad_0, pad_type = hidden_states_63_pad_type_0, strides = var_1219, weight = unet_down_blocks_2_attentions_0_proj_in_weight_to_fp16, x = add_25_cast)[name = tensor("hidden_states_63_cast")]; + tensor var_1226 = const()[name = tensor("op_1226"), val = tensor([2, 1280, 1, 1024])]; + tensor inputs_25_cast = reshape(shape = var_1226, x = hidden_states_63_cast)[name = tensor("inputs_25_cast")]; + tensor var_1236 = const()[name = tensor("op_1236"), val = tensor([1])]; + tensor channels_mean_25_cast = reduce_mean(axes = var_1236, keep_dims = var_23, x = inputs_25_cast)[name = tensor("channels_mean_25_cast")]; + tensor zero_mean_25_cast = sub(x = inputs_25_cast, y = channels_mean_25_cast)[name = tensor("zero_mean_25_cast")]; + tensor zero_mean_sq_25_cast = mul(x = zero_mean_25_cast, y = zero_mean_25_cast)[name = tensor("zero_mean_sq_25_cast")]; + tensor var_1240 = const()[name = tensor("op_1240"), val = tensor([1])]; + tensor var_1241_cast = reduce_mean(axes = var_1240, keep_dims = var_23, x = zero_mean_sq_25_cast)[name = tensor("op_1241_cast")]; + tensor var_1242_to_fp16 = const()[name = tensor("op_1242_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1243_cast = add(x = var_1241_cast, y = var_1242_to_fp16)[name = tensor("op_1243_cast")]; + tensor denom_25_epsilon_0_to_fp16 = const()[name = tensor("denom_25_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_25_cast = rsqrt(epsilon = denom_25_epsilon_0_to_fp16, x = var_1243_cast)[name = tensor("denom_25_cast")]; + tensor out_25_cast = mul(x = zero_mean_25_cast, y = denom_25_cast)[name = tensor("out_25_cast")]; + tensor var_1247_to_fp16 = const()[name = tensor("op_1247_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198165888)))]; + tensor var_1248_cast = add(x = out_25_cast, y = var_1247_to_fp16)[name = tensor("op_1248_cast")]; + tensor var_1250_to_fp16 = const()[name = tensor("op_1250_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198168512)))]; + tensor hidden_states_65_cast = mul(x = var_1248_cast, y = var_1250_to_fp16)[name = tensor("hidden_states_65_cast")]; + tensor var_1257 = const()[name = tensor("op_1257"), val = tensor([1, 1])]; + tensor var_1259 = const()[name = tensor("op_1259"), val = tensor([1, 1])]; tensor q_17_pad_type_0 = const()[name = tensor("q_17_pad_type_0"), val = tensor("custom")]; tensor q_17_pad_0 = const()[name = tensor("q_17_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_17 = conv(dilations = var_1321, groups = var_1193, pad = q_17_pad_0, pad_type = q_17_pad_type_0, strides = var_1319, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight, x = hidden_states_65)[name = tensor("q_17")]; - tensor var_1325 = const()[name = tensor("op_1325"), val = tensor([1, 1])]; - tensor var_1327 = const()[name = tensor("op_1327"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(198171136)))]; + tensor q_17_cast = conv(dilations = var_1259, groups = var_31, pad = q_17_pad_0, pad_type = q_17_pad_type_0, strides = var_1257, weight = unet_down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_65_cast)[name = tensor("q_17_cast")]; + tensor var_1263 = const()[name = tensor("op_1263"), val = tensor([1, 1])]; + tensor var_1265 = const()[name = tensor("op_1265"), val = tensor([1, 1])]; tensor k_17_pad_type_0 = const()[name = tensor("k_17_pad_type_0"), val = tensor("custom")]; tensor k_17_pad_0 = const()[name = tensor("k_17_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_17 = conv(dilations = var_1327, groups = var_1193, pad = k_17_pad_0, pad_type = k_17_pad_type_0, strides = var_1325, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight, x = hidden_states_65)[name = tensor("k_17")]; - tensor var_1331 = const()[name = tensor("op_1331"), val = tensor([1, 1])]; - tensor var_1333 = const()[name = tensor("op_1333"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201448000)))]; + tensor k_17_cast = conv(dilations = var_1265, groups = var_31, pad = k_17_pad_0, pad_type = k_17_pad_type_0, strides = var_1263, weight = unet_down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_65_cast)[name = tensor("k_17_cast")]; + tensor var_1269 = const()[name = tensor("op_1269"), val = tensor([1, 1])]; + tensor var_1271 = const()[name = tensor("op_1271"), val = tensor([1, 1])]; tensor v_17_pad_type_0 = const()[name = tensor("v_17_pad_type_0"), val = tensor("custom")]; tensor v_17_pad_0 = const()[name = tensor("v_17_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_17 = conv(dilations = var_1333, groups = var_1193, pad = v_17_pad_0, pad_type = v_17_pad_type_0, strides = var_1331, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight, x = hidden_states_65)[name = tensor("v_17")]; - tensor var_1337 = const()[name = tensor("op_1337"), val = tensor([2, 20, 64, -1])]; - tensor var_1338 = reshape(shape = var_1337, x = q_17)[name = tensor("op_1338")]; - tensor var_1339 = const()[name = tensor("op_1339"), val = tensor([2, 20, 64, -1])]; - tensor var_1340 = reshape(shape = var_1339, x = k_17)[name = tensor("op_1340")]; - tensor var_1341 = const()[name = tensor("op_1341"), val = tensor([2, 20, 64, -1])]; - tensor var_1342 = reshape(shape = var_1341, x = v_17)[name = tensor("op_1342")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(204724864)))]; + tensor v_17_cast = conv(dilations = var_1271, groups = var_31, pad = v_17_pad_0, pad_type = v_17_pad_type_0, strides = var_1269, weight = unet_down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_65_cast)[name = tensor("v_17_cast")]; + tensor var_1275 = const()[name = tensor("op_1275"), val = tensor([2, 20, 64, -1])]; + tensor var_1276_cast = reshape(shape = var_1275, x = q_17_cast)[name = tensor("op_1276_cast")]; + tensor var_1277 = const()[name = tensor("op_1277"), val = tensor([2, 20, 64, -1])]; + tensor var_1278_cast = reshape(shape = var_1277, x = k_17_cast)[name = tensor("op_1278_cast")]; + tensor var_1279 = const()[name = tensor("op_1279"), val = tensor([2, 20, 64, -1])]; + tensor var_1280_cast = reshape(shape = var_1279, x = v_17_cast)[name = tensor("op_1280_cast")]; tensor attn_weights_33_transpose_x_0 = const()[name = tensor("attn_weights_33_transpose_x_0"), val = tensor(true)]; tensor attn_weights_33_transpose_y_0 = const()[name = tensor("attn_weights_33_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_33 = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1338, y = var_1340)[name = tensor("attn_weights_33")]; - tensor attn_weights_35 = mul(x = attn_weights_33, y = var_1184)[name = tensor("attn_weights_35")]; - tensor var_1346 = softmax(axis = var_1177, x = attn_weights_35)[name = tensor("op_1346")]; + tensor attn_weights_33_cast = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1276_cast, y = var_1278_cast)[name = tensor("attn_weights_33_cast")]; + tensor attn_weights_35_cast = mul(x = attn_weights_33_cast, y = var_12_to_fp16)[name = tensor("attn_weights_35_cast")]; + tensor var_1284_cast = softmax(axis = var_18, x = attn_weights_35_cast)[name = tensor("op_1284_cast")]; tensor attn_17_transpose_x_0 = const()[name = tensor("attn_17_transpose_x_0"), val = tensor(false)]; tensor attn_17_transpose_y_0 = const()[name = tensor("attn_17_transpose_y_0"), val = tensor(true)]; - tensor attn_17 = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_1342, y = var_1346)[name = tensor("attn_17")]; - tensor var_1350 = const()[name = tensor("op_1350"), val = tensor([2, 1280, 1, -1])]; - tensor input_131 = reshape(shape = var_1350, x = attn_17)[name = tensor("input_131")]; - tensor var_1355 = const()[name = tensor("op_1355"), val = tensor([1, 1])]; - tensor var_1357 = const()[name = tensor("op_1357"), val = tensor([1, 1])]; - tensor var_1359_pad_type_0 = const()[name = tensor("op_1359_pad_type_0"), val = tensor("custom")]; - tensor var_1359_pad_0 = const()[name = tensor("op_1359_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1359 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias, dilations = var_1357, groups = var_1193, pad = var_1359_pad_0, pad_type = var_1359_pad_type_0, strides = var_1355, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight, x = input_131)[name = tensor("op_1359")]; - tensor inputs_27 = add(x = var_1359, y = inputs_25)[name = tensor("inputs_27")]; - tensor var_1363 = const()[name = tensor("op_1363"), val = tensor([1])]; - tensor channels_mean_27 = reduce_mean(axes = var_1363, keep_dims = var_1188, x = inputs_27)[name = tensor("channels_mean_27")]; - tensor zero_mean_27 = sub(x = inputs_27, y = channels_mean_27)[name = tensor("zero_mean_27")]; - tensor zero_mean_sq_27 = mul(x = zero_mean_27, y = zero_mean_27)[name = tensor("zero_mean_sq_27")]; - tensor var_1367 = const()[name = tensor("op_1367"), val = tensor([1])]; - tensor var_1368 = reduce_mean(axes = var_1367, keep_dims = var_1188, x = zero_mean_sq_27)[name = tensor("op_1368")]; - tensor var_1369 = const()[name = tensor("op_1369"), val = tensor(0x1.4f8b58p-17)]; - tensor var_1370 = add(x = var_1368, y = var_1369)[name = tensor("op_1370")]; - tensor denom_27_epsilon_0 = const()[name = tensor("denom_27_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_27 = rsqrt(epsilon = denom_27_epsilon_0, x = var_1370)[name = tensor("denom_27")]; - tensor out_27 = mul(x = zero_mean_27, y = denom_27)[name = tensor("out_27")]; - tensor var_1374 = const()[name = tensor("op_1374"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267735424)))]; - tensor var_1375 = add(x = out_27, y = var_1374)[name = tensor("op_1375")]; - tensor var_1377 = const()[name = tensor("op_1377"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267740608)))]; - tensor hidden_states_67 = mul(x = var_1375, y = var_1377)[name = tensor("hidden_states_67")]; - tensor var_1384 = const()[name = tensor("op_1384"), val = tensor([1, 1])]; - tensor var_1386 = const()[name = tensor("op_1386"), val = tensor([1, 1])]; + tensor attn_17_cast = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_1280_cast, y = var_1284_cast)[name = tensor("attn_17_cast")]; + tensor var_1288 = const()[name = tensor("op_1288"), val = tensor([2, 1280, 1, -1])]; + tensor input_131_cast = reshape(shape = var_1288, x = attn_17_cast)[name = tensor("input_131_cast")]; + tensor var_1293 = const()[name = tensor("op_1293"), val = tensor([1, 1])]; + tensor var_1295 = const()[name = tensor("op_1295"), val = tensor([1, 1])]; + tensor var_1297_pad_type_0 = const()[name = tensor("op_1297_pad_type_0"), val = tensor("custom")]; + tensor var_1297_pad_0 = const()[name = tensor("op_1297_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(208001728)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(211278592)))]; + tensor var_1297_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1295, groups = var_31, pad = var_1297_pad_0, pad_type = var_1297_pad_type_0, strides = var_1293, weight = unet_down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_131_cast)[name = tensor("op_1297_cast")]; + tensor inputs_27_cast = add(x = var_1297_cast, y = inputs_25_cast)[name = tensor("inputs_27_cast")]; + tensor var_1301 = const()[name = tensor("op_1301"), val = tensor([1])]; + tensor channels_mean_27_cast = reduce_mean(axes = var_1301, keep_dims = var_23, x = inputs_27_cast)[name = tensor("channels_mean_27_cast")]; + tensor zero_mean_27_cast = sub(x = inputs_27_cast, y = channels_mean_27_cast)[name = tensor("zero_mean_27_cast")]; + tensor zero_mean_sq_27_cast = mul(x = zero_mean_27_cast, y = zero_mean_27_cast)[name = tensor("zero_mean_sq_27_cast")]; + tensor var_1305 = const()[name = tensor("op_1305"), val = tensor([1])]; + tensor var_1306_cast = reduce_mean(axes = var_1305, keep_dims = var_23, x = zero_mean_sq_27_cast)[name = tensor("op_1306_cast")]; + tensor var_1307_to_fp16 = const()[name = tensor("op_1307_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1308_cast = add(x = var_1306_cast, y = var_1307_to_fp16)[name = tensor("op_1308_cast")]; + tensor denom_27_epsilon_0_to_fp16 = const()[name = tensor("denom_27_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_27_cast = rsqrt(epsilon = denom_27_epsilon_0_to_fp16, x = var_1308_cast)[name = tensor("denom_27_cast")]; + tensor out_27_cast = mul(x = zero_mean_27_cast, y = denom_27_cast)[name = tensor("out_27_cast")]; + tensor var_1312_to_fp16 = const()[name = tensor("op_1312_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(211281216)))]; + tensor var_1313_cast = add(x = out_27_cast, y = var_1312_to_fp16)[name = tensor("op_1313_cast")]; + tensor var_1315_to_fp16 = const()[name = tensor("op_1315_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(211283840)))]; + tensor hidden_states_67_cast = mul(x = var_1313_cast, y = var_1315_to_fp16)[name = tensor("hidden_states_67_cast")]; + tensor var_1322 = const()[name = tensor("op_1322"), val = tensor([1, 1])]; + tensor var_1324 = const()[name = tensor("op_1324"), val = tensor([1, 1])]; tensor q_19_pad_type_0 = const()[name = tensor("q_19_pad_type_0"), val = tensor("custom")]; tensor q_19_pad_0 = const()[name = tensor("q_19_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_19 = conv(dilations = var_1386, groups = var_1193, pad = q_19_pad_0, pad_type = q_19_pad_type_0, strides = var_1384, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight, x = hidden_states_67)[name = tensor("q_19")]; - tensor var_1390 = const()[name = tensor("op_1390"), val = tensor([1, 1])]; - tensor var_1392 = const()[name = tensor("op_1392"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(211286464)))]; + tensor q_19_cast = conv(dilations = var_1324, groups = var_31, pad = q_19_pad_0, pad_type = q_19_pad_type_0, strides = var_1322, weight = unet_down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_67_cast)[name = tensor("q_19_cast")]; + tensor var_1328 = const()[name = tensor("op_1328"), val = tensor([1, 1])]; + tensor var_1330 = const()[name = tensor("op_1330"), val = tensor([1, 1])]; tensor k_19_pad_type_0 = const()[name = tensor("k_19_pad_type_0"), val = tensor("custom")]; tensor k_19_pad_0 = const()[name = tensor("k_19_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_19 = conv(dilations = var_1392, groups = var_1193, pad = k_19_pad_0, pad_type = k_19_pad_type_0, strides = var_1390, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_19")]; - tensor var_1396 = const()[name = tensor("op_1396"), val = tensor([1, 1])]; - tensor var_1398 = const()[name = tensor("op_1398"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214563328)))]; + tensor k_19_cast = conv(dilations = var_1330, groups = var_31, pad = k_19_pad_0, pad_type = k_19_pad_type_0, strides = var_1328, weight = unet_down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_19_cast")]; + tensor var_1334 = const()[name = tensor("op_1334"), val = tensor([1, 1])]; + tensor var_1336 = const()[name = tensor("op_1336"), val = tensor([1, 1])]; tensor v_19_pad_type_0 = const()[name = tensor("v_19_pad_type_0"), val = tensor("custom")]; tensor v_19_pad_0 = const()[name = tensor("v_19_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_19 = conv(dilations = var_1398, groups = var_1193, pad = v_19_pad_0, pad_type = v_19_pad_type_0, strides = var_1396, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_19")]; - tensor var_1402 = const()[name = tensor("op_1402"), val = tensor([2, 20, 64, -1])]; - tensor var_1403 = reshape(shape = var_1402, x = q_19)[name = tensor("op_1403")]; - tensor var_1404 = const()[name = tensor("op_1404"), val = tensor([2, 20, 64, -1])]; - tensor var_1405 = reshape(shape = var_1404, x = k_19)[name = tensor("op_1405")]; - tensor var_1406 = const()[name = tensor("op_1406"), val = tensor([2, 20, 64, -1])]; - tensor var_1407 = reshape(shape = var_1406, x = v_19)[name = tensor("op_1407")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(219806272)))]; + tensor v_19_cast = conv(dilations = var_1336, groups = var_31, pad = v_19_pad_0, pad_type = v_19_pad_type_0, strides = var_1334, weight = unet_down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_19_cast")]; + tensor var_1340 = const()[name = tensor("op_1340"), val = tensor([2, 20, 64, -1])]; + tensor var_1341_cast = reshape(shape = var_1340, x = q_19_cast)[name = tensor("op_1341_cast")]; + tensor var_1342 = const()[name = tensor("op_1342"), val = tensor([2, 20, 64, -1])]; + tensor var_1343_cast = reshape(shape = var_1342, x = k_19_cast)[name = tensor("op_1343_cast")]; + tensor var_1344 = const()[name = tensor("op_1344"), val = tensor([2, 20, 64, -1])]; + tensor var_1345_cast = reshape(shape = var_1344, x = v_19_cast)[name = tensor("op_1345_cast")]; tensor attn_weights_37_transpose_x_0 = const()[name = tensor("attn_weights_37_transpose_x_0"), val = tensor(true)]; tensor attn_weights_37_transpose_y_0 = const()[name = tensor("attn_weights_37_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_37 = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = var_1403, y = var_1405)[name = tensor("attn_weights_37")]; - tensor attn_weights_39 = mul(x = attn_weights_37, y = var_1184)[name = tensor("attn_weights_39")]; - tensor var_1411 = softmax(axis = var_1177, x = attn_weights_39)[name = tensor("op_1411")]; + tensor attn_weights_37_cast = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = var_1341_cast, y = var_1343_cast)[name = tensor("attn_weights_37_cast")]; + tensor attn_weights_39_cast = mul(x = attn_weights_37_cast, y = var_12_to_fp16)[name = tensor("attn_weights_39_cast")]; + tensor var_1349_cast = softmax(axis = var_18, x = attn_weights_39_cast)[name = tensor("op_1349_cast")]; tensor attn_19_transpose_x_0 = const()[name = tensor("attn_19_transpose_x_0"), val = tensor(false)]; tensor attn_19_transpose_y_0 = const()[name = tensor("attn_19_transpose_y_0"), val = tensor(true)]; - tensor attn_19 = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_1407, y = var_1411)[name = tensor("attn_19")]; - tensor var_1415 = const()[name = tensor("op_1415"), val = tensor([2, 1280, 1, -1])]; - tensor input_133 = reshape(shape = var_1415, x = attn_19)[name = tensor("input_133")]; - tensor var_1420 = const()[name = tensor("op_1420"), val = tensor([1, 1])]; - tensor var_1422 = const()[name = tensor("op_1422"), val = tensor([1, 1])]; - tensor var_1424_pad_type_0 = const()[name = tensor("op_1424_pad_type_0"), val = tensor("custom")]; - tensor var_1424_pad_0 = const()[name = tensor("op_1424_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1424 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias, dilations = var_1422, groups = var_1193, pad = var_1424_pad_0, pad_type = var_1424_pad_type_0, strides = var_1420, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight, x = input_133)[name = tensor("op_1424")]; - tensor inputs_29 = add(x = var_1424, y = inputs_27)[name = tensor("inputs_29")]; - tensor var_1428 = const()[name = tensor("op_1428"), val = tensor([1])]; - tensor channels_mean_29 = reduce_mean(axes = var_1428, keep_dims = var_1188, x = inputs_29)[name = tensor("channels_mean_29")]; - tensor zero_mean_29 = sub(x = inputs_29, y = channels_mean_29)[name = tensor("zero_mean_29")]; - tensor zero_mean_sq_29 = mul(x = zero_mean_29, y = zero_mean_29)[name = tensor("zero_mean_sq_29")]; - tensor var_1432 = const()[name = tensor("op_1432"), val = tensor([1])]; - tensor var_1433 = reduce_mean(axes = var_1432, keep_dims = var_1188, x = zero_mean_sq_29)[name = tensor("op_1433")]; - tensor var_1434 = const()[name = tensor("op_1434"), val = tensor(0x1.4f8b58p-17)]; - tensor var_1435 = add(x = var_1433, y = var_1434)[name = tensor("op_1435")]; - tensor denom_29_epsilon_0 = const()[name = tensor("denom_29_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_29 = rsqrt(epsilon = denom_29_epsilon_0, x = var_1435)[name = tensor("denom_29")]; - tensor out_29 = mul(x = zero_mean_29, y = denom_29)[name = tensor("out_29")]; - tensor var_1439 = const()[name = tensor("op_1439"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267745792)))]; - tensor var_1440 = add(x = out_29, y = var_1439)[name = tensor("op_1440")]; - tensor var_1442 = const()[name = tensor("op_1442"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267750976)))]; - tensor input_135 = mul(x = var_1440, y = var_1442)[name = tensor("input_135")]; - tensor var_1450 = const()[name = tensor("op_1450"), val = tensor([1, 1])]; - tensor var_1452 = const()[name = tensor("op_1452"), val = tensor([1, 1])]; - tensor var_1454_pad_type_0 = const()[name = tensor("op_1454_pad_type_0"), val = tensor("custom")]; - tensor var_1454_pad_0 = const()[name = tensor("op_1454_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1454 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias, dilations = var_1452, groups = var_1193, pad = var_1454_pad_0, pad_type = var_1454_pad_type_0, strides = var_1450, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight, x = input_135)[name = tensor("op_1454")]; - tensor var_1455_split_sizes_0 = const()[name = tensor("op_1455_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_1455_axis_0 = const()[name = tensor("op_1455_axis_0"), val = tensor(1)]; - tensor var_1455_0, tensor var_1455_1 = split(axis = var_1455_axis_0, split_sizes = var_1455_split_sizes_0, x = var_1454)[name = tensor("op_1455")]; - tensor var_1457_mode_0 = const()[name = tensor("op_1457_mode_0"), val = tensor("EXACT")]; - tensor var_1457 = gelu(mode = var_1457_mode_0, x = var_1455_1)[name = tensor("op_1457")]; - tensor input_137 = mul(x = var_1455_0, y = var_1457)[name = tensor("input_137")]; - tensor var_1461 = const()[name = tensor("op_1461"), val = tensor([1, 1])]; - tensor var_1463 = const()[name = tensor("op_1463"), val = tensor([1, 1])]; - tensor var_1465_pad_type_0 = const()[name = tensor("op_1465_pad_type_0"), val = tensor("custom")]; - tensor var_1465_pad_0 = const()[name = tensor("op_1465_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1465 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias, dilations = var_1463, groups = var_1193, pad = var_1465_pad_0, pad_type = var_1465_pad_type_0, strides = var_1461, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight, x = input_137)[name = tensor("op_1465")]; - tensor inputs_31 = add(x = var_1465, y = inputs_29)[name = tensor("inputs_31")]; - tensor var_1475 = const()[name = tensor("op_1475"), val = tensor([1])]; - tensor channels_mean_31 = reduce_mean(axes = var_1475, keep_dims = var_1188, x = inputs_31)[name = tensor("channels_mean_31")]; - tensor zero_mean_31 = sub(x = inputs_31, y = channels_mean_31)[name = tensor("zero_mean_31")]; - tensor zero_mean_sq_31 = mul(x = zero_mean_31, y = zero_mean_31)[name = tensor("zero_mean_sq_31")]; - tensor var_1479 = const()[name = tensor("op_1479"), val = tensor([1])]; - tensor var_1480 = reduce_mean(axes = var_1479, keep_dims = var_1188, x = zero_mean_sq_31)[name = tensor("op_1480")]; - tensor var_1481 = const()[name = tensor("op_1481"), val = tensor(0x1.4f8b58p-17)]; - tensor var_1482 = add(x = var_1480, y = var_1481)[name = tensor("op_1482")]; - tensor denom_31_epsilon_0 = const()[name = tensor("denom_31_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_31 = rsqrt(epsilon = denom_31_epsilon_0, x = var_1482)[name = tensor("denom_31")]; - tensor out_31 = mul(x = zero_mean_31, y = denom_31)[name = tensor("out_31")]; - tensor var_1486 = const()[name = tensor("op_1486"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267756160)))]; - tensor var_1487 = add(x = out_31, y = var_1486)[name = tensor("op_1487")]; - tensor var_1489 = const()[name = tensor("op_1489"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267761344)))]; - tensor hidden_states_71 = mul(x = var_1487, y = var_1489)[name = tensor("hidden_states_71")]; - tensor var_1496 = const()[name = tensor("op_1496"), val = tensor([1, 1])]; - tensor var_1498 = const()[name = tensor("op_1498"), val = tensor([1, 1])]; + tensor attn_19_cast = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_1345_cast, y = var_1349_cast)[name = tensor("attn_19_cast")]; + tensor var_1353 = const()[name = tensor("op_1353"), val = tensor([2, 1280, 1, -1])]; + tensor input_133_cast = reshape(shape = var_1353, x = attn_19_cast)[name = tensor("input_133_cast")]; + tensor var_1358 = const()[name = tensor("op_1358"), val = tensor([1, 1])]; + tensor var_1360 = const()[name = tensor("op_1360"), val = tensor([1, 1])]; + tensor var_1362_pad_type_0 = const()[name = tensor("op_1362_pad_type_0"), val = tensor("custom")]; + tensor var_1362_pad_0 = const()[name = tensor("op_1362_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(225049216)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(228326080)))]; + tensor var_1362_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1360, groups = var_31, pad = var_1362_pad_0, pad_type = var_1362_pad_type_0, strides = var_1358, weight = unet_down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_133_cast)[name = tensor("op_1362_cast")]; + tensor inputs_29_cast = add(x = var_1362_cast, y = inputs_27_cast)[name = tensor("inputs_29_cast")]; + tensor var_1366 = const()[name = tensor("op_1366"), val = tensor([1])]; + tensor channels_mean_29_cast = reduce_mean(axes = var_1366, keep_dims = var_23, x = inputs_29_cast)[name = tensor("channels_mean_29_cast")]; + tensor zero_mean_29_cast = sub(x = inputs_29_cast, y = channels_mean_29_cast)[name = tensor("zero_mean_29_cast")]; + tensor zero_mean_sq_29_cast = mul(x = zero_mean_29_cast, y = zero_mean_29_cast)[name = tensor("zero_mean_sq_29_cast")]; + tensor var_1370 = const()[name = tensor("op_1370"), val = tensor([1])]; + tensor var_1371_cast = reduce_mean(axes = var_1370, keep_dims = var_23, x = zero_mean_sq_29_cast)[name = tensor("op_1371_cast")]; + tensor var_1372_to_fp16 = const()[name = tensor("op_1372_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1373_cast = add(x = var_1371_cast, y = var_1372_to_fp16)[name = tensor("op_1373_cast")]; + tensor denom_29_epsilon_0_to_fp16 = const()[name = tensor("denom_29_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_29_cast = rsqrt(epsilon = denom_29_epsilon_0_to_fp16, x = var_1373_cast)[name = tensor("denom_29_cast")]; + tensor out_29_cast = mul(x = zero_mean_29_cast, y = denom_29_cast)[name = tensor("out_29_cast")]; + tensor var_1377_to_fp16 = const()[name = tensor("op_1377_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(228328704)))]; + tensor var_1378_cast = add(x = out_29_cast, y = var_1377_to_fp16)[name = tensor("op_1378_cast")]; + tensor var_1380_to_fp16 = const()[name = tensor("op_1380_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(228331328)))]; + tensor input_135_cast = mul(x = var_1378_cast, y = var_1380_to_fp16)[name = tensor("input_135_cast")]; + tensor var_1388 = const()[name = tensor("op_1388"), val = tensor([1, 1])]; + tensor var_1390 = const()[name = tensor("op_1390"), val = tensor([1, 1])]; + tensor var_1392_pad_type_0 = const()[name = tensor("op_1392_pad_type_0"), val = tensor("custom")]; + tensor var_1392_pad_0 = const()[name = tensor("op_1392_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(228333952)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254548416)))]; + tensor var_1392_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_1390, groups = var_31, pad = var_1392_pad_0, pad_type = var_1392_pad_type_0, strides = var_1388, weight = unet_down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_135_cast)[name = tensor("op_1392_cast")]; + tensor var_1393_split_sizes_0 = const()[name = tensor("op_1393_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1393_axis_0 = const()[name = tensor("op_1393_axis_0"), val = tensor(1)]; + tensor var_1393_cast_0, tensor var_1393_cast_1 = split(axis = var_1393_axis_0, split_sizes = var_1393_split_sizes_0, x = var_1392_cast)[name = tensor("op_1393_cast")]; + tensor var_1395_mode_0 = const()[name = tensor("op_1395_mode_0"), val = tensor("EXACT")]; + tensor var_1395_cast = gelu(mode = var_1395_mode_0, x = var_1393_cast_1)[name = tensor("op_1395_cast")]; + tensor input_137_cast = mul(x = var_1393_cast_0, y = var_1395_cast)[name = tensor("input_137_cast")]; + tensor var_1399 = const()[name = tensor("op_1399"), val = tensor([1, 1])]; + tensor var_1401 = const()[name = tensor("op_1401"), val = tensor([1, 1])]; + tensor var_1403_pad_type_0 = const()[name = tensor("op_1403_pad_type_0"), val = tensor("custom")]; + tensor var_1403_pad_0 = const()[name = tensor("op_1403_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(254568960)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267676224)))]; + tensor var_1403_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1401, groups = var_31, pad = var_1403_pad_0, pad_type = var_1403_pad_type_0, strides = var_1399, weight = unet_down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_137_cast)[name = tensor("op_1403_cast")]; + tensor inputs_31_cast = add(x = var_1403_cast, y = inputs_29_cast)[name = tensor("inputs_31_cast")]; + tensor var_1413 = const()[name = tensor("op_1413"), val = tensor([1])]; + tensor channels_mean_31_cast = reduce_mean(axes = var_1413, keep_dims = var_23, x = inputs_31_cast)[name = tensor("channels_mean_31_cast")]; + tensor zero_mean_31_cast = sub(x = inputs_31_cast, y = channels_mean_31_cast)[name = tensor("zero_mean_31_cast")]; + tensor zero_mean_sq_31_cast = mul(x = zero_mean_31_cast, y = zero_mean_31_cast)[name = tensor("zero_mean_sq_31_cast")]; + tensor var_1417 = const()[name = tensor("op_1417"), val = tensor([1])]; + tensor var_1418_cast = reduce_mean(axes = var_1417, keep_dims = var_23, x = zero_mean_sq_31_cast)[name = tensor("op_1418_cast")]; + tensor var_1419_to_fp16 = const()[name = tensor("op_1419_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1420_cast = add(x = var_1418_cast, y = var_1419_to_fp16)[name = tensor("op_1420_cast")]; + tensor denom_31_epsilon_0_to_fp16 = const()[name = tensor("denom_31_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_31_cast = rsqrt(epsilon = denom_31_epsilon_0_to_fp16, x = var_1420_cast)[name = tensor("denom_31_cast")]; + tensor out_31_cast = mul(x = zero_mean_31_cast, y = denom_31_cast)[name = tensor("out_31_cast")]; + tensor var_1424_to_fp16 = const()[name = tensor("op_1424_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267678848)))]; + tensor var_1425_cast = add(x = out_31_cast, y = var_1424_to_fp16)[name = tensor("op_1425_cast")]; + tensor var_1427_to_fp16 = const()[name = tensor("op_1427_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267681472)))]; + tensor hidden_states_71_cast = mul(x = var_1425_cast, y = var_1427_to_fp16)[name = tensor("hidden_states_71_cast")]; + tensor var_1434 = const()[name = tensor("op_1434"), val = tensor([1, 1])]; + tensor var_1436 = const()[name = tensor("op_1436"), val = tensor([1, 1])]; tensor q_21_pad_type_0 = const()[name = tensor("q_21_pad_type_0"), val = tensor("custom")]; tensor q_21_pad_0 = const()[name = tensor("q_21_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_21 = conv(dilations = var_1498, groups = var_1193, pad = q_21_pad_0, pad_type = q_21_pad_type_0, strides = var_1496, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight, x = hidden_states_71)[name = tensor("q_21")]; - tensor var_1502 = const()[name = tensor("op_1502"), val = tensor([1, 1])]; - tensor var_1504 = const()[name = tensor("op_1504"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(267684096)))]; + tensor q_21_cast = conv(dilations = var_1436, groups = var_31, pad = q_21_pad_0, pad_type = q_21_pad_type_0, strides = var_1434, weight = unet_down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_71_cast)[name = tensor("q_21_cast")]; + tensor var_1440 = const()[name = tensor("op_1440"), val = tensor([1, 1])]; + tensor var_1442 = const()[name = tensor("op_1442"), val = tensor([1, 1])]; tensor k_21_pad_type_0 = const()[name = tensor("k_21_pad_type_0"), val = tensor("custom")]; tensor k_21_pad_0 = const()[name = tensor("k_21_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_21 = conv(dilations = var_1504, groups = var_1193, pad = k_21_pad_0, pad_type = k_21_pad_type_0, strides = var_1502, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight, x = hidden_states_71)[name = tensor("k_21")]; - tensor var_1508 = const()[name = tensor("op_1508"), val = tensor([1, 1])]; - tensor var_1510 = const()[name = tensor("op_1510"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(270960960)))]; + tensor k_21_cast = conv(dilations = var_1442, groups = var_31, pad = k_21_pad_0, pad_type = k_21_pad_type_0, strides = var_1440, weight = unet_down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_71_cast)[name = tensor("k_21_cast")]; + tensor var_1446 = const()[name = tensor("op_1446"), val = tensor([1, 1])]; + tensor var_1448 = const()[name = tensor("op_1448"), val = tensor([1, 1])]; tensor v_21_pad_type_0 = const()[name = tensor("v_21_pad_type_0"), val = tensor("custom")]; tensor v_21_pad_0 = const()[name = tensor("v_21_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_21 = conv(dilations = var_1510, groups = var_1193, pad = v_21_pad_0, pad_type = v_21_pad_type_0, strides = var_1508, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight, x = hidden_states_71)[name = tensor("v_21")]; - tensor var_1514 = const()[name = tensor("op_1514"), val = tensor([2, 20, 64, -1])]; - tensor var_1515 = reshape(shape = var_1514, x = q_21)[name = tensor("op_1515")]; - tensor var_1516 = const()[name = tensor("op_1516"), val = tensor([2, 20, 64, -1])]; - tensor var_1517 = reshape(shape = var_1516, x = k_21)[name = tensor("op_1517")]; - tensor var_1518 = const()[name = tensor("op_1518"), val = tensor([2, 20, 64, -1])]; - tensor var_1519 = reshape(shape = var_1518, x = v_21)[name = tensor("op_1519")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(274237824)))]; + tensor v_21_cast = conv(dilations = var_1448, groups = var_31, pad = v_21_pad_0, pad_type = v_21_pad_type_0, strides = var_1446, weight = unet_down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_71_cast)[name = tensor("v_21_cast")]; + tensor var_1452 = const()[name = tensor("op_1452"), val = tensor([2, 20, 64, -1])]; + tensor var_1453_cast = reshape(shape = var_1452, x = q_21_cast)[name = tensor("op_1453_cast")]; + tensor var_1454 = const()[name = tensor("op_1454"), val = tensor([2, 20, 64, -1])]; + tensor var_1455_cast = reshape(shape = var_1454, x = k_21_cast)[name = tensor("op_1455_cast")]; + tensor var_1456 = const()[name = tensor("op_1456"), val = tensor([2, 20, 64, -1])]; + tensor var_1457_cast = reshape(shape = var_1456, x = v_21_cast)[name = tensor("op_1457_cast")]; tensor attn_weights_41_transpose_x_0 = const()[name = tensor("attn_weights_41_transpose_x_0"), val = tensor(true)]; tensor attn_weights_41_transpose_y_0 = const()[name = tensor("attn_weights_41_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_41 = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1515, y = var_1517)[name = tensor("attn_weights_41")]; - tensor attn_weights_43 = mul(x = attn_weights_41, y = var_1184)[name = tensor("attn_weights_43")]; - tensor var_1523 = softmax(axis = var_1177, x = attn_weights_43)[name = tensor("op_1523")]; + tensor attn_weights_41_cast = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1453_cast, y = var_1455_cast)[name = tensor("attn_weights_41_cast")]; + tensor attn_weights_43_cast = mul(x = attn_weights_41_cast, y = var_12_to_fp16)[name = tensor("attn_weights_43_cast")]; + tensor var_1461_cast = softmax(axis = var_18, x = attn_weights_43_cast)[name = tensor("op_1461_cast")]; tensor attn_21_transpose_x_0 = const()[name = tensor("attn_21_transpose_x_0"), val = tensor(false)]; tensor attn_21_transpose_y_0 = const()[name = tensor("attn_21_transpose_y_0"), val = tensor(true)]; - tensor attn_21 = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_1519, y = var_1523)[name = tensor("attn_21")]; - tensor var_1527 = const()[name = tensor("op_1527"), val = tensor([2, 1280, 1, -1])]; - tensor input_139 = reshape(shape = var_1527, x = attn_21)[name = tensor("input_139")]; - tensor var_1532 = const()[name = tensor("op_1532"), val = tensor([1, 1])]; - tensor var_1534 = const()[name = tensor("op_1534"), val = tensor([1, 1])]; - tensor var_1536_pad_type_0 = const()[name = tensor("op_1536_pad_type_0"), val = tensor("custom")]; - tensor var_1536_pad_0 = const()[name = tensor("op_1536_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1536 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias, dilations = var_1534, groups = var_1193, pad = var_1536_pad_0, pad_type = var_1536_pad_type_0, strides = var_1532, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight, x = input_139)[name = tensor("op_1536")]; - tensor inputs_33 = add(x = var_1536, y = inputs_31)[name = tensor("inputs_33")]; - tensor var_1540 = const()[name = tensor("op_1540"), val = tensor([1])]; - tensor channels_mean_33 = reduce_mean(axes = var_1540, keep_dims = var_1188, x = inputs_33)[name = tensor("channels_mean_33")]; - tensor zero_mean_33 = sub(x = inputs_33, y = channels_mean_33)[name = tensor("zero_mean_33")]; - tensor zero_mean_sq_33 = mul(x = zero_mean_33, y = zero_mean_33)[name = tensor("zero_mean_sq_33")]; - tensor var_1544 = const()[name = tensor("op_1544"), val = tensor([1])]; - tensor var_1545 = reduce_mean(axes = var_1544, keep_dims = var_1188, x = zero_mean_sq_33)[name = tensor("op_1545")]; - tensor var_1546 = const()[name = tensor("op_1546"), val = tensor(0x1.4f8b58p-17)]; - tensor var_1547 = add(x = var_1545, y = var_1546)[name = tensor("op_1547")]; - tensor denom_33_epsilon_0 = const()[name = tensor("denom_33_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_33 = rsqrt(epsilon = denom_33_epsilon_0, x = var_1547)[name = tensor("denom_33")]; - tensor out_33 = mul(x = zero_mean_33, y = denom_33)[name = tensor("out_33")]; - tensor var_1551 = const()[name = tensor("op_1551"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267766528)))]; - tensor var_1552 = add(x = out_33, y = var_1551)[name = tensor("op_1552")]; - tensor var_1554 = const()[name = tensor("op_1554"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267771712)))]; - tensor hidden_states_73 = mul(x = var_1552, y = var_1554)[name = tensor("hidden_states_73")]; - tensor var_1561 = const()[name = tensor("op_1561"), val = tensor([1, 1])]; - tensor var_1563 = const()[name = tensor("op_1563"), val = tensor([1, 1])]; + tensor attn_21_cast = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_1457_cast, y = var_1461_cast)[name = tensor("attn_21_cast")]; + tensor var_1465 = const()[name = tensor("op_1465"), val = tensor([2, 1280, 1, -1])]; + tensor input_139_cast = reshape(shape = var_1465, x = attn_21_cast)[name = tensor("input_139_cast")]; + tensor var_1470 = const()[name = tensor("op_1470"), val = tensor([1, 1])]; + tensor var_1472 = const()[name = tensor("op_1472"), val = tensor([1, 1])]; + tensor var_1474_pad_type_0 = const()[name = tensor("op_1474_pad_type_0"), val = tensor("custom")]; + tensor var_1474_pad_0 = const()[name = tensor("op_1474_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(277514688)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280791552)))]; + tensor var_1474_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_1472, groups = var_31, pad = var_1474_pad_0, pad_type = var_1474_pad_type_0, strides = var_1470, weight = unet_down_blocks_2_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_139_cast)[name = tensor("op_1474_cast")]; + tensor inputs_33_cast = add(x = var_1474_cast, y = inputs_31_cast)[name = tensor("inputs_33_cast")]; + tensor var_1478 = const()[name = tensor("op_1478"), val = tensor([1])]; + tensor channels_mean_33_cast = reduce_mean(axes = var_1478, keep_dims = var_23, x = inputs_33_cast)[name = tensor("channels_mean_33_cast")]; + tensor zero_mean_33_cast = sub(x = inputs_33_cast, y = channels_mean_33_cast)[name = tensor("zero_mean_33_cast")]; + tensor zero_mean_sq_33_cast = mul(x = zero_mean_33_cast, y = zero_mean_33_cast)[name = tensor("zero_mean_sq_33_cast")]; + tensor var_1482 = const()[name = tensor("op_1482"), val = tensor([1])]; + tensor var_1483_cast = reduce_mean(axes = var_1482, keep_dims = var_23, x = zero_mean_sq_33_cast)[name = tensor("op_1483_cast")]; + tensor var_1484_to_fp16 = const()[name = tensor("op_1484_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1485_cast = add(x = var_1483_cast, y = var_1484_to_fp16)[name = tensor("op_1485_cast")]; + tensor denom_33_epsilon_0_to_fp16 = const()[name = tensor("denom_33_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_33_cast = rsqrt(epsilon = denom_33_epsilon_0_to_fp16, x = var_1485_cast)[name = tensor("denom_33_cast")]; + tensor out_33_cast = mul(x = zero_mean_33_cast, y = denom_33_cast)[name = tensor("out_33_cast")]; + tensor var_1489_to_fp16 = const()[name = tensor("op_1489_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280794176)))]; + tensor var_1490_cast = add(x = out_33_cast, y = var_1489_to_fp16)[name = tensor("op_1490_cast")]; + tensor var_1492_to_fp16 = const()[name = tensor("op_1492_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280796800)))]; + tensor hidden_states_73_cast = mul(x = var_1490_cast, y = var_1492_to_fp16)[name = tensor("hidden_states_73_cast")]; + tensor var_1499 = const()[name = tensor("op_1499"), val = tensor([1, 1])]; + tensor var_1501 = const()[name = tensor("op_1501"), val = tensor([1, 1])]; tensor q_23_pad_type_0 = const()[name = tensor("q_23_pad_type_0"), val = tensor("custom")]; tensor q_23_pad_0 = const()[name = tensor("q_23_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_23 = conv(dilations = var_1563, groups = var_1193, pad = q_23_pad_0, pad_type = q_23_pad_type_0, strides = var_1561, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight, x = hidden_states_73)[name = tensor("q_23")]; - tensor var_1567 = const()[name = tensor("op_1567"), val = tensor([1, 1])]; - tensor var_1569 = const()[name = tensor("op_1569"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(280799424)))]; + tensor q_23_cast = conv(dilations = var_1501, groups = var_31, pad = q_23_pad_0, pad_type = q_23_pad_type_0, strides = var_1499, weight = unet_down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_73_cast)[name = tensor("q_23_cast")]; + tensor var_1505 = const()[name = tensor("op_1505"), val = tensor([1, 1])]; + tensor var_1507 = const()[name = tensor("op_1507"), val = tensor([1, 1])]; tensor k_23_pad_type_0 = const()[name = tensor("k_23_pad_type_0"), val = tensor("custom")]; tensor k_23_pad_0 = const()[name = tensor("k_23_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_23 = conv(dilations = var_1569, groups = var_1193, pad = k_23_pad_0, pad_type = k_23_pad_type_0, strides = var_1567, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_23")]; - tensor var_1573 = const()[name = tensor("op_1573"), val = tensor([1, 1])]; - tensor var_1575 = const()[name = tensor("op_1575"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(284076288)))]; + tensor k_23_cast = conv(dilations = var_1507, groups = var_31, pad = k_23_pad_0, pad_type = k_23_pad_type_0, strides = var_1505, weight = unet_down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_23_cast")]; + tensor var_1511 = const()[name = tensor("op_1511"), val = tensor([1, 1])]; + tensor var_1513 = const()[name = tensor("op_1513"), val = tensor([1, 1])]; tensor v_23_pad_type_0 = const()[name = tensor("v_23_pad_type_0"), val = tensor("custom")]; tensor v_23_pad_0 = const()[name = tensor("v_23_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_23 = conv(dilations = var_1575, groups = var_1193, pad = v_23_pad_0, pad_type = v_23_pad_type_0, strides = var_1573, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_23")]; - tensor var_1579 = const()[name = tensor("op_1579"), val = tensor([2, 20, 64, -1])]; - tensor var_1580 = reshape(shape = var_1579, x = q_23)[name = tensor("op_1580")]; - tensor var_1581 = const()[name = tensor("op_1581"), val = tensor([2, 20, 64, -1])]; - tensor var_1582 = reshape(shape = var_1581, x = k_23)[name = tensor("op_1582")]; - tensor var_1583 = const()[name = tensor("op_1583"), val = tensor([2, 20, 64, -1])]; - tensor var_1584 = reshape(shape = var_1583, x = v_23)[name = tensor("op_1584")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(289319232)))]; + tensor v_23_cast = conv(dilations = var_1513, groups = var_31, pad = v_23_pad_0, pad_type = v_23_pad_type_0, strides = var_1511, weight = unet_down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_23_cast")]; + tensor var_1517 = const()[name = tensor("op_1517"), val = tensor([2, 20, 64, -1])]; + tensor var_1518_cast = reshape(shape = var_1517, x = q_23_cast)[name = tensor("op_1518_cast")]; + tensor var_1519 = const()[name = tensor("op_1519"), val = tensor([2, 20, 64, -1])]; + tensor var_1520_cast = reshape(shape = var_1519, x = k_23_cast)[name = tensor("op_1520_cast")]; + tensor var_1521 = const()[name = tensor("op_1521"), val = tensor([2, 20, 64, -1])]; + tensor var_1522_cast = reshape(shape = var_1521, x = v_23_cast)[name = tensor("op_1522_cast")]; tensor attn_weights_45_transpose_x_0 = const()[name = tensor("attn_weights_45_transpose_x_0"), val = tensor(true)]; tensor attn_weights_45_transpose_y_0 = const()[name = tensor("attn_weights_45_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_45 = matmul(transpose_x = attn_weights_45_transpose_x_0, transpose_y = attn_weights_45_transpose_y_0, x = var_1580, y = var_1582)[name = tensor("attn_weights_45")]; - tensor attn_weights_47 = mul(x = attn_weights_45, y = var_1184)[name = tensor("attn_weights_47")]; - tensor var_1588 = softmax(axis = var_1177, x = attn_weights_47)[name = tensor("op_1588")]; + tensor attn_weights_45_cast = matmul(transpose_x = attn_weights_45_transpose_x_0, transpose_y = attn_weights_45_transpose_y_0, x = var_1518_cast, y = var_1520_cast)[name = tensor("attn_weights_45_cast")]; + tensor attn_weights_47_cast = mul(x = attn_weights_45_cast, y = var_12_to_fp16)[name = tensor("attn_weights_47_cast")]; + tensor var_1526_cast = softmax(axis = var_18, x = attn_weights_47_cast)[name = tensor("op_1526_cast")]; tensor attn_23_transpose_x_0 = const()[name = tensor("attn_23_transpose_x_0"), val = tensor(false)]; tensor attn_23_transpose_y_0 = const()[name = tensor("attn_23_transpose_y_0"), val = tensor(true)]; - tensor attn_23 = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_1584, y = var_1588)[name = tensor("attn_23")]; - tensor var_1592 = const()[name = tensor("op_1592"), val = tensor([2, 1280, 1, -1])]; - tensor input_141 = reshape(shape = var_1592, x = attn_23)[name = tensor("input_141")]; - tensor var_1597 = const()[name = tensor("op_1597"), val = tensor([1, 1])]; - tensor var_1599 = const()[name = tensor("op_1599"), val = tensor([1, 1])]; - tensor var_1601_pad_type_0 = const()[name = tensor("op_1601_pad_type_0"), val = tensor("custom")]; - tensor var_1601_pad_0 = const()[name = tensor("op_1601_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1601 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias, dilations = var_1599, groups = var_1193, pad = var_1601_pad_0, pad_type = var_1601_pad_type_0, strides = var_1597, weight = down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight, x = input_141)[name = tensor("op_1601")]; - tensor inputs_35 = add(x = var_1601, y = inputs_33)[name = tensor("inputs_35")]; - tensor var_1605 = const()[name = tensor("op_1605"), val = tensor([1])]; - tensor channels_mean_35 = reduce_mean(axes = var_1605, keep_dims = var_1188, x = inputs_35)[name = tensor("channels_mean_35")]; - tensor zero_mean_35 = sub(x = inputs_35, y = channels_mean_35)[name = tensor("zero_mean_35")]; - tensor zero_mean_sq_35 = mul(x = zero_mean_35, y = zero_mean_35)[name = tensor("zero_mean_sq_35")]; - tensor var_1609 = const()[name = tensor("op_1609"), val = tensor([1])]; - tensor var_1610 = reduce_mean(axes = var_1609, keep_dims = var_1188, x = zero_mean_sq_35)[name = tensor("op_1610")]; - tensor var_1611 = const()[name = tensor("op_1611"), val = tensor(0x1.4f8b58p-17)]; - tensor var_1612 = add(x = var_1610, y = var_1611)[name = tensor("op_1612")]; - tensor denom_35_epsilon_0 = const()[name = tensor("denom_35_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_35 = rsqrt(epsilon = denom_35_epsilon_0, x = var_1612)[name = tensor("denom_35")]; - tensor out_35 = mul(x = zero_mean_35, y = denom_35)[name = tensor("out_35")]; - tensor var_1616 = const()[name = tensor("op_1616"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267776896)))]; - tensor var_1617 = add(x = out_35, y = var_1616)[name = tensor("op_1617")]; - tensor var_1619 = const()[name = tensor("op_1619"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267782080)))]; - tensor input_143 = mul(x = var_1617, y = var_1619)[name = tensor("input_143")]; - tensor var_1627 = const()[name = tensor("op_1627"), val = tensor([1, 1])]; - tensor var_1629 = const()[name = tensor("op_1629"), val = tensor([1, 1])]; - tensor var_1631_pad_type_0 = const()[name = tensor("op_1631_pad_type_0"), val = tensor("custom")]; - tensor var_1631_pad_0 = const()[name = tensor("op_1631_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1631 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias, dilations = var_1629, groups = var_1193, pad = var_1631_pad_0, pad_type = var_1631_pad_type_0, strides = var_1627, weight = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight, x = input_143)[name = tensor("op_1631")]; - tensor var_1632_split_sizes_0 = const()[name = tensor("op_1632_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_1632_axis_0 = const()[name = tensor("op_1632_axis_0"), val = tensor(1)]; - tensor var_1632_0, tensor var_1632_1 = split(axis = var_1632_axis_0, split_sizes = var_1632_split_sizes_0, x = var_1631)[name = tensor("op_1632")]; - tensor var_1634_mode_0 = const()[name = tensor("op_1634_mode_0"), val = tensor("EXACT")]; - tensor var_1634 = gelu(mode = var_1634_mode_0, x = var_1632_1)[name = tensor("op_1634")]; - tensor input_145 = mul(x = var_1632_0, y = var_1634)[name = tensor("input_145")]; - tensor var_1638 = const()[name = tensor("op_1638"), val = tensor([1, 1])]; - tensor var_1640 = const()[name = tensor("op_1640"), val = tensor([1, 1])]; - tensor var_1642_pad_type_0 = const()[name = tensor("op_1642_pad_type_0"), val = tensor("custom")]; - tensor var_1642_pad_0 = const()[name = tensor("op_1642_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1642 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias, dilations = var_1640, groups = var_1193, pad = var_1642_pad_0, pad_type = var_1642_pad_type_0, strides = var_1638, weight = down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight, x = input_145)[name = tensor("op_1642")]; - tensor inputs_37 = add(x = var_1642, y = inputs_35)[name = tensor("inputs_37")]; - tensor var_1652 = const()[name = tensor("op_1652"), val = tensor([1])]; - tensor channels_mean_37 = reduce_mean(axes = var_1652, keep_dims = var_1188, x = inputs_37)[name = tensor("channels_mean_37")]; - tensor zero_mean_37 = sub(x = inputs_37, y = channels_mean_37)[name = tensor("zero_mean_37")]; - tensor zero_mean_sq_37 = mul(x = zero_mean_37, y = zero_mean_37)[name = tensor("zero_mean_sq_37")]; - tensor var_1656 = const()[name = tensor("op_1656"), val = tensor([1])]; - tensor var_1657 = reduce_mean(axes = var_1656, keep_dims = var_1188, x = zero_mean_sq_37)[name = tensor("op_1657")]; - tensor var_1658 = const()[name = tensor("op_1658"), val = tensor(0x1.4f8b58p-17)]; - tensor var_1659 = add(x = var_1657, y = var_1658)[name = tensor("op_1659")]; - tensor denom_37_epsilon_0 = const()[name = tensor("denom_37_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_37 = rsqrt(epsilon = denom_37_epsilon_0, x = var_1659)[name = tensor("denom_37")]; - tensor out_37 = mul(x = zero_mean_37, y = denom_37)[name = tensor("out_37")]; - tensor var_1663 = const()[name = tensor("op_1663"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267787264)))]; - tensor var_1664 = add(x = out_37, y = var_1663)[name = tensor("op_1664")]; - tensor var_1666 = const()[name = tensor("op_1666"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267792448)))]; - tensor hidden_states_77 = mul(x = var_1664, y = var_1666)[name = tensor("hidden_states_77")]; - tensor var_1673 = const()[name = tensor("op_1673"), val = tensor([1, 1])]; - tensor var_1675 = const()[name = tensor("op_1675"), val = tensor([1, 1])]; + tensor attn_23_cast = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_1522_cast, y = var_1526_cast)[name = tensor("attn_23_cast")]; + tensor var_1530 = const()[name = tensor("op_1530"), val = tensor([2, 1280, 1, -1])]; + tensor input_141_cast = reshape(shape = var_1530, x = attn_23_cast)[name = tensor("input_141_cast")]; + tensor var_1535 = const()[name = tensor("op_1535"), val = tensor([1, 1])]; + tensor var_1537 = const()[name = tensor("op_1537"), val = tensor([1, 1])]; + tensor var_1539_pad_type_0 = const()[name = tensor("op_1539_pad_type_0"), val = tensor("custom")]; + tensor var_1539_pad_0 = const()[name = tensor("op_1539_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(294562176)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297839040)))]; + tensor var_1539_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_1537, groups = var_31, pad = var_1539_pad_0, pad_type = var_1539_pad_type_0, strides = var_1535, weight = unet_down_blocks_2_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_141_cast)[name = tensor("op_1539_cast")]; + tensor inputs_35_cast = add(x = var_1539_cast, y = inputs_33_cast)[name = tensor("inputs_35_cast")]; + tensor var_1543 = const()[name = tensor("op_1543"), val = tensor([1])]; + tensor channels_mean_35_cast = reduce_mean(axes = var_1543, keep_dims = var_23, x = inputs_35_cast)[name = tensor("channels_mean_35_cast")]; + tensor zero_mean_35_cast = sub(x = inputs_35_cast, y = channels_mean_35_cast)[name = tensor("zero_mean_35_cast")]; + tensor zero_mean_sq_35_cast = mul(x = zero_mean_35_cast, y = zero_mean_35_cast)[name = tensor("zero_mean_sq_35_cast")]; + tensor var_1547 = const()[name = tensor("op_1547"), val = tensor([1])]; + tensor var_1548_cast = reduce_mean(axes = var_1547, keep_dims = var_23, x = zero_mean_sq_35_cast)[name = tensor("op_1548_cast")]; + tensor var_1549_to_fp16 = const()[name = tensor("op_1549_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1550_cast = add(x = var_1548_cast, y = var_1549_to_fp16)[name = tensor("op_1550_cast")]; + tensor denom_35_epsilon_0_to_fp16 = const()[name = tensor("denom_35_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_35_cast = rsqrt(epsilon = denom_35_epsilon_0_to_fp16, x = var_1550_cast)[name = tensor("denom_35_cast")]; + tensor out_35_cast = mul(x = zero_mean_35_cast, y = denom_35_cast)[name = tensor("out_35_cast")]; + tensor var_1554_to_fp16 = const()[name = tensor("op_1554_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297841664)))]; + tensor var_1555_cast = add(x = out_35_cast, y = var_1554_to_fp16)[name = tensor("op_1555_cast")]; + tensor var_1557_to_fp16 = const()[name = tensor("op_1557_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297844288)))]; + tensor input_143_cast = mul(x = var_1555_cast, y = var_1557_to_fp16)[name = tensor("input_143_cast")]; + tensor var_1565 = const()[name = tensor("op_1565"), val = tensor([1, 1])]; + tensor var_1567 = const()[name = tensor("op_1567"), val = tensor([1, 1])]; + tensor var_1569_pad_type_0 = const()[name = tensor("op_1569_pad_type_0"), val = tensor("custom")]; + tensor var_1569_pad_0 = const()[name = tensor("op_1569_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(297846912)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324061376)))]; + tensor var_1569_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_1567, groups = var_31, pad = var_1569_pad_0, pad_type = var_1569_pad_type_0, strides = var_1565, weight = unet_down_blocks_2_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_143_cast)[name = tensor("op_1569_cast")]; + tensor var_1570_split_sizes_0 = const()[name = tensor("op_1570_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1570_axis_0 = const()[name = tensor("op_1570_axis_0"), val = tensor(1)]; + tensor var_1570_cast_0, tensor var_1570_cast_1 = split(axis = var_1570_axis_0, split_sizes = var_1570_split_sizes_0, x = var_1569_cast)[name = tensor("op_1570_cast")]; + tensor var_1572_mode_0 = const()[name = tensor("op_1572_mode_0"), val = tensor("EXACT")]; + tensor var_1572_cast = gelu(mode = var_1572_mode_0, x = var_1570_cast_1)[name = tensor("op_1572_cast")]; + tensor input_145_cast = mul(x = var_1570_cast_0, y = var_1572_cast)[name = tensor("input_145_cast")]; + tensor var_1576 = const()[name = tensor("op_1576"), val = tensor([1, 1])]; + tensor var_1578 = const()[name = tensor("op_1578"), val = tensor([1, 1])]; + tensor var_1580_pad_type_0 = const()[name = tensor("op_1580_pad_type_0"), val = tensor("custom")]; + tensor var_1580_pad_0 = const()[name = tensor("op_1580_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(324081920)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(337189184)))]; + tensor var_1580_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_1578, groups = var_31, pad = var_1580_pad_0, pad_type = var_1580_pad_type_0, strides = var_1576, weight = unet_down_blocks_2_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_145_cast)[name = tensor("op_1580_cast")]; + tensor inputs_37_cast = add(x = var_1580_cast, y = inputs_35_cast)[name = tensor("inputs_37_cast")]; + tensor var_1590 = const()[name = tensor("op_1590"), val = tensor([1])]; + tensor channels_mean_37_cast = reduce_mean(axes = var_1590, keep_dims = var_23, x = inputs_37_cast)[name = tensor("channels_mean_37_cast")]; + tensor zero_mean_37_cast = sub(x = inputs_37_cast, y = channels_mean_37_cast)[name = tensor("zero_mean_37_cast")]; + tensor zero_mean_sq_37_cast = mul(x = zero_mean_37_cast, y = zero_mean_37_cast)[name = tensor("zero_mean_sq_37_cast")]; + tensor var_1594 = const()[name = tensor("op_1594"), val = tensor([1])]; + tensor var_1595_cast = reduce_mean(axes = var_1594, keep_dims = var_23, x = zero_mean_sq_37_cast)[name = tensor("op_1595_cast")]; + tensor var_1596_to_fp16 = const()[name = tensor("op_1596_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1597_cast = add(x = var_1595_cast, y = var_1596_to_fp16)[name = tensor("op_1597_cast")]; + tensor denom_37_epsilon_0_to_fp16 = const()[name = tensor("denom_37_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_37_cast = rsqrt(epsilon = denom_37_epsilon_0_to_fp16, x = var_1597_cast)[name = tensor("denom_37_cast")]; + tensor out_37_cast = mul(x = zero_mean_37_cast, y = denom_37_cast)[name = tensor("out_37_cast")]; + tensor var_1601_to_fp16 = const()[name = tensor("op_1601_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(337191808)))]; + tensor var_1602_cast = add(x = out_37_cast, y = var_1601_to_fp16)[name = tensor("op_1602_cast")]; + tensor var_1604_to_fp16 = const()[name = tensor("op_1604_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(337194432)))]; + tensor hidden_states_77_cast = mul(x = var_1602_cast, y = var_1604_to_fp16)[name = tensor("hidden_states_77_cast")]; + tensor var_1611 = const()[name = tensor("op_1611"), val = tensor([1, 1])]; + tensor var_1613 = const()[name = tensor("op_1613"), val = tensor([1, 1])]; tensor q_25_pad_type_0 = const()[name = tensor("q_25_pad_type_0"), val = tensor("custom")]; tensor q_25_pad_0 = const()[name = tensor("q_25_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_25 = conv(dilations = var_1675, groups = var_1193, pad = q_25_pad_0, pad_type = q_25_pad_type_0, strides = var_1673, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight, x = hidden_states_77)[name = tensor("q_25")]; - tensor var_1679 = const()[name = tensor("op_1679"), val = tensor([1, 1])]; - tensor var_1681 = const()[name = tensor("op_1681"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(337197056)))]; + tensor q_25_cast = conv(dilations = var_1613, groups = var_31, pad = q_25_pad_0, pad_type = q_25_pad_type_0, strides = var_1611, weight = unet_down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_77_cast)[name = tensor("q_25_cast")]; + tensor var_1617 = const()[name = tensor("op_1617"), val = tensor([1, 1])]; + tensor var_1619 = const()[name = tensor("op_1619"), val = tensor([1, 1])]; tensor k_25_pad_type_0 = const()[name = tensor("k_25_pad_type_0"), val = tensor("custom")]; tensor k_25_pad_0 = const()[name = tensor("k_25_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_25 = conv(dilations = var_1681, groups = var_1193, pad = k_25_pad_0, pad_type = k_25_pad_type_0, strides = var_1679, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight, x = hidden_states_77)[name = tensor("k_25")]; - tensor var_1685 = const()[name = tensor("op_1685"), val = tensor([1, 1])]; - tensor var_1687 = const()[name = tensor("op_1687"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(340473920)))]; + tensor k_25_cast = conv(dilations = var_1619, groups = var_31, pad = k_25_pad_0, pad_type = k_25_pad_type_0, strides = var_1617, weight = unet_down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_77_cast)[name = tensor("k_25_cast")]; + tensor var_1623 = const()[name = tensor("op_1623"), val = tensor([1, 1])]; + tensor var_1625 = const()[name = tensor("op_1625"), val = tensor([1, 1])]; tensor v_25_pad_type_0 = const()[name = tensor("v_25_pad_type_0"), val = tensor("custom")]; tensor v_25_pad_0 = const()[name = tensor("v_25_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_25 = conv(dilations = var_1687, groups = var_1193, pad = v_25_pad_0, pad_type = v_25_pad_type_0, strides = var_1685, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight, x = hidden_states_77)[name = tensor("v_25")]; - tensor var_1691 = const()[name = tensor("op_1691"), val = tensor([2, 20, 64, -1])]; - tensor var_1692 = reshape(shape = var_1691, x = q_25)[name = tensor("op_1692")]; - tensor var_1693 = const()[name = tensor("op_1693"), val = tensor([2, 20, 64, -1])]; - tensor var_1694 = reshape(shape = var_1693, x = k_25)[name = tensor("op_1694")]; - tensor var_1695 = const()[name = tensor("op_1695"), val = tensor([2, 20, 64, -1])]; - tensor var_1696 = reshape(shape = var_1695, x = v_25)[name = tensor("op_1696")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(343750784)))]; + tensor v_25_cast = conv(dilations = var_1625, groups = var_31, pad = v_25_pad_0, pad_type = v_25_pad_type_0, strides = var_1623, weight = unet_down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_77_cast)[name = tensor("v_25_cast")]; + tensor var_1629 = const()[name = tensor("op_1629"), val = tensor([2, 20, 64, -1])]; + tensor var_1630_cast = reshape(shape = var_1629, x = q_25_cast)[name = tensor("op_1630_cast")]; + tensor var_1631 = const()[name = tensor("op_1631"), val = tensor([2, 20, 64, -1])]; + tensor var_1632_cast = reshape(shape = var_1631, x = k_25_cast)[name = tensor("op_1632_cast")]; + tensor var_1633 = const()[name = tensor("op_1633"), val = tensor([2, 20, 64, -1])]; + tensor var_1634_cast = reshape(shape = var_1633, x = v_25_cast)[name = tensor("op_1634_cast")]; tensor attn_weights_49_transpose_x_0 = const()[name = tensor("attn_weights_49_transpose_x_0"), val = tensor(true)]; tensor attn_weights_49_transpose_y_0 = const()[name = tensor("attn_weights_49_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_49 = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_1692, y = var_1694)[name = tensor("attn_weights_49")]; - tensor attn_weights_51 = mul(x = attn_weights_49, y = var_1184)[name = tensor("attn_weights_51")]; - tensor var_1700 = softmax(axis = var_1177, x = attn_weights_51)[name = tensor("op_1700")]; + tensor attn_weights_49_cast = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_1630_cast, y = var_1632_cast)[name = tensor("attn_weights_49_cast")]; + tensor attn_weights_51_cast = mul(x = attn_weights_49_cast, y = var_12_to_fp16)[name = tensor("attn_weights_51_cast")]; + tensor var_1638_cast = softmax(axis = var_18, x = attn_weights_51_cast)[name = tensor("op_1638_cast")]; tensor attn_25_transpose_x_0 = const()[name = tensor("attn_25_transpose_x_0"), val = tensor(false)]; tensor attn_25_transpose_y_0 = const()[name = tensor("attn_25_transpose_y_0"), val = tensor(true)]; - tensor attn_25 = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_1696, y = var_1700)[name = tensor("attn_25")]; - tensor var_1704 = const()[name = tensor("op_1704"), val = tensor([2, 1280, 1, -1])]; - tensor input_147 = reshape(shape = var_1704, x = attn_25)[name = tensor("input_147")]; - tensor var_1709 = const()[name = tensor("op_1709"), val = tensor([1, 1])]; - tensor var_1711 = const()[name = tensor("op_1711"), val = tensor([1, 1])]; - tensor var_1713_pad_type_0 = const()[name = tensor("op_1713_pad_type_0"), val = tensor("custom")]; - tensor var_1713_pad_0 = const()[name = tensor("op_1713_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1713 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias, dilations = var_1711, groups = var_1193, pad = var_1713_pad_0, pad_type = var_1713_pad_type_0, strides = var_1709, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight, x = input_147)[name = tensor("op_1713")]; - tensor inputs_39 = add(x = var_1713, y = inputs_37)[name = tensor("inputs_39")]; - tensor var_1717 = const()[name = tensor("op_1717"), val = tensor([1])]; - tensor channels_mean_39 = reduce_mean(axes = var_1717, keep_dims = var_1188, x = inputs_39)[name = tensor("channels_mean_39")]; - tensor zero_mean_39 = sub(x = inputs_39, y = channels_mean_39)[name = tensor("zero_mean_39")]; - tensor zero_mean_sq_39 = mul(x = zero_mean_39, y = zero_mean_39)[name = tensor("zero_mean_sq_39")]; - tensor var_1721 = const()[name = tensor("op_1721"), val = tensor([1])]; - tensor var_1722 = reduce_mean(axes = var_1721, keep_dims = var_1188, x = zero_mean_sq_39)[name = tensor("op_1722")]; - tensor var_1723 = const()[name = tensor("op_1723"), val = tensor(0x1.4f8b58p-17)]; - tensor var_1724 = add(x = var_1722, y = var_1723)[name = tensor("op_1724")]; - tensor denom_39_epsilon_0 = const()[name = tensor("denom_39_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_39 = rsqrt(epsilon = denom_39_epsilon_0, x = var_1724)[name = tensor("denom_39")]; - tensor out_39 = mul(x = zero_mean_39, y = denom_39)[name = tensor("out_39")]; - tensor var_1728 = const()[name = tensor("op_1728"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267797632)))]; - tensor var_1729 = add(x = out_39, y = var_1728)[name = tensor("op_1729")]; - tensor var_1731 = const()[name = tensor("op_1731"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267802816)))]; - tensor hidden_states_79 = mul(x = var_1729, y = var_1731)[name = tensor("hidden_states_79")]; - tensor var_1738 = const()[name = tensor("op_1738"), val = tensor([1, 1])]; - tensor var_1740 = const()[name = tensor("op_1740"), val = tensor([1, 1])]; + tensor attn_25_cast = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_1634_cast, y = var_1638_cast)[name = tensor("attn_25_cast")]; + tensor var_1642 = const()[name = tensor("op_1642"), val = tensor([2, 1280, 1, -1])]; + tensor input_147_cast = reshape(shape = var_1642, x = attn_25_cast)[name = tensor("input_147_cast")]; + tensor var_1647 = const()[name = tensor("op_1647"), val = tensor([1, 1])]; + tensor var_1649 = const()[name = tensor("op_1649"), val = tensor([1, 1])]; + tensor var_1651_pad_type_0 = const()[name = tensor("op_1651_pad_type_0"), val = tensor("custom")]; + tensor var_1651_pad_0 = const()[name = tensor("op_1651_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(347027648)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350304512)))]; + tensor var_1651_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_1649, groups = var_31, pad = var_1651_pad_0, pad_type = var_1651_pad_type_0, strides = var_1647, weight = unet_down_blocks_2_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_147_cast)[name = tensor("op_1651_cast")]; + tensor inputs_39_cast = add(x = var_1651_cast, y = inputs_37_cast)[name = tensor("inputs_39_cast")]; + tensor var_1655 = const()[name = tensor("op_1655"), val = tensor([1])]; + tensor channels_mean_39_cast = reduce_mean(axes = var_1655, keep_dims = var_23, x = inputs_39_cast)[name = tensor("channels_mean_39_cast")]; + tensor zero_mean_39_cast = sub(x = inputs_39_cast, y = channels_mean_39_cast)[name = tensor("zero_mean_39_cast")]; + tensor zero_mean_sq_39_cast = mul(x = zero_mean_39_cast, y = zero_mean_39_cast)[name = tensor("zero_mean_sq_39_cast")]; + tensor var_1659 = const()[name = tensor("op_1659"), val = tensor([1])]; + tensor var_1660_cast = reduce_mean(axes = var_1659, keep_dims = var_23, x = zero_mean_sq_39_cast)[name = tensor("op_1660_cast")]; + tensor var_1661_to_fp16 = const()[name = tensor("op_1661_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1662_cast = add(x = var_1660_cast, y = var_1661_to_fp16)[name = tensor("op_1662_cast")]; + tensor denom_39_epsilon_0_to_fp16 = const()[name = tensor("denom_39_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_39_cast = rsqrt(epsilon = denom_39_epsilon_0_to_fp16, x = var_1662_cast)[name = tensor("denom_39_cast")]; + tensor out_39_cast = mul(x = zero_mean_39_cast, y = denom_39_cast)[name = tensor("out_39_cast")]; + tensor var_1666_to_fp16 = const()[name = tensor("op_1666_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350307136)))]; + tensor var_1667_cast = add(x = out_39_cast, y = var_1666_to_fp16)[name = tensor("op_1667_cast")]; + tensor var_1669_to_fp16 = const()[name = tensor("op_1669_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350309760)))]; + tensor hidden_states_79_cast = mul(x = var_1667_cast, y = var_1669_to_fp16)[name = tensor("hidden_states_79_cast")]; + tensor var_1676 = const()[name = tensor("op_1676"), val = tensor([1, 1])]; + tensor var_1678 = const()[name = tensor("op_1678"), val = tensor([1, 1])]; tensor q_27_pad_type_0 = const()[name = tensor("q_27_pad_type_0"), val = tensor("custom")]; tensor q_27_pad_0 = const()[name = tensor("q_27_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_27 = conv(dilations = var_1740, groups = var_1193, pad = q_27_pad_0, pad_type = q_27_pad_type_0, strides = var_1738, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight, x = hidden_states_79)[name = tensor("q_27")]; - tensor var_1744 = const()[name = tensor("op_1744"), val = tensor([1, 1])]; - tensor var_1746 = const()[name = tensor("op_1746"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(350312384)))]; + tensor q_27_cast = conv(dilations = var_1678, groups = var_31, pad = q_27_pad_0, pad_type = q_27_pad_type_0, strides = var_1676, weight = unet_down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_79_cast)[name = tensor("q_27_cast")]; + tensor var_1682 = const()[name = tensor("op_1682"), val = tensor([1, 1])]; + tensor var_1684 = const()[name = tensor("op_1684"), val = tensor([1, 1])]; tensor k_27_pad_type_0 = const()[name = tensor("k_27_pad_type_0"), val = tensor("custom")]; tensor k_27_pad_0 = const()[name = tensor("k_27_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_27 = conv(dilations = var_1746, groups = var_1193, pad = k_27_pad_0, pad_type = k_27_pad_type_0, strides = var_1744, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_27")]; - tensor var_1750 = const()[name = tensor("op_1750"), val = tensor([1, 1])]; - tensor var_1752 = const()[name = tensor("op_1752"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(353589248)))]; + tensor k_27_cast = conv(dilations = var_1684, groups = var_31, pad = k_27_pad_0, pad_type = k_27_pad_type_0, strides = var_1682, weight = unet_down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_27_cast")]; + tensor var_1688 = const()[name = tensor("op_1688"), val = tensor([1, 1])]; + tensor var_1690 = const()[name = tensor("op_1690"), val = tensor([1, 1])]; tensor v_27_pad_type_0 = const()[name = tensor("v_27_pad_type_0"), val = tensor("custom")]; tensor v_27_pad_0 = const()[name = tensor("v_27_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_27 = conv(dilations = var_1752, groups = var_1193, pad = v_27_pad_0, pad_type = v_27_pad_type_0, strides = var_1750, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_27")]; - tensor var_1756 = const()[name = tensor("op_1756"), val = tensor([2, 20, 64, -1])]; - tensor var_1757 = reshape(shape = var_1756, x = q_27)[name = tensor("op_1757")]; - tensor var_1758 = const()[name = tensor("op_1758"), val = tensor([2, 20, 64, -1])]; - tensor var_1759 = reshape(shape = var_1758, x = k_27)[name = tensor("op_1759")]; - tensor var_1760 = const()[name = tensor("op_1760"), val = tensor([2, 20, 64, -1])]; - tensor var_1761 = reshape(shape = var_1760, x = v_27)[name = tensor("op_1761")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(358832192)))]; + tensor v_27_cast = conv(dilations = var_1690, groups = var_31, pad = v_27_pad_0, pad_type = v_27_pad_type_0, strides = var_1688, weight = unet_down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_27_cast")]; + tensor var_1694 = const()[name = tensor("op_1694"), val = tensor([2, 20, 64, -1])]; + tensor var_1695_cast = reshape(shape = var_1694, x = q_27_cast)[name = tensor("op_1695_cast")]; + tensor var_1696 = const()[name = tensor("op_1696"), val = tensor([2, 20, 64, -1])]; + tensor var_1697_cast = reshape(shape = var_1696, x = k_27_cast)[name = tensor("op_1697_cast")]; + tensor var_1698 = const()[name = tensor("op_1698"), val = tensor([2, 20, 64, -1])]; + tensor var_1699_cast = reshape(shape = var_1698, x = v_27_cast)[name = tensor("op_1699_cast")]; tensor attn_weights_53_transpose_x_0 = const()[name = tensor("attn_weights_53_transpose_x_0"), val = tensor(true)]; tensor attn_weights_53_transpose_y_0 = const()[name = tensor("attn_weights_53_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_53 = matmul(transpose_x = attn_weights_53_transpose_x_0, transpose_y = attn_weights_53_transpose_y_0, x = var_1757, y = var_1759)[name = tensor("attn_weights_53")]; - tensor attn_weights_55 = mul(x = attn_weights_53, y = var_1184)[name = tensor("attn_weights_55")]; - tensor var_1765 = softmax(axis = var_1177, x = attn_weights_55)[name = tensor("op_1765")]; + tensor attn_weights_53_cast = matmul(transpose_x = attn_weights_53_transpose_x_0, transpose_y = attn_weights_53_transpose_y_0, x = var_1695_cast, y = var_1697_cast)[name = tensor("attn_weights_53_cast")]; + tensor attn_weights_55_cast = mul(x = attn_weights_53_cast, y = var_12_to_fp16)[name = tensor("attn_weights_55_cast")]; + tensor var_1703_cast = softmax(axis = var_18, x = attn_weights_55_cast)[name = tensor("op_1703_cast")]; tensor attn_27_transpose_x_0 = const()[name = tensor("attn_27_transpose_x_0"), val = tensor(false)]; tensor attn_27_transpose_y_0 = const()[name = tensor("attn_27_transpose_y_0"), val = tensor(true)]; - tensor attn_27 = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_1761, y = var_1765)[name = tensor("attn_27")]; - tensor var_1769 = const()[name = tensor("op_1769"), val = tensor([2, 1280, 1, -1])]; - tensor input_149 = reshape(shape = var_1769, x = attn_27)[name = tensor("input_149")]; - tensor var_1774 = const()[name = tensor("op_1774"), val = tensor([1, 1])]; - tensor var_1776 = const()[name = tensor("op_1776"), val = tensor([1, 1])]; - tensor var_1778_pad_type_0 = const()[name = tensor("op_1778_pad_type_0"), val = tensor("custom")]; - tensor var_1778_pad_0 = const()[name = tensor("op_1778_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1778 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias, dilations = var_1776, groups = var_1193, pad = var_1778_pad_0, pad_type = var_1778_pad_type_0, strides = var_1774, weight = down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight, x = input_149)[name = tensor("op_1778")]; - tensor inputs_41 = add(x = var_1778, y = inputs_39)[name = tensor("inputs_41")]; - tensor var_1782 = const()[name = tensor("op_1782"), val = tensor([1])]; - tensor channels_mean_41 = reduce_mean(axes = var_1782, keep_dims = var_1188, x = inputs_41)[name = tensor("channels_mean_41")]; - tensor zero_mean_41 = sub(x = inputs_41, y = channels_mean_41)[name = tensor("zero_mean_41")]; - tensor zero_mean_sq_41 = mul(x = zero_mean_41, y = zero_mean_41)[name = tensor("zero_mean_sq_41")]; - tensor var_1786 = const()[name = tensor("op_1786"), val = tensor([1])]; - tensor var_1787 = reduce_mean(axes = var_1786, keep_dims = var_1188, x = zero_mean_sq_41)[name = tensor("op_1787")]; - tensor var_1788 = const()[name = tensor("op_1788"), val = tensor(0x1.4f8b58p-17)]; - tensor var_1789 = add(x = var_1787, y = var_1788)[name = tensor("op_1789")]; - tensor denom_41_epsilon_0 = const()[name = tensor("denom_41_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_41 = rsqrt(epsilon = denom_41_epsilon_0, x = var_1789)[name = tensor("denom_41")]; - tensor out_41 = mul(x = zero_mean_41, y = denom_41)[name = tensor("out_41")]; - tensor var_1793 = const()[name = tensor("op_1793"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267808000)))]; - tensor var_1794 = add(x = out_41, y = var_1793)[name = tensor("op_1794")]; - tensor var_1796 = const()[name = tensor("op_1796"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267813184)))]; - tensor input_151 = mul(x = var_1794, y = var_1796)[name = tensor("input_151")]; - tensor var_1804 = const()[name = tensor("op_1804"), val = tensor([1, 1])]; - tensor var_1806 = const()[name = tensor("op_1806"), val = tensor([1, 1])]; - tensor var_1808_pad_type_0 = const()[name = tensor("op_1808_pad_type_0"), val = tensor("custom")]; - tensor var_1808_pad_0 = const()[name = tensor("op_1808_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1808 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias, dilations = var_1806, groups = var_1193, pad = var_1808_pad_0, pad_type = var_1808_pad_type_0, strides = var_1804, weight = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight, x = input_151)[name = tensor("op_1808")]; - tensor var_1809_split_sizes_0 = const()[name = tensor("op_1809_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_1809_axis_0 = const()[name = tensor("op_1809_axis_0"), val = tensor(1)]; - tensor var_1809_0, tensor var_1809_1 = split(axis = var_1809_axis_0, split_sizes = var_1809_split_sizes_0, x = var_1808)[name = tensor("op_1809")]; - tensor var_1811_mode_0 = const()[name = tensor("op_1811_mode_0"), val = tensor("EXACT")]; - tensor var_1811 = gelu(mode = var_1811_mode_0, x = var_1809_1)[name = tensor("op_1811")]; - tensor input_153 = mul(x = var_1809_0, y = var_1811)[name = tensor("input_153")]; - tensor var_1815 = const()[name = tensor("op_1815"), val = tensor([1, 1])]; - tensor var_1817 = const()[name = tensor("op_1817"), val = tensor([1, 1])]; - tensor var_1819_pad_type_0 = const()[name = tensor("op_1819_pad_type_0"), val = tensor("custom")]; - tensor var_1819_pad_0 = const()[name = tensor("op_1819_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1819 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias, dilations = var_1817, groups = var_1193, pad = var_1819_pad_0, pad_type = var_1819_pad_type_0, strides = var_1815, weight = down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight, x = input_153)[name = tensor("op_1819")]; - tensor inputs_43 = add(x = var_1819, y = inputs_41)[name = tensor("inputs_43")]; - tensor var_1829 = const()[name = tensor("op_1829"), val = tensor([1])]; - tensor channels_mean_43 = reduce_mean(axes = var_1829, keep_dims = var_1188, x = inputs_43)[name = tensor("channels_mean_43")]; - tensor zero_mean_43 = sub(x = inputs_43, y = channels_mean_43)[name = tensor("zero_mean_43")]; - tensor zero_mean_sq_43 = mul(x = zero_mean_43, y = zero_mean_43)[name = tensor("zero_mean_sq_43")]; - tensor var_1833 = const()[name = tensor("op_1833"), val = tensor([1])]; - tensor var_1834 = reduce_mean(axes = var_1833, keep_dims = var_1188, x = zero_mean_sq_43)[name = tensor("op_1834")]; - tensor var_1835 = const()[name = tensor("op_1835"), val = tensor(0x1.4f8b58p-17)]; - tensor var_1836 = add(x = var_1834, y = var_1835)[name = tensor("op_1836")]; - tensor denom_43_epsilon_0 = const()[name = tensor("denom_43_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_43 = rsqrt(epsilon = denom_43_epsilon_0, x = var_1836)[name = tensor("denom_43")]; - tensor out_43 = mul(x = zero_mean_43, y = denom_43)[name = tensor("out_43")]; - tensor var_1840 = const()[name = tensor("op_1840"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267818368)))]; - tensor var_1841 = add(x = out_43, y = var_1840)[name = tensor("op_1841")]; - tensor var_1843 = const()[name = tensor("op_1843"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267823552)))]; - tensor hidden_states_83 = mul(x = var_1841, y = var_1843)[name = tensor("hidden_states_83")]; - tensor var_1850 = const()[name = tensor("op_1850"), val = tensor([1, 1])]; - tensor var_1852 = const()[name = tensor("op_1852"), val = tensor([1, 1])]; + tensor attn_27_cast = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_1699_cast, y = var_1703_cast)[name = tensor("attn_27_cast")]; + tensor var_1707 = const()[name = tensor("op_1707"), val = tensor([2, 1280, 1, -1])]; + tensor input_149_cast = reshape(shape = var_1707, x = attn_27_cast)[name = tensor("input_149_cast")]; + tensor var_1712 = const()[name = tensor("op_1712"), val = tensor([1, 1])]; + tensor var_1714 = const()[name = tensor("op_1714"), val = tensor([1, 1])]; + tensor var_1716_pad_type_0 = const()[name = tensor("op_1716_pad_type_0"), val = tensor("custom")]; + tensor var_1716_pad_0 = const()[name = tensor("op_1716_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(364075136)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(367352000)))]; + tensor var_1716_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_1714, groups = var_31, pad = var_1716_pad_0, pad_type = var_1716_pad_type_0, strides = var_1712, weight = unet_down_blocks_2_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_149_cast)[name = tensor("op_1716_cast")]; + tensor inputs_41_cast = add(x = var_1716_cast, y = inputs_39_cast)[name = tensor("inputs_41_cast")]; + tensor var_1720 = const()[name = tensor("op_1720"), val = tensor([1])]; + tensor channels_mean_41_cast = reduce_mean(axes = var_1720, keep_dims = var_23, x = inputs_41_cast)[name = tensor("channels_mean_41_cast")]; + tensor zero_mean_41_cast = sub(x = inputs_41_cast, y = channels_mean_41_cast)[name = tensor("zero_mean_41_cast")]; + tensor zero_mean_sq_41_cast = mul(x = zero_mean_41_cast, y = zero_mean_41_cast)[name = tensor("zero_mean_sq_41_cast")]; + tensor var_1724 = const()[name = tensor("op_1724"), val = tensor([1])]; + tensor var_1725_cast = reduce_mean(axes = var_1724, keep_dims = var_23, x = zero_mean_sq_41_cast)[name = tensor("op_1725_cast")]; + tensor var_1726_to_fp16 = const()[name = tensor("op_1726_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1727_cast = add(x = var_1725_cast, y = var_1726_to_fp16)[name = tensor("op_1727_cast")]; + tensor denom_41_epsilon_0_to_fp16 = const()[name = tensor("denom_41_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_41_cast = rsqrt(epsilon = denom_41_epsilon_0_to_fp16, x = var_1727_cast)[name = tensor("denom_41_cast")]; + tensor out_41_cast = mul(x = zero_mean_41_cast, y = denom_41_cast)[name = tensor("out_41_cast")]; + tensor var_1731_to_fp16 = const()[name = tensor("op_1731_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(367354624)))]; + tensor var_1732_cast = add(x = out_41_cast, y = var_1731_to_fp16)[name = tensor("op_1732_cast")]; + tensor var_1734_to_fp16 = const()[name = tensor("op_1734_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(367357248)))]; + tensor input_151_cast = mul(x = var_1732_cast, y = var_1734_to_fp16)[name = tensor("input_151_cast")]; + tensor var_1742 = const()[name = tensor("op_1742"), val = tensor([1, 1])]; + tensor var_1744 = const()[name = tensor("op_1744"), val = tensor([1, 1])]; + tensor var_1746_pad_type_0 = const()[name = tensor("op_1746_pad_type_0"), val = tensor("custom")]; + tensor var_1746_pad_0 = const()[name = tensor("op_1746_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(367359872)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(393574336)))]; + tensor var_1746_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_1744, groups = var_31, pad = var_1746_pad_0, pad_type = var_1746_pad_type_0, strides = var_1742, weight = unet_down_blocks_2_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_151_cast)[name = tensor("op_1746_cast")]; + tensor var_1747_split_sizes_0 = const()[name = tensor("op_1747_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1747_axis_0 = const()[name = tensor("op_1747_axis_0"), val = tensor(1)]; + tensor var_1747_cast_0, tensor var_1747_cast_1 = split(axis = var_1747_axis_0, split_sizes = var_1747_split_sizes_0, x = var_1746_cast)[name = tensor("op_1747_cast")]; + tensor var_1749_mode_0 = const()[name = tensor("op_1749_mode_0"), val = tensor("EXACT")]; + tensor var_1749_cast = gelu(mode = var_1749_mode_0, x = var_1747_cast_1)[name = tensor("op_1749_cast")]; + tensor input_153_cast = mul(x = var_1747_cast_0, y = var_1749_cast)[name = tensor("input_153_cast")]; + tensor var_1753 = const()[name = tensor("op_1753"), val = tensor([1, 1])]; + tensor var_1755 = const()[name = tensor("op_1755"), val = tensor([1, 1])]; + tensor var_1757_pad_type_0 = const()[name = tensor("op_1757_pad_type_0"), val = tensor("custom")]; + tensor var_1757_pad_0 = const()[name = tensor("op_1757_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(393594880)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406702144)))]; + tensor var_1757_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_1755, groups = var_31, pad = var_1757_pad_0, pad_type = var_1757_pad_type_0, strides = var_1753, weight = unet_down_blocks_2_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_153_cast)[name = tensor("op_1757_cast")]; + tensor inputs_43_cast = add(x = var_1757_cast, y = inputs_41_cast)[name = tensor("inputs_43_cast")]; + tensor var_1767 = const()[name = tensor("op_1767"), val = tensor([1])]; + tensor channels_mean_43_cast = reduce_mean(axes = var_1767, keep_dims = var_23, x = inputs_43_cast)[name = tensor("channels_mean_43_cast")]; + tensor zero_mean_43_cast = sub(x = inputs_43_cast, y = channels_mean_43_cast)[name = tensor("zero_mean_43_cast")]; + tensor zero_mean_sq_43_cast = mul(x = zero_mean_43_cast, y = zero_mean_43_cast)[name = tensor("zero_mean_sq_43_cast")]; + tensor var_1771 = const()[name = tensor("op_1771"), val = tensor([1])]; + tensor var_1772_cast = reduce_mean(axes = var_1771, keep_dims = var_23, x = zero_mean_sq_43_cast)[name = tensor("op_1772_cast")]; + tensor var_1773_to_fp16 = const()[name = tensor("op_1773_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1774_cast = add(x = var_1772_cast, y = var_1773_to_fp16)[name = tensor("op_1774_cast")]; + tensor denom_43_epsilon_0_to_fp16 = const()[name = tensor("denom_43_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_43_cast = rsqrt(epsilon = denom_43_epsilon_0_to_fp16, x = var_1774_cast)[name = tensor("denom_43_cast")]; + tensor out_43_cast = mul(x = zero_mean_43_cast, y = denom_43_cast)[name = tensor("out_43_cast")]; + tensor var_1778_to_fp16 = const()[name = tensor("op_1778_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406704768)))]; + tensor var_1779_cast = add(x = out_43_cast, y = var_1778_to_fp16)[name = tensor("op_1779_cast")]; + tensor var_1781_to_fp16 = const()[name = tensor("op_1781_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406707392)))]; + tensor hidden_states_83_cast = mul(x = var_1779_cast, y = var_1781_to_fp16)[name = tensor("hidden_states_83_cast")]; + tensor var_1788 = const()[name = tensor("op_1788"), val = tensor([1, 1])]; + tensor var_1790 = const()[name = tensor("op_1790"), val = tensor([1, 1])]; tensor q_29_pad_type_0 = const()[name = tensor("q_29_pad_type_0"), val = tensor("custom")]; tensor q_29_pad_0 = const()[name = tensor("q_29_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_29 = conv(dilations = var_1852, groups = var_1193, pad = q_29_pad_0, pad_type = q_29_pad_type_0, strides = var_1850, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight, x = hidden_states_83)[name = tensor("q_29")]; - tensor var_1856 = const()[name = tensor("op_1856"), val = tensor([1, 1])]; - tensor var_1858 = const()[name = tensor("op_1858"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406710016)))]; + tensor q_29_cast = conv(dilations = var_1790, groups = var_31, pad = q_29_pad_0, pad_type = q_29_pad_type_0, strides = var_1788, weight = unet_down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_83_cast)[name = tensor("q_29_cast")]; + tensor var_1794 = const()[name = tensor("op_1794"), val = tensor([1, 1])]; + tensor var_1796 = const()[name = tensor("op_1796"), val = tensor([1, 1])]; tensor k_29_pad_type_0 = const()[name = tensor("k_29_pad_type_0"), val = tensor("custom")]; tensor k_29_pad_0 = const()[name = tensor("k_29_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_29 = conv(dilations = var_1858, groups = var_1193, pad = k_29_pad_0, pad_type = k_29_pad_type_0, strides = var_1856, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight, x = hidden_states_83)[name = tensor("k_29")]; - tensor var_1862 = const()[name = tensor("op_1862"), val = tensor([1, 1])]; - tensor var_1864 = const()[name = tensor("op_1864"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(409986880)))]; + tensor k_29_cast = conv(dilations = var_1796, groups = var_31, pad = k_29_pad_0, pad_type = k_29_pad_type_0, strides = var_1794, weight = unet_down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_83_cast)[name = tensor("k_29_cast")]; + tensor var_1800 = const()[name = tensor("op_1800"), val = tensor([1, 1])]; + tensor var_1802 = const()[name = tensor("op_1802"), val = tensor([1, 1])]; tensor v_29_pad_type_0 = const()[name = tensor("v_29_pad_type_0"), val = tensor("custom")]; tensor v_29_pad_0 = const()[name = tensor("v_29_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_29 = conv(dilations = var_1864, groups = var_1193, pad = v_29_pad_0, pad_type = v_29_pad_type_0, strides = var_1862, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight, x = hidden_states_83)[name = tensor("v_29")]; - tensor var_1868 = const()[name = tensor("op_1868"), val = tensor([2, 20, 64, -1])]; - tensor var_1869 = reshape(shape = var_1868, x = q_29)[name = tensor("op_1869")]; - tensor var_1870 = const()[name = tensor("op_1870"), val = tensor([2, 20, 64, -1])]; - tensor var_1871 = reshape(shape = var_1870, x = k_29)[name = tensor("op_1871")]; - tensor var_1872 = const()[name = tensor("op_1872"), val = tensor([2, 20, 64, -1])]; - tensor var_1873 = reshape(shape = var_1872, x = v_29)[name = tensor("op_1873")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(413263744)))]; + tensor v_29_cast = conv(dilations = var_1802, groups = var_31, pad = v_29_pad_0, pad_type = v_29_pad_type_0, strides = var_1800, weight = unet_down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_83_cast)[name = tensor("v_29_cast")]; + tensor var_1806 = const()[name = tensor("op_1806"), val = tensor([2, 20, 64, -1])]; + tensor var_1807_cast = reshape(shape = var_1806, x = q_29_cast)[name = tensor("op_1807_cast")]; + tensor var_1808 = const()[name = tensor("op_1808"), val = tensor([2, 20, 64, -1])]; + tensor var_1809_cast = reshape(shape = var_1808, x = k_29_cast)[name = tensor("op_1809_cast")]; + tensor var_1810 = const()[name = tensor("op_1810"), val = tensor([2, 20, 64, -1])]; + tensor var_1811_cast = reshape(shape = var_1810, x = v_29_cast)[name = tensor("op_1811_cast")]; tensor attn_weights_57_transpose_x_0 = const()[name = tensor("attn_weights_57_transpose_x_0"), val = tensor(true)]; tensor attn_weights_57_transpose_y_0 = const()[name = tensor("attn_weights_57_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_57 = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_1869, y = var_1871)[name = tensor("attn_weights_57")]; - tensor attn_weights_59 = mul(x = attn_weights_57, y = var_1184)[name = tensor("attn_weights_59")]; - tensor var_1877 = softmax(axis = var_1177, x = attn_weights_59)[name = tensor("op_1877")]; + tensor attn_weights_57_cast = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_1807_cast, y = var_1809_cast)[name = tensor("attn_weights_57_cast")]; + tensor attn_weights_59_cast = mul(x = attn_weights_57_cast, y = var_12_to_fp16)[name = tensor("attn_weights_59_cast")]; + tensor var_1815_cast = softmax(axis = var_18, x = attn_weights_59_cast)[name = tensor("op_1815_cast")]; tensor attn_29_transpose_x_0 = const()[name = tensor("attn_29_transpose_x_0"), val = tensor(false)]; tensor attn_29_transpose_y_0 = const()[name = tensor("attn_29_transpose_y_0"), val = tensor(true)]; - tensor attn_29 = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_1873, y = var_1877)[name = tensor("attn_29")]; - tensor var_1881 = const()[name = tensor("op_1881"), val = tensor([2, 1280, 1, -1])]; - tensor input_155 = reshape(shape = var_1881, x = attn_29)[name = tensor("input_155")]; - tensor var_1886 = const()[name = tensor("op_1886"), val = tensor([1, 1])]; - tensor var_1888 = const()[name = tensor("op_1888"), val = tensor([1, 1])]; - tensor var_1890_pad_type_0 = const()[name = tensor("op_1890_pad_type_0"), val = tensor("custom")]; - tensor var_1890_pad_0 = const()[name = tensor("op_1890_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1890 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias, dilations = var_1888, groups = var_1193, pad = var_1890_pad_0, pad_type = var_1890_pad_type_0, strides = var_1886, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight, x = input_155)[name = tensor("op_1890")]; - tensor inputs_45 = add(x = var_1890, y = inputs_43)[name = tensor("inputs_45")]; - tensor var_1894 = const()[name = tensor("op_1894"), val = tensor([1])]; - tensor channels_mean_45 = reduce_mean(axes = var_1894, keep_dims = var_1188, x = inputs_45)[name = tensor("channels_mean_45")]; - tensor zero_mean_45 = sub(x = inputs_45, y = channels_mean_45)[name = tensor("zero_mean_45")]; - tensor zero_mean_sq_45 = mul(x = zero_mean_45, y = zero_mean_45)[name = tensor("zero_mean_sq_45")]; - tensor var_1898 = const()[name = tensor("op_1898"), val = tensor([1])]; - tensor var_1899 = reduce_mean(axes = var_1898, keep_dims = var_1188, x = zero_mean_sq_45)[name = tensor("op_1899")]; - tensor var_1900 = const()[name = tensor("op_1900"), val = tensor(0x1.4f8b58p-17)]; - tensor var_1901 = add(x = var_1899, y = var_1900)[name = tensor("op_1901")]; - tensor denom_45_epsilon_0 = const()[name = tensor("denom_45_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_45 = rsqrt(epsilon = denom_45_epsilon_0, x = var_1901)[name = tensor("denom_45")]; - tensor out_45 = mul(x = zero_mean_45, y = denom_45)[name = tensor("out_45")]; - tensor var_1905 = const()[name = tensor("op_1905"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267828736)))]; - tensor var_1906 = add(x = out_45, y = var_1905)[name = tensor("op_1906")]; - tensor var_1908 = const()[name = tensor("op_1908"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267833920)))]; - tensor hidden_states_85 = mul(x = var_1906, y = var_1908)[name = tensor("hidden_states_85")]; - tensor var_1915 = const()[name = tensor("op_1915"), val = tensor([1, 1])]; - tensor var_1917 = const()[name = tensor("op_1917"), val = tensor([1, 1])]; + tensor attn_29_cast = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_1811_cast, y = var_1815_cast)[name = tensor("attn_29_cast")]; + tensor var_1819 = const()[name = tensor("op_1819"), val = tensor([2, 1280, 1, -1])]; + tensor input_155_cast = reshape(shape = var_1819, x = attn_29_cast)[name = tensor("input_155_cast")]; + tensor var_1824 = const()[name = tensor("op_1824"), val = tensor([1, 1])]; + tensor var_1826 = const()[name = tensor("op_1826"), val = tensor([1, 1])]; + tensor var_1828_pad_type_0 = const()[name = tensor("op_1828_pad_type_0"), val = tensor("custom")]; + tensor var_1828_pad_0 = const()[name = tensor("op_1828_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(416540608)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419817472)))]; + tensor var_1828_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_1826, groups = var_31, pad = var_1828_pad_0, pad_type = var_1828_pad_type_0, strides = var_1824, weight = unet_down_blocks_2_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_155_cast)[name = tensor("op_1828_cast")]; + tensor inputs_45_cast = add(x = var_1828_cast, y = inputs_43_cast)[name = tensor("inputs_45_cast")]; + tensor var_1832 = const()[name = tensor("op_1832"), val = tensor([1])]; + tensor channels_mean_45_cast = reduce_mean(axes = var_1832, keep_dims = var_23, x = inputs_45_cast)[name = tensor("channels_mean_45_cast")]; + tensor zero_mean_45_cast = sub(x = inputs_45_cast, y = channels_mean_45_cast)[name = tensor("zero_mean_45_cast")]; + tensor zero_mean_sq_45_cast = mul(x = zero_mean_45_cast, y = zero_mean_45_cast)[name = tensor("zero_mean_sq_45_cast")]; + tensor var_1836 = const()[name = tensor("op_1836"), val = tensor([1])]; + tensor var_1837_cast = reduce_mean(axes = var_1836, keep_dims = var_23, x = zero_mean_sq_45_cast)[name = tensor("op_1837_cast")]; + tensor var_1838_to_fp16 = const()[name = tensor("op_1838_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1839_cast = add(x = var_1837_cast, y = var_1838_to_fp16)[name = tensor("op_1839_cast")]; + tensor denom_45_epsilon_0_to_fp16 = const()[name = tensor("denom_45_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_45_cast = rsqrt(epsilon = denom_45_epsilon_0_to_fp16, x = var_1839_cast)[name = tensor("denom_45_cast")]; + tensor out_45_cast = mul(x = zero_mean_45_cast, y = denom_45_cast)[name = tensor("out_45_cast")]; + tensor var_1843_to_fp16 = const()[name = tensor("op_1843_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419820096)))]; + tensor var_1844_cast = add(x = out_45_cast, y = var_1843_to_fp16)[name = tensor("op_1844_cast")]; + tensor var_1846_to_fp16 = const()[name = tensor("op_1846_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419822720)))]; + tensor hidden_states_85_cast = mul(x = var_1844_cast, y = var_1846_to_fp16)[name = tensor("hidden_states_85_cast")]; + tensor var_1853 = const()[name = tensor("op_1853"), val = tensor([1, 1])]; + tensor var_1855 = const()[name = tensor("op_1855"), val = tensor([1, 1])]; tensor q_31_pad_type_0 = const()[name = tensor("q_31_pad_type_0"), val = tensor("custom")]; tensor q_31_pad_0 = const()[name = tensor("q_31_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_31 = conv(dilations = var_1917, groups = var_1193, pad = q_31_pad_0, pad_type = q_31_pad_type_0, strides = var_1915, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight, x = hidden_states_85)[name = tensor("q_31")]; - tensor var_1921 = const()[name = tensor("op_1921"), val = tensor([1, 1])]; - tensor var_1923 = const()[name = tensor("op_1923"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(419825344)))]; + tensor q_31_cast = conv(dilations = var_1855, groups = var_31, pad = q_31_pad_0, pad_type = q_31_pad_type_0, strides = var_1853, weight = unet_down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_85_cast)[name = tensor("q_31_cast")]; + tensor var_1859 = const()[name = tensor("op_1859"), val = tensor([1, 1])]; + tensor var_1861 = const()[name = tensor("op_1861"), val = tensor([1, 1])]; tensor k_31_pad_type_0 = const()[name = tensor("k_31_pad_type_0"), val = tensor("custom")]; tensor k_31_pad_0 = const()[name = tensor("k_31_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_31 = conv(dilations = var_1923, groups = var_1193, pad = k_31_pad_0, pad_type = k_31_pad_type_0, strides = var_1921, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_31")]; - tensor var_1927 = const()[name = tensor("op_1927"), val = tensor([1, 1])]; - tensor var_1929 = const()[name = tensor("op_1929"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(423102208)))]; + tensor k_31_cast = conv(dilations = var_1861, groups = var_31, pad = k_31_pad_0, pad_type = k_31_pad_type_0, strides = var_1859, weight = unet_down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_31_cast")]; + tensor var_1865 = const()[name = tensor("op_1865"), val = tensor([1, 1])]; + tensor var_1867 = const()[name = tensor("op_1867"), val = tensor([1, 1])]; tensor v_31_pad_type_0 = const()[name = tensor("v_31_pad_type_0"), val = tensor("custom")]; tensor v_31_pad_0 = const()[name = tensor("v_31_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_31 = conv(dilations = var_1929, groups = var_1193, pad = v_31_pad_0, pad_type = v_31_pad_type_0, strides = var_1927, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_31")]; - tensor var_1933 = const()[name = tensor("op_1933"), val = tensor([2, 20, 64, -1])]; - tensor var_1934 = reshape(shape = var_1933, x = q_31)[name = tensor("op_1934")]; - tensor var_1935 = const()[name = tensor("op_1935"), val = tensor([2, 20, 64, -1])]; - tensor var_1936 = reshape(shape = var_1935, x = k_31)[name = tensor("op_1936")]; - tensor var_1937 = const()[name = tensor("op_1937"), val = tensor([2, 20, 64, -1])]; - tensor var_1938 = reshape(shape = var_1937, x = v_31)[name = tensor("op_1938")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(428345152)))]; + tensor v_31_cast = conv(dilations = var_1867, groups = var_31, pad = v_31_pad_0, pad_type = v_31_pad_type_0, strides = var_1865, weight = unet_down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_31_cast")]; + tensor var_1871 = const()[name = tensor("op_1871"), val = tensor([2, 20, 64, -1])]; + tensor var_1872_cast = reshape(shape = var_1871, x = q_31_cast)[name = tensor("op_1872_cast")]; + tensor var_1873 = const()[name = tensor("op_1873"), val = tensor([2, 20, 64, -1])]; + tensor var_1874_cast = reshape(shape = var_1873, x = k_31_cast)[name = tensor("op_1874_cast")]; + tensor var_1875 = const()[name = tensor("op_1875"), val = tensor([2, 20, 64, -1])]; + tensor var_1876_cast = reshape(shape = var_1875, x = v_31_cast)[name = tensor("op_1876_cast")]; tensor attn_weights_61_transpose_x_0 = const()[name = tensor("attn_weights_61_transpose_x_0"), val = tensor(true)]; tensor attn_weights_61_transpose_y_0 = const()[name = tensor("attn_weights_61_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_61 = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = var_1934, y = var_1936)[name = tensor("attn_weights_61")]; - tensor attn_weights_63 = mul(x = attn_weights_61, y = var_1184)[name = tensor("attn_weights_63")]; - tensor var_1942 = softmax(axis = var_1177, x = attn_weights_63)[name = tensor("op_1942")]; + tensor attn_weights_61_cast = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = var_1872_cast, y = var_1874_cast)[name = tensor("attn_weights_61_cast")]; + tensor attn_weights_63_cast = mul(x = attn_weights_61_cast, y = var_12_to_fp16)[name = tensor("attn_weights_63_cast")]; + tensor var_1880_cast = softmax(axis = var_18, x = attn_weights_63_cast)[name = tensor("op_1880_cast")]; tensor attn_31_transpose_x_0 = const()[name = tensor("attn_31_transpose_x_0"), val = tensor(false)]; tensor attn_31_transpose_y_0 = const()[name = tensor("attn_31_transpose_y_0"), val = tensor(true)]; - tensor attn_31 = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_1938, y = var_1942)[name = tensor("attn_31")]; - tensor var_1946 = const()[name = tensor("op_1946"), val = tensor([2, 1280, 1, -1])]; - tensor input_157 = reshape(shape = var_1946, x = attn_31)[name = tensor("input_157")]; - tensor var_1951 = const()[name = tensor("op_1951"), val = tensor([1, 1])]; - tensor var_1953 = const()[name = tensor("op_1953"), val = tensor([1, 1])]; - tensor var_1955_pad_type_0 = const()[name = tensor("op_1955_pad_type_0"), val = tensor("custom")]; - tensor var_1955_pad_0 = const()[name = tensor("op_1955_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1955 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias, dilations = var_1953, groups = var_1193, pad = var_1955_pad_0, pad_type = var_1955_pad_type_0, strides = var_1951, weight = down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight, x = input_157)[name = tensor("op_1955")]; - tensor inputs_47 = add(x = var_1955, y = inputs_45)[name = tensor("inputs_47")]; - tensor var_1959 = const()[name = tensor("op_1959"), val = tensor([1])]; - tensor channels_mean_47 = reduce_mean(axes = var_1959, keep_dims = var_1188, x = inputs_47)[name = tensor("channels_mean_47")]; - tensor zero_mean_47 = sub(x = inputs_47, y = channels_mean_47)[name = tensor("zero_mean_47")]; - tensor zero_mean_sq_47 = mul(x = zero_mean_47, y = zero_mean_47)[name = tensor("zero_mean_sq_47")]; - tensor var_1963 = const()[name = tensor("op_1963"), val = tensor([1])]; - tensor var_1964 = reduce_mean(axes = var_1963, keep_dims = var_1188, x = zero_mean_sq_47)[name = tensor("op_1964")]; - tensor var_1965 = const()[name = tensor("op_1965"), val = tensor(0x1.4f8b58p-17)]; - tensor var_1966 = add(x = var_1964, y = var_1965)[name = tensor("op_1966")]; - tensor denom_47_epsilon_0 = const()[name = tensor("denom_47_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_47 = rsqrt(epsilon = denom_47_epsilon_0, x = var_1966)[name = tensor("denom_47")]; - tensor out_47 = mul(x = zero_mean_47, y = denom_47)[name = tensor("out_47")]; - tensor var_1970 = const()[name = tensor("op_1970"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267839104)))]; - tensor var_1971 = add(x = out_47, y = var_1970)[name = tensor("op_1971")]; - tensor var_1973 = const()[name = tensor("op_1973"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267844288)))]; - tensor input_159 = mul(x = var_1971, y = var_1973)[name = tensor("input_159")]; - tensor var_1981 = const()[name = tensor("op_1981"), val = tensor([1, 1])]; - tensor var_1983 = const()[name = tensor("op_1983"), val = tensor([1, 1])]; - tensor var_1985_pad_type_0 = const()[name = tensor("op_1985_pad_type_0"), val = tensor("custom")]; - tensor var_1985_pad_0 = const()[name = tensor("op_1985_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1985 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias, dilations = var_1983, groups = var_1193, pad = var_1985_pad_0, pad_type = var_1985_pad_type_0, strides = var_1981, weight = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight, x = input_159)[name = tensor("op_1985")]; - tensor var_1986_split_sizes_0 = const()[name = tensor("op_1986_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_1986_axis_0 = const()[name = tensor("op_1986_axis_0"), val = tensor(1)]; - tensor var_1986_0, tensor var_1986_1 = split(axis = var_1986_axis_0, split_sizes = var_1986_split_sizes_0, x = var_1985)[name = tensor("op_1986")]; - tensor var_1988_mode_0 = const()[name = tensor("op_1988_mode_0"), val = tensor("EXACT")]; - tensor var_1988 = gelu(mode = var_1988_mode_0, x = var_1986_1)[name = tensor("op_1988")]; - tensor input_161 = mul(x = var_1986_0, y = var_1988)[name = tensor("input_161")]; - tensor var_1992 = const()[name = tensor("op_1992"), val = tensor([1, 1])]; - tensor var_1994 = const()[name = tensor("op_1994"), val = tensor([1, 1])]; - tensor var_1996_pad_type_0 = const()[name = tensor("op_1996_pad_type_0"), val = tensor("custom")]; - tensor var_1996_pad_0 = const()[name = tensor("op_1996_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_1996 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias, dilations = var_1994, groups = var_1193, pad = var_1996_pad_0, pad_type = var_1996_pad_type_0, strides = var_1992, weight = down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight, x = input_161)[name = tensor("op_1996")]; - tensor inputs_49 = add(x = var_1996, y = inputs_47)[name = tensor("inputs_49")]; - tensor var_2006 = const()[name = tensor("op_2006"), val = tensor([1])]; - tensor channels_mean_49 = reduce_mean(axes = var_2006, keep_dims = var_1188, x = inputs_49)[name = tensor("channels_mean_49")]; - tensor zero_mean_49 = sub(x = inputs_49, y = channels_mean_49)[name = tensor("zero_mean_49")]; - tensor zero_mean_sq_49 = mul(x = zero_mean_49, y = zero_mean_49)[name = tensor("zero_mean_sq_49")]; - tensor var_2010 = const()[name = tensor("op_2010"), val = tensor([1])]; - tensor var_2011 = reduce_mean(axes = var_2010, keep_dims = var_1188, x = zero_mean_sq_49)[name = tensor("op_2011")]; - tensor var_2012 = const()[name = tensor("op_2012"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2013 = add(x = var_2011, y = var_2012)[name = tensor("op_2013")]; - tensor denom_49_epsilon_0 = const()[name = tensor("denom_49_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_49 = rsqrt(epsilon = denom_49_epsilon_0, x = var_2013)[name = tensor("denom_49")]; - tensor out_49 = mul(x = zero_mean_49, y = denom_49)[name = tensor("out_49")]; - tensor var_2017 = const()[name = tensor("op_2017"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267849472)))]; - tensor var_2018 = add(x = out_49, y = var_2017)[name = tensor("op_2018")]; - tensor var_2020 = const()[name = tensor("op_2020"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267854656)))]; - tensor hidden_states_89 = mul(x = var_2018, y = var_2020)[name = tensor("hidden_states_89")]; - tensor var_2027 = const()[name = tensor("op_2027"), val = tensor([1, 1])]; - tensor var_2029 = const()[name = tensor("op_2029"), val = tensor([1, 1])]; + tensor attn_31_cast = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_1876_cast, y = var_1880_cast)[name = tensor("attn_31_cast")]; + tensor var_1884 = const()[name = tensor("op_1884"), val = tensor([2, 1280, 1, -1])]; + tensor input_157_cast = reshape(shape = var_1884, x = attn_31_cast)[name = tensor("input_157_cast")]; + tensor var_1889 = const()[name = tensor("op_1889"), val = tensor([1, 1])]; + tensor var_1891 = const()[name = tensor("op_1891"), val = tensor([1, 1])]; + tensor var_1893_pad_type_0 = const()[name = tensor("op_1893_pad_type_0"), val = tensor("custom")]; + tensor var_1893_pad_0 = const()[name = tensor("op_1893_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(433588096)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436864960)))]; + tensor var_1893_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_1891, groups = var_31, pad = var_1893_pad_0, pad_type = var_1893_pad_type_0, strides = var_1889, weight = unet_down_blocks_2_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_157_cast)[name = tensor("op_1893_cast")]; + tensor inputs_47_cast = add(x = var_1893_cast, y = inputs_45_cast)[name = tensor("inputs_47_cast")]; + tensor var_1897 = const()[name = tensor("op_1897"), val = tensor([1])]; + tensor channels_mean_47_cast = reduce_mean(axes = var_1897, keep_dims = var_23, x = inputs_47_cast)[name = tensor("channels_mean_47_cast")]; + tensor zero_mean_47_cast = sub(x = inputs_47_cast, y = channels_mean_47_cast)[name = tensor("zero_mean_47_cast")]; + tensor zero_mean_sq_47_cast = mul(x = zero_mean_47_cast, y = zero_mean_47_cast)[name = tensor("zero_mean_sq_47_cast")]; + tensor var_1901 = const()[name = tensor("op_1901"), val = tensor([1])]; + tensor var_1902_cast = reduce_mean(axes = var_1901, keep_dims = var_23, x = zero_mean_sq_47_cast)[name = tensor("op_1902_cast")]; + tensor var_1903_to_fp16 = const()[name = tensor("op_1903_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1904_cast = add(x = var_1902_cast, y = var_1903_to_fp16)[name = tensor("op_1904_cast")]; + tensor denom_47_epsilon_0_to_fp16 = const()[name = tensor("denom_47_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_47_cast = rsqrt(epsilon = denom_47_epsilon_0_to_fp16, x = var_1904_cast)[name = tensor("denom_47_cast")]; + tensor out_47_cast = mul(x = zero_mean_47_cast, y = denom_47_cast)[name = tensor("out_47_cast")]; + tensor var_1908_to_fp16 = const()[name = tensor("op_1908_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436867584)))]; + tensor var_1909_cast = add(x = out_47_cast, y = var_1908_to_fp16)[name = tensor("op_1909_cast")]; + tensor var_1911_to_fp16 = const()[name = tensor("op_1911_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436870208)))]; + tensor input_159_cast = mul(x = var_1909_cast, y = var_1911_to_fp16)[name = tensor("input_159_cast")]; + tensor var_1919 = const()[name = tensor("op_1919"), val = tensor([1, 1])]; + tensor var_1921 = const()[name = tensor("op_1921"), val = tensor([1, 1])]; + tensor var_1923_pad_type_0 = const()[name = tensor("op_1923_pad_type_0"), val = tensor("custom")]; + tensor var_1923_pad_0 = const()[name = tensor("op_1923_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(436872832)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(463087296)))]; + tensor var_1923_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_1921, groups = var_31, pad = var_1923_pad_0, pad_type = var_1923_pad_type_0, strides = var_1919, weight = unet_down_blocks_2_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_159_cast)[name = tensor("op_1923_cast")]; + tensor var_1924_split_sizes_0 = const()[name = tensor("op_1924_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1924_axis_0 = const()[name = tensor("op_1924_axis_0"), val = tensor(1)]; + tensor var_1924_cast_0, tensor var_1924_cast_1 = split(axis = var_1924_axis_0, split_sizes = var_1924_split_sizes_0, x = var_1923_cast)[name = tensor("op_1924_cast")]; + tensor var_1926_mode_0 = const()[name = tensor("op_1926_mode_0"), val = tensor("EXACT")]; + tensor var_1926_cast = gelu(mode = var_1926_mode_0, x = var_1924_cast_1)[name = tensor("op_1926_cast")]; + tensor input_161_cast = mul(x = var_1924_cast_0, y = var_1926_cast)[name = tensor("input_161_cast")]; + tensor var_1930 = const()[name = tensor("op_1930"), val = tensor([1, 1])]; + tensor var_1932 = const()[name = tensor("op_1932"), val = tensor([1, 1])]; + tensor var_1934_pad_type_0 = const()[name = tensor("op_1934_pad_type_0"), val = tensor("custom")]; + tensor var_1934_pad_0 = const()[name = tensor("op_1934_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(463107840)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476215104)))]; + tensor var_1934_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_1932, groups = var_31, pad = var_1934_pad_0, pad_type = var_1934_pad_type_0, strides = var_1930, weight = unet_down_blocks_2_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_161_cast)[name = tensor("op_1934_cast")]; + tensor inputs_49_cast = add(x = var_1934_cast, y = inputs_47_cast)[name = tensor("inputs_49_cast")]; + tensor var_1944 = const()[name = tensor("op_1944"), val = tensor([1])]; + tensor channels_mean_49_cast = reduce_mean(axes = var_1944, keep_dims = var_23, x = inputs_49_cast)[name = tensor("channels_mean_49_cast")]; + tensor zero_mean_49_cast = sub(x = inputs_49_cast, y = channels_mean_49_cast)[name = tensor("zero_mean_49_cast")]; + tensor zero_mean_sq_49_cast = mul(x = zero_mean_49_cast, y = zero_mean_49_cast)[name = tensor("zero_mean_sq_49_cast")]; + tensor var_1948 = const()[name = tensor("op_1948"), val = tensor([1])]; + tensor var_1949_cast = reduce_mean(axes = var_1948, keep_dims = var_23, x = zero_mean_sq_49_cast)[name = tensor("op_1949_cast")]; + tensor var_1950_to_fp16 = const()[name = tensor("op_1950_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1951_cast = add(x = var_1949_cast, y = var_1950_to_fp16)[name = tensor("op_1951_cast")]; + tensor denom_49_epsilon_0_to_fp16 = const()[name = tensor("denom_49_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_49_cast = rsqrt(epsilon = denom_49_epsilon_0_to_fp16, x = var_1951_cast)[name = tensor("denom_49_cast")]; + tensor out_49_cast = mul(x = zero_mean_49_cast, y = denom_49_cast)[name = tensor("out_49_cast")]; + tensor var_1955_to_fp16 = const()[name = tensor("op_1955_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476217728)))]; + tensor var_1956_cast = add(x = out_49_cast, y = var_1955_to_fp16)[name = tensor("op_1956_cast")]; + tensor var_1958_to_fp16 = const()[name = tensor("op_1958_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476220352)))]; + tensor hidden_states_89_cast = mul(x = var_1956_cast, y = var_1958_to_fp16)[name = tensor("hidden_states_89_cast")]; + tensor var_1965 = const()[name = tensor("op_1965"), val = tensor([1, 1])]; + tensor var_1967 = const()[name = tensor("op_1967"), val = tensor([1, 1])]; tensor q_33_pad_type_0 = const()[name = tensor("q_33_pad_type_0"), val = tensor("custom")]; tensor q_33_pad_0 = const()[name = tensor("q_33_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_33 = conv(dilations = var_2029, groups = var_1193, pad = q_33_pad_0, pad_type = q_33_pad_type_0, strides = var_2027, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_q_weight, x = hidden_states_89)[name = tensor("q_33")]; - tensor var_2033 = const()[name = tensor("op_2033"), val = tensor([1, 1])]; - tensor var_2035 = const()[name = tensor("op_2035"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(476222976)))]; + tensor q_33_cast = conv(dilations = var_1967, groups = var_31, pad = q_33_pad_0, pad_type = q_33_pad_type_0, strides = var_1965, weight = unet_down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16, x = hidden_states_89_cast)[name = tensor("q_33_cast")]; + tensor var_1971 = const()[name = tensor("op_1971"), val = tensor([1, 1])]; + tensor var_1973 = const()[name = tensor("op_1973"), val = tensor([1, 1])]; tensor k_33_pad_type_0 = const()[name = tensor("k_33_pad_type_0"), val = tensor("custom")]; tensor k_33_pad_0 = const()[name = tensor("k_33_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_33 = conv(dilations = var_2035, groups = var_1193, pad = k_33_pad_0, pad_type = k_33_pad_type_0, strides = var_2033, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_k_weight, x = hidden_states_89)[name = tensor("k_33")]; - tensor var_2039 = const()[name = tensor("op_2039"), val = tensor([1, 1])]; - tensor var_2041 = const()[name = tensor("op_2041"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(479499840)))]; + tensor k_33_cast = conv(dilations = var_1973, groups = var_31, pad = k_33_pad_0, pad_type = k_33_pad_type_0, strides = var_1971, weight = unet_down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16, x = hidden_states_89_cast)[name = tensor("k_33_cast")]; + tensor var_1977 = const()[name = tensor("op_1977"), val = tensor([1, 1])]; + tensor var_1979 = const()[name = tensor("op_1979"), val = tensor([1, 1])]; tensor v_33_pad_type_0 = const()[name = tensor("v_33_pad_type_0"), val = tensor("custom")]; tensor v_33_pad_0 = const()[name = tensor("v_33_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_33 = conv(dilations = var_2041, groups = var_1193, pad = v_33_pad_0, pad_type = v_33_pad_type_0, strides = var_2039, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_v_weight, x = hidden_states_89)[name = tensor("v_33")]; - tensor var_2045 = const()[name = tensor("op_2045"), val = tensor([2, 20, 64, -1])]; - tensor var_2046 = reshape(shape = var_2045, x = q_33)[name = tensor("op_2046")]; - tensor var_2047 = const()[name = tensor("op_2047"), val = tensor([2, 20, 64, -1])]; - tensor var_2048 = reshape(shape = var_2047, x = k_33)[name = tensor("op_2048")]; - tensor var_2049 = const()[name = tensor("op_2049"), val = tensor([2, 20, 64, -1])]; - tensor var_2050 = reshape(shape = var_2049, x = v_33)[name = tensor("op_2050")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(482776704)))]; + tensor v_33_cast = conv(dilations = var_1979, groups = var_31, pad = v_33_pad_0, pad_type = v_33_pad_type_0, strides = var_1977, weight = unet_down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16, x = hidden_states_89_cast)[name = tensor("v_33_cast")]; + tensor var_1983 = const()[name = tensor("op_1983"), val = tensor([2, 20, 64, -1])]; + tensor var_1984_cast = reshape(shape = var_1983, x = q_33_cast)[name = tensor("op_1984_cast")]; + tensor var_1985 = const()[name = tensor("op_1985"), val = tensor([2, 20, 64, -1])]; + tensor var_1986_cast = reshape(shape = var_1985, x = k_33_cast)[name = tensor("op_1986_cast")]; + tensor var_1987 = const()[name = tensor("op_1987"), val = tensor([2, 20, 64, -1])]; + tensor var_1988_cast = reshape(shape = var_1987, x = v_33_cast)[name = tensor("op_1988_cast")]; tensor attn_weights_65_transpose_x_0 = const()[name = tensor("attn_weights_65_transpose_x_0"), val = tensor(true)]; tensor attn_weights_65_transpose_y_0 = const()[name = tensor("attn_weights_65_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_65 = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2046, y = var_2048)[name = tensor("attn_weights_65")]; - tensor attn_weights_67 = mul(x = attn_weights_65, y = var_1184)[name = tensor("attn_weights_67")]; - tensor var_2054 = softmax(axis = var_1177, x = attn_weights_67)[name = tensor("op_2054")]; + tensor attn_weights_65_cast = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_1984_cast, y = var_1986_cast)[name = tensor("attn_weights_65_cast")]; + tensor attn_weights_67_cast = mul(x = attn_weights_65_cast, y = var_12_to_fp16)[name = tensor("attn_weights_67_cast")]; + tensor var_1992_cast = softmax(axis = var_18, x = attn_weights_67_cast)[name = tensor("op_1992_cast")]; tensor attn_33_transpose_x_0 = const()[name = tensor("attn_33_transpose_x_0"), val = tensor(false)]; tensor attn_33_transpose_y_0 = const()[name = tensor("attn_33_transpose_y_0"), val = tensor(true)]; - tensor attn_33 = matmul(transpose_x = attn_33_transpose_x_0, transpose_y = attn_33_transpose_y_0, x = var_2050, y = var_2054)[name = tensor("attn_33")]; - tensor var_2058 = const()[name = tensor("op_2058"), val = tensor([2, 1280, 1, -1])]; - tensor input_163 = reshape(shape = var_2058, x = attn_33)[name = tensor("input_163")]; - tensor var_2063 = const()[name = tensor("op_2063"), val = tensor([1, 1])]; - tensor var_2065 = const()[name = tensor("op_2065"), val = tensor([1, 1])]; - tensor var_2067_pad_type_0 = const()[name = tensor("op_2067_pad_type_0"), val = tensor("custom")]; - tensor var_2067_pad_0 = const()[name = tensor("op_2067_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2067 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_bias, dilations = var_2065, groups = var_1193, pad = var_2067_pad_0, pad_type = var_2067_pad_type_0, strides = var_2063, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_weight, x = input_163)[name = tensor("op_2067")]; - tensor inputs_51 = add(x = var_2067, y = inputs_49)[name = tensor("inputs_51")]; - tensor var_2071 = const()[name = tensor("op_2071"), val = tensor([1])]; - tensor channels_mean_51 = reduce_mean(axes = var_2071, keep_dims = var_1188, x = inputs_51)[name = tensor("channels_mean_51")]; - tensor zero_mean_51 = sub(x = inputs_51, y = channels_mean_51)[name = tensor("zero_mean_51")]; - tensor zero_mean_sq_51 = mul(x = zero_mean_51, y = zero_mean_51)[name = tensor("zero_mean_sq_51")]; - tensor var_2075 = const()[name = tensor("op_2075"), val = tensor([1])]; - tensor var_2076 = reduce_mean(axes = var_2075, keep_dims = var_1188, x = zero_mean_sq_51)[name = tensor("op_2076")]; - tensor var_2077 = const()[name = tensor("op_2077"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2078 = add(x = var_2076, y = var_2077)[name = tensor("op_2078")]; - tensor denom_51_epsilon_0 = const()[name = tensor("denom_51_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_51 = rsqrt(epsilon = denom_51_epsilon_0, x = var_2078)[name = tensor("denom_51")]; - tensor out_51 = mul(x = zero_mean_51, y = denom_51)[name = tensor("out_51")]; - tensor var_2082 = const()[name = tensor("op_2082"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267859840)))]; - tensor var_2083 = add(x = out_51, y = var_2082)[name = tensor("op_2083")]; - tensor var_2085 = const()[name = tensor("op_2085"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267865024)))]; - tensor hidden_states_91 = mul(x = var_2083, y = var_2085)[name = tensor("hidden_states_91")]; - tensor var_2092 = const()[name = tensor("op_2092"), val = tensor([1, 1])]; - tensor var_2094 = const()[name = tensor("op_2094"), val = tensor([1, 1])]; + tensor attn_33_cast = matmul(transpose_x = attn_33_transpose_x_0, transpose_y = attn_33_transpose_y_0, x = var_1988_cast, y = var_1992_cast)[name = tensor("attn_33_cast")]; + tensor var_1996 = const()[name = tensor("op_1996"), val = tensor([2, 1280, 1, -1])]; + tensor input_163_cast = reshape(shape = var_1996, x = attn_33_cast)[name = tensor("input_163_cast")]; + tensor var_2001 = const()[name = tensor("op_2001"), val = tensor([1, 1])]; + tensor var_2003 = const()[name = tensor("op_2003"), val = tensor([1, 1])]; + tensor var_2005_pad_type_0 = const()[name = tensor("op_2005_pad_type_0"), val = tensor("custom")]; + tensor var_2005_pad_0 = const()[name = tensor("op_2005_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(486053568)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489330432)))]; + tensor var_2005_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_2003, groups = var_31, pad = var_2005_pad_0, pad_type = var_2005_pad_type_0, strides = var_2001, weight = unet_down_blocks_2_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16, x = input_163_cast)[name = tensor("op_2005_cast")]; + tensor inputs_51_cast = add(x = var_2005_cast, y = inputs_49_cast)[name = tensor("inputs_51_cast")]; + tensor var_2009 = const()[name = tensor("op_2009"), val = tensor([1])]; + tensor channels_mean_51_cast = reduce_mean(axes = var_2009, keep_dims = var_23, x = inputs_51_cast)[name = tensor("channels_mean_51_cast")]; + tensor zero_mean_51_cast = sub(x = inputs_51_cast, y = channels_mean_51_cast)[name = tensor("zero_mean_51_cast")]; + tensor zero_mean_sq_51_cast = mul(x = zero_mean_51_cast, y = zero_mean_51_cast)[name = tensor("zero_mean_sq_51_cast")]; + tensor var_2013 = const()[name = tensor("op_2013"), val = tensor([1])]; + tensor var_2014_cast = reduce_mean(axes = var_2013, keep_dims = var_23, x = zero_mean_sq_51_cast)[name = tensor("op_2014_cast")]; + tensor var_2015_to_fp16 = const()[name = tensor("op_2015_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2016_cast = add(x = var_2014_cast, y = var_2015_to_fp16)[name = tensor("op_2016_cast")]; + tensor denom_51_epsilon_0_to_fp16 = const()[name = tensor("denom_51_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_51_cast = rsqrt(epsilon = denom_51_epsilon_0_to_fp16, x = var_2016_cast)[name = tensor("denom_51_cast")]; + tensor out_51_cast = mul(x = zero_mean_51_cast, y = denom_51_cast)[name = tensor("out_51_cast")]; + tensor var_2020_to_fp16 = const()[name = tensor("op_2020_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489333056)))]; + tensor var_2021_cast = add(x = out_51_cast, y = var_2020_to_fp16)[name = tensor("op_2021_cast")]; + tensor var_2023_to_fp16 = const()[name = tensor("op_2023_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489335680)))]; + tensor hidden_states_91_cast = mul(x = var_2021_cast, y = var_2023_to_fp16)[name = tensor("hidden_states_91_cast")]; + tensor var_2030 = const()[name = tensor("op_2030"), val = tensor([1, 1])]; + tensor var_2032 = const()[name = tensor("op_2032"), val = tensor([1, 1])]; tensor q_35_pad_type_0 = const()[name = tensor("q_35_pad_type_0"), val = tensor("custom")]; tensor q_35_pad_0 = const()[name = tensor("q_35_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_35 = conv(dilations = var_2094, groups = var_1193, pad = q_35_pad_0, pad_type = q_35_pad_type_0, strides = var_2092, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_q_weight, x = hidden_states_91)[name = tensor("q_35")]; - tensor var_2098 = const()[name = tensor("op_2098"), val = tensor([1, 1])]; - tensor var_2100 = const()[name = tensor("op_2100"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489338304)))]; + tensor q_35_cast = conv(dilations = var_2032, groups = var_31, pad = q_35_pad_0, pad_type = q_35_pad_type_0, strides = var_2030, weight = unet_down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16, x = hidden_states_91_cast)[name = tensor("q_35_cast")]; + tensor var_2036 = const()[name = tensor("op_2036"), val = tensor([1, 1])]; + tensor var_2038 = const()[name = tensor("op_2038"), val = tensor([1, 1])]; tensor k_35_pad_type_0 = const()[name = tensor("k_35_pad_type_0"), val = tensor("custom")]; tensor k_35_pad_0 = const()[name = tensor("k_35_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_35 = conv(dilations = var_2100, groups = var_1193, pad = k_35_pad_0, pad_type = k_35_pad_type_0, strides = var_2098, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_35")]; - tensor var_2104 = const()[name = tensor("op_2104"), val = tensor([1, 1])]; - tensor var_2106 = const()[name = tensor("op_2106"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(492615168)))]; + tensor k_35_cast = conv(dilations = var_2038, groups = var_31, pad = k_35_pad_0, pad_type = k_35_pad_type_0, strides = var_2036, weight = unet_down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_35_cast")]; + tensor var_2042 = const()[name = tensor("op_2042"), val = tensor([1, 1])]; + tensor var_2044 = const()[name = tensor("op_2044"), val = tensor([1, 1])]; tensor v_35_pad_type_0 = const()[name = tensor("v_35_pad_type_0"), val = tensor("custom")]; tensor v_35_pad_0 = const()[name = tensor("v_35_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_35 = conv(dilations = var_2106, groups = var_1193, pad = v_35_pad_0, pad_type = v_35_pad_type_0, strides = var_2104, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_35")]; - tensor var_2110 = const()[name = tensor("op_2110"), val = tensor([2, 20, 64, -1])]; - tensor var_2111 = reshape(shape = var_2110, x = q_35)[name = tensor("op_2111")]; - tensor var_2112 = const()[name = tensor("op_2112"), val = tensor([2, 20, 64, -1])]; - tensor var_2113 = reshape(shape = var_2112, x = k_35)[name = tensor("op_2113")]; - tensor var_2114 = const()[name = tensor("op_2114"), val = tensor([2, 20, 64, -1])]; - tensor var_2115 = reshape(shape = var_2114, x = v_35)[name = tensor("op_2115")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(497858112)))]; + tensor v_35_cast = conv(dilations = var_2044, groups = var_31, pad = v_35_pad_0, pad_type = v_35_pad_type_0, strides = var_2042, weight = unet_down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_35_cast")]; + tensor var_2048 = const()[name = tensor("op_2048"), val = tensor([2, 20, 64, -1])]; + tensor var_2049_cast = reshape(shape = var_2048, x = q_35_cast)[name = tensor("op_2049_cast")]; + tensor var_2050 = const()[name = tensor("op_2050"), val = tensor([2, 20, 64, -1])]; + tensor var_2051_cast = reshape(shape = var_2050, x = k_35_cast)[name = tensor("op_2051_cast")]; + tensor var_2052 = const()[name = tensor("op_2052"), val = tensor([2, 20, 64, -1])]; + tensor var_2053_cast = reshape(shape = var_2052, x = v_35_cast)[name = tensor("op_2053_cast")]; tensor attn_weights_69_transpose_x_0 = const()[name = tensor("attn_weights_69_transpose_x_0"), val = tensor(true)]; tensor attn_weights_69_transpose_y_0 = const()[name = tensor("attn_weights_69_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_69 = matmul(transpose_x = attn_weights_69_transpose_x_0, transpose_y = attn_weights_69_transpose_y_0, x = var_2111, y = var_2113)[name = tensor("attn_weights_69")]; - tensor attn_weights_71 = mul(x = attn_weights_69, y = var_1184)[name = tensor("attn_weights_71")]; - tensor var_2119 = softmax(axis = var_1177, x = attn_weights_71)[name = tensor("op_2119")]; + tensor attn_weights_69_cast = matmul(transpose_x = attn_weights_69_transpose_x_0, transpose_y = attn_weights_69_transpose_y_0, x = var_2049_cast, y = var_2051_cast)[name = tensor("attn_weights_69_cast")]; + tensor attn_weights_71_cast = mul(x = attn_weights_69_cast, y = var_12_to_fp16)[name = tensor("attn_weights_71_cast")]; + tensor var_2057_cast = softmax(axis = var_18, x = attn_weights_71_cast)[name = tensor("op_2057_cast")]; tensor attn_35_transpose_x_0 = const()[name = tensor("attn_35_transpose_x_0"), val = tensor(false)]; tensor attn_35_transpose_y_0 = const()[name = tensor("attn_35_transpose_y_0"), val = tensor(true)]; - tensor attn_35 = matmul(transpose_x = attn_35_transpose_x_0, transpose_y = attn_35_transpose_y_0, x = var_2115, y = var_2119)[name = tensor("attn_35")]; - tensor var_2123 = const()[name = tensor("op_2123"), val = tensor([2, 1280, 1, -1])]; - tensor input_165 = reshape(shape = var_2123, x = attn_35)[name = tensor("input_165")]; - tensor var_2128 = const()[name = tensor("op_2128"), val = tensor([1, 1])]; - tensor var_2130 = const()[name = tensor("op_2130"), val = tensor([1, 1])]; - tensor var_2132_pad_type_0 = const()[name = tensor("op_2132_pad_type_0"), val = tensor("custom")]; - tensor var_2132_pad_0 = const()[name = tensor("op_2132_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2132 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_bias, dilations = var_2130, groups = var_1193, pad = var_2132_pad_0, pad_type = var_2132_pad_type_0, strides = var_2128, weight = down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_weight, x = input_165)[name = tensor("op_2132")]; - tensor inputs_53 = add(x = var_2132, y = inputs_51)[name = tensor("inputs_53")]; - tensor var_2136 = const()[name = tensor("op_2136"), val = tensor([1])]; - tensor channels_mean_53 = reduce_mean(axes = var_2136, keep_dims = var_1188, x = inputs_53)[name = tensor("channels_mean_53")]; - tensor zero_mean_53 = sub(x = inputs_53, y = channels_mean_53)[name = tensor("zero_mean_53")]; - tensor zero_mean_sq_53 = mul(x = zero_mean_53, y = zero_mean_53)[name = tensor("zero_mean_sq_53")]; - tensor var_2140 = const()[name = tensor("op_2140"), val = tensor([1])]; - tensor var_2141 = reduce_mean(axes = var_2140, keep_dims = var_1188, x = zero_mean_sq_53)[name = tensor("op_2141")]; - tensor var_2142 = const()[name = tensor("op_2142"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2143 = add(x = var_2141, y = var_2142)[name = tensor("op_2143")]; - tensor denom_53_epsilon_0 = const()[name = tensor("denom_53_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_53 = rsqrt(epsilon = denom_53_epsilon_0, x = var_2143)[name = tensor("denom_53")]; - tensor out_53 = mul(x = zero_mean_53, y = denom_53)[name = tensor("out_53")]; - tensor var_2147 = const()[name = tensor("op_2147"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267870208)))]; - tensor var_2148 = add(x = out_53, y = var_2147)[name = tensor("op_2148")]; - tensor var_2150 = const()[name = tensor("op_2150"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267875392)))]; - tensor input_167 = mul(x = var_2148, y = var_2150)[name = tensor("input_167")]; - tensor var_2158 = const()[name = tensor("op_2158"), val = tensor([1, 1])]; - tensor var_2160 = const()[name = tensor("op_2160"), val = tensor([1, 1])]; - tensor var_2162_pad_type_0 = const()[name = tensor("op_2162_pad_type_0"), val = tensor("custom")]; - tensor var_2162_pad_0 = const()[name = tensor("op_2162_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2162 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_bias, dilations = var_2160, groups = var_1193, pad = var_2162_pad_0, pad_type = var_2162_pad_type_0, strides = var_2158, weight = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_weight, x = input_167)[name = tensor("op_2162")]; - tensor var_2163_split_sizes_0 = const()[name = tensor("op_2163_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_2163_axis_0 = const()[name = tensor("op_2163_axis_0"), val = tensor(1)]; - tensor var_2163_0, tensor var_2163_1 = split(axis = var_2163_axis_0, split_sizes = var_2163_split_sizes_0, x = var_2162)[name = tensor("op_2163")]; - tensor var_2165_mode_0 = const()[name = tensor("op_2165_mode_0"), val = tensor("EXACT")]; - tensor var_2165 = gelu(mode = var_2165_mode_0, x = var_2163_1)[name = tensor("op_2165")]; - tensor input_169 = mul(x = var_2163_0, y = var_2165)[name = tensor("input_169")]; - tensor var_2169 = const()[name = tensor("op_2169"), val = tensor([1, 1])]; - tensor var_2171 = const()[name = tensor("op_2171"), val = tensor([1, 1])]; - tensor var_2173_pad_type_0 = const()[name = tensor("op_2173_pad_type_0"), val = tensor("custom")]; - tensor var_2173_pad_0 = const()[name = tensor("op_2173_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2173 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_bias, dilations = var_2171, groups = var_1193, pad = var_2173_pad_0, pad_type = var_2173_pad_type_0, strides = var_2169, weight = down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_weight, x = input_169)[name = tensor("op_2173")]; - tensor inputs_55 = add(x = var_2173, y = inputs_53)[name = tensor("inputs_55")]; - tensor var_2183 = const()[name = tensor("op_2183"), val = tensor([1])]; - tensor channels_mean_55 = reduce_mean(axes = var_2183, keep_dims = var_1188, x = inputs_55)[name = tensor("channels_mean_55")]; - tensor zero_mean_55 = sub(x = inputs_55, y = channels_mean_55)[name = tensor("zero_mean_55")]; - tensor zero_mean_sq_55 = mul(x = zero_mean_55, y = zero_mean_55)[name = tensor("zero_mean_sq_55")]; - tensor var_2187 = const()[name = tensor("op_2187"), val = tensor([1])]; - tensor var_2188 = reduce_mean(axes = var_2187, keep_dims = var_1188, x = zero_mean_sq_55)[name = tensor("op_2188")]; - tensor var_2189 = const()[name = tensor("op_2189"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2190 = add(x = var_2188, y = var_2189)[name = tensor("op_2190")]; - tensor denom_55_epsilon_0 = const()[name = tensor("denom_55_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_55 = rsqrt(epsilon = denom_55_epsilon_0, x = var_2190)[name = tensor("denom_55")]; - tensor out_55 = mul(x = zero_mean_55, y = denom_55)[name = tensor("out_55")]; - tensor var_2194 = const()[name = tensor("op_2194"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267880576)))]; - tensor var_2195 = add(x = out_55, y = var_2194)[name = tensor("op_2195")]; - tensor var_2197 = const()[name = tensor("op_2197"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267885760)))]; - tensor hidden_states_95 = mul(x = var_2195, y = var_2197)[name = tensor("hidden_states_95")]; - tensor var_2204 = const()[name = tensor("op_2204"), val = tensor([1, 1])]; - tensor var_2206 = const()[name = tensor("op_2206"), val = tensor([1, 1])]; + tensor attn_35_cast = matmul(transpose_x = attn_35_transpose_x_0, transpose_y = attn_35_transpose_y_0, x = var_2053_cast, y = var_2057_cast)[name = tensor("attn_35_cast")]; + tensor var_2061 = const()[name = tensor("op_2061"), val = tensor([2, 1280, 1, -1])]; + tensor input_165_cast = reshape(shape = var_2061, x = attn_35_cast)[name = tensor("input_165_cast")]; + tensor var_2066 = const()[name = tensor("op_2066"), val = tensor([1, 1])]; + tensor var_2068 = const()[name = tensor("op_2068"), val = tensor([1, 1])]; + tensor var_2070_pad_type_0 = const()[name = tensor("op_2070_pad_type_0"), val = tensor("custom")]; + tensor var_2070_pad_0 = const()[name = tensor("op_2070_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503101056)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(506377920)))]; + tensor var_2070_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_2068, groups = var_31, pad = var_2070_pad_0, pad_type = var_2070_pad_type_0, strides = var_2066, weight = unet_down_blocks_2_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16, x = input_165_cast)[name = tensor("op_2070_cast")]; + tensor inputs_53_cast = add(x = var_2070_cast, y = inputs_51_cast)[name = tensor("inputs_53_cast")]; + tensor var_2074 = const()[name = tensor("op_2074"), val = tensor([1])]; + tensor channels_mean_53_cast = reduce_mean(axes = var_2074, keep_dims = var_23, x = inputs_53_cast)[name = tensor("channels_mean_53_cast")]; + tensor zero_mean_53_cast = sub(x = inputs_53_cast, y = channels_mean_53_cast)[name = tensor("zero_mean_53_cast")]; + tensor zero_mean_sq_53_cast = mul(x = zero_mean_53_cast, y = zero_mean_53_cast)[name = tensor("zero_mean_sq_53_cast")]; + tensor var_2078 = const()[name = tensor("op_2078"), val = tensor([1])]; + tensor var_2079_cast = reduce_mean(axes = var_2078, keep_dims = var_23, x = zero_mean_sq_53_cast)[name = tensor("op_2079_cast")]; + tensor var_2080_to_fp16 = const()[name = tensor("op_2080_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2081_cast = add(x = var_2079_cast, y = var_2080_to_fp16)[name = tensor("op_2081_cast")]; + tensor denom_53_epsilon_0_to_fp16 = const()[name = tensor("denom_53_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_53_cast = rsqrt(epsilon = denom_53_epsilon_0_to_fp16, x = var_2081_cast)[name = tensor("denom_53_cast")]; + tensor out_53_cast = mul(x = zero_mean_53_cast, y = denom_53_cast)[name = tensor("out_53_cast")]; + tensor var_2085_to_fp16 = const()[name = tensor("op_2085_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(506380544)))]; + tensor var_2086_cast = add(x = out_53_cast, y = var_2085_to_fp16)[name = tensor("op_2086_cast")]; + tensor var_2088_to_fp16 = const()[name = tensor("op_2088_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(506383168)))]; + tensor input_167_cast = mul(x = var_2086_cast, y = var_2088_to_fp16)[name = tensor("input_167_cast")]; + tensor var_2096 = const()[name = tensor("op_2096"), val = tensor([1, 1])]; + tensor var_2098 = const()[name = tensor("op_2098"), val = tensor([1, 1])]; + tensor var_2100_pad_type_0 = const()[name = tensor("op_2100_pad_type_0"), val = tensor("custom")]; + tensor var_2100_pad_0 = const()[name = tensor("op_2100_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(506385792)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(532600256)))]; + tensor var_2100_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_2098, groups = var_31, pad = var_2100_pad_0, pad_type = var_2100_pad_type_0, strides = var_2096, weight = unet_down_blocks_2_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16, x = input_167_cast)[name = tensor("op_2100_cast")]; + tensor var_2101_split_sizes_0 = const()[name = tensor("op_2101_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2101_axis_0 = const()[name = tensor("op_2101_axis_0"), val = tensor(1)]; + tensor var_2101_cast_0, tensor var_2101_cast_1 = split(axis = var_2101_axis_0, split_sizes = var_2101_split_sizes_0, x = var_2100_cast)[name = tensor("op_2101_cast")]; + tensor var_2103_mode_0 = const()[name = tensor("op_2103_mode_0"), val = tensor("EXACT")]; + tensor var_2103_cast = gelu(mode = var_2103_mode_0, x = var_2101_cast_1)[name = tensor("op_2103_cast")]; + tensor input_169_cast = mul(x = var_2101_cast_0, y = var_2103_cast)[name = tensor("input_169_cast")]; + tensor var_2107 = const()[name = tensor("op_2107"), val = tensor([1, 1])]; + tensor var_2109 = const()[name = tensor("op_2109"), val = tensor([1, 1])]; + tensor var_2111_pad_type_0 = const()[name = tensor("op_2111_pad_type_0"), val = tensor("custom")]; + tensor var_2111_pad_0 = const()[name = tensor("op_2111_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(532620800)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(545728064)))]; + tensor var_2111_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_2109, groups = var_31, pad = var_2111_pad_0, pad_type = var_2111_pad_type_0, strides = var_2107, weight = unet_down_blocks_2_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16, x = input_169_cast)[name = tensor("op_2111_cast")]; + tensor inputs_55_cast = add(x = var_2111_cast, y = inputs_53_cast)[name = tensor("inputs_55_cast")]; + tensor var_2121 = const()[name = tensor("op_2121"), val = tensor([1])]; + tensor channels_mean_55_cast = reduce_mean(axes = var_2121, keep_dims = var_23, x = inputs_55_cast)[name = tensor("channels_mean_55_cast")]; + tensor zero_mean_55_cast = sub(x = inputs_55_cast, y = channels_mean_55_cast)[name = tensor("zero_mean_55_cast")]; + tensor zero_mean_sq_55_cast = mul(x = zero_mean_55_cast, y = zero_mean_55_cast)[name = tensor("zero_mean_sq_55_cast")]; + tensor var_2125 = const()[name = tensor("op_2125"), val = tensor([1])]; + tensor var_2126_cast = reduce_mean(axes = var_2125, keep_dims = var_23, x = zero_mean_sq_55_cast)[name = tensor("op_2126_cast")]; + tensor var_2127_to_fp16 = const()[name = tensor("op_2127_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2128_cast = add(x = var_2126_cast, y = var_2127_to_fp16)[name = tensor("op_2128_cast")]; + tensor denom_55_epsilon_0_to_fp16 = const()[name = tensor("denom_55_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_55_cast = rsqrt(epsilon = denom_55_epsilon_0_to_fp16, x = var_2128_cast)[name = tensor("denom_55_cast")]; + tensor out_55_cast = mul(x = zero_mean_55_cast, y = denom_55_cast)[name = tensor("out_55_cast")]; + tensor var_2132_to_fp16 = const()[name = tensor("op_2132_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(545730688)))]; + tensor var_2133_cast = add(x = out_55_cast, y = var_2132_to_fp16)[name = tensor("op_2133_cast")]; + tensor var_2135_to_fp16 = const()[name = tensor("op_2135_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(545733312)))]; + tensor hidden_states_95_cast = mul(x = var_2133_cast, y = var_2135_to_fp16)[name = tensor("hidden_states_95_cast")]; + tensor var_2142 = const()[name = tensor("op_2142"), val = tensor([1, 1])]; + tensor var_2144 = const()[name = tensor("op_2144"), val = tensor([1, 1])]; tensor q_37_pad_type_0 = const()[name = tensor("q_37_pad_type_0"), val = tensor("custom")]; tensor q_37_pad_0 = const()[name = tensor("q_37_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_37 = conv(dilations = var_2206, groups = var_1193, pad = q_37_pad_0, pad_type = q_37_pad_type_0, strides = var_2204, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_q_weight, x = hidden_states_95)[name = tensor("q_37")]; - tensor var_2210 = const()[name = tensor("op_2210"), val = tensor([1, 1])]; - tensor var_2212 = const()[name = tensor("op_2212"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(545735936)))]; + tensor q_37_cast = conv(dilations = var_2144, groups = var_31, pad = q_37_pad_0, pad_type = q_37_pad_type_0, strides = var_2142, weight = unet_down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16, x = hidden_states_95_cast)[name = tensor("q_37_cast")]; + tensor var_2148 = const()[name = tensor("op_2148"), val = tensor([1, 1])]; + tensor var_2150 = const()[name = tensor("op_2150"), val = tensor([1, 1])]; tensor k_37_pad_type_0 = const()[name = tensor("k_37_pad_type_0"), val = tensor("custom")]; tensor k_37_pad_0 = const()[name = tensor("k_37_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_37 = conv(dilations = var_2212, groups = var_1193, pad = k_37_pad_0, pad_type = k_37_pad_type_0, strides = var_2210, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_k_weight, x = hidden_states_95)[name = tensor("k_37")]; - tensor var_2216 = const()[name = tensor("op_2216"), val = tensor([1, 1])]; - tensor var_2218 = const()[name = tensor("op_2218"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(549012800)))]; + tensor k_37_cast = conv(dilations = var_2150, groups = var_31, pad = k_37_pad_0, pad_type = k_37_pad_type_0, strides = var_2148, weight = unet_down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16, x = hidden_states_95_cast)[name = tensor("k_37_cast")]; + tensor var_2154 = const()[name = tensor("op_2154"), val = tensor([1, 1])]; + tensor var_2156 = const()[name = tensor("op_2156"), val = tensor([1, 1])]; tensor v_37_pad_type_0 = const()[name = tensor("v_37_pad_type_0"), val = tensor("custom")]; tensor v_37_pad_0 = const()[name = tensor("v_37_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_37 = conv(dilations = var_2218, groups = var_1193, pad = v_37_pad_0, pad_type = v_37_pad_type_0, strides = var_2216, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_v_weight, x = hidden_states_95)[name = tensor("v_37")]; - tensor var_2222 = const()[name = tensor("op_2222"), val = tensor([2, 20, 64, -1])]; - tensor var_2223 = reshape(shape = var_2222, x = q_37)[name = tensor("op_2223")]; - tensor var_2224 = const()[name = tensor("op_2224"), val = tensor([2, 20, 64, -1])]; - tensor var_2225 = reshape(shape = var_2224, x = k_37)[name = tensor("op_2225")]; - tensor var_2226 = const()[name = tensor("op_2226"), val = tensor([2, 20, 64, -1])]; - tensor var_2227 = reshape(shape = var_2226, x = v_37)[name = tensor("op_2227")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(552289664)))]; + tensor v_37_cast = conv(dilations = var_2156, groups = var_31, pad = v_37_pad_0, pad_type = v_37_pad_type_0, strides = var_2154, weight = unet_down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16, x = hidden_states_95_cast)[name = tensor("v_37_cast")]; + tensor var_2160 = const()[name = tensor("op_2160"), val = tensor([2, 20, 64, -1])]; + tensor var_2161_cast = reshape(shape = var_2160, x = q_37_cast)[name = tensor("op_2161_cast")]; + tensor var_2162 = const()[name = tensor("op_2162"), val = tensor([2, 20, 64, -1])]; + tensor var_2163_cast = reshape(shape = var_2162, x = k_37_cast)[name = tensor("op_2163_cast")]; + tensor var_2164 = const()[name = tensor("op_2164"), val = tensor([2, 20, 64, -1])]; + tensor var_2165_cast = reshape(shape = var_2164, x = v_37_cast)[name = tensor("op_2165_cast")]; tensor attn_weights_73_transpose_x_0 = const()[name = tensor("attn_weights_73_transpose_x_0"), val = tensor(true)]; tensor attn_weights_73_transpose_y_0 = const()[name = tensor("attn_weights_73_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_73 = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2223, y = var_2225)[name = tensor("attn_weights_73")]; - tensor attn_weights_75 = mul(x = attn_weights_73, y = var_1184)[name = tensor("attn_weights_75")]; - tensor var_2231 = softmax(axis = var_1177, x = attn_weights_75)[name = tensor("op_2231")]; + tensor attn_weights_73_cast = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2161_cast, y = var_2163_cast)[name = tensor("attn_weights_73_cast")]; + tensor attn_weights_75_cast = mul(x = attn_weights_73_cast, y = var_12_to_fp16)[name = tensor("attn_weights_75_cast")]; + tensor var_2169_cast = softmax(axis = var_18, x = attn_weights_75_cast)[name = tensor("op_2169_cast")]; tensor attn_37_transpose_x_0 = const()[name = tensor("attn_37_transpose_x_0"), val = tensor(false)]; tensor attn_37_transpose_y_0 = const()[name = tensor("attn_37_transpose_y_0"), val = tensor(true)]; - tensor attn_37 = matmul(transpose_x = attn_37_transpose_x_0, transpose_y = attn_37_transpose_y_0, x = var_2227, y = var_2231)[name = tensor("attn_37")]; - tensor var_2235 = const()[name = tensor("op_2235"), val = tensor([2, 1280, 1, -1])]; - tensor input_171 = reshape(shape = var_2235, x = attn_37)[name = tensor("input_171")]; - tensor var_2240 = const()[name = tensor("op_2240"), val = tensor([1, 1])]; - tensor var_2242 = const()[name = tensor("op_2242"), val = tensor([1, 1])]; - tensor var_2244_pad_type_0 = const()[name = tensor("op_2244_pad_type_0"), val = tensor("custom")]; - tensor var_2244_pad_0 = const()[name = tensor("op_2244_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2244 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_bias, dilations = var_2242, groups = var_1193, pad = var_2244_pad_0, pad_type = var_2244_pad_type_0, strides = var_2240, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_weight, x = input_171)[name = tensor("op_2244")]; - tensor inputs_57 = add(x = var_2244, y = inputs_55)[name = tensor("inputs_57")]; - tensor var_2248 = const()[name = tensor("op_2248"), val = tensor([1])]; - tensor channels_mean_57 = reduce_mean(axes = var_2248, keep_dims = var_1188, x = inputs_57)[name = tensor("channels_mean_57")]; - tensor zero_mean_57 = sub(x = inputs_57, y = channels_mean_57)[name = tensor("zero_mean_57")]; - tensor zero_mean_sq_57 = mul(x = zero_mean_57, y = zero_mean_57)[name = tensor("zero_mean_sq_57")]; - tensor var_2252 = const()[name = tensor("op_2252"), val = tensor([1])]; - tensor var_2253 = reduce_mean(axes = var_2252, keep_dims = var_1188, x = zero_mean_sq_57)[name = tensor("op_2253")]; - tensor var_2254 = const()[name = tensor("op_2254"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2255 = add(x = var_2253, y = var_2254)[name = tensor("op_2255")]; - tensor denom_57_epsilon_0 = const()[name = tensor("denom_57_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_57 = rsqrt(epsilon = denom_57_epsilon_0, x = var_2255)[name = tensor("denom_57")]; - tensor out_57 = mul(x = zero_mean_57, y = denom_57)[name = tensor("out_57")]; - tensor var_2259 = const()[name = tensor("op_2259"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267890944)))]; - tensor var_2260 = add(x = out_57, y = var_2259)[name = tensor("op_2260")]; - tensor var_2262 = const()[name = tensor("op_2262"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267896128)))]; - tensor hidden_states_97 = mul(x = var_2260, y = var_2262)[name = tensor("hidden_states_97")]; - tensor var_2269 = const()[name = tensor("op_2269"), val = tensor([1, 1])]; - tensor var_2271 = const()[name = tensor("op_2271"), val = tensor([1, 1])]; + tensor attn_37_cast = matmul(transpose_x = attn_37_transpose_x_0, transpose_y = attn_37_transpose_y_0, x = var_2165_cast, y = var_2169_cast)[name = tensor("attn_37_cast")]; + tensor var_2173 = const()[name = tensor("op_2173"), val = tensor([2, 1280, 1, -1])]; + tensor input_171_cast = reshape(shape = var_2173, x = attn_37_cast)[name = tensor("input_171_cast")]; + tensor var_2178 = const()[name = tensor("op_2178"), val = tensor([1, 1])]; + tensor var_2180 = const()[name = tensor("op_2180"), val = tensor([1, 1])]; + tensor var_2182_pad_type_0 = const()[name = tensor("op_2182_pad_type_0"), val = tensor("custom")]; + tensor var_2182_pad_0 = const()[name = tensor("op_2182_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(555566528)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(558843392)))]; + tensor var_2182_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_2180, groups = var_31, pad = var_2182_pad_0, pad_type = var_2182_pad_type_0, strides = var_2178, weight = unet_down_blocks_2_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16, x = input_171_cast)[name = tensor("op_2182_cast")]; + tensor inputs_57_cast = add(x = var_2182_cast, y = inputs_55_cast)[name = tensor("inputs_57_cast")]; + tensor var_2186 = const()[name = tensor("op_2186"), val = tensor([1])]; + tensor channels_mean_57_cast = reduce_mean(axes = var_2186, keep_dims = var_23, x = inputs_57_cast)[name = tensor("channels_mean_57_cast")]; + tensor zero_mean_57_cast = sub(x = inputs_57_cast, y = channels_mean_57_cast)[name = tensor("zero_mean_57_cast")]; + tensor zero_mean_sq_57_cast = mul(x = zero_mean_57_cast, y = zero_mean_57_cast)[name = tensor("zero_mean_sq_57_cast")]; + tensor var_2190 = const()[name = tensor("op_2190"), val = tensor([1])]; + tensor var_2191_cast = reduce_mean(axes = var_2190, keep_dims = var_23, x = zero_mean_sq_57_cast)[name = tensor("op_2191_cast")]; + tensor var_2192_to_fp16 = const()[name = tensor("op_2192_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2193_cast = add(x = var_2191_cast, y = var_2192_to_fp16)[name = tensor("op_2193_cast")]; + tensor denom_57_epsilon_0_to_fp16 = const()[name = tensor("denom_57_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_57_cast = rsqrt(epsilon = denom_57_epsilon_0_to_fp16, x = var_2193_cast)[name = tensor("denom_57_cast")]; + tensor out_57_cast = mul(x = zero_mean_57_cast, y = denom_57_cast)[name = tensor("out_57_cast")]; + tensor var_2197_to_fp16 = const()[name = tensor("op_2197_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(558846016)))]; + tensor var_2198_cast = add(x = out_57_cast, y = var_2197_to_fp16)[name = tensor("op_2198_cast")]; + tensor var_2200_to_fp16 = const()[name = tensor("op_2200_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(558848640)))]; + tensor hidden_states_97_cast = mul(x = var_2198_cast, y = var_2200_to_fp16)[name = tensor("hidden_states_97_cast")]; + tensor var_2207 = const()[name = tensor("op_2207"), val = tensor([1, 1])]; + tensor var_2209 = const()[name = tensor("op_2209"), val = tensor([1, 1])]; tensor q_39_pad_type_0 = const()[name = tensor("q_39_pad_type_0"), val = tensor("custom")]; tensor q_39_pad_0 = const()[name = tensor("q_39_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_39 = conv(dilations = var_2271, groups = var_1193, pad = q_39_pad_0, pad_type = q_39_pad_type_0, strides = var_2269, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_q_weight, x = hidden_states_97)[name = tensor("q_39")]; - tensor var_2275 = const()[name = tensor("op_2275"), val = tensor([1, 1])]; - tensor var_2277 = const()[name = tensor("op_2277"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(558851264)))]; + tensor q_39_cast = conv(dilations = var_2209, groups = var_31, pad = q_39_pad_0, pad_type = q_39_pad_type_0, strides = var_2207, weight = unet_down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16, x = hidden_states_97_cast)[name = tensor("q_39_cast")]; + tensor var_2213 = const()[name = tensor("op_2213"), val = tensor([1, 1])]; + tensor var_2215 = const()[name = tensor("op_2215"), val = tensor([1, 1])]; tensor k_39_pad_type_0 = const()[name = tensor("k_39_pad_type_0"), val = tensor("custom")]; tensor k_39_pad_0 = const()[name = tensor("k_39_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_39 = conv(dilations = var_2277, groups = var_1193, pad = k_39_pad_0, pad_type = k_39_pad_type_0, strides = var_2275, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_39")]; - tensor var_2281 = const()[name = tensor("op_2281"), val = tensor([1, 1])]; - tensor var_2283 = const()[name = tensor("op_2283"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(562128128)))]; + tensor k_39_cast = conv(dilations = var_2215, groups = var_31, pad = k_39_pad_0, pad_type = k_39_pad_type_0, strides = var_2213, weight = unet_down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_39_cast")]; + tensor var_2219 = const()[name = tensor("op_2219"), val = tensor([1, 1])]; + tensor var_2221 = const()[name = tensor("op_2221"), val = tensor([1, 1])]; tensor v_39_pad_type_0 = const()[name = tensor("v_39_pad_type_0"), val = tensor("custom")]; tensor v_39_pad_0 = const()[name = tensor("v_39_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_39 = conv(dilations = var_2283, groups = var_1193, pad = v_39_pad_0, pad_type = v_39_pad_type_0, strides = var_2281, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_39")]; - tensor var_2287 = const()[name = tensor("op_2287"), val = tensor([2, 20, 64, -1])]; - tensor var_2288 = reshape(shape = var_2287, x = q_39)[name = tensor("op_2288")]; - tensor var_2289 = const()[name = tensor("op_2289"), val = tensor([2, 20, 64, -1])]; - tensor var_2290 = reshape(shape = var_2289, x = k_39)[name = tensor("op_2290")]; - tensor var_2291 = const()[name = tensor("op_2291"), val = tensor([2, 20, 64, -1])]; - tensor var_2292 = reshape(shape = var_2291, x = v_39)[name = tensor("op_2292")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(567371072)))]; + tensor v_39_cast = conv(dilations = var_2221, groups = var_31, pad = v_39_pad_0, pad_type = v_39_pad_type_0, strides = var_2219, weight = unet_down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_39_cast")]; + tensor var_2225 = const()[name = tensor("op_2225"), val = tensor([2, 20, 64, -1])]; + tensor var_2226_cast = reshape(shape = var_2225, x = q_39_cast)[name = tensor("op_2226_cast")]; + tensor var_2227 = const()[name = tensor("op_2227"), val = tensor([2, 20, 64, -1])]; + tensor var_2228_cast = reshape(shape = var_2227, x = k_39_cast)[name = tensor("op_2228_cast")]; + tensor var_2229 = const()[name = tensor("op_2229"), val = tensor([2, 20, 64, -1])]; + tensor var_2230_cast = reshape(shape = var_2229, x = v_39_cast)[name = tensor("op_2230_cast")]; tensor attn_weights_77_transpose_x_0 = const()[name = tensor("attn_weights_77_transpose_x_0"), val = tensor(true)]; tensor attn_weights_77_transpose_y_0 = const()[name = tensor("attn_weights_77_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_77 = matmul(transpose_x = attn_weights_77_transpose_x_0, transpose_y = attn_weights_77_transpose_y_0, x = var_2288, y = var_2290)[name = tensor("attn_weights_77")]; - tensor attn_weights_79 = mul(x = attn_weights_77, y = var_1184)[name = tensor("attn_weights_79")]; - tensor var_2296 = softmax(axis = var_1177, x = attn_weights_79)[name = tensor("op_2296")]; + tensor attn_weights_77_cast = matmul(transpose_x = attn_weights_77_transpose_x_0, transpose_y = attn_weights_77_transpose_y_0, x = var_2226_cast, y = var_2228_cast)[name = tensor("attn_weights_77_cast")]; + tensor attn_weights_79_cast = mul(x = attn_weights_77_cast, y = var_12_to_fp16)[name = tensor("attn_weights_79_cast")]; + tensor var_2234_cast = softmax(axis = var_18, x = attn_weights_79_cast)[name = tensor("op_2234_cast")]; tensor attn_39_transpose_x_0 = const()[name = tensor("attn_39_transpose_x_0"), val = tensor(false)]; tensor attn_39_transpose_y_0 = const()[name = tensor("attn_39_transpose_y_0"), val = tensor(true)]; - tensor attn_39 = matmul(transpose_x = attn_39_transpose_x_0, transpose_y = attn_39_transpose_y_0, x = var_2292, y = var_2296)[name = tensor("attn_39")]; - tensor var_2300 = const()[name = tensor("op_2300"), val = tensor([2, 1280, 1, -1])]; - tensor input_173 = reshape(shape = var_2300, x = attn_39)[name = tensor("input_173")]; - tensor var_2305 = const()[name = tensor("op_2305"), val = tensor([1, 1])]; - tensor var_2307 = const()[name = tensor("op_2307"), val = tensor([1, 1])]; - tensor var_2309_pad_type_0 = const()[name = tensor("op_2309_pad_type_0"), val = tensor("custom")]; - tensor var_2309_pad_0 = const()[name = tensor("op_2309_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2309 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_bias, dilations = var_2307, groups = var_1193, pad = var_2309_pad_0, pad_type = var_2309_pad_type_0, strides = var_2305, weight = down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_weight, x = input_173)[name = tensor("op_2309")]; - tensor inputs_59 = add(x = var_2309, y = inputs_57)[name = tensor("inputs_59")]; - tensor var_2313 = const()[name = tensor("op_2313"), val = tensor([1])]; - tensor channels_mean_59 = reduce_mean(axes = var_2313, keep_dims = var_1188, x = inputs_59)[name = tensor("channels_mean_59")]; - tensor zero_mean_59 = sub(x = inputs_59, y = channels_mean_59)[name = tensor("zero_mean_59")]; - tensor zero_mean_sq_59 = mul(x = zero_mean_59, y = zero_mean_59)[name = tensor("zero_mean_sq_59")]; - tensor var_2317 = const()[name = tensor("op_2317"), val = tensor([1])]; - tensor var_2318 = reduce_mean(axes = var_2317, keep_dims = var_1188, x = zero_mean_sq_59)[name = tensor("op_2318")]; - tensor var_2319 = const()[name = tensor("op_2319"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2320 = add(x = var_2318, y = var_2319)[name = tensor("op_2320")]; - tensor denom_59_epsilon_0 = const()[name = tensor("denom_59_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_59 = rsqrt(epsilon = denom_59_epsilon_0, x = var_2320)[name = tensor("denom_59")]; - tensor out_59 = mul(x = zero_mean_59, y = denom_59)[name = tensor("out_59")]; - tensor var_2324 = const()[name = tensor("op_2324"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267901312)))]; - tensor var_2325 = add(x = out_59, y = var_2324)[name = tensor("op_2325")]; - tensor var_2327 = const()[name = tensor("op_2327"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267906496)))]; - tensor input_175 = mul(x = var_2325, y = var_2327)[name = tensor("input_175")]; - tensor var_2335 = const()[name = tensor("op_2335"), val = tensor([1, 1])]; - tensor var_2337 = const()[name = tensor("op_2337"), val = tensor([1, 1])]; - tensor var_2339_pad_type_0 = const()[name = tensor("op_2339_pad_type_0"), val = tensor("custom")]; - tensor var_2339_pad_0 = const()[name = tensor("op_2339_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2339 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_bias, dilations = var_2337, groups = var_1193, pad = var_2339_pad_0, pad_type = var_2339_pad_type_0, strides = var_2335, weight = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_weight, x = input_175)[name = tensor("op_2339")]; - tensor var_2340_split_sizes_0 = const()[name = tensor("op_2340_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_2340_axis_0 = const()[name = tensor("op_2340_axis_0"), val = tensor(1)]; - tensor var_2340_0, tensor var_2340_1 = split(axis = var_2340_axis_0, split_sizes = var_2340_split_sizes_0, x = var_2339)[name = tensor("op_2340")]; - tensor var_2342_mode_0 = const()[name = tensor("op_2342_mode_0"), val = tensor("EXACT")]; - tensor var_2342 = gelu(mode = var_2342_mode_0, x = var_2340_1)[name = tensor("op_2342")]; - tensor input_177 = mul(x = var_2340_0, y = var_2342)[name = tensor("input_177")]; - tensor var_2346 = const()[name = tensor("op_2346"), val = tensor([1, 1])]; - tensor var_2348 = const()[name = tensor("op_2348"), val = tensor([1, 1])]; - tensor var_2350_pad_type_0 = const()[name = tensor("op_2350_pad_type_0"), val = tensor("custom")]; - tensor var_2350_pad_0 = const()[name = tensor("op_2350_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2350 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_bias, dilations = var_2348, groups = var_1193, pad = var_2350_pad_0, pad_type = var_2350_pad_type_0, strides = var_2346, weight = down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_weight, x = input_177)[name = tensor("op_2350")]; - tensor inputs_61 = add(x = var_2350, y = inputs_59)[name = tensor("inputs_61")]; - tensor var_2360 = const()[name = tensor("op_2360"), val = tensor([1])]; - tensor channels_mean_61 = reduce_mean(axes = var_2360, keep_dims = var_1188, x = inputs_61)[name = tensor("channels_mean_61")]; - tensor zero_mean_61 = sub(x = inputs_61, y = channels_mean_61)[name = tensor("zero_mean_61")]; - tensor zero_mean_sq_61 = mul(x = zero_mean_61, y = zero_mean_61)[name = tensor("zero_mean_sq_61")]; - tensor var_2364 = const()[name = tensor("op_2364"), val = tensor([1])]; - tensor var_2365 = reduce_mean(axes = var_2364, keep_dims = var_1188, x = zero_mean_sq_61)[name = tensor("op_2365")]; - tensor var_2366 = const()[name = tensor("op_2366"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2367 = add(x = var_2365, y = var_2366)[name = tensor("op_2367")]; - tensor denom_61_epsilon_0 = const()[name = tensor("denom_61_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_61 = rsqrt(epsilon = denom_61_epsilon_0, x = var_2367)[name = tensor("denom_61")]; - tensor out_61 = mul(x = zero_mean_61, y = denom_61)[name = tensor("out_61")]; - tensor var_2371 = const()[name = tensor("op_2371"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267911680)))]; - tensor var_2372 = add(x = out_61, y = var_2371)[name = tensor("op_2372")]; - tensor var_2374 = const()[name = tensor("op_2374"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267916864)))]; - tensor hidden_states_101 = mul(x = var_2372, y = var_2374)[name = tensor("hidden_states_101")]; - tensor var_2381 = const()[name = tensor("op_2381"), val = tensor([1, 1])]; - tensor var_2383 = const()[name = tensor("op_2383"), val = tensor([1, 1])]; + tensor attn_39_cast = matmul(transpose_x = attn_39_transpose_x_0, transpose_y = attn_39_transpose_y_0, x = var_2230_cast, y = var_2234_cast)[name = tensor("attn_39_cast")]; + tensor var_2238 = const()[name = tensor("op_2238"), val = tensor([2, 1280, 1, -1])]; + tensor input_173_cast = reshape(shape = var_2238, x = attn_39_cast)[name = tensor("input_173_cast")]; + tensor var_2243 = const()[name = tensor("op_2243"), val = tensor([1, 1])]; + tensor var_2245 = const()[name = tensor("op_2245"), val = tensor([1, 1])]; + tensor var_2247_pad_type_0 = const()[name = tensor("op_2247_pad_type_0"), val = tensor("custom")]; + tensor var_2247_pad_0 = const()[name = tensor("op_2247_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(572614016)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(575890880)))]; + tensor var_2247_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_2245, groups = var_31, pad = var_2247_pad_0, pad_type = var_2247_pad_type_0, strides = var_2243, weight = unet_down_blocks_2_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16, x = input_173_cast)[name = tensor("op_2247_cast")]; + tensor inputs_59_cast = add(x = var_2247_cast, y = inputs_57_cast)[name = tensor("inputs_59_cast")]; + tensor var_2251 = const()[name = tensor("op_2251"), val = tensor([1])]; + tensor channels_mean_59_cast = reduce_mean(axes = var_2251, keep_dims = var_23, x = inputs_59_cast)[name = tensor("channels_mean_59_cast")]; + tensor zero_mean_59_cast = sub(x = inputs_59_cast, y = channels_mean_59_cast)[name = tensor("zero_mean_59_cast")]; + tensor zero_mean_sq_59_cast = mul(x = zero_mean_59_cast, y = zero_mean_59_cast)[name = tensor("zero_mean_sq_59_cast")]; + tensor var_2255 = const()[name = tensor("op_2255"), val = tensor([1])]; + tensor var_2256_cast = reduce_mean(axes = var_2255, keep_dims = var_23, x = zero_mean_sq_59_cast)[name = tensor("op_2256_cast")]; + tensor var_2257_to_fp16 = const()[name = tensor("op_2257_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2258_cast = add(x = var_2256_cast, y = var_2257_to_fp16)[name = tensor("op_2258_cast")]; + tensor denom_59_epsilon_0_to_fp16 = const()[name = tensor("denom_59_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_59_cast = rsqrt(epsilon = denom_59_epsilon_0_to_fp16, x = var_2258_cast)[name = tensor("denom_59_cast")]; + tensor out_59_cast = mul(x = zero_mean_59_cast, y = denom_59_cast)[name = tensor("out_59_cast")]; + tensor var_2262_to_fp16 = const()[name = tensor("op_2262_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(575893504)))]; + tensor var_2263_cast = add(x = out_59_cast, y = var_2262_to_fp16)[name = tensor("op_2263_cast")]; + tensor var_2265_to_fp16 = const()[name = tensor("op_2265_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(575896128)))]; + tensor input_175_cast = mul(x = var_2263_cast, y = var_2265_to_fp16)[name = tensor("input_175_cast")]; + tensor var_2273 = const()[name = tensor("op_2273"), val = tensor([1, 1])]; + tensor var_2275 = const()[name = tensor("op_2275"), val = tensor([1, 1])]; + tensor var_2277_pad_type_0 = const()[name = tensor("op_2277_pad_type_0"), val = tensor("custom")]; + tensor var_2277_pad_0 = const()[name = tensor("op_2277_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(575898752)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602113216)))]; + tensor var_2277_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_2275, groups = var_31, pad = var_2277_pad_0, pad_type = var_2277_pad_type_0, strides = var_2273, weight = unet_down_blocks_2_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16, x = input_175_cast)[name = tensor("op_2277_cast")]; + tensor var_2278_split_sizes_0 = const()[name = tensor("op_2278_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2278_axis_0 = const()[name = tensor("op_2278_axis_0"), val = tensor(1)]; + tensor var_2278_cast_0, tensor var_2278_cast_1 = split(axis = var_2278_axis_0, split_sizes = var_2278_split_sizes_0, x = var_2277_cast)[name = tensor("op_2278_cast")]; + tensor var_2280_mode_0 = const()[name = tensor("op_2280_mode_0"), val = tensor("EXACT")]; + tensor var_2280_cast = gelu(mode = var_2280_mode_0, x = var_2278_cast_1)[name = tensor("op_2280_cast")]; + tensor input_177_cast = mul(x = var_2278_cast_0, y = var_2280_cast)[name = tensor("input_177_cast")]; + tensor var_2284 = const()[name = tensor("op_2284"), val = tensor([1, 1])]; + tensor var_2286 = const()[name = tensor("op_2286"), val = tensor([1, 1])]; + tensor var_2288_pad_type_0 = const()[name = tensor("op_2288_pad_type_0"), val = tensor("custom")]; + tensor var_2288_pad_0 = const()[name = tensor("op_2288_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602133760)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(615241024)))]; + tensor var_2288_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_2286, groups = var_31, pad = var_2288_pad_0, pad_type = var_2288_pad_type_0, strides = var_2284, weight = unet_down_blocks_2_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16, x = input_177_cast)[name = tensor("op_2288_cast")]; + tensor inputs_61_cast = add(x = var_2288_cast, y = inputs_59_cast)[name = tensor("inputs_61_cast")]; + tensor var_2298 = const()[name = tensor("op_2298"), val = tensor([1])]; + tensor channels_mean_61_cast = reduce_mean(axes = var_2298, keep_dims = var_23, x = inputs_61_cast)[name = tensor("channels_mean_61_cast")]; + tensor zero_mean_61_cast = sub(x = inputs_61_cast, y = channels_mean_61_cast)[name = tensor("zero_mean_61_cast")]; + tensor zero_mean_sq_61_cast = mul(x = zero_mean_61_cast, y = zero_mean_61_cast)[name = tensor("zero_mean_sq_61_cast")]; + tensor var_2302 = const()[name = tensor("op_2302"), val = tensor([1])]; + tensor var_2303_cast = reduce_mean(axes = var_2302, keep_dims = var_23, x = zero_mean_sq_61_cast)[name = tensor("op_2303_cast")]; + tensor var_2304_to_fp16 = const()[name = tensor("op_2304_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2305_cast = add(x = var_2303_cast, y = var_2304_to_fp16)[name = tensor("op_2305_cast")]; + tensor denom_61_epsilon_0_to_fp16 = const()[name = tensor("denom_61_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_61_cast = rsqrt(epsilon = denom_61_epsilon_0_to_fp16, x = var_2305_cast)[name = tensor("denom_61_cast")]; + tensor out_61_cast = mul(x = zero_mean_61_cast, y = denom_61_cast)[name = tensor("out_61_cast")]; + tensor var_2309_to_fp16 = const()[name = tensor("op_2309_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(615243648)))]; + tensor var_2310_cast = add(x = out_61_cast, y = var_2309_to_fp16)[name = tensor("op_2310_cast")]; + tensor var_2312_to_fp16 = const()[name = tensor("op_2312_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(615246272)))]; + tensor hidden_states_101_cast = mul(x = var_2310_cast, y = var_2312_to_fp16)[name = tensor("hidden_states_101_cast")]; + tensor var_2319 = const()[name = tensor("op_2319"), val = tensor([1, 1])]; + tensor var_2321 = const()[name = tensor("op_2321"), val = tensor([1, 1])]; tensor q_41_pad_type_0 = const()[name = tensor("q_41_pad_type_0"), val = tensor("custom")]; tensor q_41_pad_0 = const()[name = tensor("q_41_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_41 = conv(dilations = var_2383, groups = var_1193, pad = q_41_pad_0, pad_type = q_41_pad_type_0, strides = var_2381, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_q_weight, x = hidden_states_101)[name = tensor("q_41")]; - tensor var_2387 = const()[name = tensor("op_2387"), val = tensor([1, 1])]; - tensor var_2389 = const()[name = tensor("op_2389"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(615248896)))]; + tensor q_41_cast = conv(dilations = var_2321, groups = var_31, pad = q_41_pad_0, pad_type = q_41_pad_type_0, strides = var_2319, weight = unet_down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16, x = hidden_states_101_cast)[name = tensor("q_41_cast")]; + tensor var_2325 = const()[name = tensor("op_2325"), val = tensor([1, 1])]; + tensor var_2327 = const()[name = tensor("op_2327"), val = tensor([1, 1])]; tensor k_41_pad_type_0 = const()[name = tensor("k_41_pad_type_0"), val = tensor("custom")]; tensor k_41_pad_0 = const()[name = tensor("k_41_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_41 = conv(dilations = var_2389, groups = var_1193, pad = k_41_pad_0, pad_type = k_41_pad_type_0, strides = var_2387, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_k_weight, x = hidden_states_101)[name = tensor("k_41")]; - tensor var_2393 = const()[name = tensor("op_2393"), val = tensor([1, 1])]; - tensor var_2395 = const()[name = tensor("op_2395"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(618525760)))]; + tensor k_41_cast = conv(dilations = var_2327, groups = var_31, pad = k_41_pad_0, pad_type = k_41_pad_type_0, strides = var_2325, weight = unet_down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16, x = hidden_states_101_cast)[name = tensor("k_41_cast")]; + tensor var_2331 = const()[name = tensor("op_2331"), val = tensor([1, 1])]; + tensor var_2333 = const()[name = tensor("op_2333"), val = tensor([1, 1])]; tensor v_41_pad_type_0 = const()[name = tensor("v_41_pad_type_0"), val = tensor("custom")]; tensor v_41_pad_0 = const()[name = tensor("v_41_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_41 = conv(dilations = var_2395, groups = var_1193, pad = v_41_pad_0, pad_type = v_41_pad_type_0, strides = var_2393, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_v_weight, x = hidden_states_101)[name = tensor("v_41")]; - tensor var_2399 = const()[name = tensor("op_2399"), val = tensor([2, 20, 64, -1])]; - tensor var_2400 = reshape(shape = var_2399, x = q_41)[name = tensor("op_2400")]; - tensor var_2401 = const()[name = tensor("op_2401"), val = tensor([2, 20, 64, -1])]; - tensor var_2402 = reshape(shape = var_2401, x = k_41)[name = tensor("op_2402")]; - tensor var_2403 = const()[name = tensor("op_2403"), val = tensor([2, 20, 64, -1])]; - tensor var_2404 = reshape(shape = var_2403, x = v_41)[name = tensor("op_2404")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621802624)))]; + tensor v_41_cast = conv(dilations = var_2333, groups = var_31, pad = v_41_pad_0, pad_type = v_41_pad_type_0, strides = var_2331, weight = unet_down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16, x = hidden_states_101_cast)[name = tensor("v_41_cast")]; + tensor var_2337 = const()[name = tensor("op_2337"), val = tensor([2, 20, 64, -1])]; + tensor var_2338_cast = reshape(shape = var_2337, x = q_41_cast)[name = tensor("op_2338_cast")]; + tensor var_2339 = const()[name = tensor("op_2339"), val = tensor([2, 20, 64, -1])]; + tensor var_2340_cast = reshape(shape = var_2339, x = k_41_cast)[name = tensor("op_2340_cast")]; + tensor var_2341 = const()[name = tensor("op_2341"), val = tensor([2, 20, 64, -1])]; + tensor var_2342_cast = reshape(shape = var_2341, x = v_41_cast)[name = tensor("op_2342_cast")]; tensor attn_weights_81_transpose_x_0 = const()[name = tensor("attn_weights_81_transpose_x_0"), val = tensor(true)]; tensor attn_weights_81_transpose_y_0 = const()[name = tensor("attn_weights_81_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_81 = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_2400, y = var_2402)[name = tensor("attn_weights_81")]; - tensor attn_weights_83 = mul(x = attn_weights_81, y = var_1184)[name = tensor("attn_weights_83")]; - tensor var_2408 = softmax(axis = var_1177, x = attn_weights_83)[name = tensor("op_2408")]; + tensor attn_weights_81_cast = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_2338_cast, y = var_2340_cast)[name = tensor("attn_weights_81_cast")]; + tensor attn_weights_83_cast = mul(x = attn_weights_81_cast, y = var_12_to_fp16)[name = tensor("attn_weights_83_cast")]; + tensor var_2346_cast = softmax(axis = var_18, x = attn_weights_83_cast)[name = tensor("op_2346_cast")]; tensor attn_41_transpose_x_0 = const()[name = tensor("attn_41_transpose_x_0"), val = tensor(false)]; tensor attn_41_transpose_y_0 = const()[name = tensor("attn_41_transpose_y_0"), val = tensor(true)]; - tensor attn_41 = matmul(transpose_x = attn_41_transpose_x_0, transpose_y = attn_41_transpose_y_0, x = var_2404, y = var_2408)[name = tensor("attn_41")]; - tensor var_2412 = const()[name = tensor("op_2412"), val = tensor([2, 1280, 1, -1])]; - tensor input_179 = reshape(shape = var_2412, x = attn_41)[name = tensor("input_179")]; - tensor var_2417 = const()[name = tensor("op_2417"), val = tensor([1, 1])]; - tensor var_2419 = const()[name = tensor("op_2419"), val = tensor([1, 1])]; - tensor var_2421_pad_type_0 = const()[name = tensor("op_2421_pad_type_0"), val = tensor("custom")]; - tensor var_2421_pad_0 = const()[name = tensor("op_2421_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2421 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_bias, dilations = var_2419, groups = var_1193, pad = var_2421_pad_0, pad_type = var_2421_pad_type_0, strides = var_2417, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_weight, x = input_179)[name = tensor("op_2421")]; - tensor inputs_63 = add(x = var_2421, y = inputs_61)[name = tensor("inputs_63")]; - tensor var_2425 = const()[name = tensor("op_2425"), val = tensor([1])]; - tensor channels_mean_63 = reduce_mean(axes = var_2425, keep_dims = var_1188, x = inputs_63)[name = tensor("channels_mean_63")]; - tensor zero_mean_63 = sub(x = inputs_63, y = channels_mean_63)[name = tensor("zero_mean_63")]; - tensor zero_mean_sq_63 = mul(x = zero_mean_63, y = zero_mean_63)[name = tensor("zero_mean_sq_63")]; - tensor var_2429 = const()[name = tensor("op_2429"), val = tensor([1])]; - tensor var_2430 = reduce_mean(axes = var_2429, keep_dims = var_1188, x = zero_mean_sq_63)[name = tensor("op_2430")]; - tensor var_2431 = const()[name = tensor("op_2431"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2432 = add(x = var_2430, y = var_2431)[name = tensor("op_2432")]; - tensor denom_63_epsilon_0 = const()[name = tensor("denom_63_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_63 = rsqrt(epsilon = denom_63_epsilon_0, x = var_2432)[name = tensor("denom_63")]; - tensor out_63 = mul(x = zero_mean_63, y = denom_63)[name = tensor("out_63")]; - tensor var_2436 = const()[name = tensor("op_2436"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267922048)))]; - tensor var_2437 = add(x = out_63, y = var_2436)[name = tensor("op_2437")]; - tensor var_2439 = const()[name = tensor("op_2439"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267927232)))]; - tensor hidden_states_103 = mul(x = var_2437, y = var_2439)[name = tensor("hidden_states_103")]; - tensor var_2446 = const()[name = tensor("op_2446"), val = tensor([1, 1])]; - tensor var_2448 = const()[name = tensor("op_2448"), val = tensor([1, 1])]; + tensor attn_41_cast = matmul(transpose_x = attn_41_transpose_x_0, transpose_y = attn_41_transpose_y_0, x = var_2342_cast, y = var_2346_cast)[name = tensor("attn_41_cast")]; + tensor var_2350 = const()[name = tensor("op_2350"), val = tensor([2, 1280, 1, -1])]; + tensor input_179_cast = reshape(shape = var_2350, x = attn_41_cast)[name = tensor("input_179_cast")]; + tensor var_2355 = const()[name = tensor("op_2355"), val = tensor([1, 1])]; + tensor var_2357 = const()[name = tensor("op_2357"), val = tensor([1, 1])]; + tensor var_2359_pad_type_0 = const()[name = tensor("op_2359_pad_type_0"), val = tensor("custom")]; + tensor var_2359_pad_0 = const()[name = tensor("op_2359_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(625079488)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(628356352)))]; + tensor var_2359_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_2357, groups = var_31, pad = var_2359_pad_0, pad_type = var_2359_pad_type_0, strides = var_2355, weight = unet_down_blocks_2_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16, x = input_179_cast)[name = tensor("op_2359_cast")]; + tensor inputs_63_cast = add(x = var_2359_cast, y = inputs_61_cast)[name = tensor("inputs_63_cast")]; + tensor var_2363 = const()[name = tensor("op_2363"), val = tensor([1])]; + tensor channels_mean_63_cast = reduce_mean(axes = var_2363, keep_dims = var_23, x = inputs_63_cast)[name = tensor("channels_mean_63_cast")]; + tensor zero_mean_63_cast = sub(x = inputs_63_cast, y = channels_mean_63_cast)[name = tensor("zero_mean_63_cast")]; + tensor zero_mean_sq_63_cast = mul(x = zero_mean_63_cast, y = zero_mean_63_cast)[name = tensor("zero_mean_sq_63_cast")]; + tensor var_2367 = const()[name = tensor("op_2367"), val = tensor([1])]; + tensor var_2368_cast = reduce_mean(axes = var_2367, keep_dims = var_23, x = zero_mean_sq_63_cast)[name = tensor("op_2368_cast")]; + tensor var_2369_to_fp16 = const()[name = tensor("op_2369_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2370_cast = add(x = var_2368_cast, y = var_2369_to_fp16)[name = tensor("op_2370_cast")]; + tensor denom_63_epsilon_0_to_fp16 = const()[name = tensor("denom_63_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_63_cast = rsqrt(epsilon = denom_63_epsilon_0_to_fp16, x = var_2370_cast)[name = tensor("denom_63_cast")]; + tensor out_63_cast = mul(x = zero_mean_63_cast, y = denom_63_cast)[name = tensor("out_63_cast")]; + tensor var_2374_to_fp16 = const()[name = tensor("op_2374_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(628358976)))]; + tensor var_2375_cast = add(x = out_63_cast, y = var_2374_to_fp16)[name = tensor("op_2375_cast")]; + tensor var_2377_to_fp16 = const()[name = tensor("op_2377_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(628361600)))]; + tensor hidden_states_103_cast = mul(x = var_2375_cast, y = var_2377_to_fp16)[name = tensor("hidden_states_103_cast")]; + tensor var_2384 = const()[name = tensor("op_2384"), val = tensor([1, 1])]; + tensor var_2386 = const()[name = tensor("op_2386"), val = tensor([1, 1])]; tensor q_43_pad_type_0 = const()[name = tensor("q_43_pad_type_0"), val = tensor("custom")]; tensor q_43_pad_0 = const()[name = tensor("q_43_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_43 = conv(dilations = var_2448, groups = var_1193, pad = q_43_pad_0, pad_type = q_43_pad_type_0, strides = var_2446, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_q_weight, x = hidden_states_103)[name = tensor("q_43")]; - tensor var_2452 = const()[name = tensor("op_2452"), val = tensor([1, 1])]; - tensor var_2454 = const()[name = tensor("op_2454"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(628364224)))]; + tensor q_43_cast = conv(dilations = var_2386, groups = var_31, pad = q_43_pad_0, pad_type = q_43_pad_type_0, strides = var_2384, weight = unet_down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16, x = hidden_states_103_cast)[name = tensor("q_43_cast")]; + tensor var_2390 = const()[name = tensor("op_2390"), val = tensor([1, 1])]; + tensor var_2392 = const()[name = tensor("op_2392"), val = tensor([1, 1])]; tensor k_43_pad_type_0 = const()[name = tensor("k_43_pad_type_0"), val = tensor("custom")]; tensor k_43_pad_0 = const()[name = tensor("k_43_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_43 = conv(dilations = var_2454, groups = var_1193, pad = k_43_pad_0, pad_type = k_43_pad_type_0, strides = var_2452, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_43")]; - tensor var_2458 = const()[name = tensor("op_2458"), val = tensor([1, 1])]; - tensor var_2460 = const()[name = tensor("op_2460"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(631641088)))]; + tensor k_43_cast = conv(dilations = var_2392, groups = var_31, pad = k_43_pad_0, pad_type = k_43_pad_type_0, strides = var_2390, weight = unet_down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_43_cast")]; + tensor var_2396 = const()[name = tensor("op_2396"), val = tensor([1, 1])]; + tensor var_2398 = const()[name = tensor("op_2398"), val = tensor([1, 1])]; tensor v_43_pad_type_0 = const()[name = tensor("v_43_pad_type_0"), val = tensor("custom")]; tensor v_43_pad_0 = const()[name = tensor("v_43_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_43 = conv(dilations = var_2460, groups = var_1193, pad = v_43_pad_0, pad_type = v_43_pad_type_0, strides = var_2458, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_43")]; - tensor var_2464 = const()[name = tensor("op_2464"), val = tensor([2, 20, 64, -1])]; - tensor var_2465 = reshape(shape = var_2464, x = q_43)[name = tensor("op_2465")]; - tensor var_2466 = const()[name = tensor("op_2466"), val = tensor([2, 20, 64, -1])]; - tensor var_2467 = reshape(shape = var_2466, x = k_43)[name = tensor("op_2467")]; - tensor var_2468 = const()[name = tensor("op_2468"), val = tensor([2, 20, 64, -1])]; - tensor var_2469 = reshape(shape = var_2468, x = v_43)[name = tensor("op_2469")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636884032)))]; + tensor v_43_cast = conv(dilations = var_2398, groups = var_31, pad = v_43_pad_0, pad_type = v_43_pad_type_0, strides = var_2396, weight = unet_down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_43_cast")]; + tensor var_2402 = const()[name = tensor("op_2402"), val = tensor([2, 20, 64, -1])]; + tensor var_2403_cast = reshape(shape = var_2402, x = q_43_cast)[name = tensor("op_2403_cast")]; + tensor var_2404 = const()[name = tensor("op_2404"), val = tensor([2, 20, 64, -1])]; + tensor var_2405_cast = reshape(shape = var_2404, x = k_43_cast)[name = tensor("op_2405_cast")]; + tensor var_2406 = const()[name = tensor("op_2406"), val = tensor([2, 20, 64, -1])]; + tensor var_2407_cast = reshape(shape = var_2406, x = v_43_cast)[name = tensor("op_2407_cast")]; tensor attn_weights_85_transpose_x_0 = const()[name = tensor("attn_weights_85_transpose_x_0"), val = tensor(true)]; tensor attn_weights_85_transpose_y_0 = const()[name = tensor("attn_weights_85_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_85 = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = var_2465, y = var_2467)[name = tensor("attn_weights_85")]; - tensor attn_weights_87 = mul(x = attn_weights_85, y = var_1184)[name = tensor("attn_weights_87")]; - tensor var_2473 = softmax(axis = var_1177, x = attn_weights_87)[name = tensor("op_2473")]; + tensor attn_weights_85_cast = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = var_2403_cast, y = var_2405_cast)[name = tensor("attn_weights_85_cast")]; + tensor attn_weights_87_cast = mul(x = attn_weights_85_cast, y = var_12_to_fp16)[name = tensor("attn_weights_87_cast")]; + tensor var_2411_cast = softmax(axis = var_18, x = attn_weights_87_cast)[name = tensor("op_2411_cast")]; tensor attn_43_transpose_x_0 = const()[name = tensor("attn_43_transpose_x_0"), val = tensor(false)]; tensor attn_43_transpose_y_0 = const()[name = tensor("attn_43_transpose_y_0"), val = tensor(true)]; - tensor attn_43 = matmul(transpose_x = attn_43_transpose_x_0, transpose_y = attn_43_transpose_y_0, x = var_2469, y = var_2473)[name = tensor("attn_43")]; - tensor var_2477 = const()[name = tensor("op_2477"), val = tensor([2, 1280, 1, -1])]; - tensor input_181 = reshape(shape = var_2477, x = attn_43)[name = tensor("input_181")]; - tensor var_2482 = const()[name = tensor("op_2482"), val = tensor([1, 1])]; - tensor var_2484 = const()[name = tensor("op_2484"), val = tensor([1, 1])]; - tensor var_2486_pad_type_0 = const()[name = tensor("op_2486_pad_type_0"), val = tensor("custom")]; - tensor var_2486_pad_0 = const()[name = tensor("op_2486_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2486 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_bias, dilations = var_2484, groups = var_1193, pad = var_2486_pad_0, pad_type = var_2486_pad_type_0, strides = var_2482, weight = down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_weight, x = input_181)[name = tensor("op_2486")]; - tensor inputs_65 = add(x = var_2486, y = inputs_63)[name = tensor("inputs_65")]; - tensor var_2490 = const()[name = tensor("op_2490"), val = tensor([1])]; - tensor channels_mean_65 = reduce_mean(axes = var_2490, keep_dims = var_1188, x = inputs_65)[name = tensor("channels_mean_65")]; - tensor zero_mean_65 = sub(x = inputs_65, y = channels_mean_65)[name = tensor("zero_mean_65")]; - tensor zero_mean_sq_65 = mul(x = zero_mean_65, y = zero_mean_65)[name = tensor("zero_mean_sq_65")]; - tensor var_2494 = const()[name = tensor("op_2494"), val = tensor([1])]; - tensor var_2495 = reduce_mean(axes = var_2494, keep_dims = var_1188, x = zero_mean_sq_65)[name = tensor("op_2495")]; - tensor var_2496 = const()[name = tensor("op_2496"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2497 = add(x = var_2495, y = var_2496)[name = tensor("op_2497")]; - tensor denom_65_epsilon_0 = const()[name = tensor("denom_65_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_65 = rsqrt(epsilon = denom_65_epsilon_0, x = var_2497)[name = tensor("denom_65")]; - tensor out_65 = mul(x = zero_mean_65, y = denom_65)[name = tensor("out_65")]; - tensor var_2501 = const()[name = tensor("op_2501"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267932416)))]; - tensor var_2502 = add(x = out_65, y = var_2501)[name = tensor("op_2502")]; - tensor var_2504 = const()[name = tensor("op_2504"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267937600)))]; - tensor input_183 = mul(x = var_2502, y = var_2504)[name = tensor("input_183")]; - tensor var_2512 = const()[name = tensor("op_2512"), val = tensor([1, 1])]; - tensor var_2514 = const()[name = tensor("op_2514"), val = tensor([1, 1])]; - tensor var_2516_pad_type_0 = const()[name = tensor("op_2516_pad_type_0"), val = tensor("custom")]; - tensor var_2516_pad_0 = const()[name = tensor("op_2516_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2516 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_bias, dilations = var_2514, groups = var_1193, pad = var_2516_pad_0, pad_type = var_2516_pad_type_0, strides = var_2512, weight = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_weight, x = input_183)[name = tensor("op_2516")]; - tensor var_2517_split_sizes_0 = const()[name = tensor("op_2517_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_2517_axis_0 = const()[name = tensor("op_2517_axis_0"), val = tensor(1)]; - tensor var_2517_0, tensor var_2517_1 = split(axis = var_2517_axis_0, split_sizes = var_2517_split_sizes_0, x = var_2516)[name = tensor("op_2517")]; - tensor var_2519_mode_0 = const()[name = tensor("op_2519_mode_0"), val = tensor("EXACT")]; - tensor var_2519 = gelu(mode = var_2519_mode_0, x = var_2517_1)[name = tensor("op_2519")]; - tensor input_185 = mul(x = var_2517_0, y = var_2519)[name = tensor("input_185")]; - tensor var_2523 = const()[name = tensor("op_2523"), val = tensor([1, 1])]; - tensor var_2525 = const()[name = tensor("op_2525"), val = tensor([1, 1])]; - tensor var_2527_pad_type_0 = const()[name = tensor("op_2527_pad_type_0"), val = tensor("custom")]; - tensor var_2527_pad_0 = const()[name = tensor("op_2527_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2527 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_bias, dilations = var_2525, groups = var_1193, pad = var_2527_pad_0, pad_type = var_2527_pad_type_0, strides = var_2523, weight = down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_weight, x = input_185)[name = tensor("op_2527")]; - tensor inputs_67 = add(x = var_2527, y = inputs_65)[name = tensor("inputs_67")]; - tensor var_2537 = const()[name = tensor("op_2537"), val = tensor([1])]; - tensor channels_mean_67 = reduce_mean(axes = var_2537, keep_dims = var_1188, x = inputs_67)[name = tensor("channels_mean_67")]; - tensor zero_mean_67 = sub(x = inputs_67, y = channels_mean_67)[name = tensor("zero_mean_67")]; - tensor zero_mean_sq_67 = mul(x = zero_mean_67, y = zero_mean_67)[name = tensor("zero_mean_sq_67")]; - tensor var_2541 = const()[name = tensor("op_2541"), val = tensor([1])]; - tensor var_2542 = reduce_mean(axes = var_2541, keep_dims = var_1188, x = zero_mean_sq_67)[name = tensor("op_2542")]; - tensor var_2543 = const()[name = tensor("op_2543"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2544 = add(x = var_2542, y = var_2543)[name = tensor("op_2544")]; - tensor denom_67_epsilon_0 = const()[name = tensor("denom_67_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_67 = rsqrt(epsilon = denom_67_epsilon_0, x = var_2544)[name = tensor("denom_67")]; - tensor out_67 = mul(x = zero_mean_67, y = denom_67)[name = tensor("out_67")]; - tensor var_2548 = const()[name = tensor("op_2548"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267942784)))]; - tensor var_2549 = add(x = out_67, y = var_2548)[name = tensor("op_2549")]; - tensor var_2551 = const()[name = tensor("op_2551"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267947968)))]; - tensor hidden_states_107 = mul(x = var_2549, y = var_2551)[name = tensor("hidden_states_107")]; - tensor var_2558 = const()[name = tensor("op_2558"), val = tensor([1, 1])]; - tensor var_2560 = const()[name = tensor("op_2560"), val = tensor([1, 1])]; + tensor attn_43_cast = matmul(transpose_x = attn_43_transpose_x_0, transpose_y = attn_43_transpose_y_0, x = var_2407_cast, y = var_2411_cast)[name = tensor("attn_43_cast")]; + tensor var_2415 = const()[name = tensor("op_2415"), val = tensor([2, 1280, 1, -1])]; + tensor input_181_cast = reshape(shape = var_2415, x = attn_43_cast)[name = tensor("input_181_cast")]; + tensor var_2420 = const()[name = tensor("op_2420"), val = tensor([1, 1])]; + tensor var_2422 = const()[name = tensor("op_2422"), val = tensor([1, 1])]; + tensor var_2424_pad_type_0 = const()[name = tensor("op_2424_pad_type_0"), val = tensor("custom")]; + tensor var_2424_pad_0 = const()[name = tensor("op_2424_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642126976)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645403840)))]; + tensor var_2424_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_2422, groups = var_31, pad = var_2424_pad_0, pad_type = var_2424_pad_type_0, strides = var_2420, weight = unet_down_blocks_2_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16, x = input_181_cast)[name = tensor("op_2424_cast")]; + tensor inputs_65_cast = add(x = var_2424_cast, y = inputs_63_cast)[name = tensor("inputs_65_cast")]; + tensor var_2428 = const()[name = tensor("op_2428"), val = tensor([1])]; + tensor channels_mean_65_cast = reduce_mean(axes = var_2428, keep_dims = var_23, x = inputs_65_cast)[name = tensor("channels_mean_65_cast")]; + tensor zero_mean_65_cast = sub(x = inputs_65_cast, y = channels_mean_65_cast)[name = tensor("zero_mean_65_cast")]; + tensor zero_mean_sq_65_cast = mul(x = zero_mean_65_cast, y = zero_mean_65_cast)[name = tensor("zero_mean_sq_65_cast")]; + tensor var_2432 = const()[name = tensor("op_2432"), val = tensor([1])]; + tensor var_2433_cast = reduce_mean(axes = var_2432, keep_dims = var_23, x = zero_mean_sq_65_cast)[name = tensor("op_2433_cast")]; + tensor var_2434_to_fp16 = const()[name = tensor("op_2434_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2435_cast = add(x = var_2433_cast, y = var_2434_to_fp16)[name = tensor("op_2435_cast")]; + tensor denom_65_epsilon_0_to_fp16 = const()[name = tensor("denom_65_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_65_cast = rsqrt(epsilon = denom_65_epsilon_0_to_fp16, x = var_2435_cast)[name = tensor("denom_65_cast")]; + tensor out_65_cast = mul(x = zero_mean_65_cast, y = denom_65_cast)[name = tensor("out_65_cast")]; + tensor var_2439_to_fp16 = const()[name = tensor("op_2439_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645406464)))]; + tensor var_2440_cast = add(x = out_65_cast, y = var_2439_to_fp16)[name = tensor("op_2440_cast")]; + tensor var_2442_to_fp16 = const()[name = tensor("op_2442_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645409088)))]; + tensor input_183_cast = mul(x = var_2440_cast, y = var_2442_to_fp16)[name = tensor("input_183_cast")]; + tensor var_2450 = const()[name = tensor("op_2450"), val = tensor([1, 1])]; + tensor var_2452 = const()[name = tensor("op_2452"), val = tensor([1, 1])]; + tensor var_2454_pad_type_0 = const()[name = tensor("op_2454_pad_type_0"), val = tensor("custom")]; + tensor var_2454_pad_0 = const()[name = tensor("op_2454_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645411712)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(671626176)))]; + tensor var_2454_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_2452, groups = var_31, pad = var_2454_pad_0, pad_type = var_2454_pad_type_0, strides = var_2450, weight = unet_down_blocks_2_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16, x = input_183_cast)[name = tensor("op_2454_cast")]; + tensor var_2455_split_sizes_0 = const()[name = tensor("op_2455_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2455_axis_0 = const()[name = tensor("op_2455_axis_0"), val = tensor(1)]; + tensor var_2455_cast_0, tensor var_2455_cast_1 = split(axis = var_2455_axis_0, split_sizes = var_2455_split_sizes_0, x = var_2454_cast)[name = tensor("op_2455_cast")]; + tensor var_2457_mode_0 = const()[name = tensor("op_2457_mode_0"), val = tensor("EXACT")]; + tensor var_2457_cast = gelu(mode = var_2457_mode_0, x = var_2455_cast_1)[name = tensor("op_2457_cast")]; + tensor input_185_cast = mul(x = var_2455_cast_0, y = var_2457_cast)[name = tensor("input_185_cast")]; + tensor var_2461 = const()[name = tensor("op_2461"), val = tensor([1, 1])]; + tensor var_2463 = const()[name = tensor("op_2463"), val = tensor([1, 1])]; + tensor var_2465_pad_type_0 = const()[name = tensor("op_2465_pad_type_0"), val = tensor("custom")]; + tensor var_2465_pad_0 = const()[name = tensor("op_2465_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(671646720)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(684753984)))]; + tensor var_2465_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_2463, groups = var_31, pad = var_2465_pad_0, pad_type = var_2465_pad_type_0, strides = var_2461, weight = unet_down_blocks_2_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16, x = input_185_cast)[name = tensor("op_2465_cast")]; + tensor inputs_67_cast = add(x = var_2465_cast, y = inputs_65_cast)[name = tensor("inputs_67_cast")]; + tensor var_2475 = const()[name = tensor("op_2475"), val = tensor([1])]; + tensor channels_mean_67_cast = reduce_mean(axes = var_2475, keep_dims = var_23, x = inputs_67_cast)[name = tensor("channels_mean_67_cast")]; + tensor zero_mean_67_cast = sub(x = inputs_67_cast, y = channels_mean_67_cast)[name = tensor("zero_mean_67_cast")]; + tensor zero_mean_sq_67_cast = mul(x = zero_mean_67_cast, y = zero_mean_67_cast)[name = tensor("zero_mean_sq_67_cast")]; + tensor var_2479 = const()[name = tensor("op_2479"), val = tensor([1])]; + tensor var_2480_cast = reduce_mean(axes = var_2479, keep_dims = var_23, x = zero_mean_sq_67_cast)[name = tensor("op_2480_cast")]; + tensor var_2481_to_fp16 = const()[name = tensor("op_2481_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2482_cast = add(x = var_2480_cast, y = var_2481_to_fp16)[name = tensor("op_2482_cast")]; + tensor denom_67_epsilon_0_to_fp16 = const()[name = tensor("denom_67_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_67_cast = rsqrt(epsilon = denom_67_epsilon_0_to_fp16, x = var_2482_cast)[name = tensor("denom_67_cast")]; + tensor out_67_cast = mul(x = zero_mean_67_cast, y = denom_67_cast)[name = tensor("out_67_cast")]; + tensor var_2486_to_fp16 = const()[name = tensor("op_2486_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(684756608)))]; + tensor var_2487_cast = add(x = out_67_cast, y = var_2486_to_fp16)[name = tensor("op_2487_cast")]; + tensor var_2489_to_fp16 = const()[name = tensor("op_2489_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(684759232)))]; + tensor hidden_states_107_cast = mul(x = var_2487_cast, y = var_2489_to_fp16)[name = tensor("hidden_states_107_cast")]; + tensor var_2496 = const()[name = tensor("op_2496"), val = tensor([1, 1])]; + tensor var_2498 = const()[name = tensor("op_2498"), val = tensor([1, 1])]; tensor q_45_pad_type_0 = const()[name = tensor("q_45_pad_type_0"), val = tensor("custom")]; tensor q_45_pad_0 = const()[name = tensor("q_45_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_45 = conv(dilations = var_2560, groups = var_1193, pad = q_45_pad_0, pad_type = q_45_pad_type_0, strides = var_2558, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_q_weight, x = hidden_states_107)[name = tensor("q_45")]; - tensor var_2564 = const()[name = tensor("op_2564"), val = tensor([1, 1])]; - tensor var_2566 = const()[name = tensor("op_2566"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(684761856)))]; + tensor q_45_cast = conv(dilations = var_2498, groups = var_31, pad = q_45_pad_0, pad_type = q_45_pad_type_0, strides = var_2496, weight = unet_down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16, x = hidden_states_107_cast)[name = tensor("q_45_cast")]; + tensor var_2502 = const()[name = tensor("op_2502"), val = tensor([1, 1])]; + tensor var_2504 = const()[name = tensor("op_2504"), val = tensor([1, 1])]; tensor k_45_pad_type_0 = const()[name = tensor("k_45_pad_type_0"), val = tensor("custom")]; tensor k_45_pad_0 = const()[name = tensor("k_45_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_45 = conv(dilations = var_2566, groups = var_1193, pad = k_45_pad_0, pad_type = k_45_pad_type_0, strides = var_2564, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_k_weight, x = hidden_states_107)[name = tensor("k_45")]; - tensor var_2570 = const()[name = tensor("op_2570"), val = tensor([1, 1])]; - tensor var_2572 = const()[name = tensor("op_2572"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(688038720)))]; + tensor k_45_cast = conv(dilations = var_2504, groups = var_31, pad = k_45_pad_0, pad_type = k_45_pad_type_0, strides = var_2502, weight = unet_down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16, x = hidden_states_107_cast)[name = tensor("k_45_cast")]; + tensor var_2508 = const()[name = tensor("op_2508"), val = tensor([1, 1])]; + tensor var_2510 = const()[name = tensor("op_2510"), val = tensor([1, 1])]; tensor v_45_pad_type_0 = const()[name = tensor("v_45_pad_type_0"), val = tensor("custom")]; tensor v_45_pad_0 = const()[name = tensor("v_45_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_45 = conv(dilations = var_2572, groups = var_1193, pad = v_45_pad_0, pad_type = v_45_pad_type_0, strides = var_2570, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_v_weight, x = hidden_states_107)[name = tensor("v_45")]; - tensor var_2576 = const()[name = tensor("op_2576"), val = tensor([2, 20, 64, -1])]; - tensor var_2577 = reshape(shape = var_2576, x = q_45)[name = tensor("op_2577")]; - tensor var_2578 = const()[name = tensor("op_2578"), val = tensor([2, 20, 64, -1])]; - tensor var_2579 = reshape(shape = var_2578, x = k_45)[name = tensor("op_2579")]; - tensor var_2580 = const()[name = tensor("op_2580"), val = tensor([2, 20, 64, -1])]; - tensor var_2581 = reshape(shape = var_2580, x = v_45)[name = tensor("op_2581")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(691315584)))]; + tensor v_45_cast = conv(dilations = var_2510, groups = var_31, pad = v_45_pad_0, pad_type = v_45_pad_type_0, strides = var_2508, weight = unet_down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16, x = hidden_states_107_cast)[name = tensor("v_45_cast")]; + tensor var_2514 = const()[name = tensor("op_2514"), val = tensor([2, 20, 64, -1])]; + tensor var_2515_cast = reshape(shape = var_2514, x = q_45_cast)[name = tensor("op_2515_cast")]; + tensor var_2516 = const()[name = tensor("op_2516"), val = tensor([2, 20, 64, -1])]; + tensor var_2517_cast = reshape(shape = var_2516, x = k_45_cast)[name = tensor("op_2517_cast")]; + tensor var_2518 = const()[name = tensor("op_2518"), val = tensor([2, 20, 64, -1])]; + tensor var_2519_cast = reshape(shape = var_2518, x = v_45_cast)[name = tensor("op_2519_cast")]; tensor attn_weights_89_transpose_x_0 = const()[name = tensor("attn_weights_89_transpose_x_0"), val = tensor(true)]; tensor attn_weights_89_transpose_y_0 = const()[name = tensor("attn_weights_89_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_89 = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_2577, y = var_2579)[name = tensor("attn_weights_89")]; - tensor attn_weights_91 = mul(x = attn_weights_89, y = var_1184)[name = tensor("attn_weights_91")]; - tensor var_2585 = softmax(axis = var_1177, x = attn_weights_91)[name = tensor("op_2585")]; + tensor attn_weights_89_cast = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_2515_cast, y = var_2517_cast)[name = tensor("attn_weights_89_cast")]; + tensor attn_weights_91_cast = mul(x = attn_weights_89_cast, y = var_12_to_fp16)[name = tensor("attn_weights_91_cast")]; + tensor var_2523_cast = softmax(axis = var_18, x = attn_weights_91_cast)[name = tensor("op_2523_cast")]; tensor attn_45_transpose_x_0 = const()[name = tensor("attn_45_transpose_x_0"), val = tensor(false)]; tensor attn_45_transpose_y_0 = const()[name = tensor("attn_45_transpose_y_0"), val = tensor(true)]; - tensor attn_45 = matmul(transpose_x = attn_45_transpose_x_0, transpose_y = attn_45_transpose_y_0, x = var_2581, y = var_2585)[name = tensor("attn_45")]; - tensor var_2589 = const()[name = tensor("op_2589"), val = tensor([2, 1280, 1, -1])]; - tensor input_187 = reshape(shape = var_2589, x = attn_45)[name = tensor("input_187")]; - tensor var_2594 = const()[name = tensor("op_2594"), val = tensor([1, 1])]; - tensor var_2596 = const()[name = tensor("op_2596"), val = tensor([1, 1])]; - tensor var_2598_pad_type_0 = const()[name = tensor("op_2598_pad_type_0"), val = tensor("custom")]; - tensor var_2598_pad_0 = const()[name = tensor("op_2598_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2598 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_bias, dilations = var_2596, groups = var_1193, pad = var_2598_pad_0, pad_type = var_2598_pad_type_0, strides = var_2594, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_weight, x = input_187)[name = tensor("op_2598")]; - tensor inputs_69 = add(x = var_2598, y = inputs_67)[name = tensor("inputs_69")]; - tensor var_2602 = const()[name = tensor("op_2602"), val = tensor([1])]; - tensor channels_mean_69 = reduce_mean(axes = var_2602, keep_dims = var_1188, x = inputs_69)[name = tensor("channels_mean_69")]; - tensor zero_mean_69 = sub(x = inputs_69, y = channels_mean_69)[name = tensor("zero_mean_69")]; - tensor zero_mean_sq_69 = mul(x = zero_mean_69, y = zero_mean_69)[name = tensor("zero_mean_sq_69")]; - tensor var_2606 = const()[name = tensor("op_2606"), val = tensor([1])]; - tensor var_2607 = reduce_mean(axes = var_2606, keep_dims = var_1188, x = zero_mean_sq_69)[name = tensor("op_2607")]; - tensor var_2608 = const()[name = tensor("op_2608"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2609 = add(x = var_2607, y = var_2608)[name = tensor("op_2609")]; - tensor denom_69_epsilon_0 = const()[name = tensor("denom_69_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_69 = rsqrt(epsilon = denom_69_epsilon_0, x = var_2609)[name = tensor("denom_69")]; - tensor out_69 = mul(x = zero_mean_69, y = denom_69)[name = tensor("out_69")]; - tensor var_2613 = const()[name = tensor("op_2613"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267953152)))]; - tensor var_2614 = add(x = out_69, y = var_2613)[name = tensor("op_2614")]; - tensor var_2616 = const()[name = tensor("op_2616"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267958336)))]; - tensor hidden_states_109 = mul(x = var_2614, y = var_2616)[name = tensor("hidden_states_109")]; - tensor var_2623 = const()[name = tensor("op_2623"), val = tensor([1, 1])]; - tensor var_2625 = const()[name = tensor("op_2625"), val = tensor([1, 1])]; + tensor attn_45_cast = matmul(transpose_x = attn_45_transpose_x_0, transpose_y = attn_45_transpose_y_0, x = var_2519_cast, y = var_2523_cast)[name = tensor("attn_45_cast")]; + tensor var_2527 = const()[name = tensor("op_2527"), val = tensor([2, 1280, 1, -1])]; + tensor input_187_cast = reshape(shape = var_2527, x = attn_45_cast)[name = tensor("input_187_cast")]; + tensor var_2532 = const()[name = tensor("op_2532"), val = tensor([1, 1])]; + tensor var_2534 = const()[name = tensor("op_2534"), val = tensor([1, 1])]; + tensor var_2536_pad_type_0 = const()[name = tensor("op_2536_pad_type_0"), val = tensor("custom")]; + tensor var_2536_pad_0 = const()[name = tensor("op_2536_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(694592448)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(697869312)))]; + tensor var_2536_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_2534, groups = var_31, pad = var_2536_pad_0, pad_type = var_2536_pad_type_0, strides = var_2532, weight = unet_down_blocks_2_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16, x = input_187_cast)[name = tensor("op_2536_cast")]; + tensor inputs_69_cast = add(x = var_2536_cast, y = inputs_67_cast)[name = tensor("inputs_69_cast")]; + tensor var_2540 = const()[name = tensor("op_2540"), val = tensor([1])]; + tensor channels_mean_69_cast = reduce_mean(axes = var_2540, keep_dims = var_23, x = inputs_69_cast)[name = tensor("channels_mean_69_cast")]; + tensor zero_mean_69_cast = sub(x = inputs_69_cast, y = channels_mean_69_cast)[name = tensor("zero_mean_69_cast")]; + tensor zero_mean_sq_69_cast = mul(x = zero_mean_69_cast, y = zero_mean_69_cast)[name = tensor("zero_mean_sq_69_cast")]; + tensor var_2544 = const()[name = tensor("op_2544"), val = tensor([1])]; + tensor var_2545_cast = reduce_mean(axes = var_2544, keep_dims = var_23, x = zero_mean_sq_69_cast)[name = tensor("op_2545_cast")]; + tensor var_2546_to_fp16 = const()[name = tensor("op_2546_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2547_cast = add(x = var_2545_cast, y = var_2546_to_fp16)[name = tensor("op_2547_cast")]; + tensor denom_69_epsilon_0_to_fp16 = const()[name = tensor("denom_69_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_69_cast = rsqrt(epsilon = denom_69_epsilon_0_to_fp16, x = var_2547_cast)[name = tensor("denom_69_cast")]; + tensor out_69_cast = mul(x = zero_mean_69_cast, y = denom_69_cast)[name = tensor("out_69_cast")]; + tensor var_2551_to_fp16 = const()[name = tensor("op_2551_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(697871936)))]; + tensor var_2552_cast = add(x = out_69_cast, y = var_2551_to_fp16)[name = tensor("op_2552_cast")]; + tensor var_2554_to_fp16 = const()[name = tensor("op_2554_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(697874560)))]; + tensor hidden_states_109_cast = mul(x = var_2552_cast, y = var_2554_to_fp16)[name = tensor("hidden_states_109_cast")]; + tensor var_2561 = const()[name = tensor("op_2561"), val = tensor([1, 1])]; + tensor var_2563 = const()[name = tensor("op_2563"), val = tensor([1, 1])]; tensor q_47_pad_type_0 = const()[name = tensor("q_47_pad_type_0"), val = tensor("custom")]; tensor q_47_pad_0 = const()[name = tensor("q_47_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_47 = conv(dilations = var_2625, groups = var_1193, pad = q_47_pad_0, pad_type = q_47_pad_type_0, strides = var_2623, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_q_weight, x = hidden_states_109)[name = tensor("q_47")]; - tensor var_2629 = const()[name = tensor("op_2629"), val = tensor([1, 1])]; - tensor var_2631 = const()[name = tensor("op_2631"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(697877184)))]; + tensor q_47_cast = conv(dilations = var_2563, groups = var_31, pad = q_47_pad_0, pad_type = q_47_pad_type_0, strides = var_2561, weight = unet_down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16, x = hidden_states_109_cast)[name = tensor("q_47_cast")]; + tensor var_2567 = const()[name = tensor("op_2567"), val = tensor([1, 1])]; + tensor var_2569 = const()[name = tensor("op_2569"), val = tensor([1, 1])]; tensor k_47_pad_type_0 = const()[name = tensor("k_47_pad_type_0"), val = tensor("custom")]; tensor k_47_pad_0 = const()[name = tensor("k_47_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_47 = conv(dilations = var_2631, groups = var_1193, pad = k_47_pad_0, pad_type = k_47_pad_type_0, strides = var_2629, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_47")]; - tensor var_2635 = const()[name = tensor("op_2635"), val = tensor([1, 1])]; - tensor var_2637 = const()[name = tensor("op_2637"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(701154048)))]; + tensor k_47_cast = conv(dilations = var_2569, groups = var_31, pad = k_47_pad_0, pad_type = k_47_pad_type_0, strides = var_2567, weight = unet_down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_47_cast")]; + tensor var_2573 = const()[name = tensor("op_2573"), val = tensor([1, 1])]; + tensor var_2575 = const()[name = tensor("op_2575"), val = tensor([1, 1])]; tensor v_47_pad_type_0 = const()[name = tensor("v_47_pad_type_0"), val = tensor("custom")]; tensor v_47_pad_0 = const()[name = tensor("v_47_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_47 = conv(dilations = var_2637, groups = var_1193, pad = v_47_pad_0, pad_type = v_47_pad_type_0, strides = var_2635, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_47")]; - tensor var_2641 = const()[name = tensor("op_2641"), val = tensor([2, 20, 64, -1])]; - tensor var_2642 = reshape(shape = var_2641, x = q_47)[name = tensor("op_2642")]; - tensor var_2643 = const()[name = tensor("op_2643"), val = tensor([2, 20, 64, -1])]; - tensor var_2644 = reshape(shape = var_2643, x = k_47)[name = tensor("op_2644")]; - tensor var_2645 = const()[name = tensor("op_2645"), val = tensor([2, 20, 64, -1])]; - tensor var_2646 = reshape(shape = var_2645, x = v_47)[name = tensor("op_2646")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(706396992)))]; + tensor v_47_cast = conv(dilations = var_2575, groups = var_31, pad = v_47_pad_0, pad_type = v_47_pad_type_0, strides = var_2573, weight = unet_down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_47_cast")]; + tensor var_2579 = const()[name = tensor("op_2579"), val = tensor([2, 20, 64, -1])]; + tensor var_2580_cast = reshape(shape = var_2579, x = q_47_cast)[name = tensor("op_2580_cast")]; + tensor var_2581 = const()[name = tensor("op_2581"), val = tensor([2, 20, 64, -1])]; + tensor var_2582_cast = reshape(shape = var_2581, x = k_47_cast)[name = tensor("op_2582_cast")]; + tensor var_2583 = const()[name = tensor("op_2583"), val = tensor([2, 20, 64, -1])]; + tensor var_2584_cast = reshape(shape = var_2583, x = v_47_cast)[name = tensor("op_2584_cast")]; tensor attn_weights_93_transpose_x_0 = const()[name = tensor("attn_weights_93_transpose_x_0"), val = tensor(true)]; tensor attn_weights_93_transpose_y_0 = const()[name = tensor("attn_weights_93_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_93 = matmul(transpose_x = attn_weights_93_transpose_x_0, transpose_y = attn_weights_93_transpose_y_0, x = var_2642, y = var_2644)[name = tensor("attn_weights_93")]; - tensor attn_weights_95 = mul(x = attn_weights_93, y = var_1184)[name = tensor("attn_weights_95")]; - tensor var_2650 = softmax(axis = var_1177, x = attn_weights_95)[name = tensor("op_2650")]; + tensor attn_weights_93_cast = matmul(transpose_x = attn_weights_93_transpose_x_0, transpose_y = attn_weights_93_transpose_y_0, x = var_2580_cast, y = var_2582_cast)[name = tensor("attn_weights_93_cast")]; + tensor attn_weights_95_cast = mul(x = attn_weights_93_cast, y = var_12_to_fp16)[name = tensor("attn_weights_95_cast")]; + tensor var_2588_cast = softmax(axis = var_18, x = attn_weights_95_cast)[name = tensor("op_2588_cast")]; tensor attn_47_transpose_x_0 = const()[name = tensor("attn_47_transpose_x_0"), val = tensor(false)]; tensor attn_47_transpose_y_0 = const()[name = tensor("attn_47_transpose_y_0"), val = tensor(true)]; - tensor attn_47 = matmul(transpose_x = attn_47_transpose_x_0, transpose_y = attn_47_transpose_y_0, x = var_2646, y = var_2650)[name = tensor("attn_47")]; - tensor var_2654 = const()[name = tensor("op_2654"), val = tensor([2, 1280, 1, -1])]; - tensor input_189 = reshape(shape = var_2654, x = attn_47)[name = tensor("input_189")]; - tensor var_2659 = const()[name = tensor("op_2659"), val = tensor([1, 1])]; - tensor var_2661 = const()[name = tensor("op_2661"), val = tensor([1, 1])]; - tensor var_2663_pad_type_0 = const()[name = tensor("op_2663_pad_type_0"), val = tensor("custom")]; - tensor var_2663_pad_0 = const()[name = tensor("op_2663_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2663 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_bias, dilations = var_2661, groups = var_1193, pad = var_2663_pad_0, pad_type = var_2663_pad_type_0, strides = var_2659, weight = down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_weight, x = input_189)[name = tensor("op_2663")]; - tensor inputs_71 = add(x = var_2663, y = inputs_69)[name = tensor("inputs_71")]; - tensor var_2667 = const()[name = tensor("op_2667"), val = tensor([1])]; - tensor channels_mean_71 = reduce_mean(axes = var_2667, keep_dims = var_1188, x = inputs_71)[name = tensor("channels_mean_71")]; - tensor zero_mean_71 = sub(x = inputs_71, y = channels_mean_71)[name = tensor("zero_mean_71")]; - tensor zero_mean_sq_71 = mul(x = zero_mean_71, y = zero_mean_71)[name = tensor("zero_mean_sq_71")]; - tensor var_2671 = const()[name = tensor("op_2671"), val = tensor([1])]; - tensor var_2672 = reduce_mean(axes = var_2671, keep_dims = var_1188, x = zero_mean_sq_71)[name = tensor("op_2672")]; - tensor var_2673 = const()[name = tensor("op_2673"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2674 = add(x = var_2672, y = var_2673)[name = tensor("op_2674")]; - tensor denom_71_epsilon_0 = const()[name = tensor("denom_71_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_71 = rsqrt(epsilon = denom_71_epsilon_0, x = var_2674)[name = tensor("denom_71")]; - tensor out_71 = mul(x = zero_mean_71, y = denom_71)[name = tensor("out_71")]; - tensor var_2678 = const()[name = tensor("op_2678"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267963520)))]; - tensor var_2679 = add(x = out_71, y = var_2678)[name = tensor("op_2679")]; - tensor var_2681 = const()[name = tensor("op_2681"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267968704)))]; - tensor input_191 = mul(x = var_2679, y = var_2681)[name = tensor("input_191")]; - tensor var_2689 = const()[name = tensor("op_2689"), val = tensor([1, 1])]; - tensor var_2691 = const()[name = tensor("op_2691"), val = tensor([1, 1])]; - tensor var_2693_pad_type_0 = const()[name = tensor("op_2693_pad_type_0"), val = tensor("custom")]; - tensor var_2693_pad_0 = const()[name = tensor("op_2693_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2693 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_bias, dilations = var_2691, groups = var_1193, pad = var_2693_pad_0, pad_type = var_2693_pad_type_0, strides = var_2689, weight = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_weight, x = input_191)[name = tensor("op_2693")]; - tensor var_2694_split_sizes_0 = const()[name = tensor("op_2694_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_2694_axis_0 = const()[name = tensor("op_2694_axis_0"), val = tensor(1)]; - tensor var_2694_0, tensor var_2694_1 = split(axis = var_2694_axis_0, split_sizes = var_2694_split_sizes_0, x = var_2693)[name = tensor("op_2694")]; - tensor var_2696_mode_0 = const()[name = tensor("op_2696_mode_0"), val = tensor("EXACT")]; - tensor var_2696 = gelu(mode = var_2696_mode_0, x = var_2694_1)[name = tensor("op_2696")]; - tensor input_193 = mul(x = var_2694_0, y = var_2696)[name = tensor("input_193")]; - tensor var_2700 = const()[name = tensor("op_2700"), val = tensor([1, 1])]; - tensor var_2702 = const()[name = tensor("op_2702"), val = tensor([1, 1])]; - tensor var_2704_pad_type_0 = const()[name = tensor("op_2704_pad_type_0"), val = tensor("custom")]; - tensor var_2704_pad_0 = const()[name = tensor("op_2704_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2704 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_bias, dilations = var_2702, groups = var_1193, pad = var_2704_pad_0, pad_type = var_2704_pad_type_0, strides = var_2700, weight = down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_weight, x = input_193)[name = tensor("op_2704")]; - tensor inputs_73 = add(x = var_2704, y = inputs_71)[name = tensor("inputs_73")]; - tensor var_2714 = const()[name = tensor("op_2714"), val = tensor([1])]; - tensor channels_mean_73 = reduce_mean(axes = var_2714, keep_dims = var_1188, x = inputs_73)[name = tensor("channels_mean_73")]; - tensor zero_mean_73 = sub(x = inputs_73, y = channels_mean_73)[name = tensor("zero_mean_73")]; - tensor zero_mean_sq_73 = mul(x = zero_mean_73, y = zero_mean_73)[name = tensor("zero_mean_sq_73")]; - tensor var_2718 = const()[name = tensor("op_2718"), val = tensor([1])]; - tensor var_2719 = reduce_mean(axes = var_2718, keep_dims = var_1188, x = zero_mean_sq_73)[name = tensor("op_2719")]; - tensor var_2720 = const()[name = tensor("op_2720"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2721 = add(x = var_2719, y = var_2720)[name = tensor("op_2721")]; - tensor denom_73_epsilon_0 = const()[name = tensor("denom_73_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_73 = rsqrt(epsilon = denom_73_epsilon_0, x = var_2721)[name = tensor("denom_73")]; - tensor out_73 = mul(x = zero_mean_73, y = denom_73)[name = tensor("out_73")]; - tensor var_2725 = const()[name = tensor("op_2725"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267973888)))]; - tensor var_2726 = add(x = out_73, y = var_2725)[name = tensor("op_2726")]; - tensor var_2728 = const()[name = tensor("op_2728"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267979072)))]; - tensor hidden_states_113 = mul(x = var_2726, y = var_2728)[name = tensor("hidden_states_113")]; - tensor var_2735 = const()[name = tensor("op_2735"), val = tensor([1, 1])]; - tensor var_2737 = const()[name = tensor("op_2737"), val = tensor([1, 1])]; + tensor attn_47_cast = matmul(transpose_x = attn_47_transpose_x_0, transpose_y = attn_47_transpose_y_0, x = var_2584_cast, y = var_2588_cast)[name = tensor("attn_47_cast")]; + tensor var_2592 = const()[name = tensor("op_2592"), val = tensor([2, 1280, 1, -1])]; + tensor input_189_cast = reshape(shape = var_2592, x = attn_47_cast)[name = tensor("input_189_cast")]; + tensor var_2597 = const()[name = tensor("op_2597"), val = tensor([1, 1])]; + tensor var_2599 = const()[name = tensor("op_2599"), val = tensor([1, 1])]; + tensor var_2601_pad_type_0 = const()[name = tensor("op_2601_pad_type_0"), val = tensor("custom")]; + tensor var_2601_pad_0 = const()[name = tensor("op_2601_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(711639936)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(714916800)))]; + tensor var_2601_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_2599, groups = var_31, pad = var_2601_pad_0, pad_type = var_2601_pad_type_0, strides = var_2597, weight = unet_down_blocks_2_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16, x = input_189_cast)[name = tensor("op_2601_cast")]; + tensor inputs_71_cast = add(x = var_2601_cast, y = inputs_69_cast)[name = tensor("inputs_71_cast")]; + tensor var_2605 = const()[name = tensor("op_2605"), val = tensor([1])]; + tensor channels_mean_71_cast = reduce_mean(axes = var_2605, keep_dims = var_23, x = inputs_71_cast)[name = tensor("channels_mean_71_cast")]; + tensor zero_mean_71_cast = sub(x = inputs_71_cast, y = channels_mean_71_cast)[name = tensor("zero_mean_71_cast")]; + tensor zero_mean_sq_71_cast = mul(x = zero_mean_71_cast, y = zero_mean_71_cast)[name = tensor("zero_mean_sq_71_cast")]; + tensor var_2609 = const()[name = tensor("op_2609"), val = tensor([1])]; + tensor var_2610_cast = reduce_mean(axes = var_2609, keep_dims = var_23, x = zero_mean_sq_71_cast)[name = tensor("op_2610_cast")]; + tensor var_2611_to_fp16 = const()[name = tensor("op_2611_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2612_cast = add(x = var_2610_cast, y = var_2611_to_fp16)[name = tensor("op_2612_cast")]; + tensor denom_71_epsilon_0_to_fp16 = const()[name = tensor("denom_71_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_71_cast = rsqrt(epsilon = denom_71_epsilon_0_to_fp16, x = var_2612_cast)[name = tensor("denom_71_cast")]; + tensor out_71_cast = mul(x = zero_mean_71_cast, y = denom_71_cast)[name = tensor("out_71_cast")]; + tensor var_2616_to_fp16 = const()[name = tensor("op_2616_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(714919424)))]; + tensor var_2617_cast = add(x = out_71_cast, y = var_2616_to_fp16)[name = tensor("op_2617_cast")]; + tensor var_2619_to_fp16 = const()[name = tensor("op_2619_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(714922048)))]; + tensor input_191_cast = mul(x = var_2617_cast, y = var_2619_to_fp16)[name = tensor("input_191_cast")]; + tensor var_2627 = const()[name = tensor("op_2627"), val = tensor([1, 1])]; + tensor var_2629 = const()[name = tensor("op_2629"), val = tensor([1, 1])]; + tensor var_2631_pad_type_0 = const()[name = tensor("op_2631_pad_type_0"), val = tensor("custom")]; + tensor var_2631_pad_0 = const()[name = tensor("op_2631_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(714924672)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(741139136)))]; + tensor var_2631_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_2629, groups = var_31, pad = var_2631_pad_0, pad_type = var_2631_pad_type_0, strides = var_2627, weight = unet_down_blocks_2_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16, x = input_191_cast)[name = tensor("op_2631_cast")]; + tensor var_2632_split_sizes_0 = const()[name = tensor("op_2632_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2632_axis_0 = const()[name = tensor("op_2632_axis_0"), val = tensor(1)]; + tensor var_2632_cast_0, tensor var_2632_cast_1 = split(axis = var_2632_axis_0, split_sizes = var_2632_split_sizes_0, x = var_2631_cast)[name = tensor("op_2632_cast")]; + tensor var_2634_mode_0 = const()[name = tensor("op_2634_mode_0"), val = tensor("EXACT")]; + tensor var_2634_cast = gelu(mode = var_2634_mode_0, x = var_2632_cast_1)[name = tensor("op_2634_cast")]; + tensor input_193_cast = mul(x = var_2632_cast_0, y = var_2634_cast)[name = tensor("input_193_cast")]; + tensor var_2638 = const()[name = tensor("op_2638"), val = tensor([1, 1])]; + tensor var_2640 = const()[name = tensor("op_2640"), val = tensor([1, 1])]; + tensor var_2642_pad_type_0 = const()[name = tensor("op_2642_pad_type_0"), val = tensor("custom")]; + tensor var_2642_pad_0 = const()[name = tensor("op_2642_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(741159680)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(754266944)))]; + tensor var_2642_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_2640, groups = var_31, pad = var_2642_pad_0, pad_type = var_2642_pad_type_0, strides = var_2638, weight = unet_down_blocks_2_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16, x = input_193_cast)[name = tensor("op_2642_cast")]; + tensor inputs_73_cast = add(x = var_2642_cast, y = inputs_71_cast)[name = tensor("inputs_73_cast")]; + tensor var_2652 = const()[name = tensor("op_2652"), val = tensor([1])]; + tensor channels_mean_73_cast = reduce_mean(axes = var_2652, keep_dims = var_23, x = inputs_73_cast)[name = tensor("channels_mean_73_cast")]; + tensor zero_mean_73_cast = sub(x = inputs_73_cast, y = channels_mean_73_cast)[name = tensor("zero_mean_73_cast")]; + tensor zero_mean_sq_73_cast = mul(x = zero_mean_73_cast, y = zero_mean_73_cast)[name = tensor("zero_mean_sq_73_cast")]; + tensor var_2656 = const()[name = tensor("op_2656"), val = tensor([1])]; + tensor var_2657_cast = reduce_mean(axes = var_2656, keep_dims = var_23, x = zero_mean_sq_73_cast)[name = tensor("op_2657_cast")]; + tensor var_2658_to_fp16 = const()[name = tensor("op_2658_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2659_cast = add(x = var_2657_cast, y = var_2658_to_fp16)[name = tensor("op_2659_cast")]; + tensor denom_73_epsilon_0_to_fp16 = const()[name = tensor("denom_73_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_73_cast = rsqrt(epsilon = denom_73_epsilon_0_to_fp16, x = var_2659_cast)[name = tensor("denom_73_cast")]; + tensor out_73_cast = mul(x = zero_mean_73_cast, y = denom_73_cast)[name = tensor("out_73_cast")]; + tensor var_2663_to_fp16 = const()[name = tensor("op_2663_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(754269568)))]; + tensor var_2664_cast = add(x = out_73_cast, y = var_2663_to_fp16)[name = tensor("op_2664_cast")]; + tensor var_2666_to_fp16 = const()[name = tensor("op_2666_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(754272192)))]; + tensor hidden_states_113_cast = mul(x = var_2664_cast, y = var_2666_to_fp16)[name = tensor("hidden_states_113_cast")]; + tensor var_2673 = const()[name = tensor("op_2673"), val = tensor([1, 1])]; + tensor var_2675 = const()[name = tensor("op_2675"), val = tensor([1, 1])]; tensor q_49_pad_type_0 = const()[name = tensor("q_49_pad_type_0"), val = tensor("custom")]; tensor q_49_pad_0 = const()[name = tensor("q_49_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_49 = conv(dilations = var_2737, groups = var_1193, pad = q_49_pad_0, pad_type = q_49_pad_type_0, strides = var_2735, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_q_weight, x = hidden_states_113)[name = tensor("q_49")]; - tensor var_2741 = const()[name = tensor("op_2741"), val = tensor([1, 1])]; - tensor var_2743 = const()[name = tensor("op_2743"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(754274816)))]; + tensor q_49_cast = conv(dilations = var_2675, groups = var_31, pad = q_49_pad_0, pad_type = q_49_pad_type_0, strides = var_2673, weight = unet_down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16, x = hidden_states_113_cast)[name = tensor("q_49_cast")]; + tensor var_2679 = const()[name = tensor("op_2679"), val = tensor([1, 1])]; + tensor var_2681 = const()[name = tensor("op_2681"), val = tensor([1, 1])]; tensor k_49_pad_type_0 = const()[name = tensor("k_49_pad_type_0"), val = tensor("custom")]; tensor k_49_pad_0 = const()[name = tensor("k_49_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_49 = conv(dilations = var_2743, groups = var_1193, pad = k_49_pad_0, pad_type = k_49_pad_type_0, strides = var_2741, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_k_weight, x = hidden_states_113)[name = tensor("k_49")]; - tensor var_2747 = const()[name = tensor("op_2747"), val = tensor([1, 1])]; - tensor var_2749 = const()[name = tensor("op_2749"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(757551680)))]; + tensor k_49_cast = conv(dilations = var_2681, groups = var_31, pad = k_49_pad_0, pad_type = k_49_pad_type_0, strides = var_2679, weight = unet_down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16, x = hidden_states_113_cast)[name = tensor("k_49_cast")]; + tensor var_2685 = const()[name = tensor("op_2685"), val = tensor([1, 1])]; + tensor var_2687 = const()[name = tensor("op_2687"), val = tensor([1, 1])]; tensor v_49_pad_type_0 = const()[name = tensor("v_49_pad_type_0"), val = tensor("custom")]; tensor v_49_pad_0 = const()[name = tensor("v_49_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_49 = conv(dilations = var_2749, groups = var_1193, pad = v_49_pad_0, pad_type = v_49_pad_type_0, strides = var_2747, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_v_weight, x = hidden_states_113)[name = tensor("v_49")]; - tensor var_2753 = const()[name = tensor("op_2753"), val = tensor([2, 20, 64, -1])]; - tensor var_2754 = reshape(shape = var_2753, x = q_49)[name = tensor("op_2754")]; - tensor var_2755 = const()[name = tensor("op_2755"), val = tensor([2, 20, 64, -1])]; - tensor var_2756 = reshape(shape = var_2755, x = k_49)[name = tensor("op_2756")]; - tensor var_2757 = const()[name = tensor("op_2757"), val = tensor([2, 20, 64, -1])]; - tensor var_2758 = reshape(shape = var_2757, x = v_49)[name = tensor("op_2758")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(760828544)))]; + tensor v_49_cast = conv(dilations = var_2687, groups = var_31, pad = v_49_pad_0, pad_type = v_49_pad_type_0, strides = var_2685, weight = unet_down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16, x = hidden_states_113_cast)[name = tensor("v_49_cast")]; + tensor var_2691 = const()[name = tensor("op_2691"), val = tensor([2, 20, 64, -1])]; + tensor var_2692_cast = reshape(shape = var_2691, x = q_49_cast)[name = tensor("op_2692_cast")]; + tensor var_2693 = const()[name = tensor("op_2693"), val = tensor([2, 20, 64, -1])]; + tensor var_2694_cast = reshape(shape = var_2693, x = k_49_cast)[name = tensor("op_2694_cast")]; + tensor var_2695 = const()[name = tensor("op_2695"), val = tensor([2, 20, 64, -1])]; + tensor var_2696_cast = reshape(shape = var_2695, x = v_49_cast)[name = tensor("op_2696_cast")]; tensor attn_weights_97_transpose_x_0 = const()[name = tensor("attn_weights_97_transpose_x_0"), val = tensor(true)]; tensor attn_weights_97_transpose_y_0 = const()[name = tensor("attn_weights_97_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_97 = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_2754, y = var_2756)[name = tensor("attn_weights_97")]; - tensor attn_weights_99 = mul(x = attn_weights_97, y = var_1184)[name = tensor("attn_weights_99")]; - tensor var_2762 = softmax(axis = var_1177, x = attn_weights_99)[name = tensor("op_2762")]; + tensor attn_weights_97_cast = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_2692_cast, y = var_2694_cast)[name = tensor("attn_weights_97_cast")]; + tensor attn_weights_99_cast = mul(x = attn_weights_97_cast, y = var_12_to_fp16)[name = tensor("attn_weights_99_cast")]; + tensor var_2700_cast = softmax(axis = var_18, x = attn_weights_99_cast)[name = tensor("op_2700_cast")]; tensor attn_49_transpose_x_0 = const()[name = tensor("attn_49_transpose_x_0"), val = tensor(false)]; tensor attn_49_transpose_y_0 = const()[name = tensor("attn_49_transpose_y_0"), val = tensor(true)]; - tensor attn_49 = matmul(transpose_x = attn_49_transpose_x_0, transpose_y = attn_49_transpose_y_0, x = var_2758, y = var_2762)[name = tensor("attn_49")]; - tensor var_2766 = const()[name = tensor("op_2766"), val = tensor([2, 1280, 1, -1])]; - tensor input_195 = reshape(shape = var_2766, x = attn_49)[name = tensor("input_195")]; - tensor var_2771 = const()[name = tensor("op_2771"), val = tensor([1, 1])]; - tensor var_2773 = const()[name = tensor("op_2773"), val = tensor([1, 1])]; - tensor var_2775_pad_type_0 = const()[name = tensor("op_2775_pad_type_0"), val = tensor("custom")]; - tensor var_2775_pad_0 = const()[name = tensor("op_2775_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2775 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_bias, dilations = var_2773, groups = var_1193, pad = var_2775_pad_0, pad_type = var_2775_pad_type_0, strides = var_2771, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_weight, x = input_195)[name = tensor("op_2775")]; - tensor inputs_75 = add(x = var_2775, y = inputs_73)[name = tensor("inputs_75")]; - tensor var_2779 = const()[name = tensor("op_2779"), val = tensor([1])]; - tensor channels_mean_75 = reduce_mean(axes = var_2779, keep_dims = var_1188, x = inputs_75)[name = tensor("channels_mean_75")]; - tensor zero_mean_75 = sub(x = inputs_75, y = channels_mean_75)[name = tensor("zero_mean_75")]; - tensor zero_mean_sq_75 = mul(x = zero_mean_75, y = zero_mean_75)[name = tensor("zero_mean_sq_75")]; - tensor var_2783 = const()[name = tensor("op_2783"), val = tensor([1])]; - tensor var_2784 = reduce_mean(axes = var_2783, keep_dims = var_1188, x = zero_mean_sq_75)[name = tensor("op_2784")]; - tensor var_2785 = const()[name = tensor("op_2785"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2786 = add(x = var_2784, y = var_2785)[name = tensor("op_2786")]; - tensor denom_75_epsilon_0 = const()[name = tensor("denom_75_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_75 = rsqrt(epsilon = denom_75_epsilon_0, x = var_2786)[name = tensor("denom_75")]; - tensor out_75 = mul(x = zero_mean_75, y = denom_75)[name = tensor("out_75")]; - tensor var_2790 = const()[name = tensor("op_2790"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267984256)))]; - tensor var_2791 = add(x = out_75, y = var_2790)[name = tensor("op_2791")]; - tensor var_2793 = const()[name = tensor("op_2793"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267989440)))]; - tensor hidden_states_115 = mul(x = var_2791, y = var_2793)[name = tensor("hidden_states_115")]; - tensor var_2800 = const()[name = tensor("op_2800"), val = tensor([1, 1])]; - tensor var_2802 = const()[name = tensor("op_2802"), val = tensor([1, 1])]; + tensor attn_49_cast = matmul(transpose_x = attn_49_transpose_x_0, transpose_y = attn_49_transpose_y_0, x = var_2696_cast, y = var_2700_cast)[name = tensor("attn_49_cast")]; + tensor var_2704 = const()[name = tensor("op_2704"), val = tensor([2, 1280, 1, -1])]; + tensor input_195_cast = reshape(shape = var_2704, x = attn_49_cast)[name = tensor("input_195_cast")]; + tensor var_2709 = const()[name = tensor("op_2709"), val = tensor([1, 1])]; + tensor var_2711 = const()[name = tensor("op_2711"), val = tensor([1, 1])]; + tensor var_2713_pad_type_0 = const()[name = tensor("op_2713_pad_type_0"), val = tensor("custom")]; + tensor var_2713_pad_0 = const()[name = tensor("op_2713_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(764105408)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(767382272)))]; + tensor var_2713_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_2711, groups = var_31, pad = var_2713_pad_0, pad_type = var_2713_pad_type_0, strides = var_2709, weight = unet_down_blocks_2_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16, x = input_195_cast)[name = tensor("op_2713_cast")]; + tensor inputs_75_cast = add(x = var_2713_cast, y = inputs_73_cast)[name = tensor("inputs_75_cast")]; + tensor var_2717 = const()[name = tensor("op_2717"), val = tensor([1])]; + tensor channels_mean_75_cast = reduce_mean(axes = var_2717, keep_dims = var_23, x = inputs_75_cast)[name = tensor("channels_mean_75_cast")]; + tensor zero_mean_75_cast = sub(x = inputs_75_cast, y = channels_mean_75_cast)[name = tensor("zero_mean_75_cast")]; + tensor zero_mean_sq_75_cast = mul(x = zero_mean_75_cast, y = zero_mean_75_cast)[name = tensor("zero_mean_sq_75_cast")]; + tensor var_2721 = const()[name = tensor("op_2721"), val = tensor([1])]; + tensor var_2722_cast = reduce_mean(axes = var_2721, keep_dims = var_23, x = zero_mean_sq_75_cast)[name = tensor("op_2722_cast")]; + tensor var_2723_to_fp16 = const()[name = tensor("op_2723_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2724_cast = add(x = var_2722_cast, y = var_2723_to_fp16)[name = tensor("op_2724_cast")]; + tensor denom_75_epsilon_0_to_fp16 = const()[name = tensor("denom_75_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_75_cast = rsqrt(epsilon = denom_75_epsilon_0_to_fp16, x = var_2724_cast)[name = tensor("denom_75_cast")]; + tensor out_75_cast = mul(x = zero_mean_75_cast, y = denom_75_cast)[name = tensor("out_75_cast")]; + tensor var_2728_to_fp16 = const()[name = tensor("op_2728_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(767384896)))]; + tensor var_2729_cast = add(x = out_75_cast, y = var_2728_to_fp16)[name = tensor("op_2729_cast")]; + tensor var_2731_to_fp16 = const()[name = tensor("op_2731_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(767387520)))]; + tensor hidden_states_115_cast = mul(x = var_2729_cast, y = var_2731_to_fp16)[name = tensor("hidden_states_115_cast")]; + tensor var_2738 = const()[name = tensor("op_2738"), val = tensor([1, 1])]; + tensor var_2740 = const()[name = tensor("op_2740"), val = tensor([1, 1])]; tensor q_51_pad_type_0 = const()[name = tensor("q_51_pad_type_0"), val = tensor("custom")]; tensor q_51_pad_0 = const()[name = tensor("q_51_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_51 = conv(dilations = var_2802, groups = var_1193, pad = q_51_pad_0, pad_type = q_51_pad_type_0, strides = var_2800, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_q_weight, x = hidden_states_115)[name = tensor("q_51")]; - tensor var_2806 = const()[name = tensor("op_2806"), val = tensor([1, 1])]; - tensor var_2808 = const()[name = tensor("op_2808"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(767390144)))]; + tensor q_51_cast = conv(dilations = var_2740, groups = var_31, pad = q_51_pad_0, pad_type = q_51_pad_type_0, strides = var_2738, weight = unet_down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16, x = hidden_states_115_cast)[name = tensor("q_51_cast")]; + tensor var_2744 = const()[name = tensor("op_2744"), val = tensor([1, 1])]; + tensor var_2746 = const()[name = tensor("op_2746"), val = tensor([1, 1])]; tensor k_51_pad_type_0 = const()[name = tensor("k_51_pad_type_0"), val = tensor("custom")]; tensor k_51_pad_0 = const()[name = tensor("k_51_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_51 = conv(dilations = var_2808, groups = var_1193, pad = k_51_pad_0, pad_type = k_51_pad_type_0, strides = var_2806, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_51")]; - tensor var_2812 = const()[name = tensor("op_2812"), val = tensor([1, 1])]; - tensor var_2814 = const()[name = tensor("op_2814"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(770667008)))]; + tensor k_51_cast = conv(dilations = var_2746, groups = var_31, pad = k_51_pad_0, pad_type = k_51_pad_type_0, strides = var_2744, weight = unet_down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_51_cast")]; + tensor var_2750 = const()[name = tensor("op_2750"), val = tensor([1, 1])]; + tensor var_2752 = const()[name = tensor("op_2752"), val = tensor([1, 1])]; tensor v_51_pad_type_0 = const()[name = tensor("v_51_pad_type_0"), val = tensor("custom")]; tensor v_51_pad_0 = const()[name = tensor("v_51_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_51 = conv(dilations = var_2814, groups = var_1193, pad = v_51_pad_0, pad_type = v_51_pad_type_0, strides = var_2812, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_51")]; - tensor var_2818 = const()[name = tensor("op_2818"), val = tensor([2, 20, 64, -1])]; - tensor var_2819 = reshape(shape = var_2818, x = q_51)[name = tensor("op_2819")]; - tensor var_2820 = const()[name = tensor("op_2820"), val = tensor([2, 20, 64, -1])]; - tensor var_2821 = reshape(shape = var_2820, x = k_51)[name = tensor("op_2821")]; - tensor var_2822 = const()[name = tensor("op_2822"), val = tensor([2, 20, 64, -1])]; - tensor var_2823 = reshape(shape = var_2822, x = v_51)[name = tensor("op_2823")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(775909952)))]; + tensor v_51_cast = conv(dilations = var_2752, groups = var_31, pad = v_51_pad_0, pad_type = v_51_pad_type_0, strides = var_2750, weight = unet_down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_51_cast")]; + tensor var_2756 = const()[name = tensor("op_2756"), val = tensor([2, 20, 64, -1])]; + tensor var_2757_cast = reshape(shape = var_2756, x = q_51_cast)[name = tensor("op_2757_cast")]; + tensor var_2758 = const()[name = tensor("op_2758"), val = tensor([2, 20, 64, -1])]; + tensor var_2759_cast = reshape(shape = var_2758, x = k_51_cast)[name = tensor("op_2759_cast")]; + tensor var_2760 = const()[name = tensor("op_2760"), val = tensor([2, 20, 64, -1])]; + tensor var_2761_cast = reshape(shape = var_2760, x = v_51_cast)[name = tensor("op_2761_cast")]; tensor attn_weights_101_transpose_x_0 = const()[name = tensor("attn_weights_101_transpose_x_0"), val = tensor(true)]; tensor attn_weights_101_transpose_y_0 = const()[name = tensor("attn_weights_101_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_101 = matmul(transpose_x = attn_weights_101_transpose_x_0, transpose_y = attn_weights_101_transpose_y_0, x = var_2819, y = var_2821)[name = tensor("attn_weights_101")]; - tensor attn_weights_103 = mul(x = attn_weights_101, y = var_1184)[name = tensor("attn_weights_103")]; - tensor var_2827 = softmax(axis = var_1177, x = attn_weights_103)[name = tensor("op_2827")]; + tensor attn_weights_101_cast = matmul(transpose_x = attn_weights_101_transpose_x_0, transpose_y = attn_weights_101_transpose_y_0, x = var_2757_cast, y = var_2759_cast)[name = tensor("attn_weights_101_cast")]; + tensor attn_weights_103_cast = mul(x = attn_weights_101_cast, y = var_12_to_fp16)[name = tensor("attn_weights_103_cast")]; + tensor var_2765_cast = softmax(axis = var_18, x = attn_weights_103_cast)[name = tensor("op_2765_cast")]; tensor attn_51_transpose_x_0 = const()[name = tensor("attn_51_transpose_x_0"), val = tensor(false)]; tensor attn_51_transpose_y_0 = const()[name = tensor("attn_51_transpose_y_0"), val = tensor(true)]; - tensor attn_51 = matmul(transpose_x = attn_51_transpose_x_0, transpose_y = attn_51_transpose_y_0, x = var_2823, y = var_2827)[name = tensor("attn_51")]; - tensor var_2831 = const()[name = tensor("op_2831"), val = tensor([2, 1280, 1, -1])]; - tensor input_197 = reshape(shape = var_2831, x = attn_51)[name = tensor("input_197")]; - tensor var_2836 = const()[name = tensor("op_2836"), val = tensor([1, 1])]; - tensor var_2838 = const()[name = tensor("op_2838"), val = tensor([1, 1])]; - tensor var_2840_pad_type_0 = const()[name = tensor("op_2840_pad_type_0"), val = tensor("custom")]; - tensor var_2840_pad_0 = const()[name = tensor("op_2840_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2840 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_bias, dilations = var_2838, groups = var_1193, pad = var_2840_pad_0, pad_type = var_2840_pad_type_0, strides = var_2836, weight = down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_weight, x = input_197)[name = tensor("op_2840")]; - tensor inputs_77 = add(x = var_2840, y = inputs_75)[name = tensor("inputs_77")]; - tensor var_2844 = const()[name = tensor("op_2844"), val = tensor([1])]; - tensor channels_mean_77 = reduce_mean(axes = var_2844, keep_dims = var_1188, x = inputs_77)[name = tensor("channels_mean_77")]; - tensor zero_mean_77 = sub(x = inputs_77, y = channels_mean_77)[name = tensor("zero_mean_77")]; - tensor zero_mean_sq_77 = mul(x = zero_mean_77, y = zero_mean_77)[name = tensor("zero_mean_sq_77")]; - tensor var_2848 = const()[name = tensor("op_2848"), val = tensor([1])]; - tensor var_2849 = reduce_mean(axes = var_2848, keep_dims = var_1188, x = zero_mean_sq_77)[name = tensor("op_2849")]; - tensor var_2850 = const()[name = tensor("op_2850"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2851 = add(x = var_2849, y = var_2850)[name = tensor("op_2851")]; - tensor denom_77_epsilon_0 = const()[name = tensor("denom_77_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_77 = rsqrt(epsilon = denom_77_epsilon_0, x = var_2851)[name = tensor("denom_77")]; - tensor out_77 = mul(x = zero_mean_77, y = denom_77)[name = tensor("out_77")]; - tensor var_2855 = const()[name = tensor("op_2855"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267994624)))]; - tensor var_2856 = add(x = out_77, y = var_2855)[name = tensor("op_2856")]; - tensor var_2858 = const()[name = tensor("op_2858"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10267999808)))]; - tensor input_199 = mul(x = var_2856, y = var_2858)[name = tensor("input_199")]; - tensor var_2866 = const()[name = tensor("op_2866"), val = tensor([1, 1])]; - tensor var_2868 = const()[name = tensor("op_2868"), val = tensor([1, 1])]; - tensor var_2870_pad_type_0 = const()[name = tensor("op_2870_pad_type_0"), val = tensor("custom")]; - tensor var_2870_pad_0 = const()[name = tensor("op_2870_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2870 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_bias, dilations = var_2868, groups = var_1193, pad = var_2870_pad_0, pad_type = var_2870_pad_type_0, strides = var_2866, weight = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_weight, x = input_199)[name = tensor("op_2870")]; - tensor var_2871_split_sizes_0 = const()[name = tensor("op_2871_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_2871_axis_0 = const()[name = tensor("op_2871_axis_0"), val = tensor(1)]; - tensor var_2871_0, tensor var_2871_1 = split(axis = var_2871_axis_0, split_sizes = var_2871_split_sizes_0, x = var_2870)[name = tensor("op_2871")]; - tensor var_2873_mode_0 = const()[name = tensor("op_2873_mode_0"), val = tensor("EXACT")]; - tensor var_2873 = gelu(mode = var_2873_mode_0, x = var_2871_1)[name = tensor("op_2873")]; - tensor input_201 = mul(x = var_2871_0, y = var_2873)[name = tensor("input_201")]; - tensor var_2877 = const()[name = tensor("op_2877"), val = tensor([1, 1])]; - tensor var_2879 = const()[name = tensor("op_2879"), val = tensor([1, 1])]; - tensor var_2881_pad_type_0 = const()[name = tensor("op_2881_pad_type_0"), val = tensor("custom")]; - tensor var_2881_pad_0 = const()[name = tensor("op_2881_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2881 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_bias, dilations = var_2879, groups = var_1193, pad = var_2881_pad_0, pad_type = var_2881_pad_type_0, strides = var_2877, weight = down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_weight, x = input_201)[name = tensor("op_2881")]; - tensor inputs_79 = add(x = var_2881, y = inputs_77)[name = tensor("inputs_79")]; - tensor var_2891 = const()[name = tensor("op_2891"), val = tensor([1])]; - tensor channels_mean_79 = reduce_mean(axes = var_2891, keep_dims = var_1188, x = inputs_79)[name = tensor("channels_mean_79")]; - tensor zero_mean_79 = sub(x = inputs_79, y = channels_mean_79)[name = tensor("zero_mean_79")]; - tensor zero_mean_sq_79 = mul(x = zero_mean_79, y = zero_mean_79)[name = tensor("zero_mean_sq_79")]; - tensor var_2895 = const()[name = tensor("op_2895"), val = tensor([1])]; - tensor var_2896 = reduce_mean(axes = var_2895, keep_dims = var_1188, x = zero_mean_sq_79)[name = tensor("op_2896")]; - tensor var_2897 = const()[name = tensor("op_2897"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2898 = add(x = var_2896, y = var_2897)[name = tensor("op_2898")]; - tensor denom_79_epsilon_0 = const()[name = tensor("denom_79_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_79 = rsqrt(epsilon = denom_79_epsilon_0, x = var_2898)[name = tensor("denom_79")]; - tensor out_79 = mul(x = zero_mean_79, y = denom_79)[name = tensor("out_79")]; - tensor var_2902 = const()[name = tensor("op_2902"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268004992)))]; - tensor var_2903 = add(x = out_79, y = var_2902)[name = tensor("op_2903")]; - tensor var_2905 = const()[name = tensor("op_2905"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268010176)))]; - tensor hidden_states_119 = mul(x = var_2903, y = var_2905)[name = tensor("hidden_states_119")]; - tensor var_2912 = const()[name = tensor("op_2912"), val = tensor([1, 1])]; - tensor var_2914 = const()[name = tensor("op_2914"), val = tensor([1, 1])]; + tensor attn_51_cast = matmul(transpose_x = attn_51_transpose_x_0, transpose_y = attn_51_transpose_y_0, x = var_2761_cast, y = var_2765_cast)[name = tensor("attn_51_cast")]; + tensor var_2769 = const()[name = tensor("op_2769"), val = tensor([2, 1280, 1, -1])]; + tensor input_197_cast = reshape(shape = var_2769, x = attn_51_cast)[name = tensor("input_197_cast")]; + tensor var_2774 = const()[name = tensor("op_2774"), val = tensor([1, 1])]; + tensor var_2776 = const()[name = tensor("op_2776"), val = tensor([1, 1])]; + tensor var_2778_pad_type_0 = const()[name = tensor("op_2778_pad_type_0"), val = tensor("custom")]; + tensor var_2778_pad_0 = const()[name = tensor("op_2778_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(781152896)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(784429760)))]; + tensor var_2778_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_2776, groups = var_31, pad = var_2778_pad_0, pad_type = var_2778_pad_type_0, strides = var_2774, weight = unet_down_blocks_2_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16, x = input_197_cast)[name = tensor("op_2778_cast")]; + tensor inputs_77_cast = add(x = var_2778_cast, y = inputs_75_cast)[name = tensor("inputs_77_cast")]; + tensor var_2782 = const()[name = tensor("op_2782"), val = tensor([1])]; + tensor channels_mean_77_cast = reduce_mean(axes = var_2782, keep_dims = var_23, x = inputs_77_cast)[name = tensor("channels_mean_77_cast")]; + tensor zero_mean_77_cast = sub(x = inputs_77_cast, y = channels_mean_77_cast)[name = tensor("zero_mean_77_cast")]; + tensor zero_mean_sq_77_cast = mul(x = zero_mean_77_cast, y = zero_mean_77_cast)[name = tensor("zero_mean_sq_77_cast")]; + tensor var_2786 = const()[name = tensor("op_2786"), val = tensor([1])]; + tensor var_2787_cast = reduce_mean(axes = var_2786, keep_dims = var_23, x = zero_mean_sq_77_cast)[name = tensor("op_2787_cast")]; + tensor var_2788_to_fp16 = const()[name = tensor("op_2788_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2789_cast = add(x = var_2787_cast, y = var_2788_to_fp16)[name = tensor("op_2789_cast")]; + tensor denom_77_epsilon_0_to_fp16 = const()[name = tensor("denom_77_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_77_cast = rsqrt(epsilon = denom_77_epsilon_0_to_fp16, x = var_2789_cast)[name = tensor("denom_77_cast")]; + tensor out_77_cast = mul(x = zero_mean_77_cast, y = denom_77_cast)[name = tensor("out_77_cast")]; + tensor var_2793_to_fp16 = const()[name = tensor("op_2793_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(784432384)))]; + tensor var_2794_cast = add(x = out_77_cast, y = var_2793_to_fp16)[name = tensor("op_2794_cast")]; + tensor var_2796_to_fp16 = const()[name = tensor("op_2796_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(784435008)))]; + tensor input_199_cast = mul(x = var_2794_cast, y = var_2796_to_fp16)[name = tensor("input_199_cast")]; + tensor var_2804 = const()[name = tensor("op_2804"), val = tensor([1, 1])]; + tensor var_2806 = const()[name = tensor("op_2806"), val = tensor([1, 1])]; + tensor var_2808_pad_type_0 = const()[name = tensor("op_2808_pad_type_0"), val = tensor("custom")]; + tensor var_2808_pad_0 = const()[name = tensor("op_2808_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(784437632)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(810652096)))]; + tensor var_2808_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_2806, groups = var_31, pad = var_2808_pad_0, pad_type = var_2808_pad_type_0, strides = var_2804, weight = unet_down_blocks_2_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16, x = input_199_cast)[name = tensor("op_2808_cast")]; + tensor var_2809_split_sizes_0 = const()[name = tensor("op_2809_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2809_axis_0 = const()[name = tensor("op_2809_axis_0"), val = tensor(1)]; + tensor var_2809_cast_0, tensor var_2809_cast_1 = split(axis = var_2809_axis_0, split_sizes = var_2809_split_sizes_0, x = var_2808_cast)[name = tensor("op_2809_cast")]; + tensor var_2811_mode_0 = const()[name = tensor("op_2811_mode_0"), val = tensor("EXACT")]; + tensor var_2811_cast = gelu(mode = var_2811_mode_0, x = var_2809_cast_1)[name = tensor("op_2811_cast")]; + tensor input_201_cast = mul(x = var_2809_cast_0, y = var_2811_cast)[name = tensor("input_201_cast")]; + tensor var_2815 = const()[name = tensor("op_2815"), val = tensor([1, 1])]; + tensor var_2817 = const()[name = tensor("op_2817"), val = tensor([1, 1])]; + tensor var_2819_pad_type_0 = const()[name = tensor("op_2819_pad_type_0"), val = tensor("custom")]; + tensor var_2819_pad_0 = const()[name = tensor("op_2819_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(810672640)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823779904)))]; + tensor var_2819_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_2817, groups = var_31, pad = var_2819_pad_0, pad_type = var_2819_pad_type_0, strides = var_2815, weight = unet_down_blocks_2_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16, x = input_201_cast)[name = tensor("op_2819_cast")]; + tensor inputs_79_cast = add(x = var_2819_cast, y = inputs_77_cast)[name = tensor("inputs_79_cast")]; + tensor var_2829 = const()[name = tensor("op_2829"), val = tensor([1])]; + tensor channels_mean_79_cast = reduce_mean(axes = var_2829, keep_dims = var_23, x = inputs_79_cast)[name = tensor("channels_mean_79_cast")]; + tensor zero_mean_79_cast = sub(x = inputs_79_cast, y = channels_mean_79_cast)[name = tensor("zero_mean_79_cast")]; + tensor zero_mean_sq_79_cast = mul(x = zero_mean_79_cast, y = zero_mean_79_cast)[name = tensor("zero_mean_sq_79_cast")]; + tensor var_2833 = const()[name = tensor("op_2833"), val = tensor([1])]; + tensor var_2834_cast = reduce_mean(axes = var_2833, keep_dims = var_23, x = zero_mean_sq_79_cast)[name = tensor("op_2834_cast")]; + tensor var_2835_to_fp16 = const()[name = tensor("op_2835_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2836_cast = add(x = var_2834_cast, y = var_2835_to_fp16)[name = tensor("op_2836_cast")]; + tensor denom_79_epsilon_0_to_fp16 = const()[name = tensor("denom_79_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_79_cast = rsqrt(epsilon = denom_79_epsilon_0_to_fp16, x = var_2836_cast)[name = tensor("denom_79_cast")]; + tensor out_79_cast = mul(x = zero_mean_79_cast, y = denom_79_cast)[name = tensor("out_79_cast")]; + tensor var_2840_to_fp16 = const()[name = tensor("op_2840_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823782528)))]; + tensor var_2841_cast = add(x = out_79_cast, y = var_2840_to_fp16)[name = tensor("op_2841_cast")]; + tensor var_2843_to_fp16 = const()[name = tensor("op_2843_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823785152)))]; + tensor hidden_states_119_cast = mul(x = var_2841_cast, y = var_2843_to_fp16)[name = tensor("hidden_states_119_cast")]; + tensor var_2850 = const()[name = tensor("op_2850"), val = tensor([1, 1])]; + tensor var_2852 = const()[name = tensor("op_2852"), val = tensor([1, 1])]; tensor q_53_pad_type_0 = const()[name = tensor("q_53_pad_type_0"), val = tensor("custom")]; tensor q_53_pad_0 = const()[name = tensor("q_53_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_53 = conv(dilations = var_2914, groups = var_1193, pad = q_53_pad_0, pad_type = q_53_pad_type_0, strides = var_2912, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_q_weight, x = hidden_states_119)[name = tensor("q_53")]; - tensor var_2918 = const()[name = tensor("op_2918"), val = tensor([1, 1])]; - tensor var_2920 = const()[name = tensor("op_2920"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(823787776)))]; + tensor q_53_cast = conv(dilations = var_2852, groups = var_31, pad = q_53_pad_0, pad_type = q_53_pad_type_0, strides = var_2850, weight = unet_down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16, x = hidden_states_119_cast)[name = tensor("q_53_cast")]; + tensor var_2856 = const()[name = tensor("op_2856"), val = tensor([1, 1])]; + tensor var_2858 = const()[name = tensor("op_2858"), val = tensor([1, 1])]; tensor k_53_pad_type_0 = const()[name = tensor("k_53_pad_type_0"), val = tensor("custom")]; tensor k_53_pad_0 = const()[name = tensor("k_53_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_53 = conv(dilations = var_2920, groups = var_1193, pad = k_53_pad_0, pad_type = k_53_pad_type_0, strides = var_2918, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_k_weight, x = hidden_states_119)[name = tensor("k_53")]; - tensor var_2924 = const()[name = tensor("op_2924"), val = tensor([1, 1])]; - tensor var_2926 = const()[name = tensor("op_2926"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(827064640)))]; + tensor k_53_cast = conv(dilations = var_2858, groups = var_31, pad = k_53_pad_0, pad_type = k_53_pad_type_0, strides = var_2856, weight = unet_down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16, x = hidden_states_119_cast)[name = tensor("k_53_cast")]; + tensor var_2862 = const()[name = tensor("op_2862"), val = tensor([1, 1])]; + tensor var_2864 = const()[name = tensor("op_2864"), val = tensor([1, 1])]; tensor v_53_pad_type_0 = const()[name = tensor("v_53_pad_type_0"), val = tensor("custom")]; tensor v_53_pad_0 = const()[name = tensor("v_53_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_53 = conv(dilations = var_2926, groups = var_1193, pad = v_53_pad_0, pad_type = v_53_pad_type_0, strides = var_2924, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_v_weight, x = hidden_states_119)[name = tensor("v_53")]; - tensor var_2930 = const()[name = tensor("op_2930"), val = tensor([2, 20, 64, -1])]; - tensor var_2931 = reshape(shape = var_2930, x = q_53)[name = tensor("op_2931")]; - tensor var_2932 = const()[name = tensor("op_2932"), val = tensor([2, 20, 64, -1])]; - tensor var_2933 = reshape(shape = var_2932, x = k_53)[name = tensor("op_2933")]; - tensor var_2934 = const()[name = tensor("op_2934"), val = tensor([2, 20, 64, -1])]; - tensor var_2935 = reshape(shape = var_2934, x = v_53)[name = tensor("op_2935")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(830341504)))]; + tensor v_53_cast = conv(dilations = var_2864, groups = var_31, pad = v_53_pad_0, pad_type = v_53_pad_type_0, strides = var_2862, weight = unet_down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16, x = hidden_states_119_cast)[name = tensor("v_53_cast")]; + tensor var_2868 = const()[name = tensor("op_2868"), val = tensor([2, 20, 64, -1])]; + tensor var_2869_cast = reshape(shape = var_2868, x = q_53_cast)[name = tensor("op_2869_cast")]; + tensor var_2870 = const()[name = tensor("op_2870"), val = tensor([2, 20, 64, -1])]; + tensor var_2871_cast = reshape(shape = var_2870, x = k_53_cast)[name = tensor("op_2871_cast")]; + tensor var_2872 = const()[name = tensor("op_2872"), val = tensor([2, 20, 64, -1])]; + tensor var_2873_cast = reshape(shape = var_2872, x = v_53_cast)[name = tensor("op_2873_cast")]; tensor attn_weights_105_transpose_x_0 = const()[name = tensor("attn_weights_105_transpose_x_0"), val = tensor(true)]; tensor attn_weights_105_transpose_y_0 = const()[name = tensor("attn_weights_105_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_105 = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_2931, y = var_2933)[name = tensor("attn_weights_105")]; - tensor attn_weights_107 = mul(x = attn_weights_105, y = var_1184)[name = tensor("attn_weights_107")]; - tensor var_2939 = softmax(axis = var_1177, x = attn_weights_107)[name = tensor("op_2939")]; + tensor attn_weights_105_cast = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_2869_cast, y = var_2871_cast)[name = tensor("attn_weights_105_cast")]; + tensor attn_weights_107_cast = mul(x = attn_weights_105_cast, y = var_12_to_fp16)[name = tensor("attn_weights_107_cast")]; + tensor var_2877_cast = softmax(axis = var_18, x = attn_weights_107_cast)[name = tensor("op_2877_cast")]; tensor attn_53_transpose_x_0 = const()[name = tensor("attn_53_transpose_x_0"), val = tensor(false)]; tensor attn_53_transpose_y_0 = const()[name = tensor("attn_53_transpose_y_0"), val = tensor(true)]; - tensor attn_53 = matmul(transpose_x = attn_53_transpose_x_0, transpose_y = attn_53_transpose_y_0, x = var_2935, y = var_2939)[name = tensor("attn_53")]; - tensor var_2943 = const()[name = tensor("op_2943"), val = tensor([2, 1280, 1, -1])]; - tensor input_203 = reshape(shape = var_2943, x = attn_53)[name = tensor("input_203")]; - tensor var_2948 = const()[name = tensor("op_2948"), val = tensor([1, 1])]; - tensor var_2950 = const()[name = tensor("op_2950"), val = tensor([1, 1])]; - tensor var_2952_pad_type_0 = const()[name = tensor("op_2952_pad_type_0"), val = tensor("custom")]; - tensor var_2952_pad_0 = const()[name = tensor("op_2952_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_2952 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_bias, dilations = var_2950, groups = var_1193, pad = var_2952_pad_0, pad_type = var_2952_pad_type_0, strides = var_2948, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_weight, x = input_203)[name = tensor("op_2952")]; - tensor inputs_81 = add(x = var_2952, y = inputs_79)[name = tensor("inputs_81")]; - tensor var_2956 = const()[name = tensor("op_2956"), val = tensor([1])]; - tensor channels_mean_81 = reduce_mean(axes = var_2956, keep_dims = var_1188, x = inputs_81)[name = tensor("channels_mean_81")]; - tensor zero_mean_81 = sub(x = inputs_81, y = channels_mean_81)[name = tensor("zero_mean_81")]; - tensor zero_mean_sq_81 = mul(x = zero_mean_81, y = zero_mean_81)[name = tensor("zero_mean_sq_81")]; - tensor var_2960 = const()[name = tensor("op_2960"), val = tensor([1])]; - tensor var_2961 = reduce_mean(axes = var_2960, keep_dims = var_1188, x = zero_mean_sq_81)[name = tensor("op_2961")]; - tensor var_2962 = const()[name = tensor("op_2962"), val = tensor(0x1.4f8b58p-17)]; - tensor var_2963 = add(x = var_2961, y = var_2962)[name = tensor("op_2963")]; - tensor denom_81_epsilon_0 = const()[name = tensor("denom_81_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_81 = rsqrt(epsilon = denom_81_epsilon_0, x = var_2963)[name = tensor("denom_81")]; - tensor out_81 = mul(x = zero_mean_81, y = denom_81)[name = tensor("out_81")]; - tensor var_2967 = const()[name = tensor("op_2967"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268015360)))]; - tensor var_2968 = add(x = out_81, y = var_2967)[name = tensor("op_2968")]; - tensor var_2970 = const()[name = tensor("op_2970"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268020544)))]; - tensor hidden_states_121 = mul(x = var_2968, y = var_2970)[name = tensor("hidden_states_121")]; - tensor var_2977 = const()[name = tensor("op_2977"), val = tensor([1, 1])]; - tensor var_2979 = const()[name = tensor("op_2979"), val = tensor([1, 1])]; + tensor attn_53_cast = matmul(transpose_x = attn_53_transpose_x_0, transpose_y = attn_53_transpose_y_0, x = var_2873_cast, y = var_2877_cast)[name = tensor("attn_53_cast")]; + tensor var_2881 = const()[name = tensor("op_2881"), val = tensor([2, 1280, 1, -1])]; + tensor input_203_cast = reshape(shape = var_2881, x = attn_53_cast)[name = tensor("input_203_cast")]; + tensor var_2886 = const()[name = tensor("op_2886"), val = tensor([1, 1])]; + tensor var_2888 = const()[name = tensor("op_2888"), val = tensor([1, 1])]; + tensor var_2890_pad_type_0 = const()[name = tensor("op_2890_pad_type_0"), val = tensor("custom")]; + tensor var_2890_pad_0 = const()[name = tensor("op_2890_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(833618368)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(836895232)))]; + tensor var_2890_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_2888, groups = var_31, pad = var_2890_pad_0, pad_type = var_2890_pad_type_0, strides = var_2886, weight = unet_down_blocks_2_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16, x = input_203_cast)[name = tensor("op_2890_cast")]; + tensor inputs_81_cast = add(x = var_2890_cast, y = inputs_79_cast)[name = tensor("inputs_81_cast")]; + tensor var_2894 = const()[name = tensor("op_2894"), val = tensor([1])]; + tensor channels_mean_81_cast = reduce_mean(axes = var_2894, keep_dims = var_23, x = inputs_81_cast)[name = tensor("channels_mean_81_cast")]; + tensor zero_mean_81_cast = sub(x = inputs_81_cast, y = channels_mean_81_cast)[name = tensor("zero_mean_81_cast")]; + tensor zero_mean_sq_81_cast = mul(x = zero_mean_81_cast, y = zero_mean_81_cast)[name = tensor("zero_mean_sq_81_cast")]; + tensor var_2898 = const()[name = tensor("op_2898"), val = tensor([1])]; + tensor var_2899_cast = reduce_mean(axes = var_2898, keep_dims = var_23, x = zero_mean_sq_81_cast)[name = tensor("op_2899_cast")]; + tensor var_2900_to_fp16 = const()[name = tensor("op_2900_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2901_cast = add(x = var_2899_cast, y = var_2900_to_fp16)[name = tensor("op_2901_cast")]; + tensor denom_81_epsilon_0_to_fp16 = const()[name = tensor("denom_81_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_81_cast = rsqrt(epsilon = denom_81_epsilon_0_to_fp16, x = var_2901_cast)[name = tensor("denom_81_cast")]; + tensor out_81_cast = mul(x = zero_mean_81_cast, y = denom_81_cast)[name = tensor("out_81_cast")]; + tensor var_2905_to_fp16 = const()[name = tensor("op_2905_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(836897856)))]; + tensor var_2906_cast = add(x = out_81_cast, y = var_2905_to_fp16)[name = tensor("op_2906_cast")]; + tensor var_2908_to_fp16 = const()[name = tensor("op_2908_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(836900480)))]; + tensor hidden_states_121_cast = mul(x = var_2906_cast, y = var_2908_to_fp16)[name = tensor("hidden_states_121_cast")]; + tensor var_2915 = const()[name = tensor("op_2915"), val = tensor([1, 1])]; + tensor var_2917 = const()[name = tensor("op_2917"), val = tensor([1, 1])]; tensor q_55_pad_type_0 = const()[name = tensor("q_55_pad_type_0"), val = tensor("custom")]; tensor q_55_pad_0 = const()[name = tensor("q_55_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_55 = conv(dilations = var_2979, groups = var_1193, pad = q_55_pad_0, pad_type = q_55_pad_type_0, strides = var_2977, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_q_weight, x = hidden_states_121)[name = tensor("q_55")]; - tensor var_2983 = const()[name = tensor("op_2983"), val = tensor([1, 1])]; - tensor var_2985 = const()[name = tensor("op_2985"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(836903104)))]; + tensor q_55_cast = conv(dilations = var_2917, groups = var_31, pad = q_55_pad_0, pad_type = q_55_pad_type_0, strides = var_2915, weight = unet_down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16, x = hidden_states_121_cast)[name = tensor("q_55_cast")]; + tensor var_2921 = const()[name = tensor("op_2921"), val = tensor([1, 1])]; + tensor var_2923 = const()[name = tensor("op_2923"), val = tensor([1, 1])]; tensor k_55_pad_type_0 = const()[name = tensor("k_55_pad_type_0"), val = tensor("custom")]; tensor k_55_pad_0 = const()[name = tensor("k_55_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_55 = conv(dilations = var_2985, groups = var_1193, pad = k_55_pad_0, pad_type = k_55_pad_type_0, strides = var_2983, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_55")]; - tensor var_2989 = const()[name = tensor("op_2989"), val = tensor([1, 1])]; - tensor var_2991 = const()[name = tensor("op_2991"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(840179968)))]; + tensor k_55_cast = conv(dilations = var_2923, groups = var_31, pad = k_55_pad_0, pad_type = k_55_pad_type_0, strides = var_2921, weight = unet_down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_55_cast")]; + tensor var_2927 = const()[name = tensor("op_2927"), val = tensor([1, 1])]; + tensor var_2929 = const()[name = tensor("op_2929"), val = tensor([1, 1])]; tensor v_55_pad_type_0 = const()[name = tensor("v_55_pad_type_0"), val = tensor("custom")]; tensor v_55_pad_0 = const()[name = tensor("v_55_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_55 = conv(dilations = var_2991, groups = var_1193, pad = v_55_pad_0, pad_type = v_55_pad_type_0, strides = var_2989, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_55")]; - tensor var_2995 = const()[name = tensor("op_2995"), val = tensor([2, 20, 64, -1])]; - tensor var_2996 = reshape(shape = var_2995, x = q_55)[name = tensor("op_2996")]; - tensor var_2997 = const()[name = tensor("op_2997"), val = tensor([2, 20, 64, -1])]; - tensor var_2998 = reshape(shape = var_2997, x = k_55)[name = tensor("op_2998")]; - tensor var_2999 = const()[name = tensor("op_2999"), val = tensor([2, 20, 64, -1])]; - tensor var_3000 = reshape(shape = var_2999, x = v_55)[name = tensor("op_3000")]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(845422912)))]; + tensor v_55_cast = conv(dilations = var_2929, groups = var_31, pad = v_55_pad_0, pad_type = v_55_pad_type_0, strides = var_2927, weight = unet_down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_55_cast")]; + tensor var_2933 = const()[name = tensor("op_2933"), val = tensor([2, 20, 64, -1])]; + tensor var_2934_cast = reshape(shape = var_2933, x = q_55_cast)[name = tensor("op_2934_cast")]; + tensor var_2935 = const()[name = tensor("op_2935"), val = tensor([2, 20, 64, -1])]; + tensor var_2936_cast = reshape(shape = var_2935, x = k_55_cast)[name = tensor("op_2936_cast")]; + tensor var_2937 = const()[name = tensor("op_2937"), val = tensor([2, 20, 64, -1])]; + tensor var_2938_cast = reshape(shape = var_2937, x = v_55_cast)[name = tensor("op_2938_cast")]; tensor attn_weights_109_transpose_x_0 = const()[name = tensor("attn_weights_109_transpose_x_0"), val = tensor(true)]; tensor attn_weights_109_transpose_y_0 = const()[name = tensor("attn_weights_109_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_109 = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = var_2996, y = var_2998)[name = tensor("attn_weights_109")]; - tensor attn_weights_111 = mul(x = attn_weights_109, y = var_1184)[name = tensor("attn_weights_111")]; - tensor var_3004 = softmax(axis = var_1177, x = attn_weights_111)[name = tensor("op_3004")]; + tensor attn_weights_109_cast = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = var_2934_cast, y = var_2936_cast)[name = tensor("attn_weights_109_cast")]; + tensor attn_weights_111_cast = mul(x = attn_weights_109_cast, y = var_12_to_fp16)[name = tensor("attn_weights_111_cast")]; + tensor var_2942_cast = softmax(axis = var_18, x = attn_weights_111_cast)[name = tensor("op_2942_cast")]; tensor attn_55_transpose_x_0 = const()[name = tensor("attn_55_transpose_x_0"), val = tensor(false)]; tensor attn_55_transpose_y_0 = const()[name = tensor("attn_55_transpose_y_0"), val = tensor(true)]; - tensor attn_55 = matmul(transpose_x = attn_55_transpose_x_0, transpose_y = attn_55_transpose_y_0, x = var_3000, y = var_3004)[name = tensor("attn_55")]; - tensor var_3008 = const()[name = tensor("op_3008"), val = tensor([2, 1280, 1, -1])]; - tensor input_205 = reshape(shape = var_3008, x = attn_55)[name = tensor("input_205")]; - tensor var_3013 = const()[name = tensor("op_3013"), val = tensor([1, 1])]; - tensor var_3015 = const()[name = tensor("op_3015"), val = tensor([1, 1])]; - tensor var_3017_pad_type_0 = const()[name = tensor("op_3017_pad_type_0"), val = tensor("custom")]; - tensor var_3017_pad_0 = const()[name = tensor("op_3017_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3017 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_bias, dilations = var_3015, groups = var_1193, pad = var_3017_pad_0, pad_type = var_3017_pad_type_0, strides = var_3013, weight = down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_weight, x = input_205)[name = tensor("op_3017")]; - tensor inputs_83 = add(x = var_3017, y = inputs_81)[name = tensor("inputs_83")]; - tensor var_3021 = const()[name = tensor("op_3021"), val = tensor([1])]; - tensor channels_mean_83 = reduce_mean(axes = var_3021, keep_dims = var_1188, x = inputs_83)[name = tensor("channels_mean_83")]; - tensor zero_mean_83 = sub(x = inputs_83, y = channels_mean_83)[name = tensor("zero_mean_83")]; - tensor zero_mean_sq_83 = mul(x = zero_mean_83, y = zero_mean_83)[name = tensor("zero_mean_sq_83")]; - tensor var_3025 = const()[name = tensor("op_3025"), val = tensor([1])]; - tensor var_3026 = reduce_mean(axes = var_3025, keep_dims = var_1188, x = zero_mean_sq_83)[name = tensor("op_3026")]; - tensor var_3027 = const()[name = tensor("op_3027"), val = tensor(0x1.4f8b58p-17)]; - tensor var_3028 = add(x = var_3026, y = var_3027)[name = tensor("op_3028")]; - tensor denom_83_epsilon_0 = const()[name = tensor("denom_83_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_83 = rsqrt(epsilon = denom_83_epsilon_0, x = var_3028)[name = tensor("denom_83")]; - tensor out_83 = mul(x = zero_mean_83, y = denom_83)[name = tensor("out_83")]; - tensor var_3032 = const()[name = tensor("op_3032"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268025728)))]; - tensor var_3033 = add(x = out_83, y = var_3032)[name = tensor("op_3033")]; - tensor var_3035 = const()[name = tensor("op_3035"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268030912)))]; - tensor input_207 = mul(x = var_3033, y = var_3035)[name = tensor("input_207")]; - tensor var_3043 = const()[name = tensor("op_3043"), val = tensor([1, 1])]; - tensor var_3045 = const()[name = tensor("op_3045"), val = tensor([1, 1])]; - tensor var_3047_pad_type_0 = const()[name = tensor("op_3047_pad_type_0"), val = tensor("custom")]; - tensor var_3047_pad_0 = const()[name = tensor("op_3047_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3047 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_bias, dilations = var_3045, groups = var_1193, pad = var_3047_pad_0, pad_type = var_3047_pad_type_0, strides = var_3043, weight = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_weight, x = input_207)[name = tensor("op_3047")]; - tensor var_3048_split_sizes_0 = const()[name = tensor("op_3048_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_3048_axis_0 = const()[name = tensor("op_3048_axis_0"), val = tensor(1)]; - tensor var_3048_0, tensor var_3048_1 = split(axis = var_3048_axis_0, split_sizes = var_3048_split_sizes_0, x = var_3047)[name = tensor("op_3048")]; - tensor var_3050_mode_0 = const()[name = tensor("op_3050_mode_0"), val = tensor("EXACT")]; - tensor var_3050 = gelu(mode = var_3050_mode_0, x = var_3048_1)[name = tensor("op_3050")]; - tensor input_209 = mul(x = var_3048_0, y = var_3050)[name = tensor("input_209")]; - tensor var_3054 = const()[name = tensor("op_3054"), val = tensor([1, 1])]; - tensor var_3056 = const()[name = tensor("op_3056"), val = tensor([1, 1])]; - tensor var_3058_pad_type_0 = const()[name = tensor("op_3058_pad_type_0"), val = tensor("custom")]; - tensor var_3058_pad_0 = const()[name = tensor("op_3058_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3058 = conv(bias = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_bias, dilations = var_3056, groups = var_1193, pad = var_3058_pad_0, pad_type = var_3058_pad_type_0, strides = var_3054, weight = down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_weight, x = input_209)[name = tensor("op_3058")]; - tensor hidden_states_125 = add(x = var_3058, y = inputs_83)[name = tensor("hidden_states_125")]; - tensor var_3060 = const()[name = tensor("op_3060"), val = tensor([2, 1280, 32, 32])]; - tensor input_211 = reshape(shape = var_3060, x = hidden_states_125)[name = tensor("input_211")]; - tensor var_3064 = const()[name = tensor("op_3064"), val = tensor([1, 1])]; - tensor var_3066 = const()[name = tensor("op_3066"), val = tensor([1, 1])]; + tensor attn_55_cast = matmul(transpose_x = attn_55_transpose_x_0, transpose_y = attn_55_transpose_y_0, x = var_2938_cast, y = var_2942_cast)[name = tensor("attn_55_cast")]; + tensor var_2946 = const()[name = tensor("op_2946"), val = tensor([2, 1280, 1, -1])]; + tensor input_205_cast = reshape(shape = var_2946, x = attn_55_cast)[name = tensor("input_205_cast")]; + tensor var_2951 = const()[name = tensor("op_2951"), val = tensor([1, 1])]; + tensor var_2953 = const()[name = tensor("op_2953"), val = tensor([1, 1])]; + tensor var_2955_pad_type_0 = const()[name = tensor("op_2955_pad_type_0"), val = tensor("custom")]; + tensor var_2955_pad_0 = const()[name = tensor("op_2955_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(850665856)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(853942720)))]; + tensor var_2955_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_2953, groups = var_31, pad = var_2955_pad_0, pad_type = var_2955_pad_type_0, strides = var_2951, weight = unet_down_blocks_2_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16, x = input_205_cast)[name = tensor("op_2955_cast")]; + tensor inputs_83_cast = add(x = var_2955_cast, y = inputs_81_cast)[name = tensor("inputs_83_cast")]; + tensor var_2959 = const()[name = tensor("op_2959"), val = tensor([1])]; + tensor channels_mean_83_cast = reduce_mean(axes = var_2959, keep_dims = var_23, x = inputs_83_cast)[name = tensor("channels_mean_83_cast")]; + tensor zero_mean_83_cast = sub(x = inputs_83_cast, y = channels_mean_83_cast)[name = tensor("zero_mean_83_cast")]; + tensor zero_mean_sq_83_cast = mul(x = zero_mean_83_cast, y = zero_mean_83_cast)[name = tensor("zero_mean_sq_83_cast")]; + tensor var_2963 = const()[name = tensor("op_2963"), val = tensor([1])]; + tensor var_2964_cast = reduce_mean(axes = var_2963, keep_dims = var_23, x = zero_mean_sq_83_cast)[name = tensor("op_2964_cast")]; + tensor var_2965_to_fp16 = const()[name = tensor("op_2965_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2966_cast = add(x = var_2964_cast, y = var_2965_to_fp16)[name = tensor("op_2966_cast")]; + tensor denom_83_epsilon_0_to_fp16 = const()[name = tensor("denom_83_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_83_cast = rsqrt(epsilon = denom_83_epsilon_0_to_fp16, x = var_2966_cast)[name = tensor("denom_83_cast")]; + tensor out_83_cast = mul(x = zero_mean_83_cast, y = denom_83_cast)[name = tensor("out_83_cast")]; + tensor var_2970_to_fp16 = const()[name = tensor("op_2970_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(853945344)))]; + tensor var_2971_cast = add(x = out_83_cast, y = var_2970_to_fp16)[name = tensor("op_2971_cast")]; + tensor var_2973_to_fp16 = const()[name = tensor("op_2973_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(853947968)))]; + tensor input_207_cast = mul(x = var_2971_cast, y = var_2973_to_fp16)[name = tensor("input_207_cast")]; + tensor var_2981 = const()[name = tensor("op_2981"), val = tensor([1, 1])]; + tensor var_2983 = const()[name = tensor("op_2983"), val = tensor([1, 1])]; + tensor var_2985_pad_type_0 = const()[name = tensor("op_2985_pad_type_0"), val = tensor("custom")]; + tensor var_2985_pad_0 = const()[name = tensor("op_2985_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(853950592)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(880165056)))]; + tensor var_2985_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_2983, groups = var_31, pad = var_2985_pad_0, pad_type = var_2985_pad_type_0, strides = var_2981, weight = unet_down_blocks_2_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16, x = input_207_cast)[name = tensor("op_2985_cast")]; + tensor var_2986_split_sizes_0 = const()[name = tensor("op_2986_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2986_axis_0 = const()[name = tensor("op_2986_axis_0"), val = tensor(1)]; + tensor var_2986_cast_0, tensor var_2986_cast_1 = split(axis = var_2986_axis_0, split_sizes = var_2986_split_sizes_0, x = var_2985_cast)[name = tensor("op_2986_cast")]; + tensor var_2988_mode_0 = const()[name = tensor("op_2988_mode_0"), val = tensor("EXACT")]; + tensor var_2988_cast = gelu(mode = var_2988_mode_0, x = var_2986_cast_1)[name = tensor("op_2988_cast")]; + tensor input_209_cast = mul(x = var_2986_cast_0, y = var_2988_cast)[name = tensor("input_209_cast")]; + tensor var_2992 = const()[name = tensor("op_2992"), val = tensor([1, 1])]; + tensor var_2994 = const()[name = tensor("op_2994"), val = tensor([1, 1])]; + tensor var_2996_pad_type_0 = const()[name = tensor("op_2996_pad_type_0"), val = tensor("custom")]; + tensor var_2996_pad_0 = const()[name = tensor("op_2996_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(880185600)))]; + tensor unet_down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893292864)))]; + tensor var_2996_cast = conv(bias = unet_down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_2994, groups = var_31, pad = var_2996_pad_0, pad_type = var_2996_pad_type_0, strides = var_2992, weight = unet_down_blocks_2_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16, x = input_209_cast)[name = tensor("op_2996_cast")]; + tensor hidden_states_125_cast = add(x = var_2996_cast, y = inputs_83_cast)[name = tensor("hidden_states_125_cast")]; + tensor var_2998 = const()[name = tensor("op_2998"), val = tensor([2, 1280, 32, 32])]; + tensor input_211_cast = reshape(shape = var_2998, x = hidden_states_125_cast)[name = tensor("input_211_cast")]; + tensor var_3002 = const()[name = tensor("op_3002"), val = tensor([1, 1])]; + tensor var_3004 = const()[name = tensor("op_3004"), val = tensor([1, 1])]; tensor hidden_states_127_pad_type_0 = const()[name = tensor("hidden_states_127_pad_type_0"), val = tensor("custom")]; tensor hidden_states_127_pad_0 = const()[name = tensor("hidden_states_127_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_127 = conv(bias = down_blocks_2_attentions_0_proj_out_bias, dilations = var_3066, groups = var_1193, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = var_3064, weight = down_blocks_2_attentions_0_proj_out_weight, x = input_211)[name = tensor("hidden_states_127")]; - tensor input_213 = add(x = hidden_states_127, y = hidden_states_61)[name = tensor("input_213")]; + tensor unet_down_blocks_2_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(893295488)))]; + tensor unet_down_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(896572352)))]; + tensor hidden_states_127_cast = conv(bias = unet_down_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_3004, groups = var_31, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = var_3002, weight = unet_down_blocks_2_attentions_0_proj_out_weight_to_fp16, x = input_211_cast)[name = tensor("hidden_states_127_cast")]; + tensor input_213_cast = add(x = hidden_states_127_cast, y = hidden_states_61_cast)[name = tensor("input_213_cast")]; tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([2, 32, 40, 32, 32])]; - tensor reshape_52 = reshape(shape = reshape_52_shape_0, x = input_213)[name = tensor("reshape_52")]; + tensor reshape_52_cast = reshape(shape = reshape_52_shape_0, x = input_213_cast)[name = tensor("reshape_52_cast")]; tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_39 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52)[name = tensor("reduce_mean_39")]; - tensor sub_26 = sub(x = reshape_52, y = reduce_mean_39)[name = tensor("sub_26")]; - tensor square_13 = square(x = sub_26)[name = tensor("square_13")]; + tensor reduce_mean_39_cast = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast)[name = tensor("reduce_mean_39_cast")]; + tensor sub_26_cast = sub(x = reshape_52_cast, y = reduce_mean_39_cast)[name = tensor("sub_26_cast")]; + tensor square_13_cast = square(x = sub_26_cast)[name = tensor("square_13_cast")]; tensor reduce_mean_41_axes_0 = const()[name = tensor("reduce_mean_41_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_41_keep_dims_0 = const()[name = tensor("reduce_mean_41_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_41 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13)[name = tensor("reduce_mean_41")]; - tensor add_26_y_0 = const()[name = tensor("add_26_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_26 = add(x = reduce_mean_41, y = add_26_y_0)[name = tensor("add_26")]; - tensor sqrt_13 = sqrt(x = add_26)[name = tensor("sqrt_13")]; - tensor real_div_13 = real_div(x = sub_26, y = sqrt_13)[name = tensor("real_div_13")]; + tensor reduce_mean_41_cast = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast)[name = tensor("reduce_mean_41_cast")]; + tensor add_26_y_0_to_fp16 = const()[name = tensor("add_26_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_26_cast = add(x = reduce_mean_41_cast, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast")]; + tensor sqrt_13_cast = sqrt(x = add_26_cast)[name = tensor("sqrt_13_cast")]; + tensor real_div_13_cast = real_div(x = sub_26_cast, y = sqrt_13_cast)[name = tensor("real_div_13_cast")]; tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([2, 1280, 32, 32])]; - tensor reshape_53 = reshape(shape = reshape_53_shape_0, x = real_div_13)[name = tensor("reshape_53")]; - tensor add_27_gamma_0 = const()[name = tensor("add_27_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268036096)))]; - tensor add_27_beta_0 = const()[name = tensor("add_27_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268041280)))]; - tensor add_27_epsilon_0 = const()[name = tensor("add_27_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_27 = batch_norm(beta = add_27_beta_0, epsilon = add_27_epsilon_0, gamma = add_27_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_53)[name = tensor("add_27")]; - tensor input_217 = silu(x = add_27)[name = tensor("input_217")]; - tensor var_3081 = const()[name = tensor("op_3081"), val = tensor([1, 1])]; - tensor var_3083 = const()[name = tensor("op_3083"), val = tensor([1, 1])]; + tensor reshape_53_cast = reshape(shape = reshape_53_shape_0, x = real_div_13_cast)[name = tensor("reshape_53_cast")]; + tensor add_27_gamma_0_to_fp16 = const()[name = tensor("add_27_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(896574976)))]; + tensor add_27_beta_0_to_fp16 = const()[name = tensor("add_27_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(896577600)))]; + tensor add_27_epsilon_0_to_fp16 = const()[name = tensor("add_27_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_27_cast = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_53_cast)[name = tensor("add_27_cast")]; + tensor input_217_cast = silu(x = add_27_cast)[name = tensor("input_217_cast")]; + tensor var_3019 = const()[name = tensor("op_3019"), val = tensor([1, 1])]; + tensor var_3021 = const()[name = tensor("op_3021"), val = tensor([1, 1])]; tensor hidden_states_129_pad_type_0 = const()[name = tensor("hidden_states_129_pad_type_0"), val = tensor("custom")]; tensor hidden_states_129_pad_0 = const()[name = tensor("hidden_states_129_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_129 = conv(bias = down_blocks_2_resnets_1_conv1_bias, dilations = var_3083, groups = var_1193, pad = hidden_states_129_pad_0, pad_type = hidden_states_129_pad_type_0, strides = var_3081, weight = down_blocks_2_resnets_1_conv1_weight, x = input_217)[name = tensor("hidden_states_129")]; - tensor var_3089 = const()[name = tensor("op_3089"), val = tensor([1, 1])]; - tensor var_3091 = const()[name = tensor("op_3091"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(896580224)))]; + tensor unet_down_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(926071488)))]; + tensor hidden_states_129_cast = conv(bias = unet_down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_3021, groups = var_31, pad = hidden_states_129_pad_0, pad_type = hidden_states_129_pad_type_0, strides = var_3019, weight = unet_down_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_217_cast)[name = tensor("hidden_states_129_cast")]; + tensor var_3027 = const()[name = tensor("op_3027"), val = tensor([1, 1])]; + tensor var_3029 = const()[name = tensor("op_3029"), val = tensor([1, 1])]; tensor temb_11_pad_type_0 = const()[name = tensor("temb_11_pad_type_0"), val = tensor("custom")]; tensor temb_11_pad_0 = const()[name = tensor("temb_11_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb_11 = conv(bias = down_blocks_2_resnets_1_time_emb_proj_bias, dilations = var_3091, groups = var_1193, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = var_3089, weight = down_blocks_2_resnets_1_time_emb_proj_weight, x = input_21)[name = tensor("temb_11")]; - tensor input_221 = add(x = hidden_states_129, y = temb_11)[name = tensor("input_221")]; + tensor unet_down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(926074112)))]; + tensor unet_down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(929350976)))]; + tensor temb_11_cast = conv(bias = unet_down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_3029, groups = var_31, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = var_3027, weight = unet_down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_11_cast")]; + tensor input_221_cast = add(x = hidden_states_129_cast, y = temb_11_cast)[name = tensor("input_221_cast")]; tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([2, 32, 40, 32, 32])]; - tensor reshape_56 = reshape(shape = reshape_56_shape_0, x = input_221)[name = tensor("reshape_56")]; + tensor reshape_56_cast = reshape(shape = reshape_56_shape_0, x = input_221_cast)[name = tensor("reshape_56_cast")]; tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_42_keep_dims_0 = const()[name = tensor("reduce_mean_42_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_42 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56)[name = tensor("reduce_mean_42")]; - tensor sub_28 = sub(x = reshape_56, y = reduce_mean_42)[name = tensor("sub_28")]; - tensor square_14 = square(x = sub_28)[name = tensor("square_14")]; + tensor reduce_mean_42_cast = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast)[name = tensor("reduce_mean_42_cast")]; + tensor sub_28_cast = sub(x = reshape_56_cast, y = reduce_mean_42_cast)[name = tensor("sub_28_cast")]; + tensor square_14_cast = square(x = sub_28_cast)[name = tensor("square_14_cast")]; tensor reduce_mean_44_axes_0 = const()[name = tensor("reduce_mean_44_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_44_keep_dims_0 = const()[name = tensor("reduce_mean_44_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_44 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14)[name = tensor("reduce_mean_44")]; - tensor add_28_y_0 = const()[name = tensor("add_28_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_28 = add(x = reduce_mean_44, y = add_28_y_0)[name = tensor("add_28")]; - tensor sqrt_14 = sqrt(x = add_28)[name = tensor("sqrt_14")]; - tensor real_div_14 = real_div(x = sub_28, y = sqrt_14)[name = tensor("real_div_14")]; + tensor reduce_mean_44_cast = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast)[name = tensor("reduce_mean_44_cast")]; + tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_28_cast = add(x = reduce_mean_44_cast, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast")]; + tensor sqrt_14_cast = sqrt(x = add_28_cast)[name = tensor("sqrt_14_cast")]; + tensor real_div_14_cast = real_div(x = sub_28_cast, y = sqrt_14_cast)[name = tensor("real_div_14_cast")]; tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([2, 1280, 32, 32])]; - tensor reshape_57 = reshape(shape = reshape_57_shape_0, x = real_div_14)[name = tensor("reshape_57")]; - tensor add_29_gamma_0 = const()[name = tensor("add_29_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268046464)))]; - tensor add_29_beta_0 = const()[name = tensor("add_29_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268051648)))]; - tensor add_29_epsilon_0 = const()[name = tensor("add_29_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_29 = batch_norm(beta = add_29_beta_0, epsilon = add_29_epsilon_0, gamma = add_29_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_57)[name = tensor("add_29")]; - tensor input_225 = silu(x = add_29)[name = tensor("input_225")]; - tensor var_3101 = const()[name = tensor("op_3101"), val = tensor([1, 1])]; - tensor var_3103 = const()[name = tensor("op_3103"), val = tensor([1, 1])]; + tensor reshape_57_cast = reshape(shape = reshape_57_shape_0, x = real_div_14_cast)[name = tensor("reshape_57_cast")]; + tensor add_29_gamma_0_to_fp16 = const()[name = tensor("add_29_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(929353600)))]; + tensor add_29_beta_0_to_fp16 = const()[name = tensor("add_29_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(929356224)))]; + tensor add_29_epsilon_0_to_fp16 = const()[name = tensor("add_29_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_29_cast = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_57_cast)[name = tensor("add_29_cast")]; + tensor input_225_cast = silu(x = add_29_cast)[name = tensor("input_225_cast")]; + tensor var_3039 = const()[name = tensor("op_3039"), val = tensor([1, 1])]; + tensor var_3041 = const()[name = tensor("op_3041"), val = tensor([1, 1])]; tensor hidden_states_131_pad_type_0 = const()[name = tensor("hidden_states_131_pad_type_0"), val = tensor("custom")]; tensor hidden_states_131_pad_0 = const()[name = tensor("hidden_states_131_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_131 = conv(bias = down_blocks_2_resnets_1_conv2_bias, dilations = var_3103, groups = var_1193, pad = hidden_states_131_pad_0, pad_type = hidden_states_131_pad_type_0, strides = var_3101, weight = down_blocks_2_resnets_1_conv2_weight, x = input_225)[name = tensor("hidden_states_131")]; - tensor hidden_states_133 = add(x = input_213, y = hidden_states_131)[name = tensor("hidden_states_133")]; + tensor unet_down_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(929358848)))]; + tensor unet_down_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(958850112)))]; + tensor hidden_states_131_cast = conv(bias = unet_down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_3041, groups = var_31, pad = hidden_states_131_pad_0, pad_type = hidden_states_131_pad_type_0, strides = var_3039, weight = unet_down_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_225_cast)[name = tensor("hidden_states_131_cast")]; + tensor hidden_states_133_cast = add(x = input_213_cast, y = hidden_states_131_cast)[name = tensor("hidden_states_133_cast")]; tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([2, 32, 40, 32, 32])]; - tensor reshape_60 = reshape(shape = reshape_60_shape_0, x = hidden_states_133)[name = tensor("reshape_60")]; + tensor reshape_60_cast = reshape(shape = reshape_60_shape_0, x = hidden_states_133_cast)[name = tensor("reshape_60_cast")]; tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_45 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60)[name = tensor("reduce_mean_45")]; - tensor sub_30 = sub(x = reshape_60, y = reduce_mean_45)[name = tensor("sub_30")]; - tensor square_15 = square(x = sub_30)[name = tensor("square_15")]; + tensor reduce_mean_45_cast = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast)[name = tensor("reduce_mean_45_cast")]; + tensor sub_30_cast = sub(x = reshape_60_cast, y = reduce_mean_45_cast)[name = tensor("sub_30_cast")]; + tensor square_15_cast = square(x = sub_30_cast)[name = tensor("square_15_cast")]; tensor reduce_mean_47_axes_0 = const()[name = tensor("reduce_mean_47_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_47_keep_dims_0 = const()[name = tensor("reduce_mean_47_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_47 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15)[name = tensor("reduce_mean_47")]; - tensor add_30_y_0 = const()[name = tensor("add_30_y_0"), val = tensor(0x1.0c6f7ap-20)]; - tensor add_30 = add(x = reduce_mean_47, y = add_30_y_0)[name = tensor("add_30")]; - tensor sqrt_15 = sqrt(x = add_30)[name = tensor("sqrt_15")]; - tensor real_div_15 = real_div(x = sub_30, y = sqrt_15)[name = tensor("real_div_15")]; + tensor reduce_mean_47_cast = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast)[name = tensor("reduce_mean_47_cast")]; + tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_30_cast = add(x = reduce_mean_47_cast, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast")]; + tensor sqrt_15_cast = sqrt(x = add_30_cast)[name = tensor("sqrt_15_cast")]; + tensor real_div_15_cast = real_div(x = sub_30_cast, y = sqrt_15_cast)[name = tensor("real_div_15_cast")]; tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([2, 1280, 32, 32])]; - tensor reshape_61 = reshape(shape = reshape_61_shape_0, x = real_div_15)[name = tensor("reshape_61")]; - tensor add_31_gamma_0 = const()[name = tensor("add_31_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268056832)))]; - tensor add_31_beta_0 = const()[name = tensor("add_31_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268062016)))]; - tensor add_31_epsilon_0 = const()[name = tensor("add_31_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_31 = batch_norm(beta = add_31_beta_0, epsilon = add_31_epsilon_0, gamma = add_31_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_61)[name = tensor("add_31")]; - tensor var_3141 = const()[name = tensor("op_3141"), val = tensor([1, 1])]; - tensor var_3143 = const()[name = tensor("op_3143"), val = tensor([1, 1])]; + tensor reshape_61_cast = reshape(shape = reshape_61_shape_0, x = real_div_15_cast)[name = tensor("reshape_61_cast")]; + tensor add_31_gamma_0_to_fp16 = const()[name = tensor("add_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(958852736)))]; + tensor add_31_beta_0_to_fp16 = const()[name = tensor("add_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(958855360)))]; + tensor add_31_epsilon_0_to_fp16 = const()[name = tensor("add_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_31_cast = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_61_cast)[name = tensor("add_31_cast")]; + tensor var_3079 = const()[name = tensor("op_3079"), val = tensor([1, 1])]; + tensor var_3081 = const()[name = tensor("op_3081"), val = tensor([1, 1])]; tensor hidden_states_135_pad_type_0 = const()[name = tensor("hidden_states_135_pad_type_0"), val = tensor("custom")]; tensor hidden_states_135_pad_0 = const()[name = tensor("hidden_states_135_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_135 = conv(bias = down_blocks_2_attentions_1_proj_in_bias, dilations = var_3143, groups = var_1193, pad = hidden_states_135_pad_0, pad_type = hidden_states_135_pad_type_0, strides = var_3141, weight = down_blocks_2_attentions_1_proj_in_weight, x = add_31)[name = tensor("hidden_states_135")]; - tensor var_3148 = const()[name = tensor("op_3148"), val = tensor([2, 1280, 1, 1024])]; - tensor inputs_85 = reshape(shape = var_3148, x = hidden_states_135)[name = tensor("inputs_85")]; - tensor var_3158 = const()[name = tensor("op_3158"), val = tensor([1])]; - tensor channels_mean_85 = reduce_mean(axes = var_3158, keep_dims = var_1188, x = inputs_85)[name = tensor("channels_mean_85")]; - tensor zero_mean_85 = sub(x = inputs_85, y = channels_mean_85)[name = tensor("zero_mean_85")]; - tensor zero_mean_sq_85 = mul(x = zero_mean_85, y = zero_mean_85)[name = tensor("zero_mean_sq_85")]; - tensor var_3162 = const()[name = tensor("op_3162"), val = tensor([1])]; - tensor var_3163 = reduce_mean(axes = var_3162, keep_dims = var_1188, x = zero_mean_sq_85)[name = tensor("op_3163")]; - tensor var_3164 = const()[name = tensor("op_3164"), val = tensor(0x1.4f8b58p-17)]; - tensor var_3165 = add(x = var_3163, y = var_3164)[name = tensor("op_3165")]; - tensor denom_85_epsilon_0 = const()[name = tensor("denom_85_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_85 = rsqrt(epsilon = denom_85_epsilon_0, x = var_3165)[name = tensor("denom_85")]; - tensor out_85 = mul(x = zero_mean_85, y = denom_85)[name = tensor("out_85")]; - tensor var_3169 = const()[name = tensor("op_3169"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268067200)))]; - tensor var_3170 = add(x = out_85, y = var_3169)[name = tensor("op_3170")]; - tensor var_3172 = const()[name = tensor("op_3172"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268072384)))]; - tensor hidden_states_137 = mul(x = var_3170, y = var_3172)[name = tensor("hidden_states_137")]; - tensor var_3179 = const()[name = tensor("op_3179"), val = tensor([1, 1])]; - tensor var_3181 = const()[name = tensor("op_3181"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(958857984)))]; + tensor unet_down_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(962134848)))]; + tensor hidden_states_135_cast = conv(bias = unet_down_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_3081, groups = var_31, pad = hidden_states_135_pad_0, pad_type = hidden_states_135_pad_type_0, strides = var_3079, weight = unet_down_blocks_2_attentions_1_proj_in_weight_to_fp16, x = add_31_cast)[name = tensor("hidden_states_135_cast")]; + tensor var_3086 = const()[name = tensor("op_3086"), val = tensor([2, 1280, 1, 1024])]; + tensor inputs_85_cast = reshape(shape = var_3086, x = hidden_states_135_cast)[name = tensor("inputs_85_cast")]; + tensor var_3096 = const()[name = tensor("op_3096"), val = tensor([1])]; + tensor channels_mean_85_cast = reduce_mean(axes = var_3096, keep_dims = var_23, x = inputs_85_cast)[name = tensor("channels_mean_85_cast")]; + tensor zero_mean_85_cast = sub(x = inputs_85_cast, y = channels_mean_85_cast)[name = tensor("zero_mean_85_cast")]; + tensor zero_mean_sq_85_cast = mul(x = zero_mean_85_cast, y = zero_mean_85_cast)[name = tensor("zero_mean_sq_85_cast")]; + tensor var_3100 = const()[name = tensor("op_3100"), val = tensor([1])]; + tensor var_3101_cast = reduce_mean(axes = var_3100, keep_dims = var_23, x = zero_mean_sq_85_cast)[name = tensor("op_3101_cast")]; + tensor var_3102_to_fp16 = const()[name = tensor("op_3102_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3103_cast = add(x = var_3101_cast, y = var_3102_to_fp16)[name = tensor("op_3103_cast")]; + tensor denom_85_epsilon_0_to_fp16 = const()[name = tensor("denom_85_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_85_cast = rsqrt(epsilon = denom_85_epsilon_0_to_fp16, x = var_3103_cast)[name = tensor("denom_85_cast")]; + tensor out_85_cast = mul(x = zero_mean_85_cast, y = denom_85_cast)[name = tensor("out_85_cast")]; + tensor var_3107_to_fp16 = const()[name = tensor("op_3107_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(962137472)))]; + tensor var_3108_cast = add(x = out_85_cast, y = var_3107_to_fp16)[name = tensor("op_3108_cast")]; + tensor var_3110_to_fp16 = const()[name = tensor("op_3110_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(962140096)))]; + tensor hidden_states_137_cast = mul(x = var_3108_cast, y = var_3110_to_fp16)[name = tensor("hidden_states_137_cast")]; + tensor var_3117 = const()[name = tensor("op_3117"), val = tensor([1, 1])]; + tensor var_3119 = const()[name = tensor("op_3119"), val = tensor([1, 1])]; tensor q_57_pad_type_0 = const()[name = tensor("q_57_pad_type_0"), val = tensor("custom")]; tensor q_57_pad_0 = const()[name = tensor("q_57_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_57 = conv(dilations = var_3181, groups = var_1193, pad = q_57_pad_0, pad_type = q_57_pad_type_0, strides = var_3179, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight, x = hidden_states_137)[name = tensor("q_57")]; - tensor var_3185 = const()[name = tensor("op_3185"), val = tensor([1, 1])]; - tensor var_3187 = const()[name = tensor("op_3187"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(962142720)))]; + tensor q_57_cast = conv(dilations = var_3119, groups = var_31, pad = q_57_pad_0, pad_type = q_57_pad_type_0, strides = var_3117, weight = unet_down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_137_cast)[name = tensor("q_57_cast")]; + tensor var_3123 = const()[name = tensor("op_3123"), val = tensor([1, 1])]; + tensor var_3125 = const()[name = tensor("op_3125"), val = tensor([1, 1])]; tensor k_57_pad_type_0 = const()[name = tensor("k_57_pad_type_0"), val = tensor("custom")]; tensor k_57_pad_0 = const()[name = tensor("k_57_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_57 = conv(dilations = var_3187, groups = var_1193, pad = k_57_pad_0, pad_type = k_57_pad_type_0, strides = var_3185, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight, x = hidden_states_137)[name = tensor("k_57")]; - tensor var_3191 = const()[name = tensor("op_3191"), val = tensor([1, 1])]; - tensor var_3193 = const()[name = tensor("op_3193"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(965419584)))]; + tensor k_57_cast = conv(dilations = var_3125, groups = var_31, pad = k_57_pad_0, pad_type = k_57_pad_type_0, strides = var_3123, weight = unet_down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_137_cast)[name = tensor("k_57_cast")]; + tensor var_3129 = const()[name = tensor("op_3129"), val = tensor([1, 1])]; + tensor var_3131 = const()[name = tensor("op_3131"), val = tensor([1, 1])]; tensor v_57_pad_type_0 = const()[name = tensor("v_57_pad_type_0"), val = tensor("custom")]; tensor v_57_pad_0 = const()[name = tensor("v_57_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_57 = conv(dilations = var_3193, groups = var_1193, pad = v_57_pad_0, pad_type = v_57_pad_type_0, strides = var_3191, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight, x = hidden_states_137)[name = tensor("v_57")]; - tensor var_3197 = const()[name = tensor("op_3197"), val = tensor([2, 20, 64, -1])]; - tensor var_3198 = reshape(shape = var_3197, x = q_57)[name = tensor("op_3198")]; - tensor var_3199 = const()[name = tensor("op_3199"), val = tensor([2, 20, 64, -1])]; - tensor var_3200 = reshape(shape = var_3199, x = k_57)[name = tensor("op_3200")]; - tensor var_3201 = const()[name = tensor("op_3201"), val = tensor([2, 20, 64, -1])]; - tensor var_3202 = reshape(shape = var_3201, x = v_57)[name = tensor("op_3202")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(968696448)))]; + tensor v_57_cast = conv(dilations = var_3131, groups = var_31, pad = v_57_pad_0, pad_type = v_57_pad_type_0, strides = var_3129, weight = unet_down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_137_cast)[name = tensor("v_57_cast")]; + tensor var_3135 = const()[name = tensor("op_3135"), val = tensor([2, 20, 64, -1])]; + tensor var_3136_cast = reshape(shape = var_3135, x = q_57_cast)[name = tensor("op_3136_cast")]; + tensor var_3137 = const()[name = tensor("op_3137"), val = tensor([2, 20, 64, -1])]; + tensor var_3138_cast = reshape(shape = var_3137, x = k_57_cast)[name = tensor("op_3138_cast")]; + tensor var_3139 = const()[name = tensor("op_3139"), val = tensor([2, 20, 64, -1])]; + tensor var_3140_cast = reshape(shape = var_3139, x = v_57_cast)[name = tensor("op_3140_cast")]; tensor attn_weights_113_transpose_x_0 = const()[name = tensor("attn_weights_113_transpose_x_0"), val = tensor(true)]; tensor attn_weights_113_transpose_y_0 = const()[name = tensor("attn_weights_113_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_113 = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_3198, y = var_3200)[name = tensor("attn_weights_113")]; - tensor attn_weights_115 = mul(x = attn_weights_113, y = var_1184)[name = tensor("attn_weights_115")]; - tensor var_3206 = softmax(axis = var_1177, x = attn_weights_115)[name = tensor("op_3206")]; + tensor attn_weights_113_cast = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_3136_cast, y = var_3138_cast)[name = tensor("attn_weights_113_cast")]; + tensor attn_weights_115_cast = mul(x = attn_weights_113_cast, y = var_12_to_fp16)[name = tensor("attn_weights_115_cast")]; + tensor var_3144_cast = softmax(axis = var_18, x = attn_weights_115_cast)[name = tensor("op_3144_cast")]; tensor attn_57_transpose_x_0 = const()[name = tensor("attn_57_transpose_x_0"), val = tensor(false)]; tensor attn_57_transpose_y_0 = const()[name = tensor("attn_57_transpose_y_0"), val = tensor(true)]; - tensor attn_57 = matmul(transpose_x = attn_57_transpose_x_0, transpose_y = attn_57_transpose_y_0, x = var_3202, y = var_3206)[name = tensor("attn_57")]; - tensor var_3210 = const()[name = tensor("op_3210"), val = tensor([2, 1280, 1, -1])]; - tensor input_229 = reshape(shape = var_3210, x = attn_57)[name = tensor("input_229")]; - tensor var_3215 = const()[name = tensor("op_3215"), val = tensor([1, 1])]; - tensor var_3217 = const()[name = tensor("op_3217"), val = tensor([1, 1])]; - tensor var_3219_pad_type_0 = const()[name = tensor("op_3219_pad_type_0"), val = tensor("custom")]; - tensor var_3219_pad_0 = const()[name = tensor("op_3219_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3219 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias, dilations = var_3217, groups = var_1193, pad = var_3219_pad_0, pad_type = var_3219_pad_type_0, strides = var_3215, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight, x = input_229)[name = tensor("op_3219")]; - tensor inputs_87 = add(x = var_3219, y = inputs_85)[name = tensor("inputs_87")]; - tensor var_3223 = const()[name = tensor("op_3223"), val = tensor([1])]; - tensor channels_mean_87 = reduce_mean(axes = var_3223, keep_dims = var_1188, x = inputs_87)[name = tensor("channels_mean_87")]; - tensor zero_mean_87 = sub(x = inputs_87, y = channels_mean_87)[name = tensor("zero_mean_87")]; - tensor zero_mean_sq_87 = mul(x = zero_mean_87, y = zero_mean_87)[name = tensor("zero_mean_sq_87")]; - tensor var_3227 = const()[name = tensor("op_3227"), val = tensor([1])]; - tensor var_3228 = reduce_mean(axes = var_3227, keep_dims = var_1188, x = zero_mean_sq_87)[name = tensor("op_3228")]; - tensor var_3229 = const()[name = tensor("op_3229"), val = tensor(0x1.4f8b58p-17)]; - tensor var_3230 = add(x = var_3228, y = var_3229)[name = tensor("op_3230")]; - tensor denom_87_epsilon_0 = const()[name = tensor("denom_87_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_87 = rsqrt(epsilon = denom_87_epsilon_0, x = var_3230)[name = tensor("denom_87")]; - tensor out_87 = mul(x = zero_mean_87, y = denom_87)[name = tensor("out_87")]; - tensor var_3234 = const()[name = tensor("op_3234"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268077568)))]; - tensor var_3235 = add(x = out_87, y = var_3234)[name = tensor("op_3235")]; - tensor var_3237 = const()[name = tensor("op_3237"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268082752)))]; - tensor hidden_states_139 = mul(x = var_3235, y = var_3237)[name = tensor("hidden_states_139")]; - tensor var_3244 = const()[name = tensor("op_3244"), val = tensor([1, 1])]; - tensor var_3246 = const()[name = tensor("op_3246"), val = tensor([1, 1])]; + tensor attn_57_cast = matmul(transpose_x = attn_57_transpose_x_0, transpose_y = attn_57_transpose_y_0, x = var_3140_cast, y = var_3144_cast)[name = tensor("attn_57_cast")]; + tensor var_3148 = const()[name = tensor("op_3148"), val = tensor([2, 1280, 1, -1])]; + tensor input_229_cast = reshape(shape = var_3148, x = attn_57_cast)[name = tensor("input_229_cast")]; + tensor var_3153 = const()[name = tensor("op_3153"), val = tensor([1, 1])]; + tensor var_3155 = const()[name = tensor("op_3155"), val = tensor([1, 1])]; + tensor var_3157_pad_type_0 = const()[name = tensor("op_3157_pad_type_0"), val = tensor("custom")]; + tensor var_3157_pad_0 = const()[name = tensor("op_3157_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(971973312)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(975250176)))]; + tensor var_3157_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3155, groups = var_31, pad = var_3157_pad_0, pad_type = var_3157_pad_type_0, strides = var_3153, weight = unet_down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_229_cast)[name = tensor("op_3157_cast")]; + tensor inputs_87_cast = add(x = var_3157_cast, y = inputs_85_cast)[name = tensor("inputs_87_cast")]; + tensor var_3161 = const()[name = tensor("op_3161"), val = tensor([1])]; + tensor channels_mean_87_cast = reduce_mean(axes = var_3161, keep_dims = var_23, x = inputs_87_cast)[name = tensor("channels_mean_87_cast")]; + tensor zero_mean_87_cast = sub(x = inputs_87_cast, y = channels_mean_87_cast)[name = tensor("zero_mean_87_cast")]; + tensor zero_mean_sq_87_cast = mul(x = zero_mean_87_cast, y = zero_mean_87_cast)[name = tensor("zero_mean_sq_87_cast")]; + tensor var_3165 = const()[name = tensor("op_3165"), val = tensor([1])]; + tensor var_3166_cast = reduce_mean(axes = var_3165, keep_dims = var_23, x = zero_mean_sq_87_cast)[name = tensor("op_3166_cast")]; + tensor var_3167_to_fp16 = const()[name = tensor("op_3167_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3168_cast = add(x = var_3166_cast, y = var_3167_to_fp16)[name = tensor("op_3168_cast")]; + tensor denom_87_epsilon_0_to_fp16 = const()[name = tensor("denom_87_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_87_cast = rsqrt(epsilon = denom_87_epsilon_0_to_fp16, x = var_3168_cast)[name = tensor("denom_87_cast")]; + tensor out_87_cast = mul(x = zero_mean_87_cast, y = denom_87_cast)[name = tensor("out_87_cast")]; + tensor var_3172_to_fp16 = const()[name = tensor("op_3172_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(975252800)))]; + tensor var_3173_cast = add(x = out_87_cast, y = var_3172_to_fp16)[name = tensor("op_3173_cast")]; + tensor var_3175_to_fp16 = const()[name = tensor("op_3175_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(975255424)))]; + tensor hidden_states_139_cast = mul(x = var_3173_cast, y = var_3175_to_fp16)[name = tensor("hidden_states_139_cast")]; + tensor var_3182 = const()[name = tensor("op_3182"), val = tensor([1, 1])]; + tensor var_3184 = const()[name = tensor("op_3184"), val = tensor([1, 1])]; tensor q_59_pad_type_0 = const()[name = tensor("q_59_pad_type_0"), val = tensor("custom")]; tensor q_59_pad_0 = const()[name = tensor("q_59_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_59 = conv(dilations = var_3246, groups = var_1193, pad = q_59_pad_0, pad_type = q_59_pad_type_0, strides = var_3244, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight, x = hidden_states_139)[name = tensor("q_59")]; - tensor var_3250 = const()[name = tensor("op_3250"), val = tensor([1, 1])]; - tensor var_3252 = const()[name = tensor("op_3252"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(975258048)))]; + tensor q_59_cast = conv(dilations = var_3184, groups = var_31, pad = q_59_pad_0, pad_type = q_59_pad_type_0, strides = var_3182, weight = unet_down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_139_cast)[name = tensor("q_59_cast")]; + tensor var_3188 = const()[name = tensor("op_3188"), val = tensor([1, 1])]; + tensor var_3190 = const()[name = tensor("op_3190"), val = tensor([1, 1])]; tensor k_59_pad_type_0 = const()[name = tensor("k_59_pad_type_0"), val = tensor("custom")]; tensor k_59_pad_0 = const()[name = tensor("k_59_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_59 = conv(dilations = var_3252, groups = var_1193, pad = k_59_pad_0, pad_type = k_59_pad_type_0, strides = var_3250, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_59")]; - tensor var_3256 = const()[name = tensor("op_3256"), val = tensor([1, 1])]; - tensor var_3258 = const()[name = tensor("op_3258"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(978534912)))]; + tensor k_59_cast = conv(dilations = var_3190, groups = var_31, pad = k_59_pad_0, pad_type = k_59_pad_type_0, strides = var_3188, weight = unet_down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_59_cast")]; + tensor var_3194 = const()[name = tensor("op_3194"), val = tensor([1, 1])]; + tensor var_3196 = const()[name = tensor("op_3196"), val = tensor([1, 1])]; tensor v_59_pad_type_0 = const()[name = tensor("v_59_pad_type_0"), val = tensor("custom")]; tensor v_59_pad_0 = const()[name = tensor("v_59_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_59 = conv(dilations = var_3258, groups = var_1193, pad = v_59_pad_0, pad_type = v_59_pad_type_0, strides = var_3256, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_59")]; - tensor var_3262 = const()[name = tensor("op_3262"), val = tensor([2, 20, 64, -1])]; - tensor var_3263 = reshape(shape = var_3262, x = q_59)[name = tensor("op_3263")]; - tensor var_3264 = const()[name = tensor("op_3264"), val = tensor([2, 20, 64, -1])]; - tensor var_3265 = reshape(shape = var_3264, x = k_59)[name = tensor("op_3265")]; - tensor var_3266 = const()[name = tensor("op_3266"), val = tensor([2, 20, 64, -1])]; - tensor var_3267 = reshape(shape = var_3266, x = v_59)[name = tensor("op_3267")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(983777856)))]; + tensor v_59_cast = conv(dilations = var_3196, groups = var_31, pad = v_59_pad_0, pad_type = v_59_pad_type_0, strides = var_3194, weight = unet_down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_59_cast")]; + tensor var_3200 = const()[name = tensor("op_3200"), val = tensor([2, 20, 64, -1])]; + tensor var_3201_cast = reshape(shape = var_3200, x = q_59_cast)[name = tensor("op_3201_cast")]; + tensor var_3202 = const()[name = tensor("op_3202"), val = tensor([2, 20, 64, -1])]; + tensor var_3203_cast = reshape(shape = var_3202, x = k_59_cast)[name = tensor("op_3203_cast")]; + tensor var_3204 = const()[name = tensor("op_3204"), val = tensor([2, 20, 64, -1])]; + tensor var_3205_cast = reshape(shape = var_3204, x = v_59_cast)[name = tensor("op_3205_cast")]; tensor attn_weights_117_transpose_x_0 = const()[name = tensor("attn_weights_117_transpose_x_0"), val = tensor(true)]; tensor attn_weights_117_transpose_y_0 = const()[name = tensor("attn_weights_117_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_117 = matmul(transpose_x = attn_weights_117_transpose_x_0, transpose_y = attn_weights_117_transpose_y_0, x = var_3263, y = var_3265)[name = tensor("attn_weights_117")]; - tensor attn_weights_119 = mul(x = attn_weights_117, y = var_1184)[name = tensor("attn_weights_119")]; - tensor var_3271 = softmax(axis = var_1177, x = attn_weights_119)[name = tensor("op_3271")]; + tensor attn_weights_117_cast = matmul(transpose_x = attn_weights_117_transpose_x_0, transpose_y = attn_weights_117_transpose_y_0, x = var_3201_cast, y = var_3203_cast)[name = tensor("attn_weights_117_cast")]; + tensor attn_weights_119_cast = mul(x = attn_weights_117_cast, y = var_12_to_fp16)[name = tensor("attn_weights_119_cast")]; + tensor var_3209_cast = softmax(axis = var_18, x = attn_weights_119_cast)[name = tensor("op_3209_cast")]; tensor attn_59_transpose_x_0 = const()[name = tensor("attn_59_transpose_x_0"), val = tensor(false)]; tensor attn_59_transpose_y_0 = const()[name = tensor("attn_59_transpose_y_0"), val = tensor(true)]; - tensor attn_59 = matmul(transpose_x = attn_59_transpose_x_0, transpose_y = attn_59_transpose_y_0, x = var_3267, y = var_3271)[name = tensor("attn_59")]; - tensor var_3275 = const()[name = tensor("op_3275"), val = tensor([2, 1280, 1, -1])]; - tensor input_231 = reshape(shape = var_3275, x = attn_59)[name = tensor("input_231")]; - tensor var_3280 = const()[name = tensor("op_3280"), val = tensor([1, 1])]; - tensor var_3282 = const()[name = tensor("op_3282"), val = tensor([1, 1])]; - tensor var_3284_pad_type_0 = const()[name = tensor("op_3284_pad_type_0"), val = tensor("custom")]; - tensor var_3284_pad_0 = const()[name = tensor("op_3284_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3284 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias, dilations = var_3282, groups = var_1193, pad = var_3284_pad_0, pad_type = var_3284_pad_type_0, strides = var_3280, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight, x = input_231)[name = tensor("op_3284")]; - tensor inputs_89 = add(x = var_3284, y = inputs_87)[name = tensor("inputs_89")]; - tensor var_3288 = const()[name = tensor("op_3288"), val = tensor([1])]; - tensor channels_mean_89 = reduce_mean(axes = var_3288, keep_dims = var_1188, x = inputs_89)[name = tensor("channels_mean_89")]; - tensor zero_mean_89 = sub(x = inputs_89, y = channels_mean_89)[name = tensor("zero_mean_89")]; - tensor zero_mean_sq_89 = mul(x = zero_mean_89, y = zero_mean_89)[name = tensor("zero_mean_sq_89")]; - tensor var_3292 = const()[name = tensor("op_3292"), val = tensor([1])]; - tensor var_3293 = reduce_mean(axes = var_3292, keep_dims = var_1188, x = zero_mean_sq_89)[name = tensor("op_3293")]; - tensor var_3294 = const()[name = tensor("op_3294"), val = tensor(0x1.4f8b58p-17)]; - tensor var_3295 = add(x = var_3293, y = var_3294)[name = tensor("op_3295")]; - tensor denom_89_epsilon_0 = const()[name = tensor("denom_89_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_89 = rsqrt(epsilon = denom_89_epsilon_0, x = var_3295)[name = tensor("denom_89")]; - tensor out_89 = mul(x = zero_mean_89, y = denom_89)[name = tensor("out_89")]; - tensor var_3299 = const()[name = tensor("op_3299"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268087936)))]; - tensor var_3300 = add(x = out_89, y = var_3299)[name = tensor("op_3300")]; - tensor var_3302 = const()[name = tensor("op_3302"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268093120)))]; - tensor input_233 = mul(x = var_3300, y = var_3302)[name = tensor("input_233")]; - tensor var_3310 = const()[name = tensor("op_3310"), val = tensor([1, 1])]; - tensor var_3312 = const()[name = tensor("op_3312"), val = tensor([1, 1])]; - tensor var_3314_pad_type_0 = const()[name = tensor("op_3314_pad_type_0"), val = tensor("custom")]; - tensor var_3314_pad_0 = const()[name = tensor("op_3314_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3314 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias, dilations = var_3312, groups = var_1193, pad = var_3314_pad_0, pad_type = var_3314_pad_type_0, strides = var_3310, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight, x = input_233)[name = tensor("op_3314")]; - tensor var_3315_split_sizes_0 = const()[name = tensor("op_3315_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_3315_axis_0 = const()[name = tensor("op_3315_axis_0"), val = tensor(1)]; - tensor var_3315_0, tensor var_3315_1 = split(axis = var_3315_axis_0, split_sizes = var_3315_split_sizes_0, x = var_3314)[name = tensor("op_3315")]; - tensor var_3317_mode_0 = const()[name = tensor("op_3317_mode_0"), val = tensor("EXACT")]; - tensor var_3317 = gelu(mode = var_3317_mode_0, x = var_3315_1)[name = tensor("op_3317")]; - tensor input_235 = mul(x = var_3315_0, y = var_3317)[name = tensor("input_235")]; - tensor var_3321 = const()[name = tensor("op_3321"), val = tensor([1, 1])]; - tensor var_3323 = const()[name = tensor("op_3323"), val = tensor([1, 1])]; - tensor var_3325_pad_type_0 = const()[name = tensor("op_3325_pad_type_0"), val = tensor("custom")]; - tensor var_3325_pad_0 = const()[name = tensor("op_3325_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3325 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias, dilations = var_3323, groups = var_1193, pad = var_3325_pad_0, pad_type = var_3325_pad_type_0, strides = var_3321, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight, x = input_235)[name = tensor("op_3325")]; - tensor inputs_91 = add(x = var_3325, y = inputs_89)[name = tensor("inputs_91")]; - tensor var_3335 = const()[name = tensor("op_3335"), val = tensor([1])]; - tensor channels_mean_91 = reduce_mean(axes = var_3335, keep_dims = var_1188, x = inputs_91)[name = tensor("channels_mean_91")]; - tensor zero_mean_91 = sub(x = inputs_91, y = channels_mean_91)[name = tensor("zero_mean_91")]; - tensor zero_mean_sq_91 = mul(x = zero_mean_91, y = zero_mean_91)[name = tensor("zero_mean_sq_91")]; - tensor var_3339 = const()[name = tensor("op_3339"), val = tensor([1])]; - tensor var_3340 = reduce_mean(axes = var_3339, keep_dims = var_1188, x = zero_mean_sq_91)[name = tensor("op_3340")]; - tensor var_3341 = const()[name = tensor("op_3341"), val = tensor(0x1.4f8b58p-17)]; - tensor var_3342 = add(x = var_3340, y = var_3341)[name = tensor("op_3342")]; - tensor denom_91_epsilon_0 = const()[name = tensor("denom_91_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_91 = rsqrt(epsilon = denom_91_epsilon_0, x = var_3342)[name = tensor("denom_91")]; - tensor out_91 = mul(x = zero_mean_91, y = denom_91)[name = tensor("out_91")]; - tensor var_3346 = const()[name = tensor("op_3346"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268098304)))]; - tensor var_3347 = add(x = out_91, y = var_3346)[name = tensor("op_3347")]; - tensor var_3349 = const()[name = tensor("op_3349"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268103488)))]; - tensor hidden_states_143 = mul(x = var_3347, y = var_3349)[name = tensor("hidden_states_143")]; - tensor var_3356 = const()[name = tensor("op_3356"), val = tensor([1, 1])]; - tensor var_3358 = const()[name = tensor("op_3358"), val = tensor([1, 1])]; + tensor attn_59_cast = matmul(transpose_x = attn_59_transpose_x_0, transpose_y = attn_59_transpose_y_0, x = var_3205_cast, y = var_3209_cast)[name = tensor("attn_59_cast")]; + tensor var_3213 = const()[name = tensor("op_3213"), val = tensor([2, 1280, 1, -1])]; + tensor input_231_cast = reshape(shape = var_3213, x = attn_59_cast)[name = tensor("input_231_cast")]; + tensor var_3218 = const()[name = tensor("op_3218"), val = tensor([1, 1])]; + tensor var_3220 = const()[name = tensor("op_3220"), val = tensor([1, 1])]; + tensor var_3222_pad_type_0 = const()[name = tensor("op_3222_pad_type_0"), val = tensor("custom")]; + tensor var_3222_pad_0 = const()[name = tensor("op_3222_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(989020800)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992297664)))]; + tensor var_3222_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3220, groups = var_31, pad = var_3222_pad_0, pad_type = var_3222_pad_type_0, strides = var_3218, weight = unet_down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_231_cast)[name = tensor("op_3222_cast")]; + tensor inputs_89_cast = add(x = var_3222_cast, y = inputs_87_cast)[name = tensor("inputs_89_cast")]; + tensor var_3226 = const()[name = tensor("op_3226"), val = tensor([1])]; + tensor channels_mean_89_cast = reduce_mean(axes = var_3226, keep_dims = var_23, x = inputs_89_cast)[name = tensor("channels_mean_89_cast")]; + tensor zero_mean_89_cast = sub(x = inputs_89_cast, y = channels_mean_89_cast)[name = tensor("zero_mean_89_cast")]; + tensor zero_mean_sq_89_cast = mul(x = zero_mean_89_cast, y = zero_mean_89_cast)[name = tensor("zero_mean_sq_89_cast")]; + tensor var_3230 = const()[name = tensor("op_3230"), val = tensor([1])]; + tensor var_3231_cast = reduce_mean(axes = var_3230, keep_dims = var_23, x = zero_mean_sq_89_cast)[name = tensor("op_3231_cast")]; + tensor var_3232_to_fp16 = const()[name = tensor("op_3232_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3233_cast = add(x = var_3231_cast, y = var_3232_to_fp16)[name = tensor("op_3233_cast")]; + tensor denom_89_epsilon_0_to_fp16 = const()[name = tensor("denom_89_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_89_cast = rsqrt(epsilon = denom_89_epsilon_0_to_fp16, x = var_3233_cast)[name = tensor("denom_89_cast")]; + tensor out_89_cast = mul(x = zero_mean_89_cast, y = denom_89_cast)[name = tensor("out_89_cast")]; + tensor var_3237_to_fp16 = const()[name = tensor("op_3237_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992300288)))]; + tensor var_3238_cast = add(x = out_89_cast, y = var_3237_to_fp16)[name = tensor("op_3238_cast")]; + tensor var_3240_to_fp16 = const()[name = tensor("op_3240_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992302912)))]; + tensor input_233_cast = mul(x = var_3238_cast, y = var_3240_to_fp16)[name = tensor("input_233_cast")]; + tensor var_3248 = const()[name = tensor("op_3248"), val = tensor([1, 1])]; + tensor var_3250 = const()[name = tensor("op_3250"), val = tensor([1, 1])]; + tensor var_3252_pad_type_0 = const()[name = tensor("op_3252_pad_type_0"), val = tensor("custom")]; + tensor var_3252_pad_0 = const()[name = tensor("op_3252_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(992305536)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1018520000)))]; + tensor var_3252_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_3250, groups = var_31, pad = var_3252_pad_0, pad_type = var_3252_pad_type_0, strides = var_3248, weight = unet_down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_233_cast)[name = tensor("op_3252_cast")]; + tensor var_3253_split_sizes_0 = const()[name = tensor("op_3253_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3253_axis_0 = const()[name = tensor("op_3253_axis_0"), val = tensor(1)]; + tensor var_3253_cast_0, tensor var_3253_cast_1 = split(axis = var_3253_axis_0, split_sizes = var_3253_split_sizes_0, x = var_3252_cast)[name = tensor("op_3253_cast")]; + tensor var_3255_mode_0 = const()[name = tensor("op_3255_mode_0"), val = tensor("EXACT")]; + tensor var_3255_cast = gelu(mode = var_3255_mode_0, x = var_3253_cast_1)[name = tensor("op_3255_cast")]; + tensor input_235_cast = mul(x = var_3253_cast_0, y = var_3255_cast)[name = tensor("input_235_cast")]; + tensor var_3259 = const()[name = tensor("op_3259"), val = tensor([1, 1])]; + tensor var_3261 = const()[name = tensor("op_3261"), val = tensor([1, 1])]; + tensor var_3263_pad_type_0 = const()[name = tensor("op_3263_pad_type_0"), val = tensor("custom")]; + tensor var_3263_pad_0 = const()[name = tensor("op_3263_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1018540544)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1031647808)))]; + tensor var_3263_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3261, groups = var_31, pad = var_3263_pad_0, pad_type = var_3263_pad_type_0, strides = var_3259, weight = unet_down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_235_cast)[name = tensor("op_3263_cast")]; + tensor inputs_91_cast = add(x = var_3263_cast, y = inputs_89_cast)[name = tensor("inputs_91_cast")]; + tensor var_3273 = const()[name = tensor("op_3273"), val = tensor([1])]; + tensor channels_mean_91_cast = reduce_mean(axes = var_3273, keep_dims = var_23, x = inputs_91_cast)[name = tensor("channels_mean_91_cast")]; + tensor zero_mean_91_cast = sub(x = inputs_91_cast, y = channels_mean_91_cast)[name = tensor("zero_mean_91_cast")]; + tensor zero_mean_sq_91_cast = mul(x = zero_mean_91_cast, y = zero_mean_91_cast)[name = tensor("zero_mean_sq_91_cast")]; + tensor var_3277 = const()[name = tensor("op_3277"), val = tensor([1])]; + tensor var_3278_cast = reduce_mean(axes = var_3277, keep_dims = var_23, x = zero_mean_sq_91_cast)[name = tensor("op_3278_cast")]; + tensor var_3279_to_fp16 = const()[name = tensor("op_3279_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3280_cast = add(x = var_3278_cast, y = var_3279_to_fp16)[name = tensor("op_3280_cast")]; + tensor denom_91_epsilon_0_to_fp16 = const()[name = tensor("denom_91_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_91_cast = rsqrt(epsilon = denom_91_epsilon_0_to_fp16, x = var_3280_cast)[name = tensor("denom_91_cast")]; + tensor out_91_cast = mul(x = zero_mean_91_cast, y = denom_91_cast)[name = tensor("out_91_cast")]; + tensor var_3284_to_fp16 = const()[name = tensor("op_3284_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1031650432)))]; + tensor var_3285_cast = add(x = out_91_cast, y = var_3284_to_fp16)[name = tensor("op_3285_cast")]; + tensor var_3287_to_fp16 = const()[name = tensor("op_3287_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1031653056)))]; + tensor hidden_states_143_cast = mul(x = var_3285_cast, y = var_3287_to_fp16)[name = tensor("hidden_states_143_cast")]; + tensor var_3294 = const()[name = tensor("op_3294"), val = tensor([1, 1])]; + tensor var_3296 = const()[name = tensor("op_3296"), val = tensor([1, 1])]; tensor q_61_pad_type_0 = const()[name = tensor("q_61_pad_type_0"), val = tensor("custom")]; tensor q_61_pad_0 = const()[name = tensor("q_61_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_61 = conv(dilations = var_3358, groups = var_1193, pad = q_61_pad_0, pad_type = q_61_pad_type_0, strides = var_3356, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight, x = hidden_states_143)[name = tensor("q_61")]; - tensor var_3362 = const()[name = tensor("op_3362"), val = tensor([1, 1])]; - tensor var_3364 = const()[name = tensor("op_3364"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1031655680)))]; + tensor q_61_cast = conv(dilations = var_3296, groups = var_31, pad = q_61_pad_0, pad_type = q_61_pad_type_0, strides = var_3294, weight = unet_down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_143_cast)[name = tensor("q_61_cast")]; + tensor var_3300 = const()[name = tensor("op_3300"), val = tensor([1, 1])]; + tensor var_3302 = const()[name = tensor("op_3302"), val = tensor([1, 1])]; tensor k_61_pad_type_0 = const()[name = tensor("k_61_pad_type_0"), val = tensor("custom")]; tensor k_61_pad_0 = const()[name = tensor("k_61_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_61 = conv(dilations = var_3364, groups = var_1193, pad = k_61_pad_0, pad_type = k_61_pad_type_0, strides = var_3362, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight, x = hidden_states_143)[name = tensor("k_61")]; - tensor var_3368 = const()[name = tensor("op_3368"), val = tensor([1, 1])]; - tensor var_3370 = const()[name = tensor("op_3370"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1034932544)))]; + tensor k_61_cast = conv(dilations = var_3302, groups = var_31, pad = k_61_pad_0, pad_type = k_61_pad_type_0, strides = var_3300, weight = unet_down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_143_cast)[name = tensor("k_61_cast")]; + tensor var_3306 = const()[name = tensor("op_3306"), val = tensor([1, 1])]; + tensor var_3308 = const()[name = tensor("op_3308"), val = tensor([1, 1])]; tensor v_61_pad_type_0 = const()[name = tensor("v_61_pad_type_0"), val = tensor("custom")]; tensor v_61_pad_0 = const()[name = tensor("v_61_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_61 = conv(dilations = var_3370, groups = var_1193, pad = v_61_pad_0, pad_type = v_61_pad_type_0, strides = var_3368, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight, x = hidden_states_143)[name = tensor("v_61")]; - tensor var_3374 = const()[name = tensor("op_3374"), val = tensor([2, 20, 64, -1])]; - tensor var_3375 = reshape(shape = var_3374, x = q_61)[name = tensor("op_3375")]; - tensor var_3376 = const()[name = tensor("op_3376"), val = tensor([2, 20, 64, -1])]; - tensor var_3377 = reshape(shape = var_3376, x = k_61)[name = tensor("op_3377")]; - tensor var_3378 = const()[name = tensor("op_3378"), val = tensor([2, 20, 64, -1])]; - tensor var_3379 = reshape(shape = var_3378, x = v_61)[name = tensor("op_3379")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1038209408)))]; + tensor v_61_cast = conv(dilations = var_3308, groups = var_31, pad = v_61_pad_0, pad_type = v_61_pad_type_0, strides = var_3306, weight = unet_down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_143_cast)[name = tensor("v_61_cast")]; + tensor var_3312 = const()[name = tensor("op_3312"), val = tensor([2, 20, 64, -1])]; + tensor var_3313_cast = reshape(shape = var_3312, x = q_61_cast)[name = tensor("op_3313_cast")]; + tensor var_3314 = const()[name = tensor("op_3314"), val = tensor([2, 20, 64, -1])]; + tensor var_3315_cast = reshape(shape = var_3314, x = k_61_cast)[name = tensor("op_3315_cast")]; + tensor var_3316 = const()[name = tensor("op_3316"), val = tensor([2, 20, 64, -1])]; + tensor var_3317_cast = reshape(shape = var_3316, x = v_61_cast)[name = tensor("op_3317_cast")]; tensor attn_weights_121_transpose_x_0 = const()[name = tensor("attn_weights_121_transpose_x_0"), val = tensor(true)]; tensor attn_weights_121_transpose_y_0 = const()[name = tensor("attn_weights_121_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_121 = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_3375, y = var_3377)[name = tensor("attn_weights_121")]; - tensor attn_weights_123 = mul(x = attn_weights_121, y = var_1184)[name = tensor("attn_weights_123")]; - tensor var_3383 = softmax(axis = var_1177, x = attn_weights_123)[name = tensor("op_3383")]; + tensor attn_weights_121_cast = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_3313_cast, y = var_3315_cast)[name = tensor("attn_weights_121_cast")]; + tensor attn_weights_123_cast = mul(x = attn_weights_121_cast, y = var_12_to_fp16)[name = tensor("attn_weights_123_cast")]; + tensor var_3321_cast = softmax(axis = var_18, x = attn_weights_123_cast)[name = tensor("op_3321_cast")]; tensor attn_61_transpose_x_0 = const()[name = tensor("attn_61_transpose_x_0"), val = tensor(false)]; tensor attn_61_transpose_y_0 = const()[name = tensor("attn_61_transpose_y_0"), val = tensor(true)]; - tensor attn_61 = matmul(transpose_x = attn_61_transpose_x_0, transpose_y = attn_61_transpose_y_0, x = var_3379, y = var_3383)[name = tensor("attn_61")]; - tensor var_3387 = const()[name = tensor("op_3387"), val = tensor([2, 1280, 1, -1])]; - tensor input_237 = reshape(shape = var_3387, x = attn_61)[name = tensor("input_237")]; - tensor var_3392 = const()[name = tensor("op_3392"), val = tensor([1, 1])]; - tensor var_3394 = const()[name = tensor("op_3394"), val = tensor([1, 1])]; - tensor var_3396_pad_type_0 = const()[name = tensor("op_3396_pad_type_0"), val = tensor("custom")]; - tensor var_3396_pad_0 = const()[name = tensor("op_3396_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3396 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias, dilations = var_3394, groups = var_1193, pad = var_3396_pad_0, pad_type = var_3396_pad_type_0, strides = var_3392, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight, x = input_237)[name = tensor("op_3396")]; - tensor inputs_93 = add(x = var_3396, y = inputs_91)[name = tensor("inputs_93")]; - tensor var_3400 = const()[name = tensor("op_3400"), val = tensor([1])]; - tensor channels_mean_93 = reduce_mean(axes = var_3400, keep_dims = var_1188, x = inputs_93)[name = tensor("channels_mean_93")]; - tensor zero_mean_93 = sub(x = inputs_93, y = channels_mean_93)[name = tensor("zero_mean_93")]; - tensor zero_mean_sq_93 = mul(x = zero_mean_93, y = zero_mean_93)[name = tensor("zero_mean_sq_93")]; - tensor var_3404 = const()[name = tensor("op_3404"), val = tensor([1])]; - tensor var_3405 = reduce_mean(axes = var_3404, keep_dims = var_1188, x = zero_mean_sq_93)[name = tensor("op_3405")]; - tensor var_3406 = const()[name = tensor("op_3406"), val = tensor(0x1.4f8b58p-17)]; - tensor var_3407 = add(x = var_3405, y = var_3406)[name = tensor("op_3407")]; - tensor denom_93_epsilon_0 = const()[name = tensor("denom_93_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_93 = rsqrt(epsilon = denom_93_epsilon_0, x = var_3407)[name = tensor("denom_93")]; - tensor out_93 = mul(x = zero_mean_93, y = denom_93)[name = tensor("out_93")]; - tensor var_3411 = const()[name = tensor("op_3411"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268108672)))]; - tensor var_3412 = add(x = out_93, y = var_3411)[name = tensor("op_3412")]; - tensor var_3414 = const()[name = tensor("op_3414"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268113856)))]; - tensor hidden_states_145 = mul(x = var_3412, y = var_3414)[name = tensor("hidden_states_145")]; - tensor var_3421 = const()[name = tensor("op_3421"), val = tensor([1, 1])]; - tensor var_3423 = const()[name = tensor("op_3423"), val = tensor([1, 1])]; + tensor attn_61_cast = matmul(transpose_x = attn_61_transpose_x_0, transpose_y = attn_61_transpose_y_0, x = var_3317_cast, y = var_3321_cast)[name = tensor("attn_61_cast")]; + tensor var_3325 = const()[name = tensor("op_3325"), val = tensor([2, 1280, 1, -1])]; + tensor input_237_cast = reshape(shape = var_3325, x = attn_61_cast)[name = tensor("input_237_cast")]; + tensor var_3330 = const()[name = tensor("op_3330"), val = tensor([1, 1])]; + tensor var_3332 = const()[name = tensor("op_3332"), val = tensor([1, 1])]; + tensor var_3334_pad_type_0 = const()[name = tensor("op_3334_pad_type_0"), val = tensor("custom")]; + tensor var_3334_pad_0 = const()[name = tensor("op_3334_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1041486272)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044763136)))]; + tensor var_3334_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_3332, groups = var_31, pad = var_3334_pad_0, pad_type = var_3334_pad_type_0, strides = var_3330, weight = unet_down_blocks_2_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_237_cast)[name = tensor("op_3334_cast")]; + tensor inputs_93_cast = add(x = var_3334_cast, y = inputs_91_cast)[name = tensor("inputs_93_cast")]; + tensor var_3338 = const()[name = tensor("op_3338"), val = tensor([1])]; + tensor channels_mean_93_cast = reduce_mean(axes = var_3338, keep_dims = var_23, x = inputs_93_cast)[name = tensor("channels_mean_93_cast")]; + tensor zero_mean_93_cast = sub(x = inputs_93_cast, y = channels_mean_93_cast)[name = tensor("zero_mean_93_cast")]; + tensor zero_mean_sq_93_cast = mul(x = zero_mean_93_cast, y = zero_mean_93_cast)[name = tensor("zero_mean_sq_93_cast")]; + tensor var_3342 = const()[name = tensor("op_3342"), val = tensor([1])]; + tensor var_3343_cast = reduce_mean(axes = var_3342, keep_dims = var_23, x = zero_mean_sq_93_cast)[name = tensor("op_3343_cast")]; + tensor var_3344_to_fp16 = const()[name = tensor("op_3344_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3345_cast = add(x = var_3343_cast, y = var_3344_to_fp16)[name = tensor("op_3345_cast")]; + tensor denom_93_epsilon_0_to_fp16 = const()[name = tensor("denom_93_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_93_cast = rsqrt(epsilon = denom_93_epsilon_0_to_fp16, x = var_3345_cast)[name = tensor("denom_93_cast")]; + tensor out_93_cast = mul(x = zero_mean_93_cast, y = denom_93_cast)[name = tensor("out_93_cast")]; + tensor var_3349_to_fp16 = const()[name = tensor("op_3349_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044765760)))]; + tensor var_3350_cast = add(x = out_93_cast, y = var_3349_to_fp16)[name = tensor("op_3350_cast")]; + tensor var_3352_to_fp16 = const()[name = tensor("op_3352_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044768384)))]; + tensor hidden_states_145_cast = mul(x = var_3350_cast, y = var_3352_to_fp16)[name = tensor("hidden_states_145_cast")]; + tensor var_3359 = const()[name = tensor("op_3359"), val = tensor([1, 1])]; + tensor var_3361 = const()[name = tensor("op_3361"), val = tensor([1, 1])]; tensor q_63_pad_type_0 = const()[name = tensor("q_63_pad_type_0"), val = tensor("custom")]; tensor q_63_pad_0 = const()[name = tensor("q_63_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_63 = conv(dilations = var_3423, groups = var_1193, pad = q_63_pad_0, pad_type = q_63_pad_type_0, strides = var_3421, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight, x = hidden_states_145)[name = tensor("q_63")]; - tensor var_3427 = const()[name = tensor("op_3427"), val = tensor([1, 1])]; - tensor var_3429 = const()[name = tensor("op_3429"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1044771008)))]; + tensor q_63_cast = conv(dilations = var_3361, groups = var_31, pad = q_63_pad_0, pad_type = q_63_pad_type_0, strides = var_3359, weight = unet_down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_145_cast)[name = tensor("q_63_cast")]; + tensor var_3365 = const()[name = tensor("op_3365"), val = tensor([1, 1])]; + tensor var_3367 = const()[name = tensor("op_3367"), val = tensor([1, 1])]; tensor k_63_pad_type_0 = const()[name = tensor("k_63_pad_type_0"), val = tensor("custom")]; tensor k_63_pad_0 = const()[name = tensor("k_63_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_63 = conv(dilations = var_3429, groups = var_1193, pad = k_63_pad_0, pad_type = k_63_pad_type_0, strides = var_3427, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_63")]; - tensor var_3433 = const()[name = tensor("op_3433"), val = tensor([1, 1])]; - tensor var_3435 = const()[name = tensor("op_3435"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1048047872)))]; + tensor k_63_cast = conv(dilations = var_3367, groups = var_31, pad = k_63_pad_0, pad_type = k_63_pad_type_0, strides = var_3365, weight = unet_down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_63_cast")]; + tensor var_3371 = const()[name = tensor("op_3371"), val = tensor([1, 1])]; + tensor var_3373 = const()[name = tensor("op_3373"), val = tensor([1, 1])]; tensor v_63_pad_type_0 = const()[name = tensor("v_63_pad_type_0"), val = tensor("custom")]; tensor v_63_pad_0 = const()[name = tensor("v_63_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_63 = conv(dilations = var_3435, groups = var_1193, pad = v_63_pad_0, pad_type = v_63_pad_type_0, strides = var_3433, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_63")]; - tensor var_3439 = const()[name = tensor("op_3439"), val = tensor([2, 20, 64, -1])]; - tensor var_3440 = reshape(shape = var_3439, x = q_63)[name = tensor("op_3440")]; - tensor var_3441 = const()[name = tensor("op_3441"), val = tensor([2, 20, 64, -1])]; - tensor var_3442 = reshape(shape = var_3441, x = k_63)[name = tensor("op_3442")]; - tensor var_3443 = const()[name = tensor("op_3443"), val = tensor([2, 20, 64, -1])]; - tensor var_3444 = reshape(shape = var_3443, x = v_63)[name = tensor("op_3444")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1053290816)))]; + tensor v_63_cast = conv(dilations = var_3373, groups = var_31, pad = v_63_pad_0, pad_type = v_63_pad_type_0, strides = var_3371, weight = unet_down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_63_cast")]; + tensor var_3377 = const()[name = tensor("op_3377"), val = tensor([2, 20, 64, -1])]; + tensor var_3378_cast = reshape(shape = var_3377, x = q_63_cast)[name = tensor("op_3378_cast")]; + tensor var_3379 = const()[name = tensor("op_3379"), val = tensor([2, 20, 64, -1])]; + tensor var_3380_cast = reshape(shape = var_3379, x = k_63_cast)[name = tensor("op_3380_cast")]; + tensor var_3381 = const()[name = tensor("op_3381"), val = tensor([2, 20, 64, -1])]; + tensor var_3382_cast = reshape(shape = var_3381, x = v_63_cast)[name = tensor("op_3382_cast")]; tensor attn_weights_125_transpose_x_0 = const()[name = tensor("attn_weights_125_transpose_x_0"), val = tensor(true)]; tensor attn_weights_125_transpose_y_0 = const()[name = tensor("attn_weights_125_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_125 = matmul(transpose_x = attn_weights_125_transpose_x_0, transpose_y = attn_weights_125_transpose_y_0, x = var_3440, y = var_3442)[name = tensor("attn_weights_125")]; - tensor attn_weights_127 = mul(x = attn_weights_125, y = var_1184)[name = tensor("attn_weights_127")]; - tensor var_3448 = softmax(axis = var_1177, x = attn_weights_127)[name = tensor("op_3448")]; + tensor attn_weights_125_cast = matmul(transpose_x = attn_weights_125_transpose_x_0, transpose_y = attn_weights_125_transpose_y_0, x = var_3378_cast, y = var_3380_cast)[name = tensor("attn_weights_125_cast")]; + tensor attn_weights_127_cast = mul(x = attn_weights_125_cast, y = var_12_to_fp16)[name = tensor("attn_weights_127_cast")]; + tensor var_3386_cast = softmax(axis = var_18, x = attn_weights_127_cast)[name = tensor("op_3386_cast")]; tensor attn_63_transpose_x_0 = const()[name = tensor("attn_63_transpose_x_0"), val = tensor(false)]; tensor attn_63_transpose_y_0 = const()[name = tensor("attn_63_transpose_y_0"), val = tensor(true)]; - tensor attn_63 = matmul(transpose_x = attn_63_transpose_x_0, transpose_y = attn_63_transpose_y_0, x = var_3444, y = var_3448)[name = tensor("attn_63")]; - tensor var_3452 = const()[name = tensor("op_3452"), val = tensor([2, 1280, 1, -1])]; - tensor input_239 = reshape(shape = var_3452, x = attn_63)[name = tensor("input_239")]; - tensor var_3457 = const()[name = tensor("op_3457"), val = tensor([1, 1])]; - tensor var_3459 = const()[name = tensor("op_3459"), val = tensor([1, 1])]; - tensor var_3461_pad_type_0 = const()[name = tensor("op_3461_pad_type_0"), val = tensor("custom")]; - tensor var_3461_pad_0 = const()[name = tensor("op_3461_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3461 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias, dilations = var_3459, groups = var_1193, pad = var_3461_pad_0, pad_type = var_3461_pad_type_0, strides = var_3457, weight = down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight, x = input_239)[name = tensor("op_3461")]; - tensor inputs_95 = add(x = var_3461, y = inputs_93)[name = tensor("inputs_95")]; - tensor var_3465 = const()[name = tensor("op_3465"), val = tensor([1])]; - tensor channels_mean_95 = reduce_mean(axes = var_3465, keep_dims = var_1188, x = inputs_95)[name = tensor("channels_mean_95")]; - tensor zero_mean_95 = sub(x = inputs_95, y = channels_mean_95)[name = tensor("zero_mean_95")]; - tensor zero_mean_sq_95 = mul(x = zero_mean_95, y = zero_mean_95)[name = tensor("zero_mean_sq_95")]; - tensor var_3469 = const()[name = tensor("op_3469"), val = tensor([1])]; - tensor var_3470 = reduce_mean(axes = var_3469, keep_dims = var_1188, x = zero_mean_sq_95)[name = tensor("op_3470")]; - tensor var_3471 = const()[name = tensor("op_3471"), val = tensor(0x1.4f8b58p-17)]; - tensor var_3472 = add(x = var_3470, y = var_3471)[name = tensor("op_3472")]; - tensor denom_95_epsilon_0 = const()[name = tensor("denom_95_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_95 = rsqrt(epsilon = denom_95_epsilon_0, x = var_3472)[name = tensor("denom_95")]; - tensor out_95 = mul(x = zero_mean_95, y = denom_95)[name = tensor("out_95")]; - tensor var_3476 = const()[name = tensor("op_3476"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268119040)))]; - tensor var_3477 = add(x = out_95, y = var_3476)[name = tensor("op_3477")]; - tensor var_3479 = const()[name = tensor("op_3479"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268124224)))]; - tensor input_241 = mul(x = var_3477, y = var_3479)[name = tensor("input_241")]; - tensor var_3487 = const()[name = tensor("op_3487"), val = tensor([1, 1])]; - tensor var_3489 = const()[name = tensor("op_3489"), val = tensor([1, 1])]; - tensor var_3491_pad_type_0 = const()[name = tensor("op_3491_pad_type_0"), val = tensor("custom")]; - tensor var_3491_pad_0 = const()[name = tensor("op_3491_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3491 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias, dilations = var_3489, groups = var_1193, pad = var_3491_pad_0, pad_type = var_3491_pad_type_0, strides = var_3487, weight = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight, x = input_241)[name = tensor("op_3491")]; - tensor var_3492_split_sizes_0 = const()[name = tensor("op_3492_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_3492_axis_0 = const()[name = tensor("op_3492_axis_0"), val = tensor(1)]; - tensor var_3492_0, tensor var_3492_1 = split(axis = var_3492_axis_0, split_sizes = var_3492_split_sizes_0, x = var_3491)[name = tensor("op_3492")]; - tensor var_3494_mode_0 = const()[name = tensor("op_3494_mode_0"), val = tensor("EXACT")]; - tensor var_3494 = gelu(mode = var_3494_mode_0, x = var_3492_1)[name = tensor("op_3494")]; - tensor input_243 = mul(x = var_3492_0, y = var_3494)[name = tensor("input_243")]; - tensor var_3498 = const()[name = tensor("op_3498"), val = tensor([1, 1])]; - tensor var_3500 = const()[name = tensor("op_3500"), val = tensor([1, 1])]; - tensor var_3502_pad_type_0 = const()[name = tensor("op_3502_pad_type_0"), val = tensor("custom")]; - tensor var_3502_pad_0 = const()[name = tensor("op_3502_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3502 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias, dilations = var_3500, groups = var_1193, pad = var_3502_pad_0, pad_type = var_3502_pad_type_0, strides = var_3498, weight = down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight, x = input_243)[name = tensor("op_3502")]; - tensor inputs_97 = add(x = var_3502, y = inputs_95)[name = tensor("inputs_97")]; - tensor var_3512 = const()[name = tensor("op_3512"), val = tensor([1])]; - tensor channels_mean_97 = reduce_mean(axes = var_3512, keep_dims = var_1188, x = inputs_97)[name = tensor("channels_mean_97")]; - tensor zero_mean_97 = sub(x = inputs_97, y = channels_mean_97)[name = tensor("zero_mean_97")]; - tensor zero_mean_sq_97 = mul(x = zero_mean_97, y = zero_mean_97)[name = tensor("zero_mean_sq_97")]; - tensor var_3516 = const()[name = tensor("op_3516"), val = tensor([1])]; - tensor var_3517 = reduce_mean(axes = var_3516, keep_dims = var_1188, x = zero_mean_sq_97)[name = tensor("op_3517")]; - tensor var_3518 = const()[name = tensor("op_3518"), val = tensor(0x1.4f8b58p-17)]; - tensor var_3519 = add(x = var_3517, y = var_3518)[name = tensor("op_3519")]; - tensor denom_97_epsilon_0 = const()[name = tensor("denom_97_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_97 = rsqrt(epsilon = denom_97_epsilon_0, x = var_3519)[name = tensor("denom_97")]; - tensor out_97 = mul(x = zero_mean_97, y = denom_97)[name = tensor("out_97")]; - tensor var_3523 = const()[name = tensor("op_3523"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268129408)))]; - tensor var_3524 = add(x = out_97, y = var_3523)[name = tensor("op_3524")]; - tensor var_3526 = const()[name = tensor("op_3526"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268134592)))]; - tensor hidden_states_149 = mul(x = var_3524, y = var_3526)[name = tensor("hidden_states_149")]; - tensor var_3533 = const()[name = tensor("op_3533"), val = tensor([1, 1])]; - tensor var_3535 = const()[name = tensor("op_3535"), val = tensor([1, 1])]; + tensor attn_63_cast = matmul(transpose_x = attn_63_transpose_x_0, transpose_y = attn_63_transpose_y_0, x = var_3382_cast, y = var_3386_cast)[name = tensor("attn_63_cast")]; + tensor var_3390 = const()[name = tensor("op_3390"), val = tensor([2, 1280, 1, -1])]; + tensor input_239_cast = reshape(shape = var_3390, x = attn_63_cast)[name = tensor("input_239_cast")]; + tensor var_3395 = const()[name = tensor("op_3395"), val = tensor([1, 1])]; + tensor var_3397 = const()[name = tensor("op_3397"), val = tensor([1, 1])]; + tensor var_3399_pad_type_0 = const()[name = tensor("op_3399_pad_type_0"), val = tensor("custom")]; + tensor var_3399_pad_0 = const()[name = tensor("op_3399_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1058533760)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1061810624)))]; + tensor var_3399_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_3397, groups = var_31, pad = var_3399_pad_0, pad_type = var_3399_pad_type_0, strides = var_3395, weight = unet_down_blocks_2_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_239_cast)[name = tensor("op_3399_cast")]; + tensor inputs_95_cast = add(x = var_3399_cast, y = inputs_93_cast)[name = tensor("inputs_95_cast")]; + tensor var_3403 = const()[name = tensor("op_3403"), val = tensor([1])]; + tensor channels_mean_95_cast = reduce_mean(axes = var_3403, keep_dims = var_23, x = inputs_95_cast)[name = tensor("channels_mean_95_cast")]; + tensor zero_mean_95_cast = sub(x = inputs_95_cast, y = channels_mean_95_cast)[name = tensor("zero_mean_95_cast")]; + tensor zero_mean_sq_95_cast = mul(x = zero_mean_95_cast, y = zero_mean_95_cast)[name = tensor("zero_mean_sq_95_cast")]; + tensor var_3407 = const()[name = tensor("op_3407"), val = tensor([1])]; + tensor var_3408_cast = reduce_mean(axes = var_3407, keep_dims = var_23, x = zero_mean_sq_95_cast)[name = tensor("op_3408_cast")]; + tensor var_3409_to_fp16 = const()[name = tensor("op_3409_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3410_cast = add(x = var_3408_cast, y = var_3409_to_fp16)[name = tensor("op_3410_cast")]; + tensor denom_95_epsilon_0_to_fp16 = const()[name = tensor("denom_95_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_95_cast = rsqrt(epsilon = denom_95_epsilon_0_to_fp16, x = var_3410_cast)[name = tensor("denom_95_cast")]; + tensor out_95_cast = mul(x = zero_mean_95_cast, y = denom_95_cast)[name = tensor("out_95_cast")]; + tensor var_3414_to_fp16 = const()[name = tensor("op_3414_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1061813248)))]; + tensor var_3415_cast = add(x = out_95_cast, y = var_3414_to_fp16)[name = tensor("op_3415_cast")]; + tensor var_3417_to_fp16 = const()[name = tensor("op_3417_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1061815872)))]; + tensor input_241_cast = mul(x = var_3415_cast, y = var_3417_to_fp16)[name = tensor("input_241_cast")]; + tensor var_3425 = const()[name = tensor("op_3425"), val = tensor([1, 1])]; + tensor var_3427 = const()[name = tensor("op_3427"), val = tensor([1, 1])]; + tensor var_3429_pad_type_0 = const()[name = tensor("op_3429_pad_type_0"), val = tensor("custom")]; + tensor var_3429_pad_0 = const()[name = tensor("op_3429_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1061818496)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1088032960)))]; + tensor var_3429_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_3427, groups = var_31, pad = var_3429_pad_0, pad_type = var_3429_pad_type_0, strides = var_3425, weight = unet_down_blocks_2_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_241_cast)[name = tensor("op_3429_cast")]; + tensor var_3430_split_sizes_0 = const()[name = tensor("op_3430_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3430_axis_0 = const()[name = tensor("op_3430_axis_0"), val = tensor(1)]; + tensor var_3430_cast_0, tensor var_3430_cast_1 = split(axis = var_3430_axis_0, split_sizes = var_3430_split_sizes_0, x = var_3429_cast)[name = tensor("op_3430_cast")]; + tensor var_3432_mode_0 = const()[name = tensor("op_3432_mode_0"), val = tensor("EXACT")]; + tensor var_3432_cast = gelu(mode = var_3432_mode_0, x = var_3430_cast_1)[name = tensor("op_3432_cast")]; + tensor input_243_cast = mul(x = var_3430_cast_0, y = var_3432_cast)[name = tensor("input_243_cast")]; + tensor var_3436 = const()[name = tensor("op_3436"), val = tensor([1, 1])]; + tensor var_3438 = const()[name = tensor("op_3438"), val = tensor([1, 1])]; + tensor var_3440_pad_type_0 = const()[name = tensor("op_3440_pad_type_0"), val = tensor("custom")]; + tensor var_3440_pad_0 = const()[name = tensor("op_3440_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1088053504)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101160768)))]; + tensor var_3440_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_3438, groups = var_31, pad = var_3440_pad_0, pad_type = var_3440_pad_type_0, strides = var_3436, weight = unet_down_blocks_2_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_243_cast)[name = tensor("op_3440_cast")]; + tensor inputs_97_cast = add(x = var_3440_cast, y = inputs_95_cast)[name = tensor("inputs_97_cast")]; + tensor var_3450 = const()[name = tensor("op_3450"), val = tensor([1])]; + tensor channels_mean_97_cast = reduce_mean(axes = var_3450, keep_dims = var_23, x = inputs_97_cast)[name = tensor("channels_mean_97_cast")]; + tensor zero_mean_97_cast = sub(x = inputs_97_cast, y = channels_mean_97_cast)[name = tensor("zero_mean_97_cast")]; + tensor zero_mean_sq_97_cast = mul(x = zero_mean_97_cast, y = zero_mean_97_cast)[name = tensor("zero_mean_sq_97_cast")]; + tensor var_3454 = const()[name = tensor("op_3454"), val = tensor([1])]; + tensor var_3455_cast = reduce_mean(axes = var_3454, keep_dims = var_23, x = zero_mean_sq_97_cast)[name = tensor("op_3455_cast")]; + tensor var_3456_to_fp16 = const()[name = tensor("op_3456_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3457_cast = add(x = var_3455_cast, y = var_3456_to_fp16)[name = tensor("op_3457_cast")]; + tensor denom_97_epsilon_0_to_fp16 = const()[name = tensor("denom_97_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_97_cast = rsqrt(epsilon = denom_97_epsilon_0_to_fp16, x = var_3457_cast)[name = tensor("denom_97_cast")]; + tensor out_97_cast = mul(x = zero_mean_97_cast, y = denom_97_cast)[name = tensor("out_97_cast")]; + tensor var_3461_to_fp16 = const()[name = tensor("op_3461_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101163392)))]; + tensor var_3462_cast = add(x = out_97_cast, y = var_3461_to_fp16)[name = tensor("op_3462_cast")]; + tensor var_3464_to_fp16 = const()[name = tensor("op_3464_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101166016)))]; + tensor hidden_states_149_cast = mul(x = var_3462_cast, y = var_3464_to_fp16)[name = tensor("hidden_states_149_cast")]; + tensor var_3471 = const()[name = tensor("op_3471"), val = tensor([1, 1])]; + tensor var_3473 = const()[name = tensor("op_3473"), val = tensor([1, 1])]; tensor q_65_pad_type_0 = const()[name = tensor("q_65_pad_type_0"), val = tensor("custom")]; tensor q_65_pad_0 = const()[name = tensor("q_65_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_65 = conv(dilations = var_3535, groups = var_1193, pad = q_65_pad_0, pad_type = q_65_pad_type_0, strides = var_3533, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight, x = hidden_states_149)[name = tensor("q_65")]; - tensor var_3539 = const()[name = tensor("op_3539"), val = tensor([1, 1])]; - tensor var_3541 = const()[name = tensor("op_3541"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1101168640)))]; + tensor q_65_cast = conv(dilations = var_3473, groups = var_31, pad = q_65_pad_0, pad_type = q_65_pad_type_0, strides = var_3471, weight = unet_down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_149_cast)[name = tensor("q_65_cast")]; + tensor var_3477 = const()[name = tensor("op_3477"), val = tensor([1, 1])]; + tensor var_3479 = const()[name = tensor("op_3479"), val = tensor([1, 1])]; tensor k_65_pad_type_0 = const()[name = tensor("k_65_pad_type_0"), val = tensor("custom")]; tensor k_65_pad_0 = const()[name = tensor("k_65_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_65 = conv(dilations = var_3541, groups = var_1193, pad = k_65_pad_0, pad_type = k_65_pad_type_0, strides = var_3539, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight, x = hidden_states_149)[name = tensor("k_65")]; - tensor var_3545 = const()[name = tensor("op_3545"), val = tensor([1, 1])]; - tensor var_3547 = const()[name = tensor("op_3547"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1104445504)))]; + tensor k_65_cast = conv(dilations = var_3479, groups = var_31, pad = k_65_pad_0, pad_type = k_65_pad_type_0, strides = var_3477, weight = unet_down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_149_cast)[name = tensor("k_65_cast")]; + tensor var_3483 = const()[name = tensor("op_3483"), val = tensor([1, 1])]; + tensor var_3485 = const()[name = tensor("op_3485"), val = tensor([1, 1])]; tensor v_65_pad_type_0 = const()[name = tensor("v_65_pad_type_0"), val = tensor("custom")]; tensor v_65_pad_0 = const()[name = tensor("v_65_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_65 = conv(dilations = var_3547, groups = var_1193, pad = v_65_pad_0, pad_type = v_65_pad_type_0, strides = var_3545, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight, x = hidden_states_149)[name = tensor("v_65")]; - tensor var_3551 = const()[name = tensor("op_3551"), val = tensor([2, 20, 64, -1])]; - tensor var_3552 = reshape(shape = var_3551, x = q_65)[name = tensor("op_3552")]; - tensor var_3553 = const()[name = tensor("op_3553"), val = tensor([2, 20, 64, -1])]; - tensor var_3554 = reshape(shape = var_3553, x = k_65)[name = tensor("op_3554")]; - tensor var_3555 = const()[name = tensor("op_3555"), val = tensor([2, 20, 64, -1])]; - tensor var_3556 = reshape(shape = var_3555, x = v_65)[name = tensor("op_3556")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1107722368)))]; + tensor v_65_cast = conv(dilations = var_3485, groups = var_31, pad = v_65_pad_0, pad_type = v_65_pad_type_0, strides = var_3483, weight = unet_down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_149_cast)[name = tensor("v_65_cast")]; + tensor var_3489 = const()[name = tensor("op_3489"), val = tensor([2, 20, 64, -1])]; + tensor var_3490_cast = reshape(shape = var_3489, x = q_65_cast)[name = tensor("op_3490_cast")]; + tensor var_3491 = const()[name = tensor("op_3491"), val = tensor([2, 20, 64, -1])]; + tensor var_3492_cast = reshape(shape = var_3491, x = k_65_cast)[name = tensor("op_3492_cast")]; + tensor var_3493 = const()[name = tensor("op_3493"), val = tensor([2, 20, 64, -1])]; + tensor var_3494_cast = reshape(shape = var_3493, x = v_65_cast)[name = tensor("op_3494_cast")]; tensor attn_weights_129_transpose_x_0 = const()[name = tensor("attn_weights_129_transpose_x_0"), val = tensor(true)]; tensor attn_weights_129_transpose_y_0 = const()[name = tensor("attn_weights_129_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_129 = matmul(transpose_x = attn_weights_129_transpose_x_0, transpose_y = attn_weights_129_transpose_y_0, x = var_3552, y = var_3554)[name = tensor("attn_weights_129")]; - tensor attn_weights_131 = mul(x = attn_weights_129, y = var_1184)[name = tensor("attn_weights_131")]; - tensor var_3560 = softmax(axis = var_1177, x = attn_weights_131)[name = tensor("op_3560")]; + tensor attn_weights_129_cast = matmul(transpose_x = attn_weights_129_transpose_x_0, transpose_y = attn_weights_129_transpose_y_0, x = var_3490_cast, y = var_3492_cast)[name = tensor("attn_weights_129_cast")]; + tensor attn_weights_131_cast = mul(x = attn_weights_129_cast, y = var_12_to_fp16)[name = tensor("attn_weights_131_cast")]; + tensor var_3498_cast = softmax(axis = var_18, x = attn_weights_131_cast)[name = tensor("op_3498_cast")]; tensor attn_65_transpose_x_0 = const()[name = tensor("attn_65_transpose_x_0"), val = tensor(false)]; tensor attn_65_transpose_y_0 = const()[name = tensor("attn_65_transpose_y_0"), val = tensor(true)]; - tensor attn_65 = matmul(transpose_x = attn_65_transpose_x_0, transpose_y = attn_65_transpose_y_0, x = var_3556, y = var_3560)[name = tensor("attn_65")]; - tensor var_3564 = const()[name = tensor("op_3564"), val = tensor([2, 1280, 1, -1])]; - tensor input_245 = reshape(shape = var_3564, x = attn_65)[name = tensor("input_245")]; - tensor var_3569 = const()[name = tensor("op_3569"), val = tensor([1, 1])]; - tensor var_3571 = const()[name = tensor("op_3571"), val = tensor([1, 1])]; - tensor var_3573_pad_type_0 = const()[name = tensor("op_3573_pad_type_0"), val = tensor("custom")]; - tensor var_3573_pad_0 = const()[name = tensor("op_3573_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3573 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias, dilations = var_3571, groups = var_1193, pad = var_3573_pad_0, pad_type = var_3573_pad_type_0, strides = var_3569, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight, x = input_245)[name = tensor("op_3573")]; - tensor inputs_99 = add(x = var_3573, y = inputs_97)[name = tensor("inputs_99")]; - tensor var_3577 = const()[name = tensor("op_3577"), val = tensor([1])]; - tensor channels_mean_99 = reduce_mean(axes = var_3577, keep_dims = var_1188, x = inputs_99)[name = tensor("channels_mean_99")]; - tensor zero_mean_99 = sub(x = inputs_99, y = channels_mean_99)[name = tensor("zero_mean_99")]; - tensor zero_mean_sq_99 = mul(x = zero_mean_99, y = zero_mean_99)[name = tensor("zero_mean_sq_99")]; - tensor var_3581 = const()[name = tensor("op_3581"), val = tensor([1])]; - tensor var_3582 = reduce_mean(axes = var_3581, keep_dims = var_1188, x = zero_mean_sq_99)[name = tensor("op_3582")]; - tensor var_3583 = const()[name = tensor("op_3583"), val = tensor(0x1.4f8b58p-17)]; - tensor var_3584 = add(x = var_3582, y = var_3583)[name = tensor("op_3584")]; - tensor denom_99_epsilon_0 = const()[name = tensor("denom_99_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_99 = rsqrt(epsilon = denom_99_epsilon_0, x = var_3584)[name = tensor("denom_99")]; - tensor out_99 = mul(x = zero_mean_99, y = denom_99)[name = tensor("out_99")]; - tensor var_3588 = const()[name = tensor("op_3588"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268139776)))]; - tensor var_3589 = add(x = out_99, y = var_3588)[name = tensor("op_3589")]; - tensor var_3591 = const()[name = tensor("op_3591"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268144960)))]; - tensor hidden_states_151 = mul(x = var_3589, y = var_3591)[name = tensor("hidden_states_151")]; - tensor var_3598 = const()[name = tensor("op_3598"), val = tensor([1, 1])]; - tensor var_3600 = const()[name = tensor("op_3600"), val = tensor([1, 1])]; + tensor attn_65_cast = matmul(transpose_x = attn_65_transpose_x_0, transpose_y = attn_65_transpose_y_0, x = var_3494_cast, y = var_3498_cast)[name = tensor("attn_65_cast")]; + tensor var_3502 = const()[name = tensor("op_3502"), val = tensor([2, 1280, 1, -1])]; + tensor input_245_cast = reshape(shape = var_3502, x = attn_65_cast)[name = tensor("input_245_cast")]; + tensor var_3507 = const()[name = tensor("op_3507"), val = tensor([1, 1])]; + tensor var_3509 = const()[name = tensor("op_3509"), val = tensor([1, 1])]; + tensor var_3511_pad_type_0 = const()[name = tensor("op_3511_pad_type_0"), val = tensor("custom")]; + tensor var_3511_pad_0 = const()[name = tensor("op_3511_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1110999232)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1114276096)))]; + tensor var_3511_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_3509, groups = var_31, pad = var_3511_pad_0, pad_type = var_3511_pad_type_0, strides = var_3507, weight = unet_down_blocks_2_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_245_cast)[name = tensor("op_3511_cast")]; + tensor inputs_99_cast = add(x = var_3511_cast, y = inputs_97_cast)[name = tensor("inputs_99_cast")]; + tensor var_3515 = const()[name = tensor("op_3515"), val = tensor([1])]; + tensor channels_mean_99_cast = reduce_mean(axes = var_3515, keep_dims = var_23, x = inputs_99_cast)[name = tensor("channels_mean_99_cast")]; + tensor zero_mean_99_cast = sub(x = inputs_99_cast, y = channels_mean_99_cast)[name = tensor("zero_mean_99_cast")]; + tensor zero_mean_sq_99_cast = mul(x = zero_mean_99_cast, y = zero_mean_99_cast)[name = tensor("zero_mean_sq_99_cast")]; + tensor var_3519 = const()[name = tensor("op_3519"), val = tensor([1])]; + tensor var_3520_cast = reduce_mean(axes = var_3519, keep_dims = var_23, x = zero_mean_sq_99_cast)[name = tensor("op_3520_cast")]; + tensor var_3521_to_fp16 = const()[name = tensor("op_3521_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3522_cast = add(x = var_3520_cast, y = var_3521_to_fp16)[name = tensor("op_3522_cast")]; + tensor denom_99_epsilon_0_to_fp16 = const()[name = tensor("denom_99_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_99_cast = rsqrt(epsilon = denom_99_epsilon_0_to_fp16, x = var_3522_cast)[name = tensor("denom_99_cast")]; + tensor out_99_cast = mul(x = zero_mean_99_cast, y = denom_99_cast)[name = tensor("out_99_cast")]; + tensor var_3526_to_fp16 = const()[name = tensor("op_3526_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1114278720)))]; + tensor var_3527_cast = add(x = out_99_cast, y = var_3526_to_fp16)[name = tensor("op_3527_cast")]; + tensor var_3529_to_fp16 = const()[name = tensor("op_3529_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1114281344)))]; + tensor hidden_states_151_cast = mul(x = var_3527_cast, y = var_3529_to_fp16)[name = tensor("hidden_states_151_cast")]; + tensor var_3536 = const()[name = tensor("op_3536"), val = tensor([1, 1])]; + tensor var_3538 = const()[name = tensor("op_3538"), val = tensor([1, 1])]; tensor q_67_pad_type_0 = const()[name = tensor("q_67_pad_type_0"), val = tensor("custom")]; tensor q_67_pad_0 = const()[name = tensor("q_67_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_67 = conv(dilations = var_3600, groups = var_1193, pad = q_67_pad_0, pad_type = q_67_pad_type_0, strides = var_3598, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight, x = hidden_states_151)[name = tensor("q_67")]; - tensor var_3604 = const()[name = tensor("op_3604"), val = tensor([1, 1])]; - tensor var_3606 = const()[name = tensor("op_3606"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1114283968)))]; + tensor q_67_cast = conv(dilations = var_3538, groups = var_31, pad = q_67_pad_0, pad_type = q_67_pad_type_0, strides = var_3536, weight = unet_down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_151_cast)[name = tensor("q_67_cast")]; + tensor var_3542 = const()[name = tensor("op_3542"), val = tensor([1, 1])]; + tensor var_3544 = const()[name = tensor("op_3544"), val = tensor([1, 1])]; tensor k_67_pad_type_0 = const()[name = tensor("k_67_pad_type_0"), val = tensor("custom")]; tensor k_67_pad_0 = const()[name = tensor("k_67_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_67 = conv(dilations = var_3606, groups = var_1193, pad = k_67_pad_0, pad_type = k_67_pad_type_0, strides = var_3604, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_67")]; - tensor var_3610 = const()[name = tensor("op_3610"), val = tensor([1, 1])]; - tensor var_3612 = const()[name = tensor("op_3612"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1117560832)))]; + tensor k_67_cast = conv(dilations = var_3544, groups = var_31, pad = k_67_pad_0, pad_type = k_67_pad_type_0, strides = var_3542, weight = unet_down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_67_cast")]; + tensor var_3548 = const()[name = tensor("op_3548"), val = tensor([1, 1])]; + tensor var_3550 = const()[name = tensor("op_3550"), val = tensor([1, 1])]; tensor v_67_pad_type_0 = const()[name = tensor("v_67_pad_type_0"), val = tensor("custom")]; tensor v_67_pad_0 = const()[name = tensor("v_67_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_67 = conv(dilations = var_3612, groups = var_1193, pad = v_67_pad_0, pad_type = v_67_pad_type_0, strides = var_3610, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_67")]; - tensor var_3616 = const()[name = tensor("op_3616"), val = tensor([2, 20, 64, -1])]; - tensor var_3617 = reshape(shape = var_3616, x = q_67)[name = tensor("op_3617")]; - tensor var_3618 = const()[name = tensor("op_3618"), val = tensor([2, 20, 64, -1])]; - tensor var_3619 = reshape(shape = var_3618, x = k_67)[name = tensor("op_3619")]; - tensor var_3620 = const()[name = tensor("op_3620"), val = tensor([2, 20, 64, -1])]; - tensor var_3621 = reshape(shape = var_3620, x = v_67)[name = tensor("op_3621")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1122803776)))]; + tensor v_67_cast = conv(dilations = var_3550, groups = var_31, pad = v_67_pad_0, pad_type = v_67_pad_type_0, strides = var_3548, weight = unet_down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_67_cast")]; + tensor var_3554 = const()[name = tensor("op_3554"), val = tensor([2, 20, 64, -1])]; + tensor var_3555_cast = reshape(shape = var_3554, x = q_67_cast)[name = tensor("op_3555_cast")]; + tensor var_3556 = const()[name = tensor("op_3556"), val = tensor([2, 20, 64, -1])]; + tensor var_3557_cast = reshape(shape = var_3556, x = k_67_cast)[name = tensor("op_3557_cast")]; + tensor var_3558 = const()[name = tensor("op_3558"), val = tensor([2, 20, 64, -1])]; + tensor var_3559_cast = reshape(shape = var_3558, x = v_67_cast)[name = tensor("op_3559_cast")]; tensor attn_weights_133_transpose_x_0 = const()[name = tensor("attn_weights_133_transpose_x_0"), val = tensor(true)]; tensor attn_weights_133_transpose_y_0 = const()[name = tensor("attn_weights_133_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_133 = matmul(transpose_x = attn_weights_133_transpose_x_0, transpose_y = attn_weights_133_transpose_y_0, x = var_3617, y = var_3619)[name = tensor("attn_weights_133")]; - tensor attn_weights_135 = mul(x = attn_weights_133, y = var_1184)[name = tensor("attn_weights_135")]; - tensor var_3625 = softmax(axis = var_1177, x = attn_weights_135)[name = tensor("op_3625")]; + tensor attn_weights_133_cast = matmul(transpose_x = attn_weights_133_transpose_x_0, transpose_y = attn_weights_133_transpose_y_0, x = var_3555_cast, y = var_3557_cast)[name = tensor("attn_weights_133_cast")]; + tensor attn_weights_135_cast = mul(x = attn_weights_133_cast, y = var_12_to_fp16)[name = tensor("attn_weights_135_cast")]; + tensor var_3563_cast = softmax(axis = var_18, x = attn_weights_135_cast)[name = tensor("op_3563_cast")]; tensor attn_67_transpose_x_0 = const()[name = tensor("attn_67_transpose_x_0"), val = tensor(false)]; tensor attn_67_transpose_y_0 = const()[name = tensor("attn_67_transpose_y_0"), val = tensor(true)]; - tensor attn_67 = matmul(transpose_x = attn_67_transpose_x_0, transpose_y = attn_67_transpose_y_0, x = var_3621, y = var_3625)[name = tensor("attn_67")]; - tensor var_3629 = const()[name = tensor("op_3629"), val = tensor([2, 1280, 1, -1])]; - tensor input_247 = reshape(shape = var_3629, x = attn_67)[name = tensor("input_247")]; - tensor var_3634 = const()[name = tensor("op_3634"), val = tensor([1, 1])]; - tensor var_3636 = const()[name = tensor("op_3636"), val = tensor([1, 1])]; - tensor var_3638_pad_type_0 = const()[name = tensor("op_3638_pad_type_0"), val = tensor("custom")]; - tensor var_3638_pad_0 = const()[name = tensor("op_3638_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3638 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias, dilations = var_3636, groups = var_1193, pad = var_3638_pad_0, pad_type = var_3638_pad_type_0, strides = var_3634, weight = down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight, x = input_247)[name = tensor("op_3638")]; - tensor inputs_101 = add(x = var_3638, y = inputs_99)[name = tensor("inputs_101")]; - tensor var_3642 = const()[name = tensor("op_3642"), val = tensor([1])]; - tensor channels_mean_101 = reduce_mean(axes = var_3642, keep_dims = var_1188, x = inputs_101)[name = tensor("channels_mean_101")]; - tensor zero_mean_101 = sub(x = inputs_101, y = channels_mean_101)[name = tensor("zero_mean_101")]; - tensor zero_mean_sq_101 = mul(x = zero_mean_101, y = zero_mean_101)[name = tensor("zero_mean_sq_101")]; - tensor var_3646 = const()[name = tensor("op_3646"), val = tensor([1])]; - tensor var_3647 = reduce_mean(axes = var_3646, keep_dims = var_1188, x = zero_mean_sq_101)[name = tensor("op_3647")]; - tensor var_3648 = const()[name = tensor("op_3648"), val = tensor(0x1.4f8b58p-17)]; - tensor var_3649 = add(x = var_3647, y = var_3648)[name = tensor("op_3649")]; - tensor denom_101_epsilon_0 = const()[name = tensor("denom_101_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_101 = rsqrt(epsilon = denom_101_epsilon_0, x = var_3649)[name = tensor("denom_101")]; - tensor out_101 = mul(x = zero_mean_101, y = denom_101)[name = tensor("out_101")]; - tensor var_3653 = const()[name = tensor("op_3653"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268150144)))]; - tensor var_3654 = add(x = out_101, y = var_3653)[name = tensor("op_3654")]; - tensor var_3656 = const()[name = tensor("op_3656"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268155328)))]; - tensor input_249 = mul(x = var_3654, y = var_3656)[name = tensor("input_249")]; - tensor var_3664 = const()[name = tensor("op_3664"), val = tensor([1, 1])]; - tensor var_3666 = const()[name = tensor("op_3666"), val = tensor([1, 1])]; - tensor var_3668_pad_type_0 = const()[name = tensor("op_3668_pad_type_0"), val = tensor("custom")]; - tensor var_3668_pad_0 = const()[name = tensor("op_3668_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3668 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias, dilations = var_3666, groups = var_1193, pad = var_3668_pad_0, pad_type = var_3668_pad_type_0, strides = var_3664, weight = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight, x = input_249)[name = tensor("op_3668")]; - tensor var_3669_split_sizes_0 = const()[name = tensor("op_3669_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_3669_axis_0 = const()[name = tensor("op_3669_axis_0"), val = tensor(1)]; - tensor var_3669_0, tensor var_3669_1 = split(axis = var_3669_axis_0, split_sizes = var_3669_split_sizes_0, x = var_3668)[name = tensor("op_3669")]; - tensor var_3671_mode_0 = const()[name = tensor("op_3671_mode_0"), val = tensor("EXACT")]; - tensor var_3671 = gelu(mode = var_3671_mode_0, x = var_3669_1)[name = tensor("op_3671")]; - tensor input_251 = mul(x = var_3669_0, y = var_3671)[name = tensor("input_251")]; - tensor var_3675 = const()[name = tensor("op_3675"), val = tensor([1, 1])]; - tensor var_3677 = const()[name = tensor("op_3677"), val = tensor([1, 1])]; - tensor var_3679_pad_type_0 = const()[name = tensor("op_3679_pad_type_0"), val = tensor("custom")]; - tensor var_3679_pad_0 = const()[name = tensor("op_3679_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3679 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias, dilations = var_3677, groups = var_1193, pad = var_3679_pad_0, pad_type = var_3679_pad_type_0, strides = var_3675, weight = down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight, x = input_251)[name = tensor("op_3679")]; - tensor inputs_103 = add(x = var_3679, y = inputs_101)[name = tensor("inputs_103")]; - tensor var_3689 = const()[name = tensor("op_3689"), val = tensor([1])]; - tensor channels_mean_103 = reduce_mean(axes = var_3689, keep_dims = var_1188, x = inputs_103)[name = tensor("channels_mean_103")]; - tensor zero_mean_103 = sub(x = inputs_103, y = channels_mean_103)[name = tensor("zero_mean_103")]; - tensor zero_mean_sq_103 = mul(x = zero_mean_103, y = zero_mean_103)[name = tensor("zero_mean_sq_103")]; - tensor var_3693 = const()[name = tensor("op_3693"), val = tensor([1])]; - tensor var_3694 = reduce_mean(axes = var_3693, keep_dims = var_1188, x = zero_mean_sq_103)[name = tensor("op_3694")]; - tensor var_3695 = const()[name = tensor("op_3695"), val = tensor(0x1.4f8b58p-17)]; - tensor var_3696 = add(x = var_3694, y = var_3695)[name = tensor("op_3696")]; - tensor denom_103_epsilon_0 = const()[name = tensor("denom_103_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_103 = rsqrt(epsilon = denom_103_epsilon_0, x = var_3696)[name = tensor("denom_103")]; - tensor out_103 = mul(x = zero_mean_103, y = denom_103)[name = tensor("out_103")]; - tensor var_3700 = const()[name = tensor("op_3700"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268160512)))]; - tensor var_3701 = add(x = out_103, y = var_3700)[name = tensor("op_3701")]; - tensor var_3703 = const()[name = tensor("op_3703"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268165696)))]; - tensor hidden_states_155 = mul(x = var_3701, y = var_3703)[name = tensor("hidden_states_155")]; - tensor var_3710 = const()[name = tensor("op_3710"), val = tensor([1, 1])]; - tensor var_3712 = const()[name = tensor("op_3712"), val = tensor([1, 1])]; + tensor attn_67_cast = matmul(transpose_x = attn_67_transpose_x_0, transpose_y = attn_67_transpose_y_0, x = var_3559_cast, y = var_3563_cast)[name = tensor("attn_67_cast")]; + tensor var_3567 = const()[name = tensor("op_3567"), val = tensor([2, 1280, 1, -1])]; + tensor input_247_cast = reshape(shape = var_3567, x = attn_67_cast)[name = tensor("input_247_cast")]; + tensor var_3572 = const()[name = tensor("op_3572"), val = tensor([1, 1])]; + tensor var_3574 = const()[name = tensor("op_3574"), val = tensor([1, 1])]; + tensor var_3576_pad_type_0 = const()[name = tensor("op_3576_pad_type_0"), val = tensor("custom")]; + tensor var_3576_pad_0 = const()[name = tensor("op_3576_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1128046720)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1131323584)))]; + tensor var_3576_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_3574, groups = var_31, pad = var_3576_pad_0, pad_type = var_3576_pad_type_0, strides = var_3572, weight = unet_down_blocks_2_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_247_cast)[name = tensor("op_3576_cast")]; + tensor inputs_101_cast = add(x = var_3576_cast, y = inputs_99_cast)[name = tensor("inputs_101_cast")]; + tensor var_3580 = const()[name = tensor("op_3580"), val = tensor([1])]; + tensor channels_mean_101_cast = reduce_mean(axes = var_3580, keep_dims = var_23, x = inputs_101_cast)[name = tensor("channels_mean_101_cast")]; + tensor zero_mean_101_cast = sub(x = inputs_101_cast, y = channels_mean_101_cast)[name = tensor("zero_mean_101_cast")]; + tensor zero_mean_sq_101_cast = mul(x = zero_mean_101_cast, y = zero_mean_101_cast)[name = tensor("zero_mean_sq_101_cast")]; + tensor var_3584 = const()[name = tensor("op_3584"), val = tensor([1])]; + tensor var_3585_cast = reduce_mean(axes = var_3584, keep_dims = var_23, x = zero_mean_sq_101_cast)[name = tensor("op_3585_cast")]; + tensor var_3586_to_fp16 = const()[name = tensor("op_3586_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3587_cast = add(x = var_3585_cast, y = var_3586_to_fp16)[name = tensor("op_3587_cast")]; + tensor denom_101_epsilon_0_to_fp16 = const()[name = tensor("denom_101_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_101_cast = rsqrt(epsilon = denom_101_epsilon_0_to_fp16, x = var_3587_cast)[name = tensor("denom_101_cast")]; + tensor out_101_cast = mul(x = zero_mean_101_cast, y = denom_101_cast)[name = tensor("out_101_cast")]; + tensor var_3591_to_fp16 = const()[name = tensor("op_3591_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1131326208)))]; + tensor var_3592_cast = add(x = out_101_cast, y = var_3591_to_fp16)[name = tensor("op_3592_cast")]; + tensor var_3594_to_fp16 = const()[name = tensor("op_3594_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1131328832)))]; + tensor input_249_cast = mul(x = var_3592_cast, y = var_3594_to_fp16)[name = tensor("input_249_cast")]; + tensor var_3602 = const()[name = tensor("op_3602"), val = tensor([1, 1])]; + tensor var_3604 = const()[name = tensor("op_3604"), val = tensor([1, 1])]; + tensor var_3606_pad_type_0 = const()[name = tensor("op_3606_pad_type_0"), val = tensor("custom")]; + tensor var_3606_pad_0 = const()[name = tensor("op_3606_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1131331456)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1157545920)))]; + tensor var_3606_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_3604, groups = var_31, pad = var_3606_pad_0, pad_type = var_3606_pad_type_0, strides = var_3602, weight = unet_down_blocks_2_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_249_cast)[name = tensor("op_3606_cast")]; + tensor var_3607_split_sizes_0 = const()[name = tensor("op_3607_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3607_axis_0 = const()[name = tensor("op_3607_axis_0"), val = tensor(1)]; + tensor var_3607_cast_0, tensor var_3607_cast_1 = split(axis = var_3607_axis_0, split_sizes = var_3607_split_sizes_0, x = var_3606_cast)[name = tensor("op_3607_cast")]; + tensor var_3609_mode_0 = const()[name = tensor("op_3609_mode_0"), val = tensor("EXACT")]; + tensor var_3609_cast = gelu(mode = var_3609_mode_0, x = var_3607_cast_1)[name = tensor("op_3609_cast")]; + tensor input_251_cast = mul(x = var_3607_cast_0, y = var_3609_cast)[name = tensor("input_251_cast")]; + tensor var_3613 = const()[name = tensor("op_3613"), val = tensor([1, 1])]; + tensor var_3615 = const()[name = tensor("op_3615"), val = tensor([1, 1])]; + tensor var_3617_pad_type_0 = const()[name = tensor("op_3617_pad_type_0"), val = tensor("custom")]; + tensor var_3617_pad_0 = const()[name = tensor("op_3617_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1157566464)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1170673728)))]; + tensor var_3617_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_3615, groups = var_31, pad = var_3617_pad_0, pad_type = var_3617_pad_type_0, strides = var_3613, weight = unet_down_blocks_2_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_251_cast)[name = tensor("op_3617_cast")]; + tensor inputs_103_cast = add(x = var_3617_cast, y = inputs_101_cast)[name = tensor("inputs_103_cast")]; + tensor var_3627 = const()[name = tensor("op_3627"), val = tensor([1])]; + tensor channels_mean_103_cast = reduce_mean(axes = var_3627, keep_dims = var_23, x = inputs_103_cast)[name = tensor("channels_mean_103_cast")]; + tensor zero_mean_103_cast = sub(x = inputs_103_cast, y = channels_mean_103_cast)[name = tensor("zero_mean_103_cast")]; + tensor zero_mean_sq_103_cast = mul(x = zero_mean_103_cast, y = zero_mean_103_cast)[name = tensor("zero_mean_sq_103_cast")]; + tensor var_3631 = const()[name = tensor("op_3631"), val = tensor([1])]; + tensor var_3632_cast = reduce_mean(axes = var_3631, keep_dims = var_23, x = zero_mean_sq_103_cast)[name = tensor("op_3632_cast")]; + tensor var_3633_to_fp16 = const()[name = tensor("op_3633_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3634_cast = add(x = var_3632_cast, y = var_3633_to_fp16)[name = tensor("op_3634_cast")]; + tensor denom_103_epsilon_0_to_fp16 = const()[name = tensor("denom_103_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_103_cast = rsqrt(epsilon = denom_103_epsilon_0_to_fp16, x = var_3634_cast)[name = tensor("denom_103_cast")]; + tensor out_103_cast = mul(x = zero_mean_103_cast, y = denom_103_cast)[name = tensor("out_103_cast")]; + tensor var_3638_to_fp16 = const()[name = tensor("op_3638_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1170676352)))]; + tensor var_3639_cast = add(x = out_103_cast, y = var_3638_to_fp16)[name = tensor("op_3639_cast")]; + tensor var_3641_to_fp16 = const()[name = tensor("op_3641_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1170678976)))]; + tensor hidden_states_155_cast = mul(x = var_3639_cast, y = var_3641_to_fp16)[name = tensor("hidden_states_155_cast")]; + tensor var_3648 = const()[name = tensor("op_3648"), val = tensor([1, 1])]; + tensor var_3650 = const()[name = tensor("op_3650"), val = tensor([1, 1])]; tensor q_69_pad_type_0 = const()[name = tensor("q_69_pad_type_0"), val = tensor("custom")]; tensor q_69_pad_0 = const()[name = tensor("q_69_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_69 = conv(dilations = var_3712, groups = var_1193, pad = q_69_pad_0, pad_type = q_69_pad_type_0, strides = var_3710, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight, x = hidden_states_155)[name = tensor("q_69")]; - tensor var_3716 = const()[name = tensor("op_3716"), val = tensor([1, 1])]; - tensor var_3718 = const()[name = tensor("op_3718"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1170681600)))]; + tensor q_69_cast = conv(dilations = var_3650, groups = var_31, pad = q_69_pad_0, pad_type = q_69_pad_type_0, strides = var_3648, weight = unet_down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_155_cast)[name = tensor("q_69_cast")]; + tensor var_3654 = const()[name = tensor("op_3654"), val = tensor([1, 1])]; + tensor var_3656 = const()[name = tensor("op_3656"), val = tensor([1, 1])]; tensor k_69_pad_type_0 = const()[name = tensor("k_69_pad_type_0"), val = tensor("custom")]; tensor k_69_pad_0 = const()[name = tensor("k_69_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_69 = conv(dilations = var_3718, groups = var_1193, pad = k_69_pad_0, pad_type = k_69_pad_type_0, strides = var_3716, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight, x = hidden_states_155)[name = tensor("k_69")]; - tensor var_3722 = const()[name = tensor("op_3722"), val = tensor([1, 1])]; - tensor var_3724 = const()[name = tensor("op_3724"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1173958464)))]; + tensor k_69_cast = conv(dilations = var_3656, groups = var_31, pad = k_69_pad_0, pad_type = k_69_pad_type_0, strides = var_3654, weight = unet_down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_155_cast)[name = tensor("k_69_cast")]; + tensor var_3660 = const()[name = tensor("op_3660"), val = tensor([1, 1])]; + tensor var_3662 = const()[name = tensor("op_3662"), val = tensor([1, 1])]; tensor v_69_pad_type_0 = const()[name = tensor("v_69_pad_type_0"), val = tensor("custom")]; tensor v_69_pad_0 = const()[name = tensor("v_69_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_69 = conv(dilations = var_3724, groups = var_1193, pad = v_69_pad_0, pad_type = v_69_pad_type_0, strides = var_3722, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight, x = hidden_states_155)[name = tensor("v_69")]; - tensor var_3728 = const()[name = tensor("op_3728"), val = tensor([2, 20, 64, -1])]; - tensor var_3729 = reshape(shape = var_3728, x = q_69)[name = tensor("op_3729")]; - tensor var_3730 = const()[name = tensor("op_3730"), val = tensor([2, 20, 64, -1])]; - tensor var_3731 = reshape(shape = var_3730, x = k_69)[name = tensor("op_3731")]; - tensor var_3732 = const()[name = tensor("op_3732"), val = tensor([2, 20, 64, -1])]; - tensor var_3733 = reshape(shape = var_3732, x = v_69)[name = tensor("op_3733")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1177235328)))]; + tensor v_69_cast = conv(dilations = var_3662, groups = var_31, pad = v_69_pad_0, pad_type = v_69_pad_type_0, strides = var_3660, weight = unet_down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_155_cast)[name = tensor("v_69_cast")]; + tensor var_3666 = const()[name = tensor("op_3666"), val = tensor([2, 20, 64, -1])]; + tensor var_3667_cast = reshape(shape = var_3666, x = q_69_cast)[name = tensor("op_3667_cast")]; + tensor var_3668 = const()[name = tensor("op_3668"), val = tensor([2, 20, 64, -1])]; + tensor var_3669_cast = reshape(shape = var_3668, x = k_69_cast)[name = tensor("op_3669_cast")]; + tensor var_3670 = const()[name = tensor("op_3670"), val = tensor([2, 20, 64, -1])]; + tensor var_3671_cast = reshape(shape = var_3670, x = v_69_cast)[name = tensor("op_3671_cast")]; tensor attn_weights_137_transpose_x_0 = const()[name = tensor("attn_weights_137_transpose_x_0"), val = tensor(true)]; tensor attn_weights_137_transpose_y_0 = const()[name = tensor("attn_weights_137_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_137 = matmul(transpose_x = attn_weights_137_transpose_x_0, transpose_y = attn_weights_137_transpose_y_0, x = var_3729, y = var_3731)[name = tensor("attn_weights_137")]; - tensor attn_weights_139 = mul(x = attn_weights_137, y = var_1184)[name = tensor("attn_weights_139")]; - tensor var_3737 = softmax(axis = var_1177, x = attn_weights_139)[name = tensor("op_3737")]; + tensor attn_weights_137_cast = matmul(transpose_x = attn_weights_137_transpose_x_0, transpose_y = attn_weights_137_transpose_y_0, x = var_3667_cast, y = var_3669_cast)[name = tensor("attn_weights_137_cast")]; + tensor attn_weights_139_cast = mul(x = attn_weights_137_cast, y = var_12_to_fp16)[name = tensor("attn_weights_139_cast")]; + tensor var_3675_cast = softmax(axis = var_18, x = attn_weights_139_cast)[name = tensor("op_3675_cast")]; tensor attn_69_transpose_x_0 = const()[name = tensor("attn_69_transpose_x_0"), val = tensor(false)]; tensor attn_69_transpose_y_0 = const()[name = tensor("attn_69_transpose_y_0"), val = tensor(true)]; - tensor attn_69 = matmul(transpose_x = attn_69_transpose_x_0, transpose_y = attn_69_transpose_y_0, x = var_3733, y = var_3737)[name = tensor("attn_69")]; - tensor var_3741 = const()[name = tensor("op_3741"), val = tensor([2, 1280, 1, -1])]; - tensor input_253 = reshape(shape = var_3741, x = attn_69)[name = tensor("input_253")]; - tensor var_3746 = const()[name = tensor("op_3746"), val = tensor([1, 1])]; - tensor var_3748 = const()[name = tensor("op_3748"), val = tensor([1, 1])]; - tensor var_3750_pad_type_0 = const()[name = tensor("op_3750_pad_type_0"), val = tensor("custom")]; - tensor var_3750_pad_0 = const()[name = tensor("op_3750_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3750 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias, dilations = var_3748, groups = var_1193, pad = var_3750_pad_0, pad_type = var_3750_pad_type_0, strides = var_3746, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight, x = input_253)[name = tensor("op_3750")]; - tensor inputs_105 = add(x = var_3750, y = inputs_103)[name = tensor("inputs_105")]; - tensor var_3754 = const()[name = tensor("op_3754"), val = tensor([1])]; - tensor channels_mean_105 = reduce_mean(axes = var_3754, keep_dims = var_1188, x = inputs_105)[name = tensor("channels_mean_105")]; - tensor zero_mean_105 = sub(x = inputs_105, y = channels_mean_105)[name = tensor("zero_mean_105")]; - tensor zero_mean_sq_105 = mul(x = zero_mean_105, y = zero_mean_105)[name = tensor("zero_mean_sq_105")]; - tensor var_3758 = const()[name = tensor("op_3758"), val = tensor([1])]; - tensor var_3759 = reduce_mean(axes = var_3758, keep_dims = var_1188, x = zero_mean_sq_105)[name = tensor("op_3759")]; - tensor var_3760 = const()[name = tensor("op_3760"), val = tensor(0x1.4f8b58p-17)]; - tensor var_3761 = add(x = var_3759, y = var_3760)[name = tensor("op_3761")]; - tensor denom_105_epsilon_0 = const()[name = tensor("denom_105_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_105 = rsqrt(epsilon = denom_105_epsilon_0, x = var_3761)[name = tensor("denom_105")]; - tensor out_105 = mul(x = zero_mean_105, y = denom_105)[name = tensor("out_105")]; - tensor var_3765 = const()[name = tensor("op_3765"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268170880)))]; - tensor var_3766 = add(x = out_105, y = var_3765)[name = tensor("op_3766")]; - tensor var_3768 = const()[name = tensor("op_3768"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268176064)))]; - tensor hidden_states_157 = mul(x = var_3766, y = var_3768)[name = tensor("hidden_states_157")]; - tensor var_3775 = const()[name = tensor("op_3775"), val = tensor([1, 1])]; - tensor var_3777 = const()[name = tensor("op_3777"), val = tensor([1, 1])]; + tensor attn_69_cast = matmul(transpose_x = attn_69_transpose_x_0, transpose_y = attn_69_transpose_y_0, x = var_3671_cast, y = var_3675_cast)[name = tensor("attn_69_cast")]; + tensor var_3679 = const()[name = tensor("op_3679"), val = tensor([2, 1280, 1, -1])]; + tensor input_253_cast = reshape(shape = var_3679, x = attn_69_cast)[name = tensor("input_253_cast")]; + tensor var_3684 = const()[name = tensor("op_3684"), val = tensor([1, 1])]; + tensor var_3686 = const()[name = tensor("op_3686"), val = tensor([1, 1])]; + tensor var_3688_pad_type_0 = const()[name = tensor("op_3688_pad_type_0"), val = tensor("custom")]; + tensor var_3688_pad_0 = const()[name = tensor("op_3688_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1180512192)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1183789056)))]; + tensor var_3688_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_3686, groups = var_31, pad = var_3688_pad_0, pad_type = var_3688_pad_type_0, strides = var_3684, weight = unet_down_blocks_2_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_253_cast)[name = tensor("op_3688_cast")]; + tensor inputs_105_cast = add(x = var_3688_cast, y = inputs_103_cast)[name = tensor("inputs_105_cast")]; + tensor var_3692 = const()[name = tensor("op_3692"), val = tensor([1])]; + tensor channels_mean_105_cast = reduce_mean(axes = var_3692, keep_dims = var_23, x = inputs_105_cast)[name = tensor("channels_mean_105_cast")]; + tensor zero_mean_105_cast = sub(x = inputs_105_cast, y = channels_mean_105_cast)[name = tensor("zero_mean_105_cast")]; + tensor zero_mean_sq_105_cast = mul(x = zero_mean_105_cast, y = zero_mean_105_cast)[name = tensor("zero_mean_sq_105_cast")]; + tensor var_3696 = const()[name = tensor("op_3696"), val = tensor([1])]; + tensor var_3697_cast = reduce_mean(axes = var_3696, keep_dims = var_23, x = zero_mean_sq_105_cast)[name = tensor("op_3697_cast")]; + tensor var_3698_to_fp16 = const()[name = tensor("op_3698_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3699_cast = add(x = var_3697_cast, y = var_3698_to_fp16)[name = tensor("op_3699_cast")]; + tensor denom_105_epsilon_0_to_fp16 = const()[name = tensor("denom_105_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_105_cast = rsqrt(epsilon = denom_105_epsilon_0_to_fp16, x = var_3699_cast)[name = tensor("denom_105_cast")]; + tensor out_105_cast = mul(x = zero_mean_105_cast, y = denom_105_cast)[name = tensor("out_105_cast")]; + tensor var_3703_to_fp16 = const()[name = tensor("op_3703_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1183791680)))]; + tensor var_3704_cast = add(x = out_105_cast, y = var_3703_to_fp16)[name = tensor("op_3704_cast")]; + tensor var_3706_to_fp16 = const()[name = tensor("op_3706_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1183794304)))]; + tensor hidden_states_157_cast = mul(x = var_3704_cast, y = var_3706_to_fp16)[name = tensor("hidden_states_157_cast")]; + tensor var_3713 = const()[name = tensor("op_3713"), val = tensor([1, 1])]; + tensor var_3715 = const()[name = tensor("op_3715"), val = tensor([1, 1])]; tensor q_71_pad_type_0 = const()[name = tensor("q_71_pad_type_0"), val = tensor("custom")]; tensor q_71_pad_0 = const()[name = tensor("q_71_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_71 = conv(dilations = var_3777, groups = var_1193, pad = q_71_pad_0, pad_type = q_71_pad_type_0, strides = var_3775, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight, x = hidden_states_157)[name = tensor("q_71")]; - tensor var_3781 = const()[name = tensor("op_3781"), val = tensor([1, 1])]; - tensor var_3783 = const()[name = tensor("op_3783"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1183796928)))]; + tensor q_71_cast = conv(dilations = var_3715, groups = var_31, pad = q_71_pad_0, pad_type = q_71_pad_type_0, strides = var_3713, weight = unet_down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_157_cast)[name = tensor("q_71_cast")]; + tensor var_3719 = const()[name = tensor("op_3719"), val = tensor([1, 1])]; + tensor var_3721 = const()[name = tensor("op_3721"), val = tensor([1, 1])]; tensor k_71_pad_type_0 = const()[name = tensor("k_71_pad_type_0"), val = tensor("custom")]; tensor k_71_pad_0 = const()[name = tensor("k_71_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_71 = conv(dilations = var_3783, groups = var_1193, pad = k_71_pad_0, pad_type = k_71_pad_type_0, strides = var_3781, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_71")]; - tensor var_3787 = const()[name = tensor("op_3787"), val = tensor([1, 1])]; - tensor var_3789 = const()[name = tensor("op_3789"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1187073792)))]; + tensor k_71_cast = conv(dilations = var_3721, groups = var_31, pad = k_71_pad_0, pad_type = k_71_pad_type_0, strides = var_3719, weight = unet_down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_71_cast")]; + tensor var_3725 = const()[name = tensor("op_3725"), val = tensor([1, 1])]; + tensor var_3727 = const()[name = tensor("op_3727"), val = tensor([1, 1])]; tensor v_71_pad_type_0 = const()[name = tensor("v_71_pad_type_0"), val = tensor("custom")]; tensor v_71_pad_0 = const()[name = tensor("v_71_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_71 = conv(dilations = var_3789, groups = var_1193, pad = v_71_pad_0, pad_type = v_71_pad_type_0, strides = var_3787, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_71")]; - tensor var_3793 = const()[name = tensor("op_3793"), val = tensor([2, 20, 64, -1])]; - tensor var_3794 = reshape(shape = var_3793, x = q_71)[name = tensor("op_3794")]; - tensor var_3795 = const()[name = tensor("op_3795"), val = tensor([2, 20, 64, -1])]; - tensor var_3796 = reshape(shape = var_3795, x = k_71)[name = tensor("op_3796")]; - tensor var_3797 = const()[name = tensor("op_3797"), val = tensor([2, 20, 64, -1])]; - tensor var_3798 = reshape(shape = var_3797, x = v_71)[name = tensor("op_3798")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1192316736)))]; + tensor v_71_cast = conv(dilations = var_3727, groups = var_31, pad = v_71_pad_0, pad_type = v_71_pad_type_0, strides = var_3725, weight = unet_down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_71_cast")]; + tensor var_3731 = const()[name = tensor("op_3731"), val = tensor([2, 20, 64, -1])]; + tensor var_3732_cast = reshape(shape = var_3731, x = q_71_cast)[name = tensor("op_3732_cast")]; + tensor var_3733 = const()[name = tensor("op_3733"), val = tensor([2, 20, 64, -1])]; + tensor var_3734_cast = reshape(shape = var_3733, x = k_71_cast)[name = tensor("op_3734_cast")]; + tensor var_3735 = const()[name = tensor("op_3735"), val = tensor([2, 20, 64, -1])]; + tensor var_3736_cast = reshape(shape = var_3735, x = v_71_cast)[name = tensor("op_3736_cast")]; tensor attn_weights_141_transpose_x_0 = const()[name = tensor("attn_weights_141_transpose_x_0"), val = tensor(true)]; tensor attn_weights_141_transpose_y_0 = const()[name = tensor("attn_weights_141_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_141 = matmul(transpose_x = attn_weights_141_transpose_x_0, transpose_y = attn_weights_141_transpose_y_0, x = var_3794, y = var_3796)[name = tensor("attn_weights_141")]; - tensor attn_weights_143 = mul(x = attn_weights_141, y = var_1184)[name = tensor("attn_weights_143")]; - tensor var_3802 = softmax(axis = var_1177, x = attn_weights_143)[name = tensor("op_3802")]; + tensor attn_weights_141_cast = matmul(transpose_x = attn_weights_141_transpose_x_0, transpose_y = attn_weights_141_transpose_y_0, x = var_3732_cast, y = var_3734_cast)[name = tensor("attn_weights_141_cast")]; + tensor attn_weights_143_cast = mul(x = attn_weights_141_cast, y = var_12_to_fp16)[name = tensor("attn_weights_143_cast")]; + tensor var_3740_cast = softmax(axis = var_18, x = attn_weights_143_cast)[name = tensor("op_3740_cast")]; tensor attn_71_transpose_x_0 = const()[name = tensor("attn_71_transpose_x_0"), val = tensor(false)]; tensor attn_71_transpose_y_0 = const()[name = tensor("attn_71_transpose_y_0"), val = tensor(true)]; - tensor attn_71 = matmul(transpose_x = attn_71_transpose_x_0, transpose_y = attn_71_transpose_y_0, x = var_3798, y = var_3802)[name = tensor("attn_71")]; - tensor var_3806 = const()[name = tensor("op_3806"), val = tensor([2, 1280, 1, -1])]; - tensor input_255 = reshape(shape = var_3806, x = attn_71)[name = tensor("input_255")]; - tensor var_3811 = const()[name = tensor("op_3811"), val = tensor([1, 1])]; - tensor var_3813 = const()[name = tensor("op_3813"), val = tensor([1, 1])]; - tensor var_3815_pad_type_0 = const()[name = tensor("op_3815_pad_type_0"), val = tensor("custom")]; - tensor var_3815_pad_0 = const()[name = tensor("op_3815_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3815 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias, dilations = var_3813, groups = var_1193, pad = var_3815_pad_0, pad_type = var_3815_pad_type_0, strides = var_3811, weight = down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight, x = input_255)[name = tensor("op_3815")]; - tensor inputs_107 = add(x = var_3815, y = inputs_105)[name = tensor("inputs_107")]; - tensor var_3819 = const()[name = tensor("op_3819"), val = tensor([1])]; - tensor channels_mean_107 = reduce_mean(axes = var_3819, keep_dims = var_1188, x = inputs_107)[name = tensor("channels_mean_107")]; - tensor zero_mean_107 = sub(x = inputs_107, y = channels_mean_107)[name = tensor("zero_mean_107")]; - tensor zero_mean_sq_107 = mul(x = zero_mean_107, y = zero_mean_107)[name = tensor("zero_mean_sq_107")]; - tensor var_3823 = const()[name = tensor("op_3823"), val = tensor([1])]; - tensor var_3824 = reduce_mean(axes = var_3823, keep_dims = var_1188, x = zero_mean_sq_107)[name = tensor("op_3824")]; - tensor var_3825 = const()[name = tensor("op_3825"), val = tensor(0x1.4f8b58p-17)]; - tensor var_3826 = add(x = var_3824, y = var_3825)[name = tensor("op_3826")]; - tensor denom_107_epsilon_0 = const()[name = tensor("denom_107_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_107 = rsqrt(epsilon = denom_107_epsilon_0, x = var_3826)[name = tensor("denom_107")]; - tensor out_107 = mul(x = zero_mean_107, y = denom_107)[name = tensor("out_107")]; - tensor var_3830 = const()[name = tensor("op_3830"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268181248)))]; - tensor var_3831 = add(x = out_107, y = var_3830)[name = tensor("op_3831")]; - tensor var_3833 = const()[name = tensor("op_3833"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268186432)))]; - tensor input_257 = mul(x = var_3831, y = var_3833)[name = tensor("input_257")]; - tensor var_3841 = const()[name = tensor("op_3841"), val = tensor([1, 1])]; - tensor var_3843 = const()[name = tensor("op_3843"), val = tensor([1, 1])]; - tensor var_3845_pad_type_0 = const()[name = tensor("op_3845_pad_type_0"), val = tensor("custom")]; - tensor var_3845_pad_0 = const()[name = tensor("op_3845_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3845 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias, dilations = var_3843, groups = var_1193, pad = var_3845_pad_0, pad_type = var_3845_pad_type_0, strides = var_3841, weight = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight, x = input_257)[name = tensor("op_3845")]; - tensor var_3846_split_sizes_0 = const()[name = tensor("op_3846_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_3846_axis_0 = const()[name = tensor("op_3846_axis_0"), val = tensor(1)]; - tensor var_3846_0, tensor var_3846_1 = split(axis = var_3846_axis_0, split_sizes = var_3846_split_sizes_0, x = var_3845)[name = tensor("op_3846")]; - tensor var_3848_mode_0 = const()[name = tensor("op_3848_mode_0"), val = tensor("EXACT")]; - tensor var_3848 = gelu(mode = var_3848_mode_0, x = var_3846_1)[name = tensor("op_3848")]; - tensor input_259 = mul(x = var_3846_0, y = var_3848)[name = tensor("input_259")]; - tensor var_3852 = const()[name = tensor("op_3852"), val = tensor([1, 1])]; - tensor var_3854 = const()[name = tensor("op_3854"), val = tensor([1, 1])]; - tensor var_3856_pad_type_0 = const()[name = tensor("op_3856_pad_type_0"), val = tensor("custom")]; - tensor var_3856_pad_0 = const()[name = tensor("op_3856_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3856 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias, dilations = var_3854, groups = var_1193, pad = var_3856_pad_0, pad_type = var_3856_pad_type_0, strides = var_3852, weight = down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight, x = input_259)[name = tensor("op_3856")]; - tensor inputs_109 = add(x = var_3856, y = inputs_107)[name = tensor("inputs_109")]; - tensor var_3866 = const()[name = tensor("op_3866"), val = tensor([1])]; - tensor channels_mean_109 = reduce_mean(axes = var_3866, keep_dims = var_1188, x = inputs_109)[name = tensor("channels_mean_109")]; - tensor zero_mean_109 = sub(x = inputs_109, y = channels_mean_109)[name = tensor("zero_mean_109")]; - tensor zero_mean_sq_109 = mul(x = zero_mean_109, y = zero_mean_109)[name = tensor("zero_mean_sq_109")]; - tensor var_3870 = const()[name = tensor("op_3870"), val = tensor([1])]; - tensor var_3871 = reduce_mean(axes = var_3870, keep_dims = var_1188, x = zero_mean_sq_109)[name = tensor("op_3871")]; - tensor var_3872 = const()[name = tensor("op_3872"), val = tensor(0x1.4f8b58p-17)]; - tensor var_3873 = add(x = var_3871, y = var_3872)[name = tensor("op_3873")]; - tensor denom_109_epsilon_0 = const()[name = tensor("denom_109_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_109 = rsqrt(epsilon = denom_109_epsilon_0, x = var_3873)[name = tensor("denom_109")]; - tensor out_109 = mul(x = zero_mean_109, y = denom_109)[name = tensor("out_109")]; - tensor var_3877 = const()[name = tensor("op_3877"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268191616)))]; - tensor var_3878 = add(x = out_109, y = var_3877)[name = tensor("op_3878")]; - tensor var_3880 = const()[name = tensor("op_3880"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268196800)))]; - tensor hidden_states_161 = mul(x = var_3878, y = var_3880)[name = tensor("hidden_states_161")]; - tensor var_3887 = const()[name = tensor("op_3887"), val = tensor([1, 1])]; - tensor var_3889 = const()[name = tensor("op_3889"), val = tensor([1, 1])]; + tensor attn_71_cast = matmul(transpose_x = attn_71_transpose_x_0, transpose_y = attn_71_transpose_y_0, x = var_3736_cast, y = var_3740_cast)[name = tensor("attn_71_cast")]; + tensor var_3744 = const()[name = tensor("op_3744"), val = tensor([2, 1280, 1, -1])]; + tensor input_255_cast = reshape(shape = var_3744, x = attn_71_cast)[name = tensor("input_255_cast")]; + tensor var_3749 = const()[name = tensor("op_3749"), val = tensor([1, 1])]; + tensor var_3751 = const()[name = tensor("op_3751"), val = tensor([1, 1])]; + tensor var_3753_pad_type_0 = const()[name = tensor("op_3753_pad_type_0"), val = tensor("custom")]; + tensor var_3753_pad_0 = const()[name = tensor("op_3753_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1197559680)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1200836544)))]; + tensor var_3753_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_3751, groups = var_31, pad = var_3753_pad_0, pad_type = var_3753_pad_type_0, strides = var_3749, weight = unet_down_blocks_2_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_255_cast)[name = tensor("op_3753_cast")]; + tensor inputs_107_cast = add(x = var_3753_cast, y = inputs_105_cast)[name = tensor("inputs_107_cast")]; + tensor var_3757 = const()[name = tensor("op_3757"), val = tensor([1])]; + tensor channels_mean_107_cast = reduce_mean(axes = var_3757, keep_dims = var_23, x = inputs_107_cast)[name = tensor("channels_mean_107_cast")]; + tensor zero_mean_107_cast = sub(x = inputs_107_cast, y = channels_mean_107_cast)[name = tensor("zero_mean_107_cast")]; + tensor zero_mean_sq_107_cast = mul(x = zero_mean_107_cast, y = zero_mean_107_cast)[name = tensor("zero_mean_sq_107_cast")]; + tensor var_3761 = const()[name = tensor("op_3761"), val = tensor([1])]; + tensor var_3762_cast = reduce_mean(axes = var_3761, keep_dims = var_23, x = zero_mean_sq_107_cast)[name = tensor("op_3762_cast")]; + tensor var_3763_to_fp16 = const()[name = tensor("op_3763_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3764_cast = add(x = var_3762_cast, y = var_3763_to_fp16)[name = tensor("op_3764_cast")]; + tensor denom_107_epsilon_0_to_fp16 = const()[name = tensor("denom_107_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_107_cast = rsqrt(epsilon = denom_107_epsilon_0_to_fp16, x = var_3764_cast)[name = tensor("denom_107_cast")]; + tensor out_107_cast = mul(x = zero_mean_107_cast, y = denom_107_cast)[name = tensor("out_107_cast")]; + tensor var_3768_to_fp16 = const()[name = tensor("op_3768_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1200839168)))]; + tensor var_3769_cast = add(x = out_107_cast, y = var_3768_to_fp16)[name = tensor("op_3769_cast")]; + tensor var_3771_to_fp16 = const()[name = tensor("op_3771_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1200841792)))]; + tensor input_257_cast = mul(x = var_3769_cast, y = var_3771_to_fp16)[name = tensor("input_257_cast")]; + tensor var_3779 = const()[name = tensor("op_3779"), val = tensor([1, 1])]; + tensor var_3781 = const()[name = tensor("op_3781"), val = tensor([1, 1])]; + tensor var_3783_pad_type_0 = const()[name = tensor("op_3783_pad_type_0"), val = tensor("custom")]; + tensor var_3783_pad_0 = const()[name = tensor("op_3783_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1200844416)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1227058880)))]; + tensor var_3783_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_3781, groups = var_31, pad = var_3783_pad_0, pad_type = var_3783_pad_type_0, strides = var_3779, weight = unet_down_blocks_2_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_257_cast)[name = tensor("op_3783_cast")]; + tensor var_3784_split_sizes_0 = const()[name = tensor("op_3784_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3784_axis_0 = const()[name = tensor("op_3784_axis_0"), val = tensor(1)]; + tensor var_3784_cast_0, tensor var_3784_cast_1 = split(axis = var_3784_axis_0, split_sizes = var_3784_split_sizes_0, x = var_3783_cast)[name = tensor("op_3784_cast")]; + tensor var_3786_mode_0 = const()[name = tensor("op_3786_mode_0"), val = tensor("EXACT")]; + tensor var_3786_cast = gelu(mode = var_3786_mode_0, x = var_3784_cast_1)[name = tensor("op_3786_cast")]; + tensor input_259_cast = mul(x = var_3784_cast_0, y = var_3786_cast)[name = tensor("input_259_cast")]; + tensor var_3790 = const()[name = tensor("op_3790"), val = tensor([1, 1])]; + tensor var_3792 = const()[name = tensor("op_3792"), val = tensor([1, 1])]; + tensor var_3794_pad_type_0 = const()[name = tensor("op_3794_pad_type_0"), val = tensor("custom")]; + tensor var_3794_pad_0 = const()[name = tensor("op_3794_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1227079424)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1240186688)))]; + tensor var_3794_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_3792, groups = var_31, pad = var_3794_pad_0, pad_type = var_3794_pad_type_0, strides = var_3790, weight = unet_down_blocks_2_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_259_cast)[name = tensor("op_3794_cast")]; + tensor inputs_109_cast = add(x = var_3794_cast, y = inputs_107_cast)[name = tensor("inputs_109_cast")]; + tensor var_3804 = const()[name = tensor("op_3804"), val = tensor([1])]; + tensor channels_mean_109_cast = reduce_mean(axes = var_3804, keep_dims = var_23, x = inputs_109_cast)[name = tensor("channels_mean_109_cast")]; + tensor zero_mean_109_cast = sub(x = inputs_109_cast, y = channels_mean_109_cast)[name = tensor("zero_mean_109_cast")]; + tensor zero_mean_sq_109_cast = mul(x = zero_mean_109_cast, y = zero_mean_109_cast)[name = tensor("zero_mean_sq_109_cast")]; + tensor var_3808 = const()[name = tensor("op_3808"), val = tensor([1])]; + tensor var_3809_cast = reduce_mean(axes = var_3808, keep_dims = var_23, x = zero_mean_sq_109_cast)[name = tensor("op_3809_cast")]; + tensor var_3810_to_fp16 = const()[name = tensor("op_3810_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3811_cast = add(x = var_3809_cast, y = var_3810_to_fp16)[name = tensor("op_3811_cast")]; + tensor denom_109_epsilon_0_to_fp16 = const()[name = tensor("denom_109_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_109_cast = rsqrt(epsilon = denom_109_epsilon_0_to_fp16, x = var_3811_cast)[name = tensor("denom_109_cast")]; + tensor out_109_cast = mul(x = zero_mean_109_cast, y = denom_109_cast)[name = tensor("out_109_cast")]; + tensor var_3815_to_fp16 = const()[name = tensor("op_3815_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1240189312)))]; + tensor var_3816_cast = add(x = out_109_cast, y = var_3815_to_fp16)[name = tensor("op_3816_cast")]; + tensor var_3818_to_fp16 = const()[name = tensor("op_3818_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1240191936)))]; + tensor hidden_states_161_cast = mul(x = var_3816_cast, y = var_3818_to_fp16)[name = tensor("hidden_states_161_cast")]; + tensor var_3825 = const()[name = tensor("op_3825"), val = tensor([1, 1])]; + tensor var_3827 = const()[name = tensor("op_3827"), val = tensor([1, 1])]; tensor q_73_pad_type_0 = const()[name = tensor("q_73_pad_type_0"), val = tensor("custom")]; tensor q_73_pad_0 = const()[name = tensor("q_73_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_73 = conv(dilations = var_3889, groups = var_1193, pad = q_73_pad_0, pad_type = q_73_pad_type_0, strides = var_3887, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_q_weight, x = hidden_states_161)[name = tensor("q_73")]; - tensor var_3893 = const()[name = tensor("op_3893"), val = tensor([1, 1])]; - tensor var_3895 = const()[name = tensor("op_3895"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1240194560)))]; + tensor q_73_cast = conv(dilations = var_3827, groups = var_31, pad = q_73_pad_0, pad_type = q_73_pad_type_0, strides = var_3825, weight = unet_down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16, x = hidden_states_161_cast)[name = tensor("q_73_cast")]; + tensor var_3831 = const()[name = tensor("op_3831"), val = tensor([1, 1])]; + tensor var_3833 = const()[name = tensor("op_3833"), val = tensor([1, 1])]; tensor k_73_pad_type_0 = const()[name = tensor("k_73_pad_type_0"), val = tensor("custom")]; tensor k_73_pad_0 = const()[name = tensor("k_73_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_73 = conv(dilations = var_3895, groups = var_1193, pad = k_73_pad_0, pad_type = k_73_pad_type_0, strides = var_3893, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_k_weight, x = hidden_states_161)[name = tensor("k_73")]; - tensor var_3899 = const()[name = tensor("op_3899"), val = tensor([1, 1])]; - tensor var_3901 = const()[name = tensor("op_3901"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1243471424)))]; + tensor k_73_cast = conv(dilations = var_3833, groups = var_31, pad = k_73_pad_0, pad_type = k_73_pad_type_0, strides = var_3831, weight = unet_down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16, x = hidden_states_161_cast)[name = tensor("k_73_cast")]; + tensor var_3837 = const()[name = tensor("op_3837"), val = tensor([1, 1])]; + tensor var_3839 = const()[name = tensor("op_3839"), val = tensor([1, 1])]; tensor v_73_pad_type_0 = const()[name = tensor("v_73_pad_type_0"), val = tensor("custom")]; tensor v_73_pad_0 = const()[name = tensor("v_73_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_73 = conv(dilations = var_3901, groups = var_1193, pad = v_73_pad_0, pad_type = v_73_pad_type_0, strides = var_3899, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_v_weight, x = hidden_states_161)[name = tensor("v_73")]; - tensor var_3905 = const()[name = tensor("op_3905"), val = tensor([2, 20, 64, -1])]; - tensor var_3906 = reshape(shape = var_3905, x = q_73)[name = tensor("op_3906")]; - tensor var_3907 = const()[name = tensor("op_3907"), val = tensor([2, 20, 64, -1])]; - tensor var_3908 = reshape(shape = var_3907, x = k_73)[name = tensor("op_3908")]; - tensor var_3909 = const()[name = tensor("op_3909"), val = tensor([2, 20, 64, -1])]; - tensor var_3910 = reshape(shape = var_3909, x = v_73)[name = tensor("op_3910")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1246748288)))]; + tensor v_73_cast = conv(dilations = var_3839, groups = var_31, pad = v_73_pad_0, pad_type = v_73_pad_type_0, strides = var_3837, weight = unet_down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16, x = hidden_states_161_cast)[name = tensor("v_73_cast")]; + tensor var_3843 = const()[name = tensor("op_3843"), val = tensor([2, 20, 64, -1])]; + tensor var_3844_cast = reshape(shape = var_3843, x = q_73_cast)[name = tensor("op_3844_cast")]; + tensor var_3845 = const()[name = tensor("op_3845"), val = tensor([2, 20, 64, -1])]; + tensor var_3846_cast = reshape(shape = var_3845, x = k_73_cast)[name = tensor("op_3846_cast")]; + tensor var_3847 = const()[name = tensor("op_3847"), val = tensor([2, 20, 64, -1])]; + tensor var_3848_cast = reshape(shape = var_3847, x = v_73_cast)[name = tensor("op_3848_cast")]; tensor attn_weights_145_transpose_x_0 = const()[name = tensor("attn_weights_145_transpose_x_0"), val = tensor(true)]; tensor attn_weights_145_transpose_y_0 = const()[name = tensor("attn_weights_145_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_145 = matmul(transpose_x = attn_weights_145_transpose_x_0, transpose_y = attn_weights_145_transpose_y_0, x = var_3906, y = var_3908)[name = tensor("attn_weights_145")]; - tensor attn_weights_147 = mul(x = attn_weights_145, y = var_1184)[name = tensor("attn_weights_147")]; - tensor var_3914 = softmax(axis = var_1177, x = attn_weights_147)[name = tensor("op_3914")]; + tensor attn_weights_145_cast = matmul(transpose_x = attn_weights_145_transpose_x_0, transpose_y = attn_weights_145_transpose_y_0, x = var_3844_cast, y = var_3846_cast)[name = tensor("attn_weights_145_cast")]; + tensor attn_weights_147_cast = mul(x = attn_weights_145_cast, y = var_12_to_fp16)[name = tensor("attn_weights_147_cast")]; + tensor var_3852_cast = softmax(axis = var_18, x = attn_weights_147_cast)[name = tensor("op_3852_cast")]; tensor attn_73_transpose_x_0 = const()[name = tensor("attn_73_transpose_x_0"), val = tensor(false)]; tensor attn_73_transpose_y_0 = const()[name = tensor("attn_73_transpose_y_0"), val = tensor(true)]; - tensor attn_73 = matmul(transpose_x = attn_73_transpose_x_0, transpose_y = attn_73_transpose_y_0, x = var_3910, y = var_3914)[name = tensor("attn_73")]; - tensor var_3918 = const()[name = tensor("op_3918"), val = tensor([2, 1280, 1, -1])]; - tensor input_261 = reshape(shape = var_3918, x = attn_73)[name = tensor("input_261")]; - tensor var_3923 = const()[name = tensor("op_3923"), val = tensor([1, 1])]; - tensor var_3925 = const()[name = tensor("op_3925"), val = tensor([1, 1])]; - tensor var_3927_pad_type_0 = const()[name = tensor("op_3927_pad_type_0"), val = tensor("custom")]; - tensor var_3927_pad_0 = const()[name = tensor("op_3927_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3927 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_bias, dilations = var_3925, groups = var_1193, pad = var_3927_pad_0, pad_type = var_3927_pad_type_0, strides = var_3923, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_weight, x = input_261)[name = tensor("op_3927")]; - tensor inputs_111 = add(x = var_3927, y = inputs_109)[name = tensor("inputs_111")]; - tensor var_3931 = const()[name = tensor("op_3931"), val = tensor([1])]; - tensor channels_mean_111 = reduce_mean(axes = var_3931, keep_dims = var_1188, x = inputs_111)[name = tensor("channels_mean_111")]; - tensor zero_mean_111 = sub(x = inputs_111, y = channels_mean_111)[name = tensor("zero_mean_111")]; - tensor zero_mean_sq_111 = mul(x = zero_mean_111, y = zero_mean_111)[name = tensor("zero_mean_sq_111")]; - tensor var_3935 = const()[name = tensor("op_3935"), val = tensor([1])]; - tensor var_3936 = reduce_mean(axes = var_3935, keep_dims = var_1188, x = zero_mean_sq_111)[name = tensor("op_3936")]; - tensor var_3937 = const()[name = tensor("op_3937"), val = tensor(0x1.4f8b58p-17)]; - tensor var_3938 = add(x = var_3936, y = var_3937)[name = tensor("op_3938")]; - tensor denom_111_epsilon_0 = const()[name = tensor("denom_111_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_111 = rsqrt(epsilon = denom_111_epsilon_0, x = var_3938)[name = tensor("denom_111")]; - tensor out_111 = mul(x = zero_mean_111, y = denom_111)[name = tensor("out_111")]; - tensor var_3942 = const()[name = tensor("op_3942"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268201984)))]; - tensor var_3943 = add(x = out_111, y = var_3942)[name = tensor("op_3943")]; - tensor var_3945 = const()[name = tensor("op_3945"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268207168)))]; - tensor hidden_states_163 = mul(x = var_3943, y = var_3945)[name = tensor("hidden_states_163")]; - tensor var_3952 = const()[name = tensor("op_3952"), val = tensor([1, 1])]; - tensor var_3954 = const()[name = tensor("op_3954"), val = tensor([1, 1])]; + tensor attn_73_cast = matmul(transpose_x = attn_73_transpose_x_0, transpose_y = attn_73_transpose_y_0, x = var_3848_cast, y = var_3852_cast)[name = tensor("attn_73_cast")]; + tensor var_3856 = const()[name = tensor("op_3856"), val = tensor([2, 1280, 1, -1])]; + tensor input_261_cast = reshape(shape = var_3856, x = attn_73_cast)[name = tensor("input_261_cast")]; + tensor var_3861 = const()[name = tensor("op_3861"), val = tensor([1, 1])]; + tensor var_3863 = const()[name = tensor("op_3863"), val = tensor([1, 1])]; + tensor var_3865_pad_type_0 = const()[name = tensor("op_3865_pad_type_0"), val = tensor("custom")]; + tensor var_3865_pad_0 = const()[name = tensor("op_3865_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1250025152)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1253302016)))]; + tensor var_3865_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_3863, groups = var_31, pad = var_3865_pad_0, pad_type = var_3865_pad_type_0, strides = var_3861, weight = unet_down_blocks_2_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16, x = input_261_cast)[name = tensor("op_3865_cast")]; + tensor inputs_111_cast = add(x = var_3865_cast, y = inputs_109_cast)[name = tensor("inputs_111_cast")]; + tensor var_3869 = const()[name = tensor("op_3869"), val = tensor([1])]; + tensor channels_mean_111_cast = reduce_mean(axes = var_3869, keep_dims = var_23, x = inputs_111_cast)[name = tensor("channels_mean_111_cast")]; + tensor zero_mean_111_cast = sub(x = inputs_111_cast, y = channels_mean_111_cast)[name = tensor("zero_mean_111_cast")]; + tensor zero_mean_sq_111_cast = mul(x = zero_mean_111_cast, y = zero_mean_111_cast)[name = tensor("zero_mean_sq_111_cast")]; + tensor var_3873 = const()[name = tensor("op_3873"), val = tensor([1])]; + tensor var_3874_cast = reduce_mean(axes = var_3873, keep_dims = var_23, x = zero_mean_sq_111_cast)[name = tensor("op_3874_cast")]; + tensor var_3875_to_fp16 = const()[name = tensor("op_3875_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3876_cast = add(x = var_3874_cast, y = var_3875_to_fp16)[name = tensor("op_3876_cast")]; + tensor denom_111_epsilon_0_to_fp16 = const()[name = tensor("denom_111_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_111_cast = rsqrt(epsilon = denom_111_epsilon_0_to_fp16, x = var_3876_cast)[name = tensor("denom_111_cast")]; + tensor out_111_cast = mul(x = zero_mean_111_cast, y = denom_111_cast)[name = tensor("out_111_cast")]; + tensor var_3880_to_fp16 = const()[name = tensor("op_3880_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1253304640)))]; + tensor var_3881_cast = add(x = out_111_cast, y = var_3880_to_fp16)[name = tensor("op_3881_cast")]; + tensor var_3883_to_fp16 = const()[name = tensor("op_3883_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1253307264)))]; + tensor hidden_states_163_cast = mul(x = var_3881_cast, y = var_3883_to_fp16)[name = tensor("hidden_states_163_cast")]; + tensor var_3890 = const()[name = tensor("op_3890"), val = tensor([1, 1])]; + tensor var_3892 = const()[name = tensor("op_3892"), val = tensor([1, 1])]; tensor q_75_pad_type_0 = const()[name = tensor("q_75_pad_type_0"), val = tensor("custom")]; tensor q_75_pad_0 = const()[name = tensor("q_75_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_75 = conv(dilations = var_3954, groups = var_1193, pad = q_75_pad_0, pad_type = q_75_pad_type_0, strides = var_3952, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_q_weight, x = hidden_states_163)[name = tensor("q_75")]; - tensor var_3958 = const()[name = tensor("op_3958"), val = tensor([1, 1])]; - tensor var_3960 = const()[name = tensor("op_3960"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1253309888)))]; + tensor q_75_cast = conv(dilations = var_3892, groups = var_31, pad = q_75_pad_0, pad_type = q_75_pad_type_0, strides = var_3890, weight = unet_down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16, x = hidden_states_163_cast)[name = tensor("q_75_cast")]; + tensor var_3896 = const()[name = tensor("op_3896"), val = tensor([1, 1])]; + tensor var_3898 = const()[name = tensor("op_3898"), val = tensor([1, 1])]; tensor k_75_pad_type_0 = const()[name = tensor("k_75_pad_type_0"), val = tensor("custom")]; tensor k_75_pad_0 = const()[name = tensor("k_75_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_75 = conv(dilations = var_3960, groups = var_1193, pad = k_75_pad_0, pad_type = k_75_pad_type_0, strides = var_3958, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_75")]; - tensor var_3964 = const()[name = tensor("op_3964"), val = tensor([1, 1])]; - tensor var_3966 = const()[name = tensor("op_3966"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1256586752)))]; + tensor k_75_cast = conv(dilations = var_3898, groups = var_31, pad = k_75_pad_0, pad_type = k_75_pad_type_0, strides = var_3896, weight = unet_down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_75_cast")]; + tensor var_3902 = const()[name = tensor("op_3902"), val = tensor([1, 1])]; + tensor var_3904 = const()[name = tensor("op_3904"), val = tensor([1, 1])]; tensor v_75_pad_type_0 = const()[name = tensor("v_75_pad_type_0"), val = tensor("custom")]; tensor v_75_pad_0 = const()[name = tensor("v_75_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_75 = conv(dilations = var_3966, groups = var_1193, pad = v_75_pad_0, pad_type = v_75_pad_type_0, strides = var_3964, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_75")]; - tensor var_3970 = const()[name = tensor("op_3970"), val = tensor([2, 20, 64, -1])]; - tensor var_3971 = reshape(shape = var_3970, x = q_75)[name = tensor("op_3971")]; - tensor var_3972 = const()[name = tensor("op_3972"), val = tensor([2, 20, 64, -1])]; - tensor var_3973 = reshape(shape = var_3972, x = k_75)[name = tensor("op_3973")]; - tensor var_3974 = const()[name = tensor("op_3974"), val = tensor([2, 20, 64, -1])]; - tensor var_3975 = reshape(shape = var_3974, x = v_75)[name = tensor("op_3975")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1261829696)))]; + tensor v_75_cast = conv(dilations = var_3904, groups = var_31, pad = v_75_pad_0, pad_type = v_75_pad_type_0, strides = var_3902, weight = unet_down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_75_cast")]; + tensor var_3908 = const()[name = tensor("op_3908"), val = tensor([2, 20, 64, -1])]; + tensor var_3909_cast = reshape(shape = var_3908, x = q_75_cast)[name = tensor("op_3909_cast")]; + tensor var_3910 = const()[name = tensor("op_3910"), val = tensor([2, 20, 64, -1])]; + tensor var_3911_cast = reshape(shape = var_3910, x = k_75_cast)[name = tensor("op_3911_cast")]; + tensor var_3912 = const()[name = tensor("op_3912"), val = tensor([2, 20, 64, -1])]; + tensor var_3913_cast = reshape(shape = var_3912, x = v_75_cast)[name = tensor("op_3913_cast")]; tensor attn_weights_149_transpose_x_0 = const()[name = tensor("attn_weights_149_transpose_x_0"), val = tensor(true)]; tensor attn_weights_149_transpose_y_0 = const()[name = tensor("attn_weights_149_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_149 = matmul(transpose_x = attn_weights_149_transpose_x_0, transpose_y = attn_weights_149_transpose_y_0, x = var_3971, y = var_3973)[name = tensor("attn_weights_149")]; - tensor attn_weights_151 = mul(x = attn_weights_149, y = var_1184)[name = tensor("attn_weights_151")]; - tensor var_3979 = softmax(axis = var_1177, x = attn_weights_151)[name = tensor("op_3979")]; + tensor attn_weights_149_cast = matmul(transpose_x = attn_weights_149_transpose_x_0, transpose_y = attn_weights_149_transpose_y_0, x = var_3909_cast, y = var_3911_cast)[name = tensor("attn_weights_149_cast")]; + tensor attn_weights_151_cast = mul(x = attn_weights_149_cast, y = var_12_to_fp16)[name = tensor("attn_weights_151_cast")]; + tensor var_3917_cast = softmax(axis = var_18, x = attn_weights_151_cast)[name = tensor("op_3917_cast")]; tensor attn_75_transpose_x_0 = const()[name = tensor("attn_75_transpose_x_0"), val = tensor(false)]; tensor attn_75_transpose_y_0 = const()[name = tensor("attn_75_transpose_y_0"), val = tensor(true)]; - tensor attn_75 = matmul(transpose_x = attn_75_transpose_x_0, transpose_y = attn_75_transpose_y_0, x = var_3975, y = var_3979)[name = tensor("attn_75")]; - tensor var_3983 = const()[name = tensor("op_3983"), val = tensor([2, 1280, 1, -1])]; - tensor input_263 = reshape(shape = var_3983, x = attn_75)[name = tensor("input_263")]; - tensor var_3988 = const()[name = tensor("op_3988"), val = tensor([1, 1])]; - tensor var_3990 = const()[name = tensor("op_3990"), val = tensor([1, 1])]; - tensor var_3992_pad_type_0 = const()[name = tensor("op_3992_pad_type_0"), val = tensor("custom")]; - tensor var_3992_pad_0 = const()[name = tensor("op_3992_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_3992 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_bias, dilations = var_3990, groups = var_1193, pad = var_3992_pad_0, pad_type = var_3992_pad_type_0, strides = var_3988, weight = down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_weight, x = input_263)[name = tensor("op_3992")]; - tensor inputs_113 = add(x = var_3992, y = inputs_111)[name = tensor("inputs_113")]; - tensor var_3996 = const()[name = tensor("op_3996"), val = tensor([1])]; - tensor channels_mean_113 = reduce_mean(axes = var_3996, keep_dims = var_1188, x = inputs_113)[name = tensor("channels_mean_113")]; - tensor zero_mean_113 = sub(x = inputs_113, y = channels_mean_113)[name = tensor("zero_mean_113")]; - tensor zero_mean_sq_113 = mul(x = zero_mean_113, y = zero_mean_113)[name = tensor("zero_mean_sq_113")]; - tensor var_4000 = const()[name = tensor("op_4000"), val = tensor([1])]; - tensor var_4001 = reduce_mean(axes = var_4000, keep_dims = var_1188, x = zero_mean_sq_113)[name = tensor("op_4001")]; - tensor var_4002 = const()[name = tensor("op_4002"), val = tensor(0x1.4f8b58p-17)]; - tensor var_4003 = add(x = var_4001, y = var_4002)[name = tensor("op_4003")]; - tensor denom_113_epsilon_0 = const()[name = tensor("denom_113_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_113 = rsqrt(epsilon = denom_113_epsilon_0, x = var_4003)[name = tensor("denom_113")]; - tensor out_113 = mul(x = zero_mean_113, y = denom_113)[name = tensor("out_113")]; - tensor var_4007 = const()[name = tensor("op_4007"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268212352)))]; - tensor var_4008 = add(x = out_113, y = var_4007)[name = tensor("op_4008")]; - tensor var_4010 = const()[name = tensor("op_4010"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268217536)))]; - tensor input_265 = mul(x = var_4008, y = var_4010)[name = tensor("input_265")]; - tensor var_4018 = const()[name = tensor("op_4018"), val = tensor([1, 1])]; - tensor var_4020 = const()[name = tensor("op_4020"), val = tensor([1, 1])]; - tensor var_4022_pad_type_0 = const()[name = tensor("op_4022_pad_type_0"), val = tensor("custom")]; - tensor var_4022_pad_0 = const()[name = tensor("op_4022_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4022 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_bias, dilations = var_4020, groups = var_1193, pad = var_4022_pad_0, pad_type = var_4022_pad_type_0, strides = var_4018, weight = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_weight, x = input_265)[name = tensor("op_4022")]; - tensor var_4023_split_sizes_0 = const()[name = tensor("op_4023_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_4023_axis_0 = const()[name = tensor("op_4023_axis_0"), val = tensor(1)]; - tensor var_4023_0, tensor var_4023_1 = split(axis = var_4023_axis_0, split_sizes = var_4023_split_sizes_0, x = var_4022)[name = tensor("op_4023")]; - tensor var_4025_mode_0 = const()[name = tensor("op_4025_mode_0"), val = tensor("EXACT")]; - tensor var_4025 = gelu(mode = var_4025_mode_0, x = var_4023_1)[name = tensor("op_4025")]; - tensor input_267 = mul(x = var_4023_0, y = var_4025)[name = tensor("input_267")]; - tensor var_4029 = const()[name = tensor("op_4029"), val = tensor([1, 1])]; - tensor var_4031 = const()[name = tensor("op_4031"), val = tensor([1, 1])]; - tensor var_4033_pad_type_0 = const()[name = tensor("op_4033_pad_type_0"), val = tensor("custom")]; - tensor var_4033_pad_0 = const()[name = tensor("op_4033_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4033 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_bias, dilations = var_4031, groups = var_1193, pad = var_4033_pad_0, pad_type = var_4033_pad_type_0, strides = var_4029, weight = down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_weight, x = input_267)[name = tensor("op_4033")]; - tensor inputs_115 = add(x = var_4033, y = inputs_113)[name = tensor("inputs_115")]; - tensor var_4043 = const()[name = tensor("op_4043"), val = tensor([1])]; - tensor channels_mean_115 = reduce_mean(axes = var_4043, keep_dims = var_1188, x = inputs_115)[name = tensor("channels_mean_115")]; - tensor zero_mean_115 = sub(x = inputs_115, y = channels_mean_115)[name = tensor("zero_mean_115")]; - tensor zero_mean_sq_115 = mul(x = zero_mean_115, y = zero_mean_115)[name = tensor("zero_mean_sq_115")]; - tensor var_4047 = const()[name = tensor("op_4047"), val = tensor([1])]; - tensor var_4048 = reduce_mean(axes = var_4047, keep_dims = var_1188, x = zero_mean_sq_115)[name = tensor("op_4048")]; - tensor var_4049 = const()[name = tensor("op_4049"), val = tensor(0x1.4f8b58p-17)]; - tensor var_4050 = add(x = var_4048, y = var_4049)[name = tensor("op_4050")]; - tensor denom_115_epsilon_0 = const()[name = tensor("denom_115_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_115 = rsqrt(epsilon = denom_115_epsilon_0, x = var_4050)[name = tensor("denom_115")]; - tensor out_115 = mul(x = zero_mean_115, y = denom_115)[name = tensor("out_115")]; - tensor var_4054 = const()[name = tensor("op_4054"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268222720)))]; - tensor var_4055 = add(x = out_115, y = var_4054)[name = tensor("op_4055")]; - tensor var_4057 = const()[name = tensor("op_4057"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268227904)))]; - tensor hidden_states_167 = mul(x = var_4055, y = var_4057)[name = tensor("hidden_states_167")]; - tensor var_4064 = const()[name = tensor("op_4064"), val = tensor([1, 1])]; - tensor var_4066 = const()[name = tensor("op_4066"), val = tensor([1, 1])]; + tensor attn_75_cast = matmul(transpose_x = attn_75_transpose_x_0, transpose_y = attn_75_transpose_y_0, x = var_3913_cast, y = var_3917_cast)[name = tensor("attn_75_cast")]; + tensor var_3921 = const()[name = tensor("op_3921"), val = tensor([2, 1280, 1, -1])]; + tensor input_263_cast = reshape(shape = var_3921, x = attn_75_cast)[name = tensor("input_263_cast")]; + tensor var_3926 = const()[name = tensor("op_3926"), val = tensor([1, 1])]; + tensor var_3928 = const()[name = tensor("op_3928"), val = tensor([1, 1])]; + tensor var_3930_pad_type_0 = const()[name = tensor("op_3930_pad_type_0"), val = tensor("custom")]; + tensor var_3930_pad_0 = const()[name = tensor("op_3930_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1267072640)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1270349504)))]; + tensor var_3930_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_3928, groups = var_31, pad = var_3930_pad_0, pad_type = var_3930_pad_type_0, strides = var_3926, weight = unet_down_blocks_2_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16, x = input_263_cast)[name = tensor("op_3930_cast")]; + tensor inputs_113_cast = add(x = var_3930_cast, y = inputs_111_cast)[name = tensor("inputs_113_cast")]; + tensor var_3934 = const()[name = tensor("op_3934"), val = tensor([1])]; + tensor channels_mean_113_cast = reduce_mean(axes = var_3934, keep_dims = var_23, x = inputs_113_cast)[name = tensor("channels_mean_113_cast")]; + tensor zero_mean_113_cast = sub(x = inputs_113_cast, y = channels_mean_113_cast)[name = tensor("zero_mean_113_cast")]; + tensor zero_mean_sq_113_cast = mul(x = zero_mean_113_cast, y = zero_mean_113_cast)[name = tensor("zero_mean_sq_113_cast")]; + tensor var_3938 = const()[name = tensor("op_3938"), val = tensor([1])]; + tensor var_3939_cast = reduce_mean(axes = var_3938, keep_dims = var_23, x = zero_mean_sq_113_cast)[name = tensor("op_3939_cast")]; + tensor var_3940_to_fp16 = const()[name = tensor("op_3940_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3941_cast = add(x = var_3939_cast, y = var_3940_to_fp16)[name = tensor("op_3941_cast")]; + tensor denom_113_epsilon_0_to_fp16 = const()[name = tensor("denom_113_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_113_cast = rsqrt(epsilon = denom_113_epsilon_0_to_fp16, x = var_3941_cast)[name = tensor("denom_113_cast")]; + tensor out_113_cast = mul(x = zero_mean_113_cast, y = denom_113_cast)[name = tensor("out_113_cast")]; + tensor var_3945_to_fp16 = const()[name = tensor("op_3945_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1270352128)))]; + tensor var_3946_cast = add(x = out_113_cast, y = var_3945_to_fp16)[name = tensor("op_3946_cast")]; + tensor var_3948_to_fp16 = const()[name = tensor("op_3948_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1270354752)))]; + tensor input_265_cast = mul(x = var_3946_cast, y = var_3948_to_fp16)[name = tensor("input_265_cast")]; + tensor var_3956 = const()[name = tensor("op_3956"), val = tensor([1, 1])]; + tensor var_3958 = const()[name = tensor("op_3958"), val = tensor([1, 1])]; + tensor var_3960_pad_type_0 = const()[name = tensor("op_3960_pad_type_0"), val = tensor("custom")]; + tensor var_3960_pad_0 = const()[name = tensor("op_3960_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1270357376)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1296571840)))]; + tensor var_3960_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_3958, groups = var_31, pad = var_3960_pad_0, pad_type = var_3960_pad_type_0, strides = var_3956, weight = unet_down_blocks_2_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16, x = input_265_cast)[name = tensor("op_3960_cast")]; + tensor var_3961_split_sizes_0 = const()[name = tensor("op_3961_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3961_axis_0 = const()[name = tensor("op_3961_axis_0"), val = tensor(1)]; + tensor var_3961_cast_0, tensor var_3961_cast_1 = split(axis = var_3961_axis_0, split_sizes = var_3961_split_sizes_0, x = var_3960_cast)[name = tensor("op_3961_cast")]; + tensor var_3963_mode_0 = const()[name = tensor("op_3963_mode_0"), val = tensor("EXACT")]; + tensor var_3963_cast = gelu(mode = var_3963_mode_0, x = var_3961_cast_1)[name = tensor("op_3963_cast")]; + tensor input_267_cast = mul(x = var_3961_cast_0, y = var_3963_cast)[name = tensor("input_267_cast")]; + tensor var_3967 = const()[name = tensor("op_3967"), val = tensor([1, 1])]; + tensor var_3969 = const()[name = tensor("op_3969"), val = tensor([1, 1])]; + tensor var_3971_pad_type_0 = const()[name = tensor("op_3971_pad_type_0"), val = tensor("custom")]; + tensor var_3971_pad_0 = const()[name = tensor("op_3971_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1296592384)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1309699648)))]; + tensor var_3971_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_3969, groups = var_31, pad = var_3971_pad_0, pad_type = var_3971_pad_type_0, strides = var_3967, weight = unet_down_blocks_2_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16, x = input_267_cast)[name = tensor("op_3971_cast")]; + tensor inputs_115_cast = add(x = var_3971_cast, y = inputs_113_cast)[name = tensor("inputs_115_cast")]; + tensor var_3981 = const()[name = tensor("op_3981"), val = tensor([1])]; + tensor channels_mean_115_cast = reduce_mean(axes = var_3981, keep_dims = var_23, x = inputs_115_cast)[name = tensor("channels_mean_115_cast")]; + tensor zero_mean_115_cast = sub(x = inputs_115_cast, y = channels_mean_115_cast)[name = tensor("zero_mean_115_cast")]; + tensor zero_mean_sq_115_cast = mul(x = zero_mean_115_cast, y = zero_mean_115_cast)[name = tensor("zero_mean_sq_115_cast")]; + tensor var_3985 = const()[name = tensor("op_3985"), val = tensor([1])]; + tensor var_3986_cast = reduce_mean(axes = var_3985, keep_dims = var_23, x = zero_mean_sq_115_cast)[name = tensor("op_3986_cast")]; + tensor var_3987_to_fp16 = const()[name = tensor("op_3987_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3988_cast = add(x = var_3986_cast, y = var_3987_to_fp16)[name = tensor("op_3988_cast")]; + tensor denom_115_epsilon_0_to_fp16 = const()[name = tensor("denom_115_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_115_cast = rsqrt(epsilon = denom_115_epsilon_0_to_fp16, x = var_3988_cast)[name = tensor("denom_115_cast")]; + tensor out_115_cast = mul(x = zero_mean_115_cast, y = denom_115_cast)[name = tensor("out_115_cast")]; + tensor var_3992_to_fp16 = const()[name = tensor("op_3992_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1309702272)))]; + tensor var_3993_cast = add(x = out_115_cast, y = var_3992_to_fp16)[name = tensor("op_3993_cast")]; + tensor var_3995_to_fp16 = const()[name = tensor("op_3995_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1309704896)))]; + tensor hidden_states_167_cast = mul(x = var_3993_cast, y = var_3995_to_fp16)[name = tensor("hidden_states_167_cast")]; + tensor var_4002 = const()[name = tensor("op_4002"), val = tensor([1, 1])]; + tensor var_4004 = const()[name = tensor("op_4004"), val = tensor([1, 1])]; tensor q_77_pad_type_0 = const()[name = tensor("q_77_pad_type_0"), val = tensor("custom")]; tensor q_77_pad_0 = const()[name = tensor("q_77_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_77 = conv(dilations = var_4066, groups = var_1193, pad = q_77_pad_0, pad_type = q_77_pad_type_0, strides = var_4064, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_q_weight, x = hidden_states_167)[name = tensor("q_77")]; - tensor var_4070 = const()[name = tensor("op_4070"), val = tensor([1, 1])]; - tensor var_4072 = const()[name = tensor("op_4072"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1309707520)))]; + tensor q_77_cast = conv(dilations = var_4004, groups = var_31, pad = q_77_pad_0, pad_type = q_77_pad_type_0, strides = var_4002, weight = unet_down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16, x = hidden_states_167_cast)[name = tensor("q_77_cast")]; + tensor var_4008 = const()[name = tensor("op_4008"), val = tensor([1, 1])]; + tensor var_4010 = const()[name = tensor("op_4010"), val = tensor([1, 1])]; tensor k_77_pad_type_0 = const()[name = tensor("k_77_pad_type_0"), val = tensor("custom")]; tensor k_77_pad_0 = const()[name = tensor("k_77_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_77 = conv(dilations = var_4072, groups = var_1193, pad = k_77_pad_0, pad_type = k_77_pad_type_0, strides = var_4070, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_k_weight, x = hidden_states_167)[name = tensor("k_77")]; - tensor var_4076 = const()[name = tensor("op_4076"), val = tensor([1, 1])]; - tensor var_4078 = const()[name = tensor("op_4078"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1312984384)))]; + tensor k_77_cast = conv(dilations = var_4010, groups = var_31, pad = k_77_pad_0, pad_type = k_77_pad_type_0, strides = var_4008, weight = unet_down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16, x = hidden_states_167_cast)[name = tensor("k_77_cast")]; + tensor var_4014 = const()[name = tensor("op_4014"), val = tensor([1, 1])]; + tensor var_4016 = const()[name = tensor("op_4016"), val = tensor([1, 1])]; tensor v_77_pad_type_0 = const()[name = tensor("v_77_pad_type_0"), val = tensor("custom")]; tensor v_77_pad_0 = const()[name = tensor("v_77_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_77 = conv(dilations = var_4078, groups = var_1193, pad = v_77_pad_0, pad_type = v_77_pad_type_0, strides = var_4076, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_v_weight, x = hidden_states_167)[name = tensor("v_77")]; - tensor var_4082 = const()[name = tensor("op_4082"), val = tensor([2, 20, 64, -1])]; - tensor var_4083 = reshape(shape = var_4082, x = q_77)[name = tensor("op_4083")]; - tensor var_4084 = const()[name = tensor("op_4084"), val = tensor([2, 20, 64, -1])]; - tensor var_4085 = reshape(shape = var_4084, x = k_77)[name = tensor("op_4085")]; - tensor var_4086 = const()[name = tensor("op_4086"), val = tensor([2, 20, 64, -1])]; - tensor var_4087 = reshape(shape = var_4086, x = v_77)[name = tensor("op_4087")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1316261248)))]; + tensor v_77_cast = conv(dilations = var_4016, groups = var_31, pad = v_77_pad_0, pad_type = v_77_pad_type_0, strides = var_4014, weight = unet_down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16, x = hidden_states_167_cast)[name = tensor("v_77_cast")]; + tensor var_4020 = const()[name = tensor("op_4020"), val = tensor([2, 20, 64, -1])]; + tensor var_4021_cast = reshape(shape = var_4020, x = q_77_cast)[name = tensor("op_4021_cast")]; + tensor var_4022 = const()[name = tensor("op_4022"), val = tensor([2, 20, 64, -1])]; + tensor var_4023_cast = reshape(shape = var_4022, x = k_77_cast)[name = tensor("op_4023_cast")]; + tensor var_4024 = const()[name = tensor("op_4024"), val = tensor([2, 20, 64, -1])]; + tensor var_4025_cast = reshape(shape = var_4024, x = v_77_cast)[name = tensor("op_4025_cast")]; tensor attn_weights_153_transpose_x_0 = const()[name = tensor("attn_weights_153_transpose_x_0"), val = tensor(true)]; tensor attn_weights_153_transpose_y_0 = const()[name = tensor("attn_weights_153_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_153 = matmul(transpose_x = attn_weights_153_transpose_x_0, transpose_y = attn_weights_153_transpose_y_0, x = var_4083, y = var_4085)[name = tensor("attn_weights_153")]; - tensor attn_weights_155 = mul(x = attn_weights_153, y = var_1184)[name = tensor("attn_weights_155")]; - tensor var_4091 = softmax(axis = var_1177, x = attn_weights_155)[name = tensor("op_4091")]; + tensor attn_weights_153_cast = matmul(transpose_x = attn_weights_153_transpose_x_0, transpose_y = attn_weights_153_transpose_y_0, x = var_4021_cast, y = var_4023_cast)[name = tensor("attn_weights_153_cast")]; + tensor attn_weights_155_cast = mul(x = attn_weights_153_cast, y = var_12_to_fp16)[name = tensor("attn_weights_155_cast")]; + tensor var_4029_cast = softmax(axis = var_18, x = attn_weights_155_cast)[name = tensor("op_4029_cast")]; tensor attn_77_transpose_x_0 = const()[name = tensor("attn_77_transpose_x_0"), val = tensor(false)]; tensor attn_77_transpose_y_0 = const()[name = tensor("attn_77_transpose_y_0"), val = tensor(true)]; - tensor attn_77 = matmul(transpose_x = attn_77_transpose_x_0, transpose_y = attn_77_transpose_y_0, x = var_4087, y = var_4091)[name = tensor("attn_77")]; - tensor var_4095 = const()[name = tensor("op_4095"), val = tensor([2, 1280, 1, -1])]; - tensor input_269 = reshape(shape = var_4095, x = attn_77)[name = tensor("input_269")]; - tensor var_4100 = const()[name = tensor("op_4100"), val = tensor([1, 1])]; - tensor var_4102 = const()[name = tensor("op_4102"), val = tensor([1, 1])]; - tensor var_4104_pad_type_0 = const()[name = tensor("op_4104_pad_type_0"), val = tensor("custom")]; - tensor var_4104_pad_0 = const()[name = tensor("op_4104_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4104 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_bias, dilations = var_4102, groups = var_1193, pad = var_4104_pad_0, pad_type = var_4104_pad_type_0, strides = var_4100, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_weight, x = input_269)[name = tensor("op_4104")]; - tensor inputs_117 = add(x = var_4104, y = inputs_115)[name = tensor("inputs_117")]; - tensor var_4108 = const()[name = tensor("op_4108"), val = tensor([1])]; - tensor channels_mean_117 = reduce_mean(axes = var_4108, keep_dims = var_1188, x = inputs_117)[name = tensor("channels_mean_117")]; - tensor zero_mean_117 = sub(x = inputs_117, y = channels_mean_117)[name = tensor("zero_mean_117")]; - tensor zero_mean_sq_117 = mul(x = zero_mean_117, y = zero_mean_117)[name = tensor("zero_mean_sq_117")]; - tensor var_4112 = const()[name = tensor("op_4112"), val = tensor([1])]; - tensor var_4113 = reduce_mean(axes = var_4112, keep_dims = var_1188, x = zero_mean_sq_117)[name = tensor("op_4113")]; - tensor var_4114 = const()[name = tensor("op_4114"), val = tensor(0x1.4f8b58p-17)]; - tensor var_4115 = add(x = var_4113, y = var_4114)[name = tensor("op_4115")]; - tensor denom_117_epsilon_0 = const()[name = tensor("denom_117_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_117 = rsqrt(epsilon = denom_117_epsilon_0, x = var_4115)[name = tensor("denom_117")]; - tensor out_117 = mul(x = zero_mean_117, y = denom_117)[name = tensor("out_117")]; - tensor var_4119 = const()[name = tensor("op_4119"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268233088)))]; - tensor var_4120 = add(x = out_117, y = var_4119)[name = tensor("op_4120")]; - tensor var_4122 = const()[name = tensor("op_4122"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268238272)))]; - tensor hidden_states_169 = mul(x = var_4120, y = var_4122)[name = tensor("hidden_states_169")]; - tensor var_4129 = const()[name = tensor("op_4129"), val = tensor([1, 1])]; - tensor var_4131 = const()[name = tensor("op_4131"), val = tensor([1, 1])]; + tensor attn_77_cast = matmul(transpose_x = attn_77_transpose_x_0, transpose_y = attn_77_transpose_y_0, x = var_4025_cast, y = var_4029_cast)[name = tensor("attn_77_cast")]; + tensor var_4033 = const()[name = tensor("op_4033"), val = tensor([2, 1280, 1, -1])]; + tensor input_269_cast = reshape(shape = var_4033, x = attn_77_cast)[name = tensor("input_269_cast")]; + tensor var_4038 = const()[name = tensor("op_4038"), val = tensor([1, 1])]; + tensor var_4040 = const()[name = tensor("op_4040"), val = tensor([1, 1])]; + tensor var_4042_pad_type_0 = const()[name = tensor("op_4042_pad_type_0"), val = tensor("custom")]; + tensor var_4042_pad_0 = const()[name = tensor("op_4042_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1319538112)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1322814976)))]; + tensor var_4042_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_4040, groups = var_31, pad = var_4042_pad_0, pad_type = var_4042_pad_type_0, strides = var_4038, weight = unet_down_blocks_2_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16, x = input_269_cast)[name = tensor("op_4042_cast")]; + tensor inputs_117_cast = add(x = var_4042_cast, y = inputs_115_cast)[name = tensor("inputs_117_cast")]; + tensor var_4046 = const()[name = tensor("op_4046"), val = tensor([1])]; + tensor channels_mean_117_cast = reduce_mean(axes = var_4046, keep_dims = var_23, x = inputs_117_cast)[name = tensor("channels_mean_117_cast")]; + tensor zero_mean_117_cast = sub(x = inputs_117_cast, y = channels_mean_117_cast)[name = tensor("zero_mean_117_cast")]; + tensor zero_mean_sq_117_cast = mul(x = zero_mean_117_cast, y = zero_mean_117_cast)[name = tensor("zero_mean_sq_117_cast")]; + tensor var_4050 = const()[name = tensor("op_4050"), val = tensor([1])]; + tensor var_4051_cast = reduce_mean(axes = var_4050, keep_dims = var_23, x = zero_mean_sq_117_cast)[name = tensor("op_4051_cast")]; + tensor var_4052_to_fp16 = const()[name = tensor("op_4052_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4053_cast = add(x = var_4051_cast, y = var_4052_to_fp16)[name = tensor("op_4053_cast")]; + tensor denom_117_epsilon_0_to_fp16 = const()[name = tensor("denom_117_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_117_cast = rsqrt(epsilon = denom_117_epsilon_0_to_fp16, x = var_4053_cast)[name = tensor("denom_117_cast")]; + tensor out_117_cast = mul(x = zero_mean_117_cast, y = denom_117_cast)[name = tensor("out_117_cast")]; + tensor var_4057_to_fp16 = const()[name = tensor("op_4057_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1322817600)))]; + tensor var_4058_cast = add(x = out_117_cast, y = var_4057_to_fp16)[name = tensor("op_4058_cast")]; + tensor var_4060_to_fp16 = const()[name = tensor("op_4060_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1322820224)))]; + tensor hidden_states_169_cast = mul(x = var_4058_cast, y = var_4060_to_fp16)[name = tensor("hidden_states_169_cast")]; + tensor var_4067 = const()[name = tensor("op_4067"), val = tensor([1, 1])]; + tensor var_4069 = const()[name = tensor("op_4069"), val = tensor([1, 1])]; tensor q_79_pad_type_0 = const()[name = tensor("q_79_pad_type_0"), val = tensor("custom")]; tensor q_79_pad_0 = const()[name = tensor("q_79_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_79 = conv(dilations = var_4131, groups = var_1193, pad = q_79_pad_0, pad_type = q_79_pad_type_0, strides = var_4129, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_q_weight, x = hidden_states_169)[name = tensor("q_79")]; - tensor var_4135 = const()[name = tensor("op_4135"), val = tensor([1, 1])]; - tensor var_4137 = const()[name = tensor("op_4137"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1322822848)))]; + tensor q_79_cast = conv(dilations = var_4069, groups = var_31, pad = q_79_pad_0, pad_type = q_79_pad_type_0, strides = var_4067, weight = unet_down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16, x = hidden_states_169_cast)[name = tensor("q_79_cast")]; + tensor var_4073 = const()[name = tensor("op_4073"), val = tensor([1, 1])]; + tensor var_4075 = const()[name = tensor("op_4075"), val = tensor([1, 1])]; tensor k_79_pad_type_0 = const()[name = tensor("k_79_pad_type_0"), val = tensor("custom")]; tensor k_79_pad_0 = const()[name = tensor("k_79_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_79 = conv(dilations = var_4137, groups = var_1193, pad = k_79_pad_0, pad_type = k_79_pad_type_0, strides = var_4135, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_79")]; - tensor var_4141 = const()[name = tensor("op_4141"), val = tensor([1, 1])]; - tensor var_4143 = const()[name = tensor("op_4143"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1326099712)))]; + tensor k_79_cast = conv(dilations = var_4075, groups = var_31, pad = k_79_pad_0, pad_type = k_79_pad_type_0, strides = var_4073, weight = unet_down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_79_cast")]; + tensor var_4079 = const()[name = tensor("op_4079"), val = tensor([1, 1])]; + tensor var_4081 = const()[name = tensor("op_4081"), val = tensor([1, 1])]; tensor v_79_pad_type_0 = const()[name = tensor("v_79_pad_type_0"), val = tensor("custom")]; tensor v_79_pad_0 = const()[name = tensor("v_79_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_79 = conv(dilations = var_4143, groups = var_1193, pad = v_79_pad_0, pad_type = v_79_pad_type_0, strides = var_4141, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_79")]; - tensor var_4147 = const()[name = tensor("op_4147"), val = tensor([2, 20, 64, -1])]; - tensor var_4148 = reshape(shape = var_4147, x = q_79)[name = tensor("op_4148")]; - tensor var_4149 = const()[name = tensor("op_4149"), val = tensor([2, 20, 64, -1])]; - tensor var_4150 = reshape(shape = var_4149, x = k_79)[name = tensor("op_4150")]; - tensor var_4151 = const()[name = tensor("op_4151"), val = tensor([2, 20, 64, -1])]; - tensor var_4152 = reshape(shape = var_4151, x = v_79)[name = tensor("op_4152")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1331342656)))]; + tensor v_79_cast = conv(dilations = var_4081, groups = var_31, pad = v_79_pad_0, pad_type = v_79_pad_type_0, strides = var_4079, weight = unet_down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_79_cast")]; + tensor var_4085 = const()[name = tensor("op_4085"), val = tensor([2, 20, 64, -1])]; + tensor var_4086_cast = reshape(shape = var_4085, x = q_79_cast)[name = tensor("op_4086_cast")]; + tensor var_4087 = const()[name = tensor("op_4087"), val = tensor([2, 20, 64, -1])]; + tensor var_4088_cast = reshape(shape = var_4087, x = k_79_cast)[name = tensor("op_4088_cast")]; + tensor var_4089 = const()[name = tensor("op_4089"), val = tensor([2, 20, 64, -1])]; + tensor var_4090_cast = reshape(shape = var_4089, x = v_79_cast)[name = tensor("op_4090_cast")]; tensor attn_weights_157_transpose_x_0 = const()[name = tensor("attn_weights_157_transpose_x_0"), val = tensor(true)]; tensor attn_weights_157_transpose_y_0 = const()[name = tensor("attn_weights_157_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_157 = matmul(transpose_x = attn_weights_157_transpose_x_0, transpose_y = attn_weights_157_transpose_y_0, x = var_4148, y = var_4150)[name = tensor("attn_weights_157")]; - tensor attn_weights_159 = mul(x = attn_weights_157, y = var_1184)[name = tensor("attn_weights_159")]; - tensor var_4156 = softmax(axis = var_1177, x = attn_weights_159)[name = tensor("op_4156")]; + tensor attn_weights_157_cast = matmul(transpose_x = attn_weights_157_transpose_x_0, transpose_y = attn_weights_157_transpose_y_0, x = var_4086_cast, y = var_4088_cast)[name = tensor("attn_weights_157_cast")]; + tensor attn_weights_159_cast = mul(x = attn_weights_157_cast, y = var_12_to_fp16)[name = tensor("attn_weights_159_cast")]; + tensor var_4094_cast = softmax(axis = var_18, x = attn_weights_159_cast)[name = tensor("op_4094_cast")]; tensor attn_79_transpose_x_0 = const()[name = tensor("attn_79_transpose_x_0"), val = tensor(false)]; tensor attn_79_transpose_y_0 = const()[name = tensor("attn_79_transpose_y_0"), val = tensor(true)]; - tensor attn_79 = matmul(transpose_x = attn_79_transpose_x_0, transpose_y = attn_79_transpose_y_0, x = var_4152, y = var_4156)[name = tensor("attn_79")]; - tensor var_4160 = const()[name = tensor("op_4160"), val = tensor([2, 1280, 1, -1])]; - tensor input_271 = reshape(shape = var_4160, x = attn_79)[name = tensor("input_271")]; - tensor var_4165 = const()[name = tensor("op_4165"), val = tensor([1, 1])]; - tensor var_4167 = const()[name = tensor("op_4167"), val = tensor([1, 1])]; - tensor var_4169_pad_type_0 = const()[name = tensor("op_4169_pad_type_0"), val = tensor("custom")]; - tensor var_4169_pad_0 = const()[name = tensor("op_4169_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4169 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_bias, dilations = var_4167, groups = var_1193, pad = var_4169_pad_0, pad_type = var_4169_pad_type_0, strides = var_4165, weight = down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_weight, x = input_271)[name = tensor("op_4169")]; - tensor inputs_119 = add(x = var_4169, y = inputs_117)[name = tensor("inputs_119")]; - tensor var_4173 = const()[name = tensor("op_4173"), val = tensor([1])]; - tensor channels_mean_119 = reduce_mean(axes = var_4173, keep_dims = var_1188, x = inputs_119)[name = tensor("channels_mean_119")]; - tensor zero_mean_119 = sub(x = inputs_119, y = channels_mean_119)[name = tensor("zero_mean_119")]; - tensor zero_mean_sq_119 = mul(x = zero_mean_119, y = zero_mean_119)[name = tensor("zero_mean_sq_119")]; - tensor var_4177 = const()[name = tensor("op_4177"), val = tensor([1])]; - tensor var_4178 = reduce_mean(axes = var_4177, keep_dims = var_1188, x = zero_mean_sq_119)[name = tensor("op_4178")]; - tensor var_4179 = const()[name = tensor("op_4179"), val = tensor(0x1.4f8b58p-17)]; - tensor var_4180 = add(x = var_4178, y = var_4179)[name = tensor("op_4180")]; - tensor denom_119_epsilon_0 = const()[name = tensor("denom_119_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_119 = rsqrt(epsilon = denom_119_epsilon_0, x = var_4180)[name = tensor("denom_119")]; - tensor out_119 = mul(x = zero_mean_119, y = denom_119)[name = tensor("out_119")]; - tensor var_4184 = const()[name = tensor("op_4184"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268243456)))]; - tensor var_4185 = add(x = out_119, y = var_4184)[name = tensor("op_4185")]; - tensor var_4187 = const()[name = tensor("op_4187"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268248640)))]; - tensor input_273 = mul(x = var_4185, y = var_4187)[name = tensor("input_273")]; - tensor var_4195 = const()[name = tensor("op_4195"), val = tensor([1, 1])]; - tensor var_4197 = const()[name = tensor("op_4197"), val = tensor([1, 1])]; - tensor var_4199_pad_type_0 = const()[name = tensor("op_4199_pad_type_0"), val = tensor("custom")]; - tensor var_4199_pad_0 = const()[name = tensor("op_4199_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4199 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_bias, dilations = var_4197, groups = var_1193, pad = var_4199_pad_0, pad_type = var_4199_pad_type_0, strides = var_4195, weight = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_weight, x = input_273)[name = tensor("op_4199")]; - tensor var_4200_split_sizes_0 = const()[name = tensor("op_4200_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_4200_axis_0 = const()[name = tensor("op_4200_axis_0"), val = tensor(1)]; - tensor var_4200_0, tensor var_4200_1 = split(axis = var_4200_axis_0, split_sizes = var_4200_split_sizes_0, x = var_4199)[name = tensor("op_4200")]; - tensor var_4202_mode_0 = const()[name = tensor("op_4202_mode_0"), val = tensor("EXACT")]; - tensor var_4202 = gelu(mode = var_4202_mode_0, x = var_4200_1)[name = tensor("op_4202")]; - tensor input_275 = mul(x = var_4200_0, y = var_4202)[name = tensor("input_275")]; - tensor var_4206 = const()[name = tensor("op_4206"), val = tensor([1, 1])]; - tensor var_4208 = const()[name = tensor("op_4208"), val = tensor([1, 1])]; - tensor var_4210_pad_type_0 = const()[name = tensor("op_4210_pad_type_0"), val = tensor("custom")]; - tensor var_4210_pad_0 = const()[name = tensor("op_4210_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4210 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_bias, dilations = var_4208, groups = var_1193, pad = var_4210_pad_0, pad_type = var_4210_pad_type_0, strides = var_4206, weight = down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_weight, x = input_275)[name = tensor("op_4210")]; - tensor inputs_121 = add(x = var_4210, y = inputs_119)[name = tensor("inputs_121")]; - tensor var_4220 = const()[name = tensor("op_4220"), val = tensor([1])]; - tensor channels_mean_121 = reduce_mean(axes = var_4220, keep_dims = var_1188, x = inputs_121)[name = tensor("channels_mean_121")]; - tensor zero_mean_121 = sub(x = inputs_121, y = channels_mean_121)[name = tensor("zero_mean_121")]; - tensor zero_mean_sq_121 = mul(x = zero_mean_121, y = zero_mean_121)[name = tensor("zero_mean_sq_121")]; - tensor var_4224 = const()[name = tensor("op_4224"), val = tensor([1])]; - tensor var_4225 = reduce_mean(axes = var_4224, keep_dims = var_1188, x = zero_mean_sq_121)[name = tensor("op_4225")]; - tensor var_4226 = const()[name = tensor("op_4226"), val = tensor(0x1.4f8b58p-17)]; - tensor var_4227 = add(x = var_4225, y = var_4226)[name = tensor("op_4227")]; - tensor denom_121_epsilon_0 = const()[name = tensor("denom_121_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_121 = rsqrt(epsilon = denom_121_epsilon_0, x = var_4227)[name = tensor("denom_121")]; - tensor out_121 = mul(x = zero_mean_121, y = denom_121)[name = tensor("out_121")]; - tensor var_4231 = const()[name = tensor("op_4231"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268253824)))]; - tensor var_4232 = add(x = out_121, y = var_4231)[name = tensor("op_4232")]; - tensor var_4234 = const()[name = tensor("op_4234"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268259008)))]; - tensor hidden_states_173 = mul(x = var_4232, y = var_4234)[name = tensor("hidden_states_173")]; - tensor var_4241 = const()[name = tensor("op_4241"), val = tensor([1, 1])]; - tensor var_4243 = const()[name = tensor("op_4243"), val = tensor([1, 1])]; + tensor attn_79_cast = matmul(transpose_x = attn_79_transpose_x_0, transpose_y = attn_79_transpose_y_0, x = var_4090_cast, y = var_4094_cast)[name = tensor("attn_79_cast")]; + tensor var_4098 = const()[name = tensor("op_4098"), val = tensor([2, 1280, 1, -1])]; + tensor input_271_cast = reshape(shape = var_4098, x = attn_79_cast)[name = tensor("input_271_cast")]; + tensor var_4103 = const()[name = tensor("op_4103"), val = tensor([1, 1])]; + tensor var_4105 = const()[name = tensor("op_4105"), val = tensor([1, 1])]; + tensor var_4107_pad_type_0 = const()[name = tensor("op_4107_pad_type_0"), val = tensor("custom")]; + tensor var_4107_pad_0 = const()[name = tensor("op_4107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1336585600)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1339862464)))]; + tensor var_4107_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_4105, groups = var_31, pad = var_4107_pad_0, pad_type = var_4107_pad_type_0, strides = var_4103, weight = unet_down_blocks_2_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16, x = input_271_cast)[name = tensor("op_4107_cast")]; + tensor inputs_119_cast = add(x = var_4107_cast, y = inputs_117_cast)[name = tensor("inputs_119_cast")]; + tensor var_4111 = const()[name = tensor("op_4111"), val = tensor([1])]; + tensor channels_mean_119_cast = reduce_mean(axes = var_4111, keep_dims = var_23, x = inputs_119_cast)[name = tensor("channels_mean_119_cast")]; + tensor zero_mean_119_cast = sub(x = inputs_119_cast, y = channels_mean_119_cast)[name = tensor("zero_mean_119_cast")]; + tensor zero_mean_sq_119_cast = mul(x = zero_mean_119_cast, y = zero_mean_119_cast)[name = tensor("zero_mean_sq_119_cast")]; + tensor var_4115 = const()[name = tensor("op_4115"), val = tensor([1])]; + tensor var_4116_cast = reduce_mean(axes = var_4115, keep_dims = var_23, x = zero_mean_sq_119_cast)[name = tensor("op_4116_cast")]; + tensor var_4117_to_fp16 = const()[name = tensor("op_4117_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4118_cast = add(x = var_4116_cast, y = var_4117_to_fp16)[name = tensor("op_4118_cast")]; + tensor denom_119_epsilon_0_to_fp16 = const()[name = tensor("denom_119_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_119_cast = rsqrt(epsilon = denom_119_epsilon_0_to_fp16, x = var_4118_cast)[name = tensor("denom_119_cast")]; + tensor out_119_cast = mul(x = zero_mean_119_cast, y = denom_119_cast)[name = tensor("out_119_cast")]; + tensor var_4122_to_fp16 = const()[name = tensor("op_4122_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1339865088)))]; + tensor var_4123_cast = add(x = out_119_cast, y = var_4122_to_fp16)[name = tensor("op_4123_cast")]; + tensor var_4125_to_fp16 = const()[name = tensor("op_4125_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1339867712)))]; + tensor input_273_cast = mul(x = var_4123_cast, y = var_4125_to_fp16)[name = tensor("input_273_cast")]; + tensor var_4133 = const()[name = tensor("op_4133"), val = tensor([1, 1])]; + tensor var_4135 = const()[name = tensor("op_4135"), val = tensor([1, 1])]; + tensor var_4137_pad_type_0 = const()[name = tensor("op_4137_pad_type_0"), val = tensor("custom")]; + tensor var_4137_pad_0 = const()[name = tensor("op_4137_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1339870336)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1366084800)))]; + tensor var_4137_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_4135, groups = var_31, pad = var_4137_pad_0, pad_type = var_4137_pad_type_0, strides = var_4133, weight = unet_down_blocks_2_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16, x = input_273_cast)[name = tensor("op_4137_cast")]; + tensor var_4138_split_sizes_0 = const()[name = tensor("op_4138_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4138_axis_0 = const()[name = tensor("op_4138_axis_0"), val = tensor(1)]; + tensor var_4138_cast_0, tensor var_4138_cast_1 = split(axis = var_4138_axis_0, split_sizes = var_4138_split_sizes_0, x = var_4137_cast)[name = tensor("op_4138_cast")]; + tensor var_4140_mode_0 = const()[name = tensor("op_4140_mode_0"), val = tensor("EXACT")]; + tensor var_4140_cast = gelu(mode = var_4140_mode_0, x = var_4138_cast_1)[name = tensor("op_4140_cast")]; + tensor input_275_cast = mul(x = var_4138_cast_0, y = var_4140_cast)[name = tensor("input_275_cast")]; + tensor var_4144 = const()[name = tensor("op_4144"), val = tensor([1, 1])]; + tensor var_4146 = const()[name = tensor("op_4146"), val = tensor([1, 1])]; + tensor var_4148_pad_type_0 = const()[name = tensor("op_4148_pad_type_0"), val = tensor("custom")]; + tensor var_4148_pad_0 = const()[name = tensor("op_4148_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1366105344)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1379212608)))]; + tensor var_4148_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_4146, groups = var_31, pad = var_4148_pad_0, pad_type = var_4148_pad_type_0, strides = var_4144, weight = unet_down_blocks_2_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16, x = input_275_cast)[name = tensor("op_4148_cast")]; + tensor inputs_121_cast = add(x = var_4148_cast, y = inputs_119_cast)[name = tensor("inputs_121_cast")]; + tensor var_4158 = const()[name = tensor("op_4158"), val = tensor([1])]; + tensor channels_mean_121_cast = reduce_mean(axes = var_4158, keep_dims = var_23, x = inputs_121_cast)[name = tensor("channels_mean_121_cast")]; + tensor zero_mean_121_cast = sub(x = inputs_121_cast, y = channels_mean_121_cast)[name = tensor("zero_mean_121_cast")]; + tensor zero_mean_sq_121_cast = mul(x = zero_mean_121_cast, y = zero_mean_121_cast)[name = tensor("zero_mean_sq_121_cast")]; + tensor var_4162 = const()[name = tensor("op_4162"), val = tensor([1])]; + tensor var_4163_cast = reduce_mean(axes = var_4162, keep_dims = var_23, x = zero_mean_sq_121_cast)[name = tensor("op_4163_cast")]; + tensor var_4164_to_fp16 = const()[name = tensor("op_4164_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4165_cast = add(x = var_4163_cast, y = var_4164_to_fp16)[name = tensor("op_4165_cast")]; + tensor denom_121_epsilon_0_to_fp16 = const()[name = tensor("denom_121_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_121_cast = rsqrt(epsilon = denom_121_epsilon_0_to_fp16, x = var_4165_cast)[name = tensor("denom_121_cast")]; + tensor out_121_cast = mul(x = zero_mean_121_cast, y = denom_121_cast)[name = tensor("out_121_cast")]; + tensor var_4169_to_fp16 = const()[name = tensor("op_4169_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1379215232)))]; + tensor var_4170_cast = add(x = out_121_cast, y = var_4169_to_fp16)[name = tensor("op_4170_cast")]; + tensor var_4172_to_fp16 = const()[name = tensor("op_4172_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1379217856)))]; + tensor hidden_states_173_cast = mul(x = var_4170_cast, y = var_4172_to_fp16)[name = tensor("hidden_states_173_cast")]; + tensor var_4179 = const()[name = tensor("op_4179"), val = tensor([1, 1])]; + tensor var_4181 = const()[name = tensor("op_4181"), val = tensor([1, 1])]; tensor q_81_pad_type_0 = const()[name = tensor("q_81_pad_type_0"), val = tensor("custom")]; tensor q_81_pad_0 = const()[name = tensor("q_81_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_81 = conv(dilations = var_4243, groups = var_1193, pad = q_81_pad_0, pad_type = q_81_pad_type_0, strides = var_4241, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_q_weight, x = hidden_states_173)[name = tensor("q_81")]; - tensor var_4247 = const()[name = tensor("op_4247"), val = tensor([1, 1])]; - tensor var_4249 = const()[name = tensor("op_4249"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1379220480)))]; + tensor q_81_cast = conv(dilations = var_4181, groups = var_31, pad = q_81_pad_0, pad_type = q_81_pad_type_0, strides = var_4179, weight = unet_down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16, x = hidden_states_173_cast)[name = tensor("q_81_cast")]; + tensor var_4185 = const()[name = tensor("op_4185"), val = tensor([1, 1])]; + tensor var_4187 = const()[name = tensor("op_4187"), val = tensor([1, 1])]; tensor k_81_pad_type_0 = const()[name = tensor("k_81_pad_type_0"), val = tensor("custom")]; tensor k_81_pad_0 = const()[name = tensor("k_81_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_81 = conv(dilations = var_4249, groups = var_1193, pad = k_81_pad_0, pad_type = k_81_pad_type_0, strides = var_4247, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_k_weight, x = hidden_states_173)[name = tensor("k_81")]; - tensor var_4253 = const()[name = tensor("op_4253"), val = tensor([1, 1])]; - tensor var_4255 = const()[name = tensor("op_4255"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1382497344)))]; + tensor k_81_cast = conv(dilations = var_4187, groups = var_31, pad = k_81_pad_0, pad_type = k_81_pad_type_0, strides = var_4185, weight = unet_down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16, x = hidden_states_173_cast)[name = tensor("k_81_cast")]; + tensor var_4191 = const()[name = tensor("op_4191"), val = tensor([1, 1])]; + tensor var_4193 = const()[name = tensor("op_4193"), val = tensor([1, 1])]; tensor v_81_pad_type_0 = const()[name = tensor("v_81_pad_type_0"), val = tensor("custom")]; tensor v_81_pad_0 = const()[name = tensor("v_81_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_81 = conv(dilations = var_4255, groups = var_1193, pad = v_81_pad_0, pad_type = v_81_pad_type_0, strides = var_4253, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_v_weight, x = hidden_states_173)[name = tensor("v_81")]; - tensor var_4259 = const()[name = tensor("op_4259"), val = tensor([2, 20, 64, -1])]; - tensor var_4260 = reshape(shape = var_4259, x = q_81)[name = tensor("op_4260")]; - tensor var_4261 = const()[name = tensor("op_4261"), val = tensor([2, 20, 64, -1])]; - tensor var_4262 = reshape(shape = var_4261, x = k_81)[name = tensor("op_4262")]; - tensor var_4263 = const()[name = tensor("op_4263"), val = tensor([2, 20, 64, -1])]; - tensor var_4264 = reshape(shape = var_4263, x = v_81)[name = tensor("op_4264")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1385774208)))]; + tensor v_81_cast = conv(dilations = var_4193, groups = var_31, pad = v_81_pad_0, pad_type = v_81_pad_type_0, strides = var_4191, weight = unet_down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16, x = hidden_states_173_cast)[name = tensor("v_81_cast")]; + tensor var_4197 = const()[name = tensor("op_4197"), val = tensor([2, 20, 64, -1])]; + tensor var_4198_cast = reshape(shape = var_4197, x = q_81_cast)[name = tensor("op_4198_cast")]; + tensor var_4199 = const()[name = tensor("op_4199"), val = tensor([2, 20, 64, -1])]; + tensor var_4200_cast = reshape(shape = var_4199, x = k_81_cast)[name = tensor("op_4200_cast")]; + tensor var_4201 = const()[name = tensor("op_4201"), val = tensor([2, 20, 64, -1])]; + tensor var_4202_cast = reshape(shape = var_4201, x = v_81_cast)[name = tensor("op_4202_cast")]; tensor attn_weights_161_transpose_x_0 = const()[name = tensor("attn_weights_161_transpose_x_0"), val = tensor(true)]; tensor attn_weights_161_transpose_y_0 = const()[name = tensor("attn_weights_161_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_161 = matmul(transpose_x = attn_weights_161_transpose_x_0, transpose_y = attn_weights_161_transpose_y_0, x = var_4260, y = var_4262)[name = tensor("attn_weights_161")]; - tensor attn_weights_163 = mul(x = attn_weights_161, y = var_1184)[name = tensor("attn_weights_163")]; - tensor var_4268 = softmax(axis = var_1177, x = attn_weights_163)[name = tensor("op_4268")]; + tensor attn_weights_161_cast = matmul(transpose_x = attn_weights_161_transpose_x_0, transpose_y = attn_weights_161_transpose_y_0, x = var_4198_cast, y = var_4200_cast)[name = tensor("attn_weights_161_cast")]; + tensor attn_weights_163_cast = mul(x = attn_weights_161_cast, y = var_12_to_fp16)[name = tensor("attn_weights_163_cast")]; + tensor var_4206_cast = softmax(axis = var_18, x = attn_weights_163_cast)[name = tensor("op_4206_cast")]; tensor attn_81_transpose_x_0 = const()[name = tensor("attn_81_transpose_x_0"), val = tensor(false)]; tensor attn_81_transpose_y_0 = const()[name = tensor("attn_81_transpose_y_0"), val = tensor(true)]; - tensor attn_81 = matmul(transpose_x = attn_81_transpose_x_0, transpose_y = attn_81_transpose_y_0, x = var_4264, y = var_4268)[name = tensor("attn_81")]; - tensor var_4272 = const()[name = tensor("op_4272"), val = tensor([2, 1280, 1, -1])]; - tensor input_277 = reshape(shape = var_4272, x = attn_81)[name = tensor("input_277")]; - tensor var_4277 = const()[name = tensor("op_4277"), val = tensor([1, 1])]; - tensor var_4279 = const()[name = tensor("op_4279"), val = tensor([1, 1])]; - tensor var_4281_pad_type_0 = const()[name = tensor("op_4281_pad_type_0"), val = tensor("custom")]; - tensor var_4281_pad_0 = const()[name = tensor("op_4281_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4281 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_bias, dilations = var_4279, groups = var_1193, pad = var_4281_pad_0, pad_type = var_4281_pad_type_0, strides = var_4277, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_weight, x = input_277)[name = tensor("op_4281")]; - tensor inputs_123 = add(x = var_4281, y = inputs_121)[name = tensor("inputs_123")]; - tensor var_4285 = const()[name = tensor("op_4285"), val = tensor([1])]; - tensor channels_mean_123 = reduce_mean(axes = var_4285, keep_dims = var_1188, x = inputs_123)[name = tensor("channels_mean_123")]; - tensor zero_mean_123 = sub(x = inputs_123, y = channels_mean_123)[name = tensor("zero_mean_123")]; - tensor zero_mean_sq_123 = mul(x = zero_mean_123, y = zero_mean_123)[name = tensor("zero_mean_sq_123")]; - tensor var_4289 = const()[name = tensor("op_4289"), val = tensor([1])]; - tensor var_4290 = reduce_mean(axes = var_4289, keep_dims = var_1188, x = zero_mean_sq_123)[name = tensor("op_4290")]; - tensor var_4291 = const()[name = tensor("op_4291"), val = tensor(0x1.4f8b58p-17)]; - tensor var_4292 = add(x = var_4290, y = var_4291)[name = tensor("op_4292")]; - tensor denom_123_epsilon_0 = const()[name = tensor("denom_123_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_123 = rsqrt(epsilon = denom_123_epsilon_0, x = var_4292)[name = tensor("denom_123")]; - tensor out_123 = mul(x = zero_mean_123, y = denom_123)[name = tensor("out_123")]; - tensor var_4296 = const()[name = tensor("op_4296"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268264192)))]; - tensor var_4297 = add(x = out_123, y = var_4296)[name = tensor("op_4297")]; - tensor var_4299 = const()[name = tensor("op_4299"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268269376)))]; - tensor hidden_states_175 = mul(x = var_4297, y = var_4299)[name = tensor("hidden_states_175")]; - tensor var_4306 = const()[name = tensor("op_4306"), val = tensor([1, 1])]; - tensor var_4308 = const()[name = tensor("op_4308"), val = tensor([1, 1])]; + tensor attn_81_cast = matmul(transpose_x = attn_81_transpose_x_0, transpose_y = attn_81_transpose_y_0, x = var_4202_cast, y = var_4206_cast)[name = tensor("attn_81_cast")]; + tensor var_4210 = const()[name = tensor("op_4210"), val = tensor([2, 1280, 1, -1])]; + tensor input_277_cast = reshape(shape = var_4210, x = attn_81_cast)[name = tensor("input_277_cast")]; + tensor var_4215 = const()[name = tensor("op_4215"), val = tensor([1, 1])]; + tensor var_4217 = const()[name = tensor("op_4217"), val = tensor([1, 1])]; + tensor var_4219_pad_type_0 = const()[name = tensor("op_4219_pad_type_0"), val = tensor("custom")]; + tensor var_4219_pad_0 = const()[name = tensor("op_4219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1389051072)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1392327936)))]; + tensor var_4219_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_4217, groups = var_31, pad = var_4219_pad_0, pad_type = var_4219_pad_type_0, strides = var_4215, weight = unet_down_blocks_2_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16, x = input_277_cast)[name = tensor("op_4219_cast")]; + tensor inputs_123_cast = add(x = var_4219_cast, y = inputs_121_cast)[name = tensor("inputs_123_cast")]; + tensor var_4223 = const()[name = tensor("op_4223"), val = tensor([1])]; + tensor channels_mean_123_cast = reduce_mean(axes = var_4223, keep_dims = var_23, x = inputs_123_cast)[name = tensor("channels_mean_123_cast")]; + tensor zero_mean_123_cast = sub(x = inputs_123_cast, y = channels_mean_123_cast)[name = tensor("zero_mean_123_cast")]; + tensor zero_mean_sq_123_cast = mul(x = zero_mean_123_cast, y = zero_mean_123_cast)[name = tensor("zero_mean_sq_123_cast")]; + tensor var_4227 = const()[name = tensor("op_4227"), val = tensor([1])]; + tensor var_4228_cast = reduce_mean(axes = var_4227, keep_dims = var_23, x = zero_mean_sq_123_cast)[name = tensor("op_4228_cast")]; + tensor var_4229_to_fp16 = const()[name = tensor("op_4229_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4230_cast = add(x = var_4228_cast, y = var_4229_to_fp16)[name = tensor("op_4230_cast")]; + tensor denom_123_epsilon_0_to_fp16 = const()[name = tensor("denom_123_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_123_cast = rsqrt(epsilon = denom_123_epsilon_0_to_fp16, x = var_4230_cast)[name = tensor("denom_123_cast")]; + tensor out_123_cast = mul(x = zero_mean_123_cast, y = denom_123_cast)[name = tensor("out_123_cast")]; + tensor var_4234_to_fp16 = const()[name = tensor("op_4234_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1392330560)))]; + tensor var_4235_cast = add(x = out_123_cast, y = var_4234_to_fp16)[name = tensor("op_4235_cast")]; + tensor var_4237_to_fp16 = const()[name = tensor("op_4237_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1392333184)))]; + tensor hidden_states_175_cast = mul(x = var_4235_cast, y = var_4237_to_fp16)[name = tensor("hidden_states_175_cast")]; + tensor var_4244 = const()[name = tensor("op_4244"), val = tensor([1, 1])]; + tensor var_4246 = const()[name = tensor("op_4246"), val = tensor([1, 1])]; tensor q_83_pad_type_0 = const()[name = tensor("q_83_pad_type_0"), val = tensor("custom")]; tensor q_83_pad_0 = const()[name = tensor("q_83_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_83 = conv(dilations = var_4308, groups = var_1193, pad = q_83_pad_0, pad_type = q_83_pad_type_0, strides = var_4306, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_q_weight, x = hidden_states_175)[name = tensor("q_83")]; - tensor var_4312 = const()[name = tensor("op_4312"), val = tensor([1, 1])]; - tensor var_4314 = const()[name = tensor("op_4314"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1392335808)))]; + tensor q_83_cast = conv(dilations = var_4246, groups = var_31, pad = q_83_pad_0, pad_type = q_83_pad_type_0, strides = var_4244, weight = unet_down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16, x = hidden_states_175_cast)[name = tensor("q_83_cast")]; + tensor var_4250 = const()[name = tensor("op_4250"), val = tensor([1, 1])]; + tensor var_4252 = const()[name = tensor("op_4252"), val = tensor([1, 1])]; tensor k_83_pad_type_0 = const()[name = tensor("k_83_pad_type_0"), val = tensor("custom")]; tensor k_83_pad_0 = const()[name = tensor("k_83_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_83 = conv(dilations = var_4314, groups = var_1193, pad = k_83_pad_0, pad_type = k_83_pad_type_0, strides = var_4312, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_83")]; - tensor var_4318 = const()[name = tensor("op_4318"), val = tensor([1, 1])]; - tensor var_4320 = const()[name = tensor("op_4320"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1395612672)))]; + tensor k_83_cast = conv(dilations = var_4252, groups = var_31, pad = k_83_pad_0, pad_type = k_83_pad_type_0, strides = var_4250, weight = unet_down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_83_cast")]; + tensor var_4256 = const()[name = tensor("op_4256"), val = tensor([1, 1])]; + tensor var_4258 = const()[name = tensor("op_4258"), val = tensor([1, 1])]; tensor v_83_pad_type_0 = const()[name = tensor("v_83_pad_type_0"), val = tensor("custom")]; tensor v_83_pad_0 = const()[name = tensor("v_83_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_83 = conv(dilations = var_4320, groups = var_1193, pad = v_83_pad_0, pad_type = v_83_pad_type_0, strides = var_4318, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_83")]; - tensor var_4324 = const()[name = tensor("op_4324"), val = tensor([2, 20, 64, -1])]; - tensor var_4325 = reshape(shape = var_4324, x = q_83)[name = tensor("op_4325")]; - tensor var_4326 = const()[name = tensor("op_4326"), val = tensor([2, 20, 64, -1])]; - tensor var_4327 = reshape(shape = var_4326, x = k_83)[name = tensor("op_4327")]; - tensor var_4328 = const()[name = tensor("op_4328"), val = tensor([2, 20, 64, -1])]; - tensor var_4329 = reshape(shape = var_4328, x = v_83)[name = tensor("op_4329")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1400855616)))]; + tensor v_83_cast = conv(dilations = var_4258, groups = var_31, pad = v_83_pad_0, pad_type = v_83_pad_type_0, strides = var_4256, weight = unet_down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_83_cast")]; + tensor var_4262 = const()[name = tensor("op_4262"), val = tensor([2, 20, 64, -1])]; + tensor var_4263_cast = reshape(shape = var_4262, x = q_83_cast)[name = tensor("op_4263_cast")]; + tensor var_4264 = const()[name = tensor("op_4264"), val = tensor([2, 20, 64, -1])]; + tensor var_4265_cast = reshape(shape = var_4264, x = k_83_cast)[name = tensor("op_4265_cast")]; + tensor var_4266 = const()[name = tensor("op_4266"), val = tensor([2, 20, 64, -1])]; + tensor var_4267_cast = reshape(shape = var_4266, x = v_83_cast)[name = tensor("op_4267_cast")]; tensor attn_weights_165_transpose_x_0 = const()[name = tensor("attn_weights_165_transpose_x_0"), val = tensor(true)]; tensor attn_weights_165_transpose_y_0 = const()[name = tensor("attn_weights_165_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_165 = matmul(transpose_x = attn_weights_165_transpose_x_0, transpose_y = attn_weights_165_transpose_y_0, x = var_4325, y = var_4327)[name = tensor("attn_weights_165")]; - tensor attn_weights_167 = mul(x = attn_weights_165, y = var_1184)[name = tensor("attn_weights_167")]; - tensor var_4333 = softmax(axis = var_1177, x = attn_weights_167)[name = tensor("op_4333")]; + tensor attn_weights_165_cast = matmul(transpose_x = attn_weights_165_transpose_x_0, transpose_y = attn_weights_165_transpose_y_0, x = var_4263_cast, y = var_4265_cast)[name = tensor("attn_weights_165_cast")]; + tensor attn_weights_167_cast = mul(x = attn_weights_165_cast, y = var_12_to_fp16)[name = tensor("attn_weights_167_cast")]; + tensor var_4271_cast = softmax(axis = var_18, x = attn_weights_167_cast)[name = tensor("op_4271_cast")]; tensor attn_83_transpose_x_0 = const()[name = tensor("attn_83_transpose_x_0"), val = tensor(false)]; tensor attn_83_transpose_y_0 = const()[name = tensor("attn_83_transpose_y_0"), val = tensor(true)]; - tensor attn_83 = matmul(transpose_x = attn_83_transpose_x_0, transpose_y = attn_83_transpose_y_0, x = var_4329, y = var_4333)[name = tensor("attn_83")]; - tensor var_4337 = const()[name = tensor("op_4337"), val = tensor([2, 1280, 1, -1])]; - tensor input_279 = reshape(shape = var_4337, x = attn_83)[name = tensor("input_279")]; - tensor var_4342 = const()[name = tensor("op_4342"), val = tensor([1, 1])]; - tensor var_4344 = const()[name = tensor("op_4344"), val = tensor([1, 1])]; - tensor var_4346_pad_type_0 = const()[name = tensor("op_4346_pad_type_0"), val = tensor("custom")]; - tensor var_4346_pad_0 = const()[name = tensor("op_4346_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4346 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_bias, dilations = var_4344, groups = var_1193, pad = var_4346_pad_0, pad_type = var_4346_pad_type_0, strides = var_4342, weight = down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_weight, x = input_279)[name = tensor("op_4346")]; - tensor inputs_125 = add(x = var_4346, y = inputs_123)[name = tensor("inputs_125")]; - tensor var_4350 = const()[name = tensor("op_4350"), val = tensor([1])]; - tensor channels_mean_125 = reduce_mean(axes = var_4350, keep_dims = var_1188, x = inputs_125)[name = tensor("channels_mean_125")]; - tensor zero_mean_125 = sub(x = inputs_125, y = channels_mean_125)[name = tensor("zero_mean_125")]; - tensor zero_mean_sq_125 = mul(x = zero_mean_125, y = zero_mean_125)[name = tensor("zero_mean_sq_125")]; - tensor var_4354 = const()[name = tensor("op_4354"), val = tensor([1])]; - tensor var_4355 = reduce_mean(axes = var_4354, keep_dims = var_1188, x = zero_mean_sq_125)[name = tensor("op_4355")]; - tensor var_4356 = const()[name = tensor("op_4356"), val = tensor(0x1.4f8b58p-17)]; - tensor var_4357 = add(x = var_4355, y = var_4356)[name = tensor("op_4357")]; - tensor denom_125_epsilon_0 = const()[name = tensor("denom_125_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_125 = rsqrt(epsilon = denom_125_epsilon_0, x = var_4357)[name = tensor("denom_125")]; - tensor out_125 = mul(x = zero_mean_125, y = denom_125)[name = tensor("out_125")]; - tensor var_4361 = const()[name = tensor("op_4361"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268274560)))]; - tensor var_4362 = add(x = out_125, y = var_4361)[name = tensor("op_4362")]; - tensor var_4364 = const()[name = tensor("op_4364"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268279744)))]; - tensor input_281 = mul(x = var_4362, y = var_4364)[name = tensor("input_281")]; - tensor var_4372 = const()[name = tensor("op_4372"), val = tensor([1, 1])]; - tensor var_4374 = const()[name = tensor("op_4374"), val = tensor([1, 1])]; - tensor var_4376_pad_type_0 = const()[name = tensor("op_4376_pad_type_0"), val = tensor("custom")]; - tensor var_4376_pad_0 = const()[name = tensor("op_4376_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4376 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_bias, dilations = var_4374, groups = var_1193, pad = var_4376_pad_0, pad_type = var_4376_pad_type_0, strides = var_4372, weight = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_weight, x = input_281)[name = tensor("op_4376")]; - tensor var_4377_split_sizes_0 = const()[name = tensor("op_4377_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_4377_axis_0 = const()[name = tensor("op_4377_axis_0"), val = tensor(1)]; - tensor var_4377_0, tensor var_4377_1 = split(axis = var_4377_axis_0, split_sizes = var_4377_split_sizes_0, x = var_4376)[name = tensor("op_4377")]; - tensor var_4379_mode_0 = const()[name = tensor("op_4379_mode_0"), val = tensor("EXACT")]; - tensor var_4379 = gelu(mode = var_4379_mode_0, x = var_4377_1)[name = tensor("op_4379")]; - tensor input_283 = mul(x = var_4377_0, y = var_4379)[name = tensor("input_283")]; - tensor var_4383 = const()[name = tensor("op_4383"), val = tensor([1, 1])]; - tensor var_4385 = const()[name = tensor("op_4385"), val = tensor([1, 1])]; - tensor var_4387_pad_type_0 = const()[name = tensor("op_4387_pad_type_0"), val = tensor("custom")]; - tensor var_4387_pad_0 = const()[name = tensor("op_4387_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4387 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_bias, dilations = var_4385, groups = var_1193, pad = var_4387_pad_0, pad_type = var_4387_pad_type_0, strides = var_4383, weight = down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_weight, x = input_283)[name = tensor("op_4387")]; - tensor inputs_127 = add(x = var_4387, y = inputs_125)[name = tensor("inputs_127")]; - tensor var_4397 = const()[name = tensor("op_4397"), val = tensor([1])]; - tensor channels_mean_127 = reduce_mean(axes = var_4397, keep_dims = var_1188, x = inputs_127)[name = tensor("channels_mean_127")]; - tensor zero_mean_127 = sub(x = inputs_127, y = channels_mean_127)[name = tensor("zero_mean_127")]; - tensor zero_mean_sq_127 = mul(x = zero_mean_127, y = zero_mean_127)[name = tensor("zero_mean_sq_127")]; - tensor var_4401 = const()[name = tensor("op_4401"), val = tensor([1])]; - tensor var_4402 = reduce_mean(axes = var_4401, keep_dims = var_1188, x = zero_mean_sq_127)[name = tensor("op_4402")]; - tensor var_4403 = const()[name = tensor("op_4403"), val = tensor(0x1.4f8b58p-17)]; - tensor var_4404 = add(x = var_4402, y = var_4403)[name = tensor("op_4404")]; - tensor denom_127_epsilon_0 = const()[name = tensor("denom_127_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_127 = rsqrt(epsilon = denom_127_epsilon_0, x = var_4404)[name = tensor("denom_127")]; - tensor out_127 = mul(x = zero_mean_127, y = denom_127)[name = tensor("out_127")]; - tensor var_4408 = const()[name = tensor("op_4408"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268284928)))]; - tensor var_4409 = add(x = out_127, y = var_4408)[name = tensor("op_4409")]; - tensor var_4411 = const()[name = tensor("op_4411"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268290112)))]; - tensor hidden_states_179 = mul(x = var_4409, y = var_4411)[name = tensor("hidden_states_179")]; - tensor var_4418 = const()[name = tensor("op_4418"), val = tensor([1, 1])]; - tensor var_4420 = const()[name = tensor("op_4420"), val = tensor([1, 1])]; + tensor attn_83_cast = matmul(transpose_x = attn_83_transpose_x_0, transpose_y = attn_83_transpose_y_0, x = var_4267_cast, y = var_4271_cast)[name = tensor("attn_83_cast")]; + tensor var_4275 = const()[name = tensor("op_4275"), val = tensor([2, 1280, 1, -1])]; + tensor input_279_cast = reshape(shape = var_4275, x = attn_83_cast)[name = tensor("input_279_cast")]; + tensor var_4280 = const()[name = tensor("op_4280"), val = tensor([1, 1])]; + tensor var_4282 = const()[name = tensor("op_4282"), val = tensor([1, 1])]; + tensor var_4284_pad_type_0 = const()[name = tensor("op_4284_pad_type_0"), val = tensor("custom")]; + tensor var_4284_pad_0 = const()[name = tensor("op_4284_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1406098560)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1409375424)))]; + tensor var_4284_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_4282, groups = var_31, pad = var_4284_pad_0, pad_type = var_4284_pad_type_0, strides = var_4280, weight = unet_down_blocks_2_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16, x = input_279_cast)[name = tensor("op_4284_cast")]; + tensor inputs_125_cast = add(x = var_4284_cast, y = inputs_123_cast)[name = tensor("inputs_125_cast")]; + tensor var_4288 = const()[name = tensor("op_4288"), val = tensor([1])]; + tensor channels_mean_125_cast = reduce_mean(axes = var_4288, keep_dims = var_23, x = inputs_125_cast)[name = tensor("channels_mean_125_cast")]; + tensor zero_mean_125_cast = sub(x = inputs_125_cast, y = channels_mean_125_cast)[name = tensor("zero_mean_125_cast")]; + tensor zero_mean_sq_125_cast = mul(x = zero_mean_125_cast, y = zero_mean_125_cast)[name = tensor("zero_mean_sq_125_cast")]; + tensor var_4292 = const()[name = tensor("op_4292"), val = tensor([1])]; + tensor var_4293_cast = reduce_mean(axes = var_4292, keep_dims = var_23, x = zero_mean_sq_125_cast)[name = tensor("op_4293_cast")]; + tensor var_4294_to_fp16 = const()[name = tensor("op_4294_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4295_cast = add(x = var_4293_cast, y = var_4294_to_fp16)[name = tensor("op_4295_cast")]; + tensor denom_125_epsilon_0_to_fp16 = const()[name = tensor("denom_125_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_125_cast = rsqrt(epsilon = denom_125_epsilon_0_to_fp16, x = var_4295_cast)[name = tensor("denom_125_cast")]; + tensor out_125_cast = mul(x = zero_mean_125_cast, y = denom_125_cast)[name = tensor("out_125_cast")]; + tensor var_4299_to_fp16 = const()[name = tensor("op_4299_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1409378048)))]; + tensor var_4300_cast = add(x = out_125_cast, y = var_4299_to_fp16)[name = tensor("op_4300_cast")]; + tensor var_4302_to_fp16 = const()[name = tensor("op_4302_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1409380672)))]; + tensor input_281_cast = mul(x = var_4300_cast, y = var_4302_to_fp16)[name = tensor("input_281_cast")]; + tensor var_4310 = const()[name = tensor("op_4310"), val = tensor([1, 1])]; + tensor var_4312 = const()[name = tensor("op_4312"), val = tensor([1, 1])]; + tensor var_4314_pad_type_0 = const()[name = tensor("op_4314_pad_type_0"), val = tensor("custom")]; + tensor var_4314_pad_0 = const()[name = tensor("op_4314_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1409383296)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1435597760)))]; + tensor var_4314_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_4312, groups = var_31, pad = var_4314_pad_0, pad_type = var_4314_pad_type_0, strides = var_4310, weight = unet_down_blocks_2_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16, x = input_281_cast)[name = tensor("op_4314_cast")]; + tensor var_4315_split_sizes_0 = const()[name = tensor("op_4315_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4315_axis_0 = const()[name = tensor("op_4315_axis_0"), val = tensor(1)]; + tensor var_4315_cast_0, tensor var_4315_cast_1 = split(axis = var_4315_axis_0, split_sizes = var_4315_split_sizes_0, x = var_4314_cast)[name = tensor("op_4315_cast")]; + tensor var_4317_mode_0 = const()[name = tensor("op_4317_mode_0"), val = tensor("EXACT")]; + tensor var_4317_cast = gelu(mode = var_4317_mode_0, x = var_4315_cast_1)[name = tensor("op_4317_cast")]; + tensor input_283_cast = mul(x = var_4315_cast_0, y = var_4317_cast)[name = tensor("input_283_cast")]; + tensor var_4321 = const()[name = tensor("op_4321"), val = tensor([1, 1])]; + tensor var_4323 = const()[name = tensor("op_4323"), val = tensor([1, 1])]; + tensor var_4325_pad_type_0 = const()[name = tensor("op_4325_pad_type_0"), val = tensor("custom")]; + tensor var_4325_pad_0 = const()[name = tensor("op_4325_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1435618304)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1448725568)))]; + tensor var_4325_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_4323, groups = var_31, pad = var_4325_pad_0, pad_type = var_4325_pad_type_0, strides = var_4321, weight = unet_down_blocks_2_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16, x = input_283_cast)[name = tensor("op_4325_cast")]; + tensor inputs_127_cast = add(x = var_4325_cast, y = inputs_125_cast)[name = tensor("inputs_127_cast")]; + tensor var_4335 = const()[name = tensor("op_4335"), val = tensor([1])]; + tensor channels_mean_127_cast = reduce_mean(axes = var_4335, keep_dims = var_23, x = inputs_127_cast)[name = tensor("channels_mean_127_cast")]; + tensor zero_mean_127_cast = sub(x = inputs_127_cast, y = channels_mean_127_cast)[name = tensor("zero_mean_127_cast")]; + tensor zero_mean_sq_127_cast = mul(x = zero_mean_127_cast, y = zero_mean_127_cast)[name = tensor("zero_mean_sq_127_cast")]; + tensor var_4339 = const()[name = tensor("op_4339"), val = tensor([1])]; + tensor var_4340_cast = reduce_mean(axes = var_4339, keep_dims = var_23, x = zero_mean_sq_127_cast)[name = tensor("op_4340_cast")]; + tensor var_4341_to_fp16 = const()[name = tensor("op_4341_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4342_cast = add(x = var_4340_cast, y = var_4341_to_fp16)[name = tensor("op_4342_cast")]; + tensor denom_127_epsilon_0_to_fp16 = const()[name = tensor("denom_127_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_127_cast = rsqrt(epsilon = denom_127_epsilon_0_to_fp16, x = var_4342_cast)[name = tensor("denom_127_cast")]; + tensor out_127_cast = mul(x = zero_mean_127_cast, y = denom_127_cast)[name = tensor("out_127_cast")]; + tensor var_4346_to_fp16 = const()[name = tensor("op_4346_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1448728192)))]; + tensor var_4347_cast = add(x = out_127_cast, y = var_4346_to_fp16)[name = tensor("op_4347_cast")]; + tensor var_4349_to_fp16 = const()[name = tensor("op_4349_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1448730816)))]; + tensor hidden_states_179_cast = mul(x = var_4347_cast, y = var_4349_to_fp16)[name = tensor("hidden_states_179_cast")]; + tensor var_4356 = const()[name = tensor("op_4356"), val = tensor([1, 1])]; + tensor var_4358 = const()[name = tensor("op_4358"), val = tensor([1, 1])]; tensor q_85_pad_type_0 = const()[name = tensor("q_85_pad_type_0"), val = tensor("custom")]; tensor q_85_pad_0 = const()[name = tensor("q_85_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_85 = conv(dilations = var_4420, groups = var_1193, pad = q_85_pad_0, pad_type = q_85_pad_type_0, strides = var_4418, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_q_weight, x = hidden_states_179)[name = tensor("q_85")]; - tensor var_4424 = const()[name = tensor("op_4424"), val = tensor([1, 1])]; - tensor var_4426 = const()[name = tensor("op_4426"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1448733440)))]; + tensor q_85_cast = conv(dilations = var_4358, groups = var_31, pad = q_85_pad_0, pad_type = q_85_pad_type_0, strides = var_4356, weight = unet_down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16, x = hidden_states_179_cast)[name = tensor("q_85_cast")]; + tensor var_4362 = const()[name = tensor("op_4362"), val = tensor([1, 1])]; + tensor var_4364 = const()[name = tensor("op_4364"), val = tensor([1, 1])]; tensor k_85_pad_type_0 = const()[name = tensor("k_85_pad_type_0"), val = tensor("custom")]; tensor k_85_pad_0 = const()[name = tensor("k_85_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_85 = conv(dilations = var_4426, groups = var_1193, pad = k_85_pad_0, pad_type = k_85_pad_type_0, strides = var_4424, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_k_weight, x = hidden_states_179)[name = tensor("k_85")]; - tensor var_4430 = const()[name = tensor("op_4430"), val = tensor([1, 1])]; - tensor var_4432 = const()[name = tensor("op_4432"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1452010304)))]; + tensor k_85_cast = conv(dilations = var_4364, groups = var_31, pad = k_85_pad_0, pad_type = k_85_pad_type_0, strides = var_4362, weight = unet_down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16, x = hidden_states_179_cast)[name = tensor("k_85_cast")]; + tensor var_4368 = const()[name = tensor("op_4368"), val = tensor([1, 1])]; + tensor var_4370 = const()[name = tensor("op_4370"), val = tensor([1, 1])]; tensor v_85_pad_type_0 = const()[name = tensor("v_85_pad_type_0"), val = tensor("custom")]; tensor v_85_pad_0 = const()[name = tensor("v_85_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_85 = conv(dilations = var_4432, groups = var_1193, pad = v_85_pad_0, pad_type = v_85_pad_type_0, strides = var_4430, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_v_weight, x = hidden_states_179)[name = tensor("v_85")]; - tensor var_4436 = const()[name = tensor("op_4436"), val = tensor([2, 20, 64, -1])]; - tensor var_4437 = reshape(shape = var_4436, x = q_85)[name = tensor("op_4437")]; - tensor var_4438 = const()[name = tensor("op_4438"), val = tensor([2, 20, 64, -1])]; - tensor var_4439 = reshape(shape = var_4438, x = k_85)[name = tensor("op_4439")]; - tensor var_4440 = const()[name = tensor("op_4440"), val = tensor([2, 20, 64, -1])]; - tensor var_4441 = reshape(shape = var_4440, x = v_85)[name = tensor("op_4441")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1455287168)))]; + tensor v_85_cast = conv(dilations = var_4370, groups = var_31, pad = v_85_pad_0, pad_type = v_85_pad_type_0, strides = var_4368, weight = unet_down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16, x = hidden_states_179_cast)[name = tensor("v_85_cast")]; + tensor var_4374 = const()[name = tensor("op_4374"), val = tensor([2, 20, 64, -1])]; + tensor var_4375_cast = reshape(shape = var_4374, x = q_85_cast)[name = tensor("op_4375_cast")]; + tensor var_4376 = const()[name = tensor("op_4376"), val = tensor([2, 20, 64, -1])]; + tensor var_4377_cast = reshape(shape = var_4376, x = k_85_cast)[name = tensor("op_4377_cast")]; + tensor var_4378 = const()[name = tensor("op_4378"), val = tensor([2, 20, 64, -1])]; + tensor var_4379_cast = reshape(shape = var_4378, x = v_85_cast)[name = tensor("op_4379_cast")]; tensor attn_weights_169_transpose_x_0 = const()[name = tensor("attn_weights_169_transpose_x_0"), val = tensor(true)]; tensor attn_weights_169_transpose_y_0 = const()[name = tensor("attn_weights_169_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_169 = matmul(transpose_x = attn_weights_169_transpose_x_0, transpose_y = attn_weights_169_transpose_y_0, x = var_4437, y = var_4439)[name = tensor("attn_weights_169")]; - tensor attn_weights_171 = mul(x = attn_weights_169, y = var_1184)[name = tensor("attn_weights_171")]; - tensor var_4445 = softmax(axis = var_1177, x = attn_weights_171)[name = tensor("op_4445")]; + tensor attn_weights_169_cast = matmul(transpose_x = attn_weights_169_transpose_x_0, transpose_y = attn_weights_169_transpose_y_0, x = var_4375_cast, y = var_4377_cast)[name = tensor("attn_weights_169_cast")]; + tensor attn_weights_171_cast = mul(x = attn_weights_169_cast, y = var_12_to_fp16)[name = tensor("attn_weights_171_cast")]; + tensor var_4383_cast = softmax(axis = var_18, x = attn_weights_171_cast)[name = tensor("op_4383_cast")]; tensor attn_85_transpose_x_0 = const()[name = tensor("attn_85_transpose_x_0"), val = tensor(false)]; tensor attn_85_transpose_y_0 = const()[name = tensor("attn_85_transpose_y_0"), val = tensor(true)]; - tensor attn_85 = matmul(transpose_x = attn_85_transpose_x_0, transpose_y = attn_85_transpose_y_0, x = var_4441, y = var_4445)[name = tensor("attn_85")]; - tensor var_4449 = const()[name = tensor("op_4449"), val = tensor([2, 1280, 1, -1])]; - tensor input_285 = reshape(shape = var_4449, x = attn_85)[name = tensor("input_285")]; - tensor var_4454 = const()[name = tensor("op_4454"), val = tensor([1, 1])]; - tensor var_4456 = const()[name = tensor("op_4456"), val = tensor([1, 1])]; - tensor var_4458_pad_type_0 = const()[name = tensor("op_4458_pad_type_0"), val = tensor("custom")]; - tensor var_4458_pad_0 = const()[name = tensor("op_4458_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4458 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_bias, dilations = var_4456, groups = var_1193, pad = var_4458_pad_0, pad_type = var_4458_pad_type_0, strides = var_4454, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_weight, x = input_285)[name = tensor("op_4458")]; - tensor inputs_129 = add(x = var_4458, y = inputs_127)[name = tensor("inputs_129")]; - tensor var_4462 = const()[name = tensor("op_4462"), val = tensor([1])]; - tensor channels_mean_129 = reduce_mean(axes = var_4462, keep_dims = var_1188, x = inputs_129)[name = tensor("channels_mean_129")]; - tensor zero_mean_129 = sub(x = inputs_129, y = channels_mean_129)[name = tensor("zero_mean_129")]; - tensor zero_mean_sq_129 = mul(x = zero_mean_129, y = zero_mean_129)[name = tensor("zero_mean_sq_129")]; - tensor var_4466 = const()[name = tensor("op_4466"), val = tensor([1])]; - tensor var_4467 = reduce_mean(axes = var_4466, keep_dims = var_1188, x = zero_mean_sq_129)[name = tensor("op_4467")]; - tensor var_4468 = const()[name = tensor("op_4468"), val = tensor(0x1.4f8b58p-17)]; - tensor var_4469 = add(x = var_4467, y = var_4468)[name = tensor("op_4469")]; - tensor denom_129_epsilon_0 = const()[name = tensor("denom_129_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_129 = rsqrt(epsilon = denom_129_epsilon_0, x = var_4469)[name = tensor("denom_129")]; - tensor out_129 = mul(x = zero_mean_129, y = denom_129)[name = tensor("out_129")]; - tensor var_4473 = const()[name = tensor("op_4473"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268295296)))]; - tensor var_4474 = add(x = out_129, y = var_4473)[name = tensor("op_4474")]; - tensor var_4476 = const()[name = tensor("op_4476"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268300480)))]; - tensor hidden_states_181 = mul(x = var_4474, y = var_4476)[name = tensor("hidden_states_181")]; - tensor var_4483 = const()[name = tensor("op_4483"), val = tensor([1, 1])]; - tensor var_4485 = const()[name = tensor("op_4485"), val = tensor([1, 1])]; + tensor attn_85_cast = matmul(transpose_x = attn_85_transpose_x_0, transpose_y = attn_85_transpose_y_0, x = var_4379_cast, y = var_4383_cast)[name = tensor("attn_85_cast")]; + tensor var_4387 = const()[name = tensor("op_4387"), val = tensor([2, 1280, 1, -1])]; + tensor input_285_cast = reshape(shape = var_4387, x = attn_85_cast)[name = tensor("input_285_cast")]; + tensor var_4392 = const()[name = tensor("op_4392"), val = tensor([1, 1])]; + tensor var_4394 = const()[name = tensor("op_4394"), val = tensor([1, 1])]; + tensor var_4396_pad_type_0 = const()[name = tensor("op_4396_pad_type_0"), val = tensor("custom")]; + tensor var_4396_pad_0 = const()[name = tensor("op_4396_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1458564032)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1461840896)))]; + tensor var_4396_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_4394, groups = var_31, pad = var_4396_pad_0, pad_type = var_4396_pad_type_0, strides = var_4392, weight = unet_down_blocks_2_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16, x = input_285_cast)[name = tensor("op_4396_cast")]; + tensor inputs_129_cast = add(x = var_4396_cast, y = inputs_127_cast)[name = tensor("inputs_129_cast")]; + tensor var_4400 = const()[name = tensor("op_4400"), val = tensor([1])]; + tensor channels_mean_129_cast = reduce_mean(axes = var_4400, keep_dims = var_23, x = inputs_129_cast)[name = tensor("channels_mean_129_cast")]; + tensor zero_mean_129_cast = sub(x = inputs_129_cast, y = channels_mean_129_cast)[name = tensor("zero_mean_129_cast")]; + tensor zero_mean_sq_129_cast = mul(x = zero_mean_129_cast, y = zero_mean_129_cast)[name = tensor("zero_mean_sq_129_cast")]; + tensor var_4404 = const()[name = tensor("op_4404"), val = tensor([1])]; + tensor var_4405_cast = reduce_mean(axes = var_4404, keep_dims = var_23, x = zero_mean_sq_129_cast)[name = tensor("op_4405_cast")]; + tensor var_4406_to_fp16 = const()[name = tensor("op_4406_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4407_cast = add(x = var_4405_cast, y = var_4406_to_fp16)[name = tensor("op_4407_cast")]; + tensor denom_129_epsilon_0_to_fp16 = const()[name = tensor("denom_129_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_129_cast = rsqrt(epsilon = denom_129_epsilon_0_to_fp16, x = var_4407_cast)[name = tensor("denom_129_cast")]; + tensor out_129_cast = mul(x = zero_mean_129_cast, y = denom_129_cast)[name = tensor("out_129_cast")]; + tensor var_4411_to_fp16 = const()[name = tensor("op_4411_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1461843520)))]; + tensor var_4412_cast = add(x = out_129_cast, y = var_4411_to_fp16)[name = tensor("op_4412_cast")]; + tensor var_4414_to_fp16 = const()[name = tensor("op_4414_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1461846144)))]; + tensor hidden_states_181_cast = mul(x = var_4412_cast, y = var_4414_to_fp16)[name = tensor("hidden_states_181_cast")]; + tensor var_4421 = const()[name = tensor("op_4421"), val = tensor([1, 1])]; + tensor var_4423 = const()[name = tensor("op_4423"), val = tensor([1, 1])]; tensor q_87_pad_type_0 = const()[name = tensor("q_87_pad_type_0"), val = tensor("custom")]; tensor q_87_pad_0 = const()[name = tensor("q_87_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_87 = conv(dilations = var_4485, groups = var_1193, pad = q_87_pad_0, pad_type = q_87_pad_type_0, strides = var_4483, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_q_weight, x = hidden_states_181)[name = tensor("q_87")]; - tensor var_4489 = const()[name = tensor("op_4489"), val = tensor([1, 1])]; - tensor var_4491 = const()[name = tensor("op_4491"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1461848768)))]; + tensor q_87_cast = conv(dilations = var_4423, groups = var_31, pad = q_87_pad_0, pad_type = q_87_pad_type_0, strides = var_4421, weight = unet_down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16, x = hidden_states_181_cast)[name = tensor("q_87_cast")]; + tensor var_4427 = const()[name = tensor("op_4427"), val = tensor([1, 1])]; + tensor var_4429 = const()[name = tensor("op_4429"), val = tensor([1, 1])]; tensor k_87_pad_type_0 = const()[name = tensor("k_87_pad_type_0"), val = tensor("custom")]; tensor k_87_pad_0 = const()[name = tensor("k_87_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_87 = conv(dilations = var_4491, groups = var_1193, pad = k_87_pad_0, pad_type = k_87_pad_type_0, strides = var_4489, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_87")]; - tensor var_4495 = const()[name = tensor("op_4495"), val = tensor([1, 1])]; - tensor var_4497 = const()[name = tensor("op_4497"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1465125632)))]; + tensor k_87_cast = conv(dilations = var_4429, groups = var_31, pad = k_87_pad_0, pad_type = k_87_pad_type_0, strides = var_4427, weight = unet_down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_87_cast")]; + tensor var_4433 = const()[name = tensor("op_4433"), val = tensor([1, 1])]; + tensor var_4435 = const()[name = tensor("op_4435"), val = tensor([1, 1])]; tensor v_87_pad_type_0 = const()[name = tensor("v_87_pad_type_0"), val = tensor("custom")]; tensor v_87_pad_0 = const()[name = tensor("v_87_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_87 = conv(dilations = var_4497, groups = var_1193, pad = v_87_pad_0, pad_type = v_87_pad_type_0, strides = var_4495, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_87")]; - tensor var_4501 = const()[name = tensor("op_4501"), val = tensor([2, 20, 64, -1])]; - tensor var_4502 = reshape(shape = var_4501, x = q_87)[name = tensor("op_4502")]; - tensor var_4503 = const()[name = tensor("op_4503"), val = tensor([2, 20, 64, -1])]; - tensor var_4504 = reshape(shape = var_4503, x = k_87)[name = tensor("op_4504")]; - tensor var_4505 = const()[name = tensor("op_4505"), val = tensor([2, 20, 64, -1])]; - tensor var_4506 = reshape(shape = var_4505, x = v_87)[name = tensor("op_4506")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1470368576)))]; + tensor v_87_cast = conv(dilations = var_4435, groups = var_31, pad = v_87_pad_0, pad_type = v_87_pad_type_0, strides = var_4433, weight = unet_down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_87_cast")]; + tensor var_4439 = const()[name = tensor("op_4439"), val = tensor([2, 20, 64, -1])]; + tensor var_4440_cast = reshape(shape = var_4439, x = q_87_cast)[name = tensor("op_4440_cast")]; + tensor var_4441 = const()[name = tensor("op_4441"), val = tensor([2, 20, 64, -1])]; + tensor var_4442_cast = reshape(shape = var_4441, x = k_87_cast)[name = tensor("op_4442_cast")]; + tensor var_4443 = const()[name = tensor("op_4443"), val = tensor([2, 20, 64, -1])]; + tensor var_4444_cast = reshape(shape = var_4443, x = v_87_cast)[name = tensor("op_4444_cast")]; tensor attn_weights_173_transpose_x_0 = const()[name = tensor("attn_weights_173_transpose_x_0"), val = tensor(true)]; tensor attn_weights_173_transpose_y_0 = const()[name = tensor("attn_weights_173_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_173 = matmul(transpose_x = attn_weights_173_transpose_x_0, transpose_y = attn_weights_173_transpose_y_0, x = var_4502, y = var_4504)[name = tensor("attn_weights_173")]; - tensor attn_weights_175 = mul(x = attn_weights_173, y = var_1184)[name = tensor("attn_weights_175")]; - tensor var_4510 = softmax(axis = var_1177, x = attn_weights_175)[name = tensor("op_4510")]; + tensor attn_weights_173_cast = matmul(transpose_x = attn_weights_173_transpose_x_0, transpose_y = attn_weights_173_transpose_y_0, x = var_4440_cast, y = var_4442_cast)[name = tensor("attn_weights_173_cast")]; + tensor attn_weights_175_cast = mul(x = attn_weights_173_cast, y = var_12_to_fp16)[name = tensor("attn_weights_175_cast")]; + tensor var_4448_cast = softmax(axis = var_18, x = attn_weights_175_cast)[name = tensor("op_4448_cast")]; tensor attn_87_transpose_x_0 = const()[name = tensor("attn_87_transpose_x_0"), val = tensor(false)]; tensor attn_87_transpose_y_0 = const()[name = tensor("attn_87_transpose_y_0"), val = tensor(true)]; - tensor attn_87 = matmul(transpose_x = attn_87_transpose_x_0, transpose_y = attn_87_transpose_y_0, x = var_4506, y = var_4510)[name = tensor("attn_87")]; - tensor var_4514 = const()[name = tensor("op_4514"), val = tensor([2, 1280, 1, -1])]; - tensor input_287 = reshape(shape = var_4514, x = attn_87)[name = tensor("input_287")]; - tensor var_4519 = const()[name = tensor("op_4519"), val = tensor([1, 1])]; - tensor var_4521 = const()[name = tensor("op_4521"), val = tensor([1, 1])]; - tensor var_4523_pad_type_0 = const()[name = tensor("op_4523_pad_type_0"), val = tensor("custom")]; - tensor var_4523_pad_0 = const()[name = tensor("op_4523_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4523 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_bias, dilations = var_4521, groups = var_1193, pad = var_4523_pad_0, pad_type = var_4523_pad_type_0, strides = var_4519, weight = down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_weight, x = input_287)[name = tensor("op_4523")]; - tensor inputs_131 = add(x = var_4523, y = inputs_129)[name = tensor("inputs_131")]; - tensor var_4527 = const()[name = tensor("op_4527"), val = tensor([1])]; - tensor channels_mean_131 = reduce_mean(axes = var_4527, keep_dims = var_1188, x = inputs_131)[name = tensor("channels_mean_131")]; - tensor zero_mean_131 = sub(x = inputs_131, y = channels_mean_131)[name = tensor("zero_mean_131")]; - tensor zero_mean_sq_131 = mul(x = zero_mean_131, y = zero_mean_131)[name = tensor("zero_mean_sq_131")]; - tensor var_4531 = const()[name = tensor("op_4531"), val = tensor([1])]; - tensor var_4532 = reduce_mean(axes = var_4531, keep_dims = var_1188, x = zero_mean_sq_131)[name = tensor("op_4532")]; - tensor var_4533 = const()[name = tensor("op_4533"), val = tensor(0x1.4f8b58p-17)]; - tensor var_4534 = add(x = var_4532, y = var_4533)[name = tensor("op_4534")]; - tensor denom_131_epsilon_0 = const()[name = tensor("denom_131_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_131 = rsqrt(epsilon = denom_131_epsilon_0, x = var_4534)[name = tensor("denom_131")]; - tensor out_131 = mul(x = zero_mean_131, y = denom_131)[name = tensor("out_131")]; - tensor var_4538 = const()[name = tensor("op_4538"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268305664)))]; - tensor var_4539 = add(x = out_131, y = var_4538)[name = tensor("op_4539")]; - tensor var_4541 = const()[name = tensor("op_4541"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268310848)))]; - tensor input_289 = mul(x = var_4539, y = var_4541)[name = tensor("input_289")]; - tensor var_4549 = const()[name = tensor("op_4549"), val = tensor([1, 1])]; - tensor var_4551 = const()[name = tensor("op_4551"), val = tensor([1, 1])]; - tensor var_4553_pad_type_0 = const()[name = tensor("op_4553_pad_type_0"), val = tensor("custom")]; - tensor var_4553_pad_0 = const()[name = tensor("op_4553_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4553 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_bias, dilations = var_4551, groups = var_1193, pad = var_4553_pad_0, pad_type = var_4553_pad_type_0, strides = var_4549, weight = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_weight, x = input_289)[name = tensor("op_4553")]; - tensor var_4554_split_sizes_0 = const()[name = tensor("op_4554_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_4554_axis_0 = const()[name = tensor("op_4554_axis_0"), val = tensor(1)]; - tensor var_4554_0, tensor var_4554_1 = split(axis = var_4554_axis_0, split_sizes = var_4554_split_sizes_0, x = var_4553)[name = tensor("op_4554")]; - tensor var_4556_mode_0 = const()[name = tensor("op_4556_mode_0"), val = tensor("EXACT")]; - tensor var_4556 = gelu(mode = var_4556_mode_0, x = var_4554_1)[name = tensor("op_4556")]; - tensor input_291 = mul(x = var_4554_0, y = var_4556)[name = tensor("input_291")]; - tensor var_4560 = const()[name = tensor("op_4560"), val = tensor([1, 1])]; - tensor var_4562 = const()[name = tensor("op_4562"), val = tensor([1, 1])]; - tensor var_4564_pad_type_0 = const()[name = tensor("op_4564_pad_type_0"), val = tensor("custom")]; - tensor var_4564_pad_0 = const()[name = tensor("op_4564_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4564 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_bias, dilations = var_4562, groups = var_1193, pad = var_4564_pad_0, pad_type = var_4564_pad_type_0, strides = var_4560, weight = down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_weight, x = input_291)[name = tensor("op_4564")]; - tensor inputs_133 = add(x = var_4564, y = inputs_131)[name = tensor("inputs_133")]; - tensor var_4574 = const()[name = tensor("op_4574"), val = tensor([1])]; - tensor channels_mean_133 = reduce_mean(axes = var_4574, keep_dims = var_1188, x = inputs_133)[name = tensor("channels_mean_133")]; - tensor zero_mean_133 = sub(x = inputs_133, y = channels_mean_133)[name = tensor("zero_mean_133")]; - tensor zero_mean_sq_133 = mul(x = zero_mean_133, y = zero_mean_133)[name = tensor("zero_mean_sq_133")]; - tensor var_4578 = const()[name = tensor("op_4578"), val = tensor([1])]; - tensor var_4579 = reduce_mean(axes = var_4578, keep_dims = var_1188, x = zero_mean_sq_133)[name = tensor("op_4579")]; - tensor var_4580 = const()[name = tensor("op_4580"), val = tensor(0x1.4f8b58p-17)]; - tensor var_4581 = add(x = var_4579, y = var_4580)[name = tensor("op_4581")]; - tensor denom_133_epsilon_0 = const()[name = tensor("denom_133_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_133 = rsqrt(epsilon = denom_133_epsilon_0, x = var_4581)[name = tensor("denom_133")]; - tensor out_133 = mul(x = zero_mean_133, y = denom_133)[name = tensor("out_133")]; - tensor var_4585 = const()[name = tensor("op_4585"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268316032)))]; - tensor var_4586 = add(x = out_133, y = var_4585)[name = tensor("op_4586")]; - tensor var_4588 = const()[name = tensor("op_4588"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268321216)))]; - tensor hidden_states_185 = mul(x = var_4586, y = var_4588)[name = tensor("hidden_states_185")]; - tensor var_4595 = const()[name = tensor("op_4595"), val = tensor([1, 1])]; - tensor var_4597 = const()[name = tensor("op_4597"), val = tensor([1, 1])]; + tensor attn_87_cast = matmul(transpose_x = attn_87_transpose_x_0, transpose_y = attn_87_transpose_y_0, x = var_4444_cast, y = var_4448_cast)[name = tensor("attn_87_cast")]; + tensor var_4452 = const()[name = tensor("op_4452"), val = tensor([2, 1280, 1, -1])]; + tensor input_287_cast = reshape(shape = var_4452, x = attn_87_cast)[name = tensor("input_287_cast")]; + tensor var_4457 = const()[name = tensor("op_4457"), val = tensor([1, 1])]; + tensor var_4459 = const()[name = tensor("op_4459"), val = tensor([1, 1])]; + tensor var_4461_pad_type_0 = const()[name = tensor("op_4461_pad_type_0"), val = tensor("custom")]; + tensor var_4461_pad_0 = const()[name = tensor("op_4461_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1475611520)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1478888384)))]; + tensor var_4461_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_4459, groups = var_31, pad = var_4461_pad_0, pad_type = var_4461_pad_type_0, strides = var_4457, weight = unet_down_blocks_2_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16, x = input_287_cast)[name = tensor("op_4461_cast")]; + tensor inputs_131_cast = add(x = var_4461_cast, y = inputs_129_cast)[name = tensor("inputs_131_cast")]; + tensor var_4465 = const()[name = tensor("op_4465"), val = tensor([1])]; + tensor channels_mean_131_cast = reduce_mean(axes = var_4465, keep_dims = var_23, x = inputs_131_cast)[name = tensor("channels_mean_131_cast")]; + tensor zero_mean_131_cast = sub(x = inputs_131_cast, y = channels_mean_131_cast)[name = tensor("zero_mean_131_cast")]; + tensor zero_mean_sq_131_cast = mul(x = zero_mean_131_cast, y = zero_mean_131_cast)[name = tensor("zero_mean_sq_131_cast")]; + tensor var_4469 = const()[name = tensor("op_4469"), val = tensor([1])]; + tensor var_4470_cast = reduce_mean(axes = var_4469, keep_dims = var_23, x = zero_mean_sq_131_cast)[name = tensor("op_4470_cast")]; + tensor var_4471_to_fp16 = const()[name = tensor("op_4471_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4472_cast = add(x = var_4470_cast, y = var_4471_to_fp16)[name = tensor("op_4472_cast")]; + tensor denom_131_epsilon_0_to_fp16 = const()[name = tensor("denom_131_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_131_cast = rsqrt(epsilon = denom_131_epsilon_0_to_fp16, x = var_4472_cast)[name = tensor("denom_131_cast")]; + tensor out_131_cast = mul(x = zero_mean_131_cast, y = denom_131_cast)[name = tensor("out_131_cast")]; + tensor var_4476_to_fp16 = const()[name = tensor("op_4476_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1478891008)))]; + tensor var_4477_cast = add(x = out_131_cast, y = var_4476_to_fp16)[name = tensor("op_4477_cast")]; + tensor var_4479_to_fp16 = const()[name = tensor("op_4479_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1478893632)))]; + tensor input_289_cast = mul(x = var_4477_cast, y = var_4479_to_fp16)[name = tensor("input_289_cast")]; + tensor var_4487 = const()[name = tensor("op_4487"), val = tensor([1, 1])]; + tensor var_4489 = const()[name = tensor("op_4489"), val = tensor([1, 1])]; + tensor var_4491_pad_type_0 = const()[name = tensor("op_4491_pad_type_0"), val = tensor("custom")]; + tensor var_4491_pad_0 = const()[name = tensor("op_4491_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1478896256)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1505110720)))]; + tensor var_4491_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_4489, groups = var_31, pad = var_4491_pad_0, pad_type = var_4491_pad_type_0, strides = var_4487, weight = unet_down_blocks_2_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16, x = input_289_cast)[name = tensor("op_4491_cast")]; + tensor var_4492_split_sizes_0 = const()[name = tensor("op_4492_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4492_axis_0 = const()[name = tensor("op_4492_axis_0"), val = tensor(1)]; + tensor var_4492_cast_0, tensor var_4492_cast_1 = split(axis = var_4492_axis_0, split_sizes = var_4492_split_sizes_0, x = var_4491_cast)[name = tensor("op_4492_cast")]; + tensor var_4494_mode_0 = const()[name = tensor("op_4494_mode_0"), val = tensor("EXACT")]; + tensor var_4494_cast = gelu(mode = var_4494_mode_0, x = var_4492_cast_1)[name = tensor("op_4494_cast")]; + tensor input_291_cast = mul(x = var_4492_cast_0, y = var_4494_cast)[name = tensor("input_291_cast")]; + tensor var_4498 = const()[name = tensor("op_4498"), val = tensor([1, 1])]; + tensor var_4500 = const()[name = tensor("op_4500"), val = tensor([1, 1])]; + tensor var_4502_pad_type_0 = const()[name = tensor("op_4502_pad_type_0"), val = tensor("custom")]; + tensor var_4502_pad_0 = const()[name = tensor("op_4502_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1505131264)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1518238528)))]; + tensor var_4502_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_4500, groups = var_31, pad = var_4502_pad_0, pad_type = var_4502_pad_type_0, strides = var_4498, weight = unet_down_blocks_2_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16, x = input_291_cast)[name = tensor("op_4502_cast")]; + tensor inputs_133_cast = add(x = var_4502_cast, y = inputs_131_cast)[name = tensor("inputs_133_cast")]; + tensor var_4512 = const()[name = tensor("op_4512"), val = tensor([1])]; + tensor channels_mean_133_cast = reduce_mean(axes = var_4512, keep_dims = var_23, x = inputs_133_cast)[name = tensor("channels_mean_133_cast")]; + tensor zero_mean_133_cast = sub(x = inputs_133_cast, y = channels_mean_133_cast)[name = tensor("zero_mean_133_cast")]; + tensor zero_mean_sq_133_cast = mul(x = zero_mean_133_cast, y = zero_mean_133_cast)[name = tensor("zero_mean_sq_133_cast")]; + tensor var_4516 = const()[name = tensor("op_4516"), val = tensor([1])]; + tensor var_4517_cast = reduce_mean(axes = var_4516, keep_dims = var_23, x = zero_mean_sq_133_cast)[name = tensor("op_4517_cast")]; + tensor var_4518_to_fp16 = const()[name = tensor("op_4518_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4519_cast = add(x = var_4517_cast, y = var_4518_to_fp16)[name = tensor("op_4519_cast")]; + tensor denom_133_epsilon_0_to_fp16 = const()[name = tensor("denom_133_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_133_cast = rsqrt(epsilon = denom_133_epsilon_0_to_fp16, x = var_4519_cast)[name = tensor("denom_133_cast")]; + tensor out_133_cast = mul(x = zero_mean_133_cast, y = denom_133_cast)[name = tensor("out_133_cast")]; + tensor var_4523_to_fp16 = const()[name = tensor("op_4523_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1518241152)))]; + tensor var_4524_cast = add(x = out_133_cast, y = var_4523_to_fp16)[name = tensor("op_4524_cast")]; + tensor var_4526_to_fp16 = const()[name = tensor("op_4526_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1518243776)))]; + tensor hidden_states_185_cast = mul(x = var_4524_cast, y = var_4526_to_fp16)[name = tensor("hidden_states_185_cast")]; + tensor var_4533 = const()[name = tensor("op_4533"), val = tensor([1, 1])]; + tensor var_4535 = const()[name = tensor("op_4535"), val = tensor([1, 1])]; tensor q_89_pad_type_0 = const()[name = tensor("q_89_pad_type_0"), val = tensor("custom")]; tensor q_89_pad_0 = const()[name = tensor("q_89_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_89 = conv(dilations = var_4597, groups = var_1193, pad = q_89_pad_0, pad_type = q_89_pad_type_0, strides = var_4595, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_q_weight, x = hidden_states_185)[name = tensor("q_89")]; - tensor var_4601 = const()[name = tensor("op_4601"), val = tensor([1, 1])]; - tensor var_4603 = const()[name = tensor("op_4603"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1518246400)))]; + tensor q_89_cast = conv(dilations = var_4535, groups = var_31, pad = q_89_pad_0, pad_type = q_89_pad_type_0, strides = var_4533, weight = unet_down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16, x = hidden_states_185_cast)[name = tensor("q_89_cast")]; + tensor var_4539 = const()[name = tensor("op_4539"), val = tensor([1, 1])]; + tensor var_4541 = const()[name = tensor("op_4541"), val = tensor([1, 1])]; tensor k_89_pad_type_0 = const()[name = tensor("k_89_pad_type_0"), val = tensor("custom")]; tensor k_89_pad_0 = const()[name = tensor("k_89_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_89 = conv(dilations = var_4603, groups = var_1193, pad = k_89_pad_0, pad_type = k_89_pad_type_0, strides = var_4601, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_k_weight, x = hidden_states_185)[name = tensor("k_89")]; - tensor var_4607 = const()[name = tensor("op_4607"), val = tensor([1, 1])]; - tensor var_4609 = const()[name = tensor("op_4609"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1521523264)))]; + tensor k_89_cast = conv(dilations = var_4541, groups = var_31, pad = k_89_pad_0, pad_type = k_89_pad_type_0, strides = var_4539, weight = unet_down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16, x = hidden_states_185_cast)[name = tensor("k_89_cast")]; + tensor var_4545 = const()[name = tensor("op_4545"), val = tensor([1, 1])]; + tensor var_4547 = const()[name = tensor("op_4547"), val = tensor([1, 1])]; tensor v_89_pad_type_0 = const()[name = tensor("v_89_pad_type_0"), val = tensor("custom")]; tensor v_89_pad_0 = const()[name = tensor("v_89_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_89 = conv(dilations = var_4609, groups = var_1193, pad = v_89_pad_0, pad_type = v_89_pad_type_0, strides = var_4607, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_v_weight, x = hidden_states_185)[name = tensor("v_89")]; - tensor var_4613 = const()[name = tensor("op_4613"), val = tensor([2, 20, 64, -1])]; - tensor var_4614 = reshape(shape = var_4613, x = q_89)[name = tensor("op_4614")]; - tensor var_4615 = const()[name = tensor("op_4615"), val = tensor([2, 20, 64, -1])]; - tensor var_4616 = reshape(shape = var_4615, x = k_89)[name = tensor("op_4616")]; - tensor var_4617 = const()[name = tensor("op_4617"), val = tensor([2, 20, 64, -1])]; - tensor var_4618 = reshape(shape = var_4617, x = v_89)[name = tensor("op_4618")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1524800128)))]; + tensor v_89_cast = conv(dilations = var_4547, groups = var_31, pad = v_89_pad_0, pad_type = v_89_pad_type_0, strides = var_4545, weight = unet_down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16, x = hidden_states_185_cast)[name = tensor("v_89_cast")]; + tensor var_4551 = const()[name = tensor("op_4551"), val = tensor([2, 20, 64, -1])]; + tensor var_4552_cast = reshape(shape = var_4551, x = q_89_cast)[name = tensor("op_4552_cast")]; + tensor var_4553 = const()[name = tensor("op_4553"), val = tensor([2, 20, 64, -1])]; + tensor var_4554_cast = reshape(shape = var_4553, x = k_89_cast)[name = tensor("op_4554_cast")]; + tensor var_4555 = const()[name = tensor("op_4555"), val = tensor([2, 20, 64, -1])]; + tensor var_4556_cast = reshape(shape = var_4555, x = v_89_cast)[name = tensor("op_4556_cast")]; tensor attn_weights_177_transpose_x_0 = const()[name = tensor("attn_weights_177_transpose_x_0"), val = tensor(true)]; tensor attn_weights_177_transpose_y_0 = const()[name = tensor("attn_weights_177_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_177 = matmul(transpose_x = attn_weights_177_transpose_x_0, transpose_y = attn_weights_177_transpose_y_0, x = var_4614, y = var_4616)[name = tensor("attn_weights_177")]; - tensor attn_weights_179 = mul(x = attn_weights_177, y = var_1184)[name = tensor("attn_weights_179")]; - tensor var_4622 = softmax(axis = var_1177, x = attn_weights_179)[name = tensor("op_4622")]; + tensor attn_weights_177_cast = matmul(transpose_x = attn_weights_177_transpose_x_0, transpose_y = attn_weights_177_transpose_y_0, x = var_4552_cast, y = var_4554_cast)[name = tensor("attn_weights_177_cast")]; + tensor attn_weights_179_cast = mul(x = attn_weights_177_cast, y = var_12_to_fp16)[name = tensor("attn_weights_179_cast")]; + tensor var_4560_cast = softmax(axis = var_18, x = attn_weights_179_cast)[name = tensor("op_4560_cast")]; tensor attn_89_transpose_x_0 = const()[name = tensor("attn_89_transpose_x_0"), val = tensor(false)]; tensor attn_89_transpose_y_0 = const()[name = tensor("attn_89_transpose_y_0"), val = tensor(true)]; - tensor attn_89 = matmul(transpose_x = attn_89_transpose_x_0, transpose_y = attn_89_transpose_y_0, x = var_4618, y = var_4622)[name = tensor("attn_89")]; - tensor var_4626 = const()[name = tensor("op_4626"), val = tensor([2, 1280, 1, -1])]; - tensor input_293 = reshape(shape = var_4626, x = attn_89)[name = tensor("input_293")]; - tensor var_4631 = const()[name = tensor("op_4631"), val = tensor([1, 1])]; - tensor var_4633 = const()[name = tensor("op_4633"), val = tensor([1, 1])]; - tensor var_4635_pad_type_0 = const()[name = tensor("op_4635_pad_type_0"), val = tensor("custom")]; - tensor var_4635_pad_0 = const()[name = tensor("op_4635_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4635 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_bias, dilations = var_4633, groups = var_1193, pad = var_4635_pad_0, pad_type = var_4635_pad_type_0, strides = var_4631, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_weight, x = input_293)[name = tensor("op_4635")]; - tensor inputs_135 = add(x = var_4635, y = inputs_133)[name = tensor("inputs_135")]; - tensor var_4639 = const()[name = tensor("op_4639"), val = tensor([1])]; - tensor channels_mean_135 = reduce_mean(axes = var_4639, keep_dims = var_1188, x = inputs_135)[name = tensor("channels_mean_135")]; - tensor zero_mean_135 = sub(x = inputs_135, y = channels_mean_135)[name = tensor("zero_mean_135")]; - tensor zero_mean_sq_135 = mul(x = zero_mean_135, y = zero_mean_135)[name = tensor("zero_mean_sq_135")]; - tensor var_4643 = const()[name = tensor("op_4643"), val = tensor([1])]; - tensor var_4644 = reduce_mean(axes = var_4643, keep_dims = var_1188, x = zero_mean_sq_135)[name = tensor("op_4644")]; - tensor var_4645 = const()[name = tensor("op_4645"), val = tensor(0x1.4f8b58p-17)]; - tensor var_4646 = add(x = var_4644, y = var_4645)[name = tensor("op_4646")]; - tensor denom_135_epsilon_0 = const()[name = tensor("denom_135_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_135 = rsqrt(epsilon = denom_135_epsilon_0, x = var_4646)[name = tensor("denom_135")]; - tensor out_135 = mul(x = zero_mean_135, y = denom_135)[name = tensor("out_135")]; - tensor var_4650 = const()[name = tensor("op_4650"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268326400)))]; - tensor var_4651 = add(x = out_135, y = var_4650)[name = tensor("op_4651")]; - tensor var_4653 = const()[name = tensor("op_4653"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268331584)))]; - tensor hidden_states_187 = mul(x = var_4651, y = var_4653)[name = tensor("hidden_states_187")]; - tensor var_4660 = const()[name = tensor("op_4660"), val = tensor([1, 1])]; - tensor var_4662 = const()[name = tensor("op_4662"), val = tensor([1, 1])]; + tensor attn_89_cast = matmul(transpose_x = attn_89_transpose_x_0, transpose_y = attn_89_transpose_y_0, x = var_4556_cast, y = var_4560_cast)[name = tensor("attn_89_cast")]; + tensor var_4564 = const()[name = tensor("op_4564"), val = tensor([2, 1280, 1, -1])]; + tensor input_293_cast = reshape(shape = var_4564, x = attn_89_cast)[name = tensor("input_293_cast")]; + tensor var_4569 = const()[name = tensor("op_4569"), val = tensor([1, 1])]; + tensor var_4571 = const()[name = tensor("op_4571"), val = tensor([1, 1])]; + tensor var_4573_pad_type_0 = const()[name = tensor("op_4573_pad_type_0"), val = tensor("custom")]; + tensor var_4573_pad_0 = const()[name = tensor("op_4573_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1528076992)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1531353856)))]; + tensor var_4573_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_4571, groups = var_31, pad = var_4573_pad_0, pad_type = var_4573_pad_type_0, strides = var_4569, weight = unet_down_blocks_2_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16, x = input_293_cast)[name = tensor("op_4573_cast")]; + tensor inputs_135_cast = add(x = var_4573_cast, y = inputs_133_cast)[name = tensor("inputs_135_cast")]; + tensor var_4577 = const()[name = tensor("op_4577"), val = tensor([1])]; + tensor channels_mean_135_cast = reduce_mean(axes = var_4577, keep_dims = var_23, x = inputs_135_cast)[name = tensor("channels_mean_135_cast")]; + tensor zero_mean_135_cast = sub(x = inputs_135_cast, y = channels_mean_135_cast)[name = tensor("zero_mean_135_cast")]; + tensor zero_mean_sq_135_cast = mul(x = zero_mean_135_cast, y = zero_mean_135_cast)[name = tensor("zero_mean_sq_135_cast")]; + tensor var_4581 = const()[name = tensor("op_4581"), val = tensor([1])]; + tensor var_4582_cast = reduce_mean(axes = var_4581, keep_dims = var_23, x = zero_mean_sq_135_cast)[name = tensor("op_4582_cast")]; + tensor var_4583_to_fp16 = const()[name = tensor("op_4583_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4584_cast = add(x = var_4582_cast, y = var_4583_to_fp16)[name = tensor("op_4584_cast")]; + tensor denom_135_epsilon_0_to_fp16 = const()[name = tensor("denom_135_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_135_cast = rsqrt(epsilon = denom_135_epsilon_0_to_fp16, x = var_4584_cast)[name = tensor("denom_135_cast")]; + tensor out_135_cast = mul(x = zero_mean_135_cast, y = denom_135_cast)[name = tensor("out_135_cast")]; + tensor var_4588_to_fp16 = const()[name = tensor("op_4588_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1531356480)))]; + tensor var_4589_cast = add(x = out_135_cast, y = var_4588_to_fp16)[name = tensor("op_4589_cast")]; + tensor var_4591_to_fp16 = const()[name = tensor("op_4591_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1531359104)))]; + tensor hidden_states_187_cast = mul(x = var_4589_cast, y = var_4591_to_fp16)[name = tensor("hidden_states_187_cast")]; + tensor var_4598 = const()[name = tensor("op_4598"), val = tensor([1, 1])]; + tensor var_4600 = const()[name = tensor("op_4600"), val = tensor([1, 1])]; tensor q_91_pad_type_0 = const()[name = tensor("q_91_pad_type_0"), val = tensor("custom")]; tensor q_91_pad_0 = const()[name = tensor("q_91_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_91 = conv(dilations = var_4662, groups = var_1193, pad = q_91_pad_0, pad_type = q_91_pad_type_0, strides = var_4660, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_q_weight, x = hidden_states_187)[name = tensor("q_91")]; - tensor var_4666 = const()[name = tensor("op_4666"), val = tensor([1, 1])]; - tensor var_4668 = const()[name = tensor("op_4668"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1531361728)))]; + tensor q_91_cast = conv(dilations = var_4600, groups = var_31, pad = q_91_pad_0, pad_type = q_91_pad_type_0, strides = var_4598, weight = unet_down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16, x = hidden_states_187_cast)[name = tensor("q_91_cast")]; + tensor var_4604 = const()[name = tensor("op_4604"), val = tensor([1, 1])]; + tensor var_4606 = const()[name = tensor("op_4606"), val = tensor([1, 1])]; tensor k_91_pad_type_0 = const()[name = tensor("k_91_pad_type_0"), val = tensor("custom")]; tensor k_91_pad_0 = const()[name = tensor("k_91_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_91 = conv(dilations = var_4668, groups = var_1193, pad = k_91_pad_0, pad_type = k_91_pad_type_0, strides = var_4666, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_91")]; - tensor var_4672 = const()[name = tensor("op_4672"), val = tensor([1, 1])]; - tensor var_4674 = const()[name = tensor("op_4674"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1534638592)))]; + tensor k_91_cast = conv(dilations = var_4606, groups = var_31, pad = k_91_pad_0, pad_type = k_91_pad_type_0, strides = var_4604, weight = unet_down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_91_cast")]; + tensor var_4610 = const()[name = tensor("op_4610"), val = tensor([1, 1])]; + tensor var_4612 = const()[name = tensor("op_4612"), val = tensor([1, 1])]; tensor v_91_pad_type_0 = const()[name = tensor("v_91_pad_type_0"), val = tensor("custom")]; tensor v_91_pad_0 = const()[name = tensor("v_91_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_91 = conv(dilations = var_4674, groups = var_1193, pad = v_91_pad_0, pad_type = v_91_pad_type_0, strides = var_4672, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_91")]; - tensor var_4678 = const()[name = tensor("op_4678"), val = tensor([2, 20, 64, -1])]; - tensor var_4679 = reshape(shape = var_4678, x = q_91)[name = tensor("op_4679")]; - tensor var_4680 = const()[name = tensor("op_4680"), val = tensor([2, 20, 64, -1])]; - tensor var_4681 = reshape(shape = var_4680, x = k_91)[name = tensor("op_4681")]; - tensor var_4682 = const()[name = tensor("op_4682"), val = tensor([2, 20, 64, -1])]; - tensor var_4683 = reshape(shape = var_4682, x = v_91)[name = tensor("op_4683")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1539881536)))]; + tensor v_91_cast = conv(dilations = var_4612, groups = var_31, pad = v_91_pad_0, pad_type = v_91_pad_type_0, strides = var_4610, weight = unet_down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_91_cast")]; + tensor var_4616 = const()[name = tensor("op_4616"), val = tensor([2, 20, 64, -1])]; + tensor var_4617_cast = reshape(shape = var_4616, x = q_91_cast)[name = tensor("op_4617_cast")]; + tensor var_4618 = const()[name = tensor("op_4618"), val = tensor([2, 20, 64, -1])]; + tensor var_4619_cast = reshape(shape = var_4618, x = k_91_cast)[name = tensor("op_4619_cast")]; + tensor var_4620 = const()[name = tensor("op_4620"), val = tensor([2, 20, 64, -1])]; + tensor var_4621_cast = reshape(shape = var_4620, x = v_91_cast)[name = tensor("op_4621_cast")]; tensor attn_weights_181_transpose_x_0 = const()[name = tensor("attn_weights_181_transpose_x_0"), val = tensor(true)]; tensor attn_weights_181_transpose_y_0 = const()[name = tensor("attn_weights_181_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_181 = matmul(transpose_x = attn_weights_181_transpose_x_0, transpose_y = attn_weights_181_transpose_y_0, x = var_4679, y = var_4681)[name = tensor("attn_weights_181")]; - tensor attn_weights_183 = mul(x = attn_weights_181, y = var_1184)[name = tensor("attn_weights_183")]; - tensor var_4687 = softmax(axis = var_1177, x = attn_weights_183)[name = tensor("op_4687")]; + tensor attn_weights_181_cast = matmul(transpose_x = attn_weights_181_transpose_x_0, transpose_y = attn_weights_181_transpose_y_0, x = var_4617_cast, y = var_4619_cast)[name = tensor("attn_weights_181_cast")]; + tensor attn_weights_183_cast = mul(x = attn_weights_181_cast, y = var_12_to_fp16)[name = tensor("attn_weights_183_cast")]; + tensor var_4625_cast = softmax(axis = var_18, x = attn_weights_183_cast)[name = tensor("op_4625_cast")]; tensor attn_91_transpose_x_0 = const()[name = tensor("attn_91_transpose_x_0"), val = tensor(false)]; tensor attn_91_transpose_y_0 = const()[name = tensor("attn_91_transpose_y_0"), val = tensor(true)]; - tensor attn_91 = matmul(transpose_x = attn_91_transpose_x_0, transpose_y = attn_91_transpose_y_0, x = var_4683, y = var_4687)[name = tensor("attn_91")]; - tensor var_4691 = const()[name = tensor("op_4691"), val = tensor([2, 1280, 1, -1])]; - tensor input_295 = reshape(shape = var_4691, x = attn_91)[name = tensor("input_295")]; - tensor var_4696 = const()[name = tensor("op_4696"), val = tensor([1, 1])]; - tensor var_4698 = const()[name = tensor("op_4698"), val = tensor([1, 1])]; - tensor var_4700_pad_type_0 = const()[name = tensor("op_4700_pad_type_0"), val = tensor("custom")]; - tensor var_4700_pad_0 = const()[name = tensor("op_4700_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4700 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_bias, dilations = var_4698, groups = var_1193, pad = var_4700_pad_0, pad_type = var_4700_pad_type_0, strides = var_4696, weight = down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_weight, x = input_295)[name = tensor("op_4700")]; - tensor inputs_137 = add(x = var_4700, y = inputs_135)[name = tensor("inputs_137")]; - tensor var_4704 = const()[name = tensor("op_4704"), val = tensor([1])]; - tensor channels_mean_137 = reduce_mean(axes = var_4704, keep_dims = var_1188, x = inputs_137)[name = tensor("channels_mean_137")]; - tensor zero_mean_137 = sub(x = inputs_137, y = channels_mean_137)[name = tensor("zero_mean_137")]; - tensor zero_mean_sq_137 = mul(x = zero_mean_137, y = zero_mean_137)[name = tensor("zero_mean_sq_137")]; - tensor var_4708 = const()[name = tensor("op_4708"), val = tensor([1])]; - tensor var_4709 = reduce_mean(axes = var_4708, keep_dims = var_1188, x = zero_mean_sq_137)[name = tensor("op_4709")]; - tensor var_4710 = const()[name = tensor("op_4710"), val = tensor(0x1.4f8b58p-17)]; - tensor var_4711 = add(x = var_4709, y = var_4710)[name = tensor("op_4711")]; - tensor denom_137_epsilon_0 = const()[name = tensor("denom_137_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_137 = rsqrt(epsilon = denom_137_epsilon_0, x = var_4711)[name = tensor("denom_137")]; - tensor out_137 = mul(x = zero_mean_137, y = denom_137)[name = tensor("out_137")]; - tensor var_4715 = const()[name = tensor("op_4715"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268336768)))]; - tensor var_4716 = add(x = out_137, y = var_4715)[name = tensor("op_4716")]; - tensor var_4718 = const()[name = tensor("op_4718"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268341952)))]; - tensor input_297 = mul(x = var_4716, y = var_4718)[name = tensor("input_297")]; - tensor var_4726 = const()[name = tensor("op_4726"), val = tensor([1, 1])]; - tensor var_4728 = const()[name = tensor("op_4728"), val = tensor([1, 1])]; - tensor var_4730_pad_type_0 = const()[name = tensor("op_4730_pad_type_0"), val = tensor("custom")]; - tensor var_4730_pad_0 = const()[name = tensor("op_4730_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4730 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_bias, dilations = var_4728, groups = var_1193, pad = var_4730_pad_0, pad_type = var_4730_pad_type_0, strides = var_4726, weight = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_weight, x = input_297)[name = tensor("op_4730")]; - tensor var_4731_split_sizes_0 = const()[name = tensor("op_4731_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_4731_axis_0 = const()[name = tensor("op_4731_axis_0"), val = tensor(1)]; - tensor var_4731_0, tensor var_4731_1 = split(axis = var_4731_axis_0, split_sizes = var_4731_split_sizes_0, x = var_4730)[name = tensor("op_4731")]; - tensor var_4733_mode_0 = const()[name = tensor("op_4733_mode_0"), val = tensor("EXACT")]; - tensor var_4733 = gelu(mode = var_4733_mode_0, x = var_4731_1)[name = tensor("op_4733")]; - tensor input_299 = mul(x = var_4731_0, y = var_4733)[name = tensor("input_299")]; - tensor var_4737 = const()[name = tensor("op_4737"), val = tensor([1, 1])]; - tensor var_4739 = const()[name = tensor("op_4739"), val = tensor([1, 1])]; - tensor var_4741_pad_type_0 = const()[name = tensor("op_4741_pad_type_0"), val = tensor("custom")]; - tensor var_4741_pad_0 = const()[name = tensor("op_4741_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4741 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_bias, dilations = var_4739, groups = var_1193, pad = var_4741_pad_0, pad_type = var_4741_pad_type_0, strides = var_4737, weight = down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_weight, x = input_299)[name = tensor("op_4741")]; - tensor inputs_139 = add(x = var_4741, y = inputs_137)[name = tensor("inputs_139")]; - tensor var_4751 = const()[name = tensor("op_4751"), val = tensor([1])]; - tensor channels_mean_139 = reduce_mean(axes = var_4751, keep_dims = var_1188, x = inputs_139)[name = tensor("channels_mean_139")]; - tensor zero_mean_139 = sub(x = inputs_139, y = channels_mean_139)[name = tensor("zero_mean_139")]; - tensor zero_mean_sq_139 = mul(x = zero_mean_139, y = zero_mean_139)[name = tensor("zero_mean_sq_139")]; - tensor var_4755 = const()[name = tensor("op_4755"), val = tensor([1])]; - tensor var_4756 = reduce_mean(axes = var_4755, keep_dims = var_1188, x = zero_mean_sq_139)[name = tensor("op_4756")]; - tensor var_4757 = const()[name = tensor("op_4757"), val = tensor(0x1.4f8b58p-17)]; - tensor var_4758 = add(x = var_4756, y = var_4757)[name = tensor("op_4758")]; - tensor denom_139_epsilon_0 = const()[name = tensor("denom_139_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_139 = rsqrt(epsilon = denom_139_epsilon_0, x = var_4758)[name = tensor("denom_139")]; - tensor out_139 = mul(x = zero_mean_139, y = denom_139)[name = tensor("out_139")]; - tensor var_4762 = const()[name = tensor("op_4762"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268347136)))]; - tensor var_4763 = add(x = out_139, y = var_4762)[name = tensor("op_4763")]; - tensor var_4765 = const()[name = tensor("op_4765"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268352320)))]; - tensor hidden_states_191 = mul(x = var_4763, y = var_4765)[name = tensor("hidden_states_191")]; - tensor var_4772 = const()[name = tensor("op_4772"), val = tensor([1, 1])]; - tensor var_4774 = const()[name = tensor("op_4774"), val = tensor([1, 1])]; + tensor attn_91_cast = matmul(transpose_x = attn_91_transpose_x_0, transpose_y = attn_91_transpose_y_0, x = var_4621_cast, y = var_4625_cast)[name = tensor("attn_91_cast")]; + tensor var_4629 = const()[name = tensor("op_4629"), val = tensor([2, 1280, 1, -1])]; + tensor input_295_cast = reshape(shape = var_4629, x = attn_91_cast)[name = tensor("input_295_cast")]; + tensor var_4634 = const()[name = tensor("op_4634"), val = tensor([1, 1])]; + tensor var_4636 = const()[name = tensor("op_4636"), val = tensor([1, 1])]; + tensor var_4638_pad_type_0 = const()[name = tensor("op_4638_pad_type_0"), val = tensor("custom")]; + tensor var_4638_pad_0 = const()[name = tensor("op_4638_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1545124480)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1548401344)))]; + tensor var_4638_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_4636, groups = var_31, pad = var_4638_pad_0, pad_type = var_4638_pad_type_0, strides = var_4634, weight = unet_down_blocks_2_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16, x = input_295_cast)[name = tensor("op_4638_cast")]; + tensor inputs_137_cast = add(x = var_4638_cast, y = inputs_135_cast)[name = tensor("inputs_137_cast")]; + tensor var_4642 = const()[name = tensor("op_4642"), val = tensor([1])]; + tensor channels_mean_137_cast = reduce_mean(axes = var_4642, keep_dims = var_23, x = inputs_137_cast)[name = tensor("channels_mean_137_cast")]; + tensor zero_mean_137_cast = sub(x = inputs_137_cast, y = channels_mean_137_cast)[name = tensor("zero_mean_137_cast")]; + tensor zero_mean_sq_137_cast = mul(x = zero_mean_137_cast, y = zero_mean_137_cast)[name = tensor("zero_mean_sq_137_cast")]; + tensor var_4646 = const()[name = tensor("op_4646"), val = tensor([1])]; + tensor var_4647_cast = reduce_mean(axes = var_4646, keep_dims = var_23, x = zero_mean_sq_137_cast)[name = tensor("op_4647_cast")]; + tensor var_4648_to_fp16 = const()[name = tensor("op_4648_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4649_cast = add(x = var_4647_cast, y = var_4648_to_fp16)[name = tensor("op_4649_cast")]; + tensor denom_137_epsilon_0_to_fp16 = const()[name = tensor("denom_137_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_137_cast = rsqrt(epsilon = denom_137_epsilon_0_to_fp16, x = var_4649_cast)[name = tensor("denom_137_cast")]; + tensor out_137_cast = mul(x = zero_mean_137_cast, y = denom_137_cast)[name = tensor("out_137_cast")]; + tensor var_4653_to_fp16 = const()[name = tensor("op_4653_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1548403968)))]; + tensor var_4654_cast = add(x = out_137_cast, y = var_4653_to_fp16)[name = tensor("op_4654_cast")]; + tensor var_4656_to_fp16 = const()[name = tensor("op_4656_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1548406592)))]; + tensor input_297_cast = mul(x = var_4654_cast, y = var_4656_to_fp16)[name = tensor("input_297_cast")]; + tensor var_4664 = const()[name = tensor("op_4664"), val = tensor([1, 1])]; + tensor var_4666 = const()[name = tensor("op_4666"), val = tensor([1, 1])]; + tensor var_4668_pad_type_0 = const()[name = tensor("op_4668_pad_type_0"), val = tensor("custom")]; + tensor var_4668_pad_0 = const()[name = tensor("op_4668_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1548409216)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1574623680)))]; + tensor var_4668_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_4666, groups = var_31, pad = var_4668_pad_0, pad_type = var_4668_pad_type_0, strides = var_4664, weight = unet_down_blocks_2_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16, x = input_297_cast)[name = tensor("op_4668_cast")]; + tensor var_4669_split_sizes_0 = const()[name = tensor("op_4669_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4669_axis_0 = const()[name = tensor("op_4669_axis_0"), val = tensor(1)]; + tensor var_4669_cast_0, tensor var_4669_cast_1 = split(axis = var_4669_axis_0, split_sizes = var_4669_split_sizes_0, x = var_4668_cast)[name = tensor("op_4669_cast")]; + tensor var_4671_mode_0 = const()[name = tensor("op_4671_mode_0"), val = tensor("EXACT")]; + tensor var_4671_cast = gelu(mode = var_4671_mode_0, x = var_4669_cast_1)[name = tensor("op_4671_cast")]; + tensor input_299_cast = mul(x = var_4669_cast_0, y = var_4671_cast)[name = tensor("input_299_cast")]; + tensor var_4675 = const()[name = tensor("op_4675"), val = tensor([1, 1])]; + tensor var_4677 = const()[name = tensor("op_4677"), val = tensor([1, 1])]; + tensor var_4679_pad_type_0 = const()[name = tensor("op_4679_pad_type_0"), val = tensor("custom")]; + tensor var_4679_pad_0 = const()[name = tensor("op_4679_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1574644224)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1587751488)))]; + tensor var_4679_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_4677, groups = var_31, pad = var_4679_pad_0, pad_type = var_4679_pad_type_0, strides = var_4675, weight = unet_down_blocks_2_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16, x = input_299_cast)[name = tensor("op_4679_cast")]; + tensor inputs_139_cast = add(x = var_4679_cast, y = inputs_137_cast)[name = tensor("inputs_139_cast")]; + tensor var_4689 = const()[name = tensor("op_4689"), val = tensor([1])]; + tensor channels_mean_139_cast = reduce_mean(axes = var_4689, keep_dims = var_23, x = inputs_139_cast)[name = tensor("channels_mean_139_cast")]; + tensor zero_mean_139_cast = sub(x = inputs_139_cast, y = channels_mean_139_cast)[name = tensor("zero_mean_139_cast")]; + tensor zero_mean_sq_139_cast = mul(x = zero_mean_139_cast, y = zero_mean_139_cast)[name = tensor("zero_mean_sq_139_cast")]; + tensor var_4693 = const()[name = tensor("op_4693"), val = tensor([1])]; + tensor var_4694_cast = reduce_mean(axes = var_4693, keep_dims = var_23, x = zero_mean_sq_139_cast)[name = tensor("op_4694_cast")]; + tensor var_4695_to_fp16 = const()[name = tensor("op_4695_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4696_cast = add(x = var_4694_cast, y = var_4695_to_fp16)[name = tensor("op_4696_cast")]; + tensor denom_139_epsilon_0_to_fp16 = const()[name = tensor("denom_139_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_139_cast = rsqrt(epsilon = denom_139_epsilon_0_to_fp16, x = var_4696_cast)[name = tensor("denom_139_cast")]; + tensor out_139_cast = mul(x = zero_mean_139_cast, y = denom_139_cast)[name = tensor("out_139_cast")]; + tensor var_4700_to_fp16 = const()[name = tensor("op_4700_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1587754112)))]; + tensor var_4701_cast = add(x = out_139_cast, y = var_4700_to_fp16)[name = tensor("op_4701_cast")]; + tensor var_4703_to_fp16 = const()[name = tensor("op_4703_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1587756736)))]; + tensor hidden_states_191_cast = mul(x = var_4701_cast, y = var_4703_to_fp16)[name = tensor("hidden_states_191_cast")]; + tensor var_4710 = const()[name = tensor("op_4710"), val = tensor([1, 1])]; + tensor var_4712 = const()[name = tensor("op_4712"), val = tensor([1, 1])]; tensor q_93_pad_type_0 = const()[name = tensor("q_93_pad_type_0"), val = tensor("custom")]; tensor q_93_pad_0 = const()[name = tensor("q_93_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_93 = conv(dilations = var_4774, groups = var_1193, pad = q_93_pad_0, pad_type = q_93_pad_type_0, strides = var_4772, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_q_weight, x = hidden_states_191)[name = tensor("q_93")]; - tensor var_4778 = const()[name = tensor("op_4778"), val = tensor([1, 1])]; - tensor var_4780 = const()[name = tensor("op_4780"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1587759360)))]; + tensor q_93_cast = conv(dilations = var_4712, groups = var_31, pad = q_93_pad_0, pad_type = q_93_pad_type_0, strides = var_4710, weight = unet_down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16, x = hidden_states_191_cast)[name = tensor("q_93_cast")]; + tensor var_4716 = const()[name = tensor("op_4716"), val = tensor([1, 1])]; + tensor var_4718 = const()[name = tensor("op_4718"), val = tensor([1, 1])]; tensor k_93_pad_type_0 = const()[name = tensor("k_93_pad_type_0"), val = tensor("custom")]; tensor k_93_pad_0 = const()[name = tensor("k_93_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_93 = conv(dilations = var_4780, groups = var_1193, pad = k_93_pad_0, pad_type = k_93_pad_type_0, strides = var_4778, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_k_weight, x = hidden_states_191)[name = tensor("k_93")]; - tensor var_4784 = const()[name = tensor("op_4784"), val = tensor([1, 1])]; - tensor var_4786 = const()[name = tensor("op_4786"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1591036224)))]; + tensor k_93_cast = conv(dilations = var_4718, groups = var_31, pad = k_93_pad_0, pad_type = k_93_pad_type_0, strides = var_4716, weight = unet_down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16, x = hidden_states_191_cast)[name = tensor("k_93_cast")]; + tensor var_4722 = const()[name = tensor("op_4722"), val = tensor([1, 1])]; + tensor var_4724 = const()[name = tensor("op_4724"), val = tensor([1, 1])]; tensor v_93_pad_type_0 = const()[name = tensor("v_93_pad_type_0"), val = tensor("custom")]; tensor v_93_pad_0 = const()[name = tensor("v_93_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_93 = conv(dilations = var_4786, groups = var_1193, pad = v_93_pad_0, pad_type = v_93_pad_type_0, strides = var_4784, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_v_weight, x = hidden_states_191)[name = tensor("v_93")]; - tensor var_4790 = const()[name = tensor("op_4790"), val = tensor([2, 20, 64, -1])]; - tensor var_4791 = reshape(shape = var_4790, x = q_93)[name = tensor("op_4791")]; - tensor var_4792 = const()[name = tensor("op_4792"), val = tensor([2, 20, 64, -1])]; - tensor var_4793 = reshape(shape = var_4792, x = k_93)[name = tensor("op_4793")]; - tensor var_4794 = const()[name = tensor("op_4794"), val = tensor([2, 20, 64, -1])]; - tensor var_4795 = reshape(shape = var_4794, x = v_93)[name = tensor("op_4795")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1594313088)))]; + tensor v_93_cast = conv(dilations = var_4724, groups = var_31, pad = v_93_pad_0, pad_type = v_93_pad_type_0, strides = var_4722, weight = unet_down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16, x = hidden_states_191_cast)[name = tensor("v_93_cast")]; + tensor var_4728 = const()[name = tensor("op_4728"), val = tensor([2, 20, 64, -1])]; + tensor var_4729_cast = reshape(shape = var_4728, x = q_93_cast)[name = tensor("op_4729_cast")]; + tensor var_4730 = const()[name = tensor("op_4730"), val = tensor([2, 20, 64, -1])]; + tensor var_4731_cast = reshape(shape = var_4730, x = k_93_cast)[name = tensor("op_4731_cast")]; + tensor var_4732 = const()[name = tensor("op_4732"), val = tensor([2, 20, 64, -1])]; + tensor var_4733_cast = reshape(shape = var_4732, x = v_93_cast)[name = tensor("op_4733_cast")]; tensor attn_weights_185_transpose_x_0 = const()[name = tensor("attn_weights_185_transpose_x_0"), val = tensor(true)]; tensor attn_weights_185_transpose_y_0 = const()[name = tensor("attn_weights_185_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_185 = matmul(transpose_x = attn_weights_185_transpose_x_0, transpose_y = attn_weights_185_transpose_y_0, x = var_4791, y = var_4793)[name = tensor("attn_weights_185")]; - tensor attn_weights_187 = mul(x = attn_weights_185, y = var_1184)[name = tensor("attn_weights_187")]; - tensor var_4799 = softmax(axis = var_1177, x = attn_weights_187)[name = tensor("op_4799")]; + tensor attn_weights_185_cast = matmul(transpose_x = attn_weights_185_transpose_x_0, transpose_y = attn_weights_185_transpose_y_0, x = var_4729_cast, y = var_4731_cast)[name = tensor("attn_weights_185_cast")]; + tensor attn_weights_187_cast = mul(x = attn_weights_185_cast, y = var_12_to_fp16)[name = tensor("attn_weights_187_cast")]; + tensor var_4737_cast = softmax(axis = var_18, x = attn_weights_187_cast)[name = tensor("op_4737_cast")]; tensor attn_93_transpose_x_0 = const()[name = tensor("attn_93_transpose_x_0"), val = tensor(false)]; tensor attn_93_transpose_y_0 = const()[name = tensor("attn_93_transpose_y_0"), val = tensor(true)]; - tensor attn_93 = matmul(transpose_x = attn_93_transpose_x_0, transpose_y = attn_93_transpose_y_0, x = var_4795, y = var_4799)[name = tensor("attn_93")]; - tensor var_4803 = const()[name = tensor("op_4803"), val = tensor([2, 1280, 1, -1])]; - tensor input_301 = reshape(shape = var_4803, x = attn_93)[name = tensor("input_301")]; - tensor var_4808 = const()[name = tensor("op_4808"), val = tensor([1, 1])]; - tensor var_4810 = const()[name = tensor("op_4810"), val = tensor([1, 1])]; - tensor var_4812_pad_type_0 = const()[name = tensor("op_4812_pad_type_0"), val = tensor("custom")]; - tensor var_4812_pad_0 = const()[name = tensor("op_4812_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4812 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_bias, dilations = var_4810, groups = var_1193, pad = var_4812_pad_0, pad_type = var_4812_pad_type_0, strides = var_4808, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_weight, x = input_301)[name = tensor("op_4812")]; - tensor inputs_141 = add(x = var_4812, y = inputs_139)[name = tensor("inputs_141")]; - tensor var_4816 = const()[name = tensor("op_4816"), val = tensor([1])]; - tensor channels_mean_141 = reduce_mean(axes = var_4816, keep_dims = var_1188, x = inputs_141)[name = tensor("channels_mean_141")]; - tensor zero_mean_141 = sub(x = inputs_141, y = channels_mean_141)[name = tensor("zero_mean_141")]; - tensor zero_mean_sq_141 = mul(x = zero_mean_141, y = zero_mean_141)[name = tensor("zero_mean_sq_141")]; - tensor var_4820 = const()[name = tensor("op_4820"), val = tensor([1])]; - tensor var_4821 = reduce_mean(axes = var_4820, keep_dims = var_1188, x = zero_mean_sq_141)[name = tensor("op_4821")]; - tensor var_4822 = const()[name = tensor("op_4822"), val = tensor(0x1.4f8b58p-17)]; - tensor var_4823 = add(x = var_4821, y = var_4822)[name = tensor("op_4823")]; - tensor denom_141_epsilon_0 = const()[name = tensor("denom_141_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_141 = rsqrt(epsilon = denom_141_epsilon_0, x = var_4823)[name = tensor("denom_141")]; - tensor out_141 = mul(x = zero_mean_141, y = denom_141)[name = tensor("out_141")]; - tensor var_4827 = const()[name = tensor("op_4827"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268357504)))]; - tensor var_4828 = add(x = out_141, y = var_4827)[name = tensor("op_4828")]; - tensor var_4830 = const()[name = tensor("op_4830"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268362688)))]; - tensor hidden_states_193 = mul(x = var_4828, y = var_4830)[name = tensor("hidden_states_193")]; - tensor var_4837 = const()[name = tensor("op_4837"), val = tensor([1, 1])]; - tensor var_4839 = const()[name = tensor("op_4839"), val = tensor([1, 1])]; + tensor attn_93_cast = matmul(transpose_x = attn_93_transpose_x_0, transpose_y = attn_93_transpose_y_0, x = var_4733_cast, y = var_4737_cast)[name = tensor("attn_93_cast")]; + tensor var_4741 = const()[name = tensor("op_4741"), val = tensor([2, 1280, 1, -1])]; + tensor input_301_cast = reshape(shape = var_4741, x = attn_93_cast)[name = tensor("input_301_cast")]; + tensor var_4746 = const()[name = tensor("op_4746"), val = tensor([1, 1])]; + tensor var_4748 = const()[name = tensor("op_4748"), val = tensor([1, 1])]; + tensor var_4750_pad_type_0 = const()[name = tensor("op_4750_pad_type_0"), val = tensor("custom")]; + tensor var_4750_pad_0 = const()[name = tensor("op_4750_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1597589952)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1600866816)))]; + tensor var_4750_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_4748, groups = var_31, pad = var_4750_pad_0, pad_type = var_4750_pad_type_0, strides = var_4746, weight = unet_down_blocks_2_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16, x = input_301_cast)[name = tensor("op_4750_cast")]; + tensor inputs_141_cast = add(x = var_4750_cast, y = inputs_139_cast)[name = tensor("inputs_141_cast")]; + tensor var_4754 = const()[name = tensor("op_4754"), val = tensor([1])]; + tensor channels_mean_141_cast = reduce_mean(axes = var_4754, keep_dims = var_23, x = inputs_141_cast)[name = tensor("channels_mean_141_cast")]; + tensor zero_mean_141_cast = sub(x = inputs_141_cast, y = channels_mean_141_cast)[name = tensor("zero_mean_141_cast")]; + tensor zero_mean_sq_141_cast = mul(x = zero_mean_141_cast, y = zero_mean_141_cast)[name = tensor("zero_mean_sq_141_cast")]; + tensor var_4758 = const()[name = tensor("op_4758"), val = tensor([1])]; + tensor var_4759_cast = reduce_mean(axes = var_4758, keep_dims = var_23, x = zero_mean_sq_141_cast)[name = tensor("op_4759_cast")]; + tensor var_4760_to_fp16 = const()[name = tensor("op_4760_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4761_cast = add(x = var_4759_cast, y = var_4760_to_fp16)[name = tensor("op_4761_cast")]; + tensor denom_141_epsilon_0_to_fp16 = const()[name = tensor("denom_141_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_141_cast = rsqrt(epsilon = denom_141_epsilon_0_to_fp16, x = var_4761_cast)[name = tensor("denom_141_cast")]; + tensor out_141_cast = mul(x = zero_mean_141_cast, y = denom_141_cast)[name = tensor("out_141_cast")]; + tensor var_4765_to_fp16 = const()[name = tensor("op_4765_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1600869440)))]; + tensor var_4766_cast = add(x = out_141_cast, y = var_4765_to_fp16)[name = tensor("op_4766_cast")]; + tensor var_4768_to_fp16 = const()[name = tensor("op_4768_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1600872064)))]; + tensor hidden_states_193_cast = mul(x = var_4766_cast, y = var_4768_to_fp16)[name = tensor("hidden_states_193_cast")]; + tensor var_4775 = const()[name = tensor("op_4775"), val = tensor([1, 1])]; + tensor var_4777 = const()[name = tensor("op_4777"), val = tensor([1, 1])]; tensor q_95_pad_type_0 = const()[name = tensor("q_95_pad_type_0"), val = tensor("custom")]; tensor q_95_pad_0 = const()[name = tensor("q_95_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_95 = conv(dilations = var_4839, groups = var_1193, pad = q_95_pad_0, pad_type = q_95_pad_type_0, strides = var_4837, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_q_weight, x = hidden_states_193)[name = tensor("q_95")]; - tensor var_4843 = const()[name = tensor("op_4843"), val = tensor([1, 1])]; - tensor var_4845 = const()[name = tensor("op_4845"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1600874688)))]; + tensor q_95_cast = conv(dilations = var_4777, groups = var_31, pad = q_95_pad_0, pad_type = q_95_pad_type_0, strides = var_4775, weight = unet_down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16, x = hidden_states_193_cast)[name = tensor("q_95_cast")]; + tensor var_4781 = const()[name = tensor("op_4781"), val = tensor([1, 1])]; + tensor var_4783 = const()[name = tensor("op_4783"), val = tensor([1, 1])]; tensor k_95_pad_type_0 = const()[name = tensor("k_95_pad_type_0"), val = tensor("custom")]; tensor k_95_pad_0 = const()[name = tensor("k_95_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_95 = conv(dilations = var_4845, groups = var_1193, pad = k_95_pad_0, pad_type = k_95_pad_type_0, strides = var_4843, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_95")]; - tensor var_4849 = const()[name = tensor("op_4849"), val = tensor([1, 1])]; - tensor var_4851 = const()[name = tensor("op_4851"), val = tensor([1, 1])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1604151552)))]; + tensor k_95_cast = conv(dilations = var_4783, groups = var_31, pad = k_95_pad_0, pad_type = k_95_pad_type_0, strides = var_4781, weight = unet_down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_95_cast")]; + tensor var_4787 = const()[name = tensor("op_4787"), val = tensor([1, 1])]; + tensor var_4789 = const()[name = tensor("op_4789"), val = tensor([1, 1])]; tensor v_95_pad_type_0 = const()[name = tensor("v_95_pad_type_0"), val = tensor("custom")]; tensor v_95_pad_0 = const()[name = tensor("v_95_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_95 = conv(dilations = var_4851, groups = var_1193, pad = v_95_pad_0, pad_type = v_95_pad_type_0, strides = var_4849, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_95")]; - tensor var_4855 = const()[name = tensor("op_4855"), val = tensor([2, 20, 64, -1])]; - tensor var_4856 = reshape(shape = var_4855, x = q_95)[name = tensor("op_4856")]; - tensor var_4857 = const()[name = tensor("op_4857"), val = tensor([2, 20, 64, -1])]; - tensor var_4858 = reshape(shape = var_4857, x = k_95)[name = tensor("op_4858")]; - tensor var_4859 = const()[name = tensor("op_4859"), val = tensor([2, 20, 64, -1])]; - tensor var_4860 = reshape(shape = var_4859, x = v_95)[name = tensor("op_4860")]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1609394496)))]; + tensor v_95_cast = conv(dilations = var_4789, groups = var_31, pad = v_95_pad_0, pad_type = v_95_pad_type_0, strides = var_4787, weight = unet_down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_95_cast")]; + tensor var_4793 = const()[name = tensor("op_4793"), val = tensor([2, 20, 64, -1])]; + tensor var_4794_cast = reshape(shape = var_4793, x = q_95_cast)[name = tensor("op_4794_cast")]; + tensor var_4795 = const()[name = tensor("op_4795"), val = tensor([2, 20, 64, -1])]; + tensor var_4796_cast = reshape(shape = var_4795, x = k_95_cast)[name = tensor("op_4796_cast")]; + tensor var_4797 = const()[name = tensor("op_4797"), val = tensor([2, 20, 64, -1])]; + tensor var_4798_cast = reshape(shape = var_4797, x = v_95_cast)[name = tensor("op_4798_cast")]; tensor attn_weights_189_transpose_x_0 = const()[name = tensor("attn_weights_189_transpose_x_0"), val = tensor(true)]; tensor attn_weights_189_transpose_y_0 = const()[name = tensor("attn_weights_189_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_189 = matmul(transpose_x = attn_weights_189_transpose_x_0, transpose_y = attn_weights_189_transpose_y_0, x = var_4856, y = var_4858)[name = tensor("attn_weights_189")]; - tensor attn_weights_191 = mul(x = attn_weights_189, y = var_1184)[name = tensor("attn_weights_191")]; - tensor var_4864 = softmax(axis = var_1177, x = attn_weights_191)[name = tensor("op_4864")]; + tensor attn_weights_189_cast = matmul(transpose_x = attn_weights_189_transpose_x_0, transpose_y = attn_weights_189_transpose_y_0, x = var_4794_cast, y = var_4796_cast)[name = tensor("attn_weights_189_cast")]; + tensor attn_weights_191_cast = mul(x = attn_weights_189_cast, y = var_12_to_fp16)[name = tensor("attn_weights_191_cast")]; + tensor var_4802_cast = softmax(axis = var_18, x = attn_weights_191_cast)[name = tensor("op_4802_cast")]; tensor attn_95_transpose_x_0 = const()[name = tensor("attn_95_transpose_x_0"), val = tensor(false)]; tensor attn_95_transpose_y_0 = const()[name = tensor("attn_95_transpose_y_0"), val = tensor(true)]; - tensor attn_95 = matmul(transpose_x = attn_95_transpose_x_0, transpose_y = attn_95_transpose_y_0, x = var_4860, y = var_4864)[name = tensor("attn_95")]; - tensor var_4868 = const()[name = tensor("op_4868"), val = tensor([2, 1280, 1, -1])]; - tensor input_303 = reshape(shape = var_4868, x = attn_95)[name = tensor("input_303")]; - tensor var_4873 = const()[name = tensor("op_4873"), val = tensor([1, 1])]; - tensor var_4875 = const()[name = tensor("op_4875"), val = tensor([1, 1])]; - tensor var_4877_pad_type_0 = const()[name = tensor("op_4877_pad_type_0"), val = tensor("custom")]; - tensor var_4877_pad_0 = const()[name = tensor("op_4877_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4877 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_bias, dilations = var_4875, groups = var_1193, pad = var_4877_pad_0, pad_type = var_4877_pad_type_0, strides = var_4873, weight = down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_weight, x = input_303)[name = tensor("op_4877")]; - tensor inputs_143 = add(x = var_4877, y = inputs_141)[name = tensor("inputs_143")]; - tensor var_4881 = const()[name = tensor("op_4881"), val = tensor([1])]; - tensor channels_mean_143 = reduce_mean(axes = var_4881, keep_dims = var_1188, x = inputs_143)[name = tensor("channels_mean_143")]; - tensor zero_mean_143 = sub(x = inputs_143, y = channels_mean_143)[name = tensor("zero_mean_143")]; - tensor zero_mean_sq_143 = mul(x = zero_mean_143, y = zero_mean_143)[name = tensor("zero_mean_sq_143")]; - tensor var_4885 = const()[name = tensor("op_4885"), val = tensor([1])]; - tensor var_4886 = reduce_mean(axes = var_4885, keep_dims = var_1188, x = zero_mean_sq_143)[name = tensor("op_4886")]; - tensor var_4887 = const()[name = tensor("op_4887"), val = tensor(0x1.4f8b58p-17)]; - tensor var_4888 = add(x = var_4886, y = var_4887)[name = tensor("op_4888")]; - tensor denom_143_epsilon_0 = const()[name = tensor("denom_143_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_143 = rsqrt(epsilon = denom_143_epsilon_0, x = var_4888)[name = tensor("denom_143")]; - tensor out_143 = mul(x = zero_mean_143, y = denom_143)[name = tensor("out_143")]; - tensor var_4892 = const()[name = tensor("op_4892"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268367872)))]; - tensor var_4893 = add(x = out_143, y = var_4892)[name = tensor("op_4893")]; - tensor var_4895 = const()[name = tensor("op_4895"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268373056)))]; - tensor input_305 = mul(x = var_4893, y = var_4895)[name = tensor("input_305")]; - tensor var_4903 = const()[name = tensor("op_4903"), val = tensor([1, 1])]; - tensor var_4905 = const()[name = tensor("op_4905"), val = tensor([1, 1])]; - tensor var_4907_pad_type_0 = const()[name = tensor("op_4907_pad_type_0"), val = tensor("custom")]; - tensor var_4907_pad_0 = const()[name = tensor("op_4907_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4907 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_bias, dilations = var_4905, groups = var_1193, pad = var_4907_pad_0, pad_type = var_4907_pad_type_0, strides = var_4903, weight = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_weight, x = input_305)[name = tensor("op_4907")]; - tensor var_4908_split_sizes_0 = const()[name = tensor("op_4908_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_4908_axis_0 = const()[name = tensor("op_4908_axis_0"), val = tensor(1)]; - tensor var_4908_0, tensor var_4908_1 = split(axis = var_4908_axis_0, split_sizes = var_4908_split_sizes_0, x = var_4907)[name = tensor("op_4908")]; - tensor var_4910_mode_0 = const()[name = tensor("op_4910_mode_0"), val = tensor("EXACT")]; - tensor var_4910 = gelu(mode = var_4910_mode_0, x = var_4908_1)[name = tensor("op_4910")]; - tensor input_307 = mul(x = var_4908_0, y = var_4910)[name = tensor("input_307")]; - tensor var_4914 = const()[name = tensor("op_4914"), val = tensor([1, 1])]; - tensor var_4916 = const()[name = tensor("op_4916"), val = tensor([1, 1])]; - tensor var_4918_pad_type_0 = const()[name = tensor("op_4918_pad_type_0"), val = tensor("custom")]; - tensor var_4918_pad_0 = const()[name = tensor("op_4918_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_4918 = conv(bias = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_bias, dilations = var_4916, groups = var_1193, pad = var_4918_pad_0, pad_type = var_4918_pad_type_0, strides = var_4914, weight = down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_weight, x = input_307)[name = tensor("op_4918")]; - tensor hidden_states_197 = add(x = var_4918, y = inputs_143)[name = tensor("hidden_states_197")]; - tensor var_4920 = const()[name = tensor("op_4920"), val = tensor([2, 1280, 32, 32])]; - tensor input_309 = reshape(shape = var_4920, x = hidden_states_197)[name = tensor("input_309")]; - tensor var_4924 = const()[name = tensor("op_4924"), val = tensor([1, 1])]; - tensor var_4926 = const()[name = tensor("op_4926"), val = tensor([1, 1])]; + tensor attn_95_cast = matmul(transpose_x = attn_95_transpose_x_0, transpose_y = attn_95_transpose_y_0, x = var_4798_cast, y = var_4802_cast)[name = tensor("attn_95_cast")]; + tensor var_4806 = const()[name = tensor("op_4806"), val = tensor([2, 1280, 1, -1])]; + tensor input_303_cast = reshape(shape = var_4806, x = attn_95_cast)[name = tensor("input_303_cast")]; + tensor var_4811 = const()[name = tensor("op_4811"), val = tensor([1, 1])]; + tensor var_4813 = const()[name = tensor("op_4813"), val = tensor([1, 1])]; + tensor var_4815_pad_type_0 = const()[name = tensor("op_4815_pad_type_0"), val = tensor("custom")]; + tensor var_4815_pad_0 = const()[name = tensor("op_4815_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1614637440)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617914304)))]; + tensor var_4815_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_4813, groups = var_31, pad = var_4815_pad_0, pad_type = var_4815_pad_type_0, strides = var_4811, weight = unet_down_blocks_2_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16, x = input_303_cast)[name = tensor("op_4815_cast")]; + tensor inputs_143_cast = add(x = var_4815_cast, y = inputs_141_cast)[name = tensor("inputs_143_cast")]; + tensor var_4819 = const()[name = tensor("op_4819"), val = tensor([1])]; + tensor channels_mean_143_cast = reduce_mean(axes = var_4819, keep_dims = var_23, x = inputs_143_cast)[name = tensor("channels_mean_143_cast")]; + tensor zero_mean_143_cast = sub(x = inputs_143_cast, y = channels_mean_143_cast)[name = tensor("zero_mean_143_cast")]; + tensor zero_mean_sq_143_cast = mul(x = zero_mean_143_cast, y = zero_mean_143_cast)[name = tensor("zero_mean_sq_143_cast")]; + tensor var_4823 = const()[name = tensor("op_4823"), val = tensor([1])]; + tensor var_4824_cast = reduce_mean(axes = var_4823, keep_dims = var_23, x = zero_mean_sq_143_cast)[name = tensor("op_4824_cast")]; + tensor var_4825_to_fp16 = const()[name = tensor("op_4825_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4826_cast = add(x = var_4824_cast, y = var_4825_to_fp16)[name = tensor("op_4826_cast")]; + tensor denom_143_epsilon_0_to_fp16 = const()[name = tensor("denom_143_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_143_cast = rsqrt(epsilon = denom_143_epsilon_0_to_fp16, x = var_4826_cast)[name = tensor("denom_143_cast")]; + tensor out_143_cast = mul(x = zero_mean_143_cast, y = denom_143_cast)[name = tensor("out_143_cast")]; + tensor var_4830_to_fp16 = const()[name = tensor("op_4830_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617916928)))]; + tensor var_4831_cast = add(x = out_143_cast, y = var_4830_to_fp16)[name = tensor("op_4831_cast")]; + tensor var_4833_to_fp16 = const()[name = tensor("op_4833_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617919552)))]; + tensor input_305_cast = mul(x = var_4831_cast, y = var_4833_to_fp16)[name = tensor("input_305_cast")]; + tensor var_4841 = const()[name = tensor("op_4841"), val = tensor([1, 1])]; + tensor var_4843 = const()[name = tensor("op_4843"), val = tensor([1, 1])]; + tensor var_4845_pad_type_0 = const()[name = tensor("op_4845_pad_type_0"), val = tensor("custom")]; + tensor var_4845_pad_0 = const()[name = tensor("op_4845_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1617922176)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1644136640)))]; + tensor var_4845_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_4843, groups = var_31, pad = var_4845_pad_0, pad_type = var_4845_pad_type_0, strides = var_4841, weight = unet_down_blocks_2_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16, x = input_305_cast)[name = tensor("op_4845_cast")]; + tensor var_4846_split_sizes_0 = const()[name = tensor("op_4846_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_4846_axis_0 = const()[name = tensor("op_4846_axis_0"), val = tensor(1)]; + tensor var_4846_cast_0, tensor var_4846_cast_1 = split(axis = var_4846_axis_0, split_sizes = var_4846_split_sizes_0, x = var_4845_cast)[name = tensor("op_4846_cast")]; + tensor var_4848_mode_0 = const()[name = tensor("op_4848_mode_0"), val = tensor("EXACT")]; + tensor var_4848_cast = gelu(mode = var_4848_mode_0, x = var_4846_cast_1)[name = tensor("op_4848_cast")]; + tensor input_307_cast = mul(x = var_4846_cast_0, y = var_4848_cast)[name = tensor("input_307_cast")]; + tensor var_4852 = const()[name = tensor("op_4852"), val = tensor([1, 1])]; + tensor var_4854 = const()[name = tensor("op_4854"), val = tensor([1, 1])]; + tensor var_4856_pad_type_0 = const()[name = tensor("op_4856_pad_type_0"), val = tensor("custom")]; + tensor var_4856_pad_0 = const()[name = tensor("op_4856_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1644157184)))]; + tensor unet_down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1657264448)))]; + tensor var_4856_cast = conv(bias = unet_down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_4854, groups = var_31, pad = var_4856_pad_0, pad_type = var_4856_pad_type_0, strides = var_4852, weight = unet_down_blocks_2_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16, x = input_307_cast)[name = tensor("op_4856_cast")]; + tensor hidden_states_197_cast = add(x = var_4856_cast, y = inputs_143_cast)[name = tensor("hidden_states_197_cast")]; + tensor var_4858 = const()[name = tensor("op_4858"), val = tensor([2, 1280, 32, 32])]; + tensor input_309_cast = reshape(shape = var_4858, x = hidden_states_197_cast)[name = tensor("input_309_cast")]; + tensor var_4862 = const()[name = tensor("op_4862"), val = tensor([1, 1])]; + tensor var_4864 = const()[name = tensor("op_4864"), val = tensor([1, 1])]; tensor hidden_states_199_pad_type_0 = const()[name = tensor("hidden_states_199_pad_type_0"), val = tensor("custom")]; tensor hidden_states_199_pad_0 = const()[name = tensor("hidden_states_199_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_199 = conv(bias = down_blocks_2_attentions_1_proj_out_bias, dilations = var_4926, groups = var_1193, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = var_4924, weight = down_blocks_2_attentions_1_proj_out_weight, x = input_309)[name = tensor("hidden_states_199")]; - tensor input_311 = add(x = hidden_states_199, y = hidden_states_133)[name = tensor("input_311")]; - tensor var_4934 = const()[name = tensor("op_4934"), val = tensor(3)]; - tensor var_4941 = const()[name = tensor("op_4941"), val = tensor(0x1p-3)]; - tensor var_4945 = const()[name = tensor("op_4945"), val = tensor(true)]; - tensor var_4950 = const()[name = tensor("op_4950"), val = tensor(1)]; + tensor unet_down_blocks_2_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1657267072)))]; + tensor unet_down_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("unet_down_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1660543936)))]; + tensor hidden_states_199_cast = conv(bias = unet_down_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_4864, groups = var_31, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = var_4862, weight = unet_down_blocks_2_attentions_1_proj_out_weight_to_fp16, x = input_309_cast)[name = tensor("hidden_states_199_cast")]; + tensor input_311_cast = add(x = hidden_states_199_cast, y = hidden_states_133_cast)[name = tensor("input_311_cast")]; tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([2, 32, 40, 32, 32])]; - tensor reshape_64 = reshape(shape = reshape_64_shape_0, x = input_311)[name = tensor("reshape_64")]; + tensor reshape_64_cast = reshape(shape = reshape_64_shape_0, x = input_311_cast)[name = tensor("reshape_64_cast")]; tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_48_keep_dims_0 = const()[name = tensor("reduce_mean_48_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_48 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64)[name = tensor("reduce_mean_48")]; - tensor sub_32 = sub(x = reshape_64, y = reduce_mean_48)[name = tensor("sub_32")]; - tensor square_16 = square(x = sub_32)[name = tensor("square_16")]; + tensor reduce_mean_48_cast = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast)[name = tensor("reduce_mean_48_cast")]; + tensor sub_32_cast = sub(x = reshape_64_cast, y = reduce_mean_48_cast)[name = tensor("sub_32_cast")]; + tensor square_16_cast = square(x = sub_32_cast)[name = tensor("square_16_cast")]; tensor reduce_mean_50_axes_0 = const()[name = tensor("reduce_mean_50_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_50_keep_dims_0 = const()[name = tensor("reduce_mean_50_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_50 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16)[name = tensor("reduce_mean_50")]; - tensor add_32_y_0 = const()[name = tensor("add_32_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_32 = add(x = reduce_mean_50, y = add_32_y_0)[name = tensor("add_32")]; - tensor sqrt_16 = sqrt(x = add_32)[name = tensor("sqrt_16")]; - tensor real_div_16 = real_div(x = sub_32, y = sqrt_16)[name = tensor("real_div_16")]; + tensor reduce_mean_50_cast = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast)[name = tensor("reduce_mean_50_cast")]; + tensor add_32_y_0_to_fp16 = const()[name = tensor("add_32_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_32_cast = add(x = reduce_mean_50_cast, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast")]; + tensor sqrt_16_cast = sqrt(x = add_32_cast)[name = tensor("sqrt_16_cast")]; + tensor real_div_16_cast = real_div(x = sub_32_cast, y = sqrt_16_cast)[name = tensor("real_div_16_cast")]; tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([2, 1280, 32, 32])]; - tensor reshape_65 = reshape(shape = reshape_65_shape_0, x = real_div_16)[name = tensor("reshape_65")]; - tensor add_33_gamma_0 = const()[name = tensor("add_33_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268378240)))]; - tensor add_33_beta_0 = const()[name = tensor("add_33_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268383424)))]; - tensor add_33_epsilon_0 = const()[name = tensor("add_33_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_33 = batch_norm(beta = add_33_beta_0, epsilon = add_33_epsilon_0, gamma = add_33_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_65)[name = tensor("add_33")]; - tensor input_315 = silu(x = add_33)[name = tensor("input_315")]; - tensor var_4968 = const()[name = tensor("op_4968"), val = tensor([1, 1])]; - tensor var_4970 = const()[name = tensor("op_4970"), val = tensor([1, 1])]; + tensor reshape_65_cast = reshape(shape = reshape_65_shape_0, x = real_div_16_cast)[name = tensor("reshape_65_cast")]; + tensor add_33_gamma_0_to_fp16 = const()[name = tensor("add_33_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1660546560)))]; + tensor add_33_beta_0_to_fp16 = const()[name = tensor("add_33_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1660549184)))]; + tensor add_33_epsilon_0_to_fp16 = const()[name = tensor("add_33_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_33_cast = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_65_cast)[name = tensor("add_33_cast")]; + tensor input_315_cast = silu(x = add_33_cast)[name = tensor("input_315_cast")]; + tensor var_4888 = const()[name = tensor("op_4888"), val = tensor([1, 1])]; + tensor var_4890 = const()[name = tensor("op_4890"), val = tensor([1, 1])]; tensor hidden_states_201_pad_type_0 = const()[name = tensor("hidden_states_201_pad_type_0"), val = tensor("custom")]; tensor hidden_states_201_pad_0 = const()[name = tensor("hidden_states_201_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_201 = conv(bias = mid_block_resnets_0_conv1_bias, dilations = var_4970, groups = var_4950, pad = hidden_states_201_pad_0, pad_type = hidden_states_201_pad_type_0, strides = var_4968, weight = mid_block_resnets_0_conv1_weight, x = input_315)[name = tensor("hidden_states_201")]; - tensor var_4976 = const()[name = tensor("op_4976"), val = tensor([1, 1])]; - tensor var_4978 = const()[name = tensor("op_4978"), val = tensor([1, 1])]; + tensor unet_mid_block_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("unet_mid_block_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1660551808)))]; + tensor unet_mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("unet_mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1690043072)))]; + tensor hidden_states_201_cast = conv(bias = unet_mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_4890, groups = var_31, pad = hidden_states_201_pad_0, pad_type = hidden_states_201_pad_type_0, strides = var_4888, weight = unet_mid_block_resnets_0_conv1_weight_to_fp16, x = input_315_cast)[name = tensor("hidden_states_201_cast")]; + tensor var_4896 = const()[name = tensor("op_4896"), val = tensor([1, 1])]; + tensor var_4898 = const()[name = tensor("op_4898"), val = tensor([1, 1])]; tensor temb_13_pad_type_0 = const()[name = tensor("temb_13_pad_type_0"), val = tensor("custom")]; tensor temb_13_pad_0 = const()[name = tensor("temb_13_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb_13 = conv(bias = mid_block_resnets_0_time_emb_proj_bias, dilations = var_4978, groups = var_4950, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = var_4976, weight = mid_block_resnets_0_time_emb_proj_weight, x = input_21)[name = tensor("temb_13")]; - tensor input_319 = add(x = hidden_states_201, y = temb_13)[name = tensor("input_319")]; + tensor unet_mid_block_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_mid_block_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1690045696)))]; + tensor unet_mid_block_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_mid_block_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1693322560)))]; + tensor temb_13_cast = conv(bias = unet_mid_block_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_4898, groups = var_31, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = var_4896, weight = unet_mid_block_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_13_cast")]; + tensor input_319_cast = add(x = hidden_states_201_cast, y = temb_13_cast)[name = tensor("input_319_cast")]; tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([2, 32, 40, 32, 32])]; - tensor reshape_68 = reshape(shape = reshape_68_shape_0, x = input_319)[name = tensor("reshape_68")]; + tensor reshape_68_cast = reshape(shape = reshape_68_shape_0, x = input_319_cast)[name = tensor("reshape_68_cast")]; tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_51_keep_dims_0 = const()[name = tensor("reduce_mean_51_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_51 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68)[name = tensor("reduce_mean_51")]; - tensor sub_34 = sub(x = reshape_68, y = reduce_mean_51)[name = tensor("sub_34")]; - tensor square_17 = square(x = sub_34)[name = tensor("square_17")]; + tensor reduce_mean_51_cast = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast)[name = tensor("reduce_mean_51_cast")]; + tensor sub_34_cast = sub(x = reshape_68_cast, y = reduce_mean_51_cast)[name = tensor("sub_34_cast")]; + tensor square_17_cast = square(x = sub_34_cast)[name = tensor("square_17_cast")]; tensor reduce_mean_53_axes_0 = const()[name = tensor("reduce_mean_53_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_53_keep_dims_0 = const()[name = tensor("reduce_mean_53_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_53 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17)[name = tensor("reduce_mean_53")]; - tensor add_34_y_0 = const()[name = tensor("add_34_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_34 = add(x = reduce_mean_53, y = add_34_y_0)[name = tensor("add_34")]; - tensor sqrt_17 = sqrt(x = add_34)[name = tensor("sqrt_17")]; - tensor real_div_17 = real_div(x = sub_34, y = sqrt_17)[name = tensor("real_div_17")]; + tensor reduce_mean_53_cast = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast)[name = tensor("reduce_mean_53_cast")]; + tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_34_cast = add(x = reduce_mean_53_cast, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast")]; + tensor sqrt_17_cast = sqrt(x = add_34_cast)[name = tensor("sqrt_17_cast")]; + tensor real_div_17_cast = real_div(x = sub_34_cast, y = sqrt_17_cast)[name = tensor("real_div_17_cast")]; tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([2, 1280, 32, 32])]; - tensor reshape_69 = reshape(shape = reshape_69_shape_0, x = real_div_17)[name = tensor("reshape_69")]; - tensor add_35_gamma_0 = const()[name = tensor("add_35_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268388608)))]; - tensor add_35_beta_0 = const()[name = tensor("add_35_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268393792)))]; - tensor add_35_epsilon_0 = const()[name = tensor("add_35_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_35 = batch_norm(beta = add_35_beta_0, epsilon = add_35_epsilon_0, gamma = add_35_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_69)[name = tensor("add_35")]; - tensor input_323 = silu(x = add_35)[name = tensor("input_323")]; - tensor var_4988 = const()[name = tensor("op_4988"), val = tensor([1, 1])]; - tensor var_4990 = const()[name = tensor("op_4990"), val = tensor([1, 1])]; + tensor reshape_69_cast = reshape(shape = reshape_69_shape_0, x = real_div_17_cast)[name = tensor("reshape_69_cast")]; + tensor add_35_gamma_0_to_fp16 = const()[name = tensor("add_35_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1693325184)))]; + tensor add_35_beta_0_to_fp16 = const()[name = tensor("add_35_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1693327808)))]; + tensor add_35_epsilon_0_to_fp16 = const()[name = tensor("add_35_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_35_cast = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_69_cast)[name = tensor("add_35_cast")]; + tensor input_323_cast = silu(x = add_35_cast)[name = tensor("input_323_cast")]; + tensor var_4908 = const()[name = tensor("op_4908"), val = tensor([1, 1])]; + tensor var_4910 = const()[name = tensor("op_4910"), val = tensor([1, 1])]; tensor hidden_states_203_pad_type_0 = const()[name = tensor("hidden_states_203_pad_type_0"), val = tensor("custom")]; tensor hidden_states_203_pad_0 = const()[name = tensor("hidden_states_203_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_203 = conv(bias = mid_block_resnets_0_conv2_bias, dilations = var_4990, groups = var_4950, pad = hidden_states_203_pad_0, pad_type = hidden_states_203_pad_type_0, strides = var_4988, weight = mid_block_resnets_0_conv2_weight, x = input_323)[name = tensor("hidden_states_203")]; - tensor hidden_states_205 = add(x = input_311, y = hidden_states_203)[name = tensor("hidden_states_205")]; + tensor unet_mid_block_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("unet_mid_block_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1693330432)))]; + tensor unet_mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("unet_mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722821696)))]; + tensor hidden_states_203_cast = conv(bias = unet_mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_4910, groups = var_31, pad = hidden_states_203_pad_0, pad_type = hidden_states_203_pad_type_0, strides = var_4908, weight = unet_mid_block_resnets_0_conv2_weight_to_fp16, x = input_323_cast)[name = tensor("hidden_states_203_cast")]; + tensor hidden_states_205_cast = add(x = input_311_cast, y = hidden_states_203_cast)[name = tensor("hidden_states_205_cast")]; tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([2, 32, 40, 32, 32])]; - tensor reshape_72 = reshape(shape = reshape_72_shape_0, x = hidden_states_205)[name = tensor("reshape_72")]; + tensor reshape_72_cast = reshape(shape = reshape_72_shape_0, x = hidden_states_205_cast)[name = tensor("reshape_72_cast")]; tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_54 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72)[name = tensor("reduce_mean_54")]; - tensor sub_36 = sub(x = reshape_72, y = reduce_mean_54)[name = tensor("sub_36")]; - tensor square_18 = square(x = sub_36)[name = tensor("square_18")]; + tensor reduce_mean_54_cast = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast)[name = tensor("reduce_mean_54_cast")]; + tensor sub_36_cast = sub(x = reshape_72_cast, y = reduce_mean_54_cast)[name = tensor("sub_36_cast")]; + tensor square_18_cast = square(x = sub_36_cast)[name = tensor("square_18_cast")]; tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_56 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18)[name = tensor("reduce_mean_56")]; - tensor add_36_y_0 = const()[name = tensor("add_36_y_0"), val = tensor(0x1.0c6f7ap-20)]; - tensor add_36 = add(x = reduce_mean_56, y = add_36_y_0)[name = tensor("add_36")]; - tensor sqrt_18 = sqrt(x = add_36)[name = tensor("sqrt_18")]; - tensor real_div_18 = real_div(x = sub_36, y = sqrt_18)[name = tensor("real_div_18")]; + tensor reduce_mean_56_cast = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast)[name = tensor("reduce_mean_56_cast")]; + tensor add_36_y_0_to_fp16 = const()[name = tensor("add_36_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_36_cast = add(x = reduce_mean_56_cast, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast")]; + tensor sqrt_18_cast = sqrt(x = add_36_cast)[name = tensor("sqrt_18_cast")]; + tensor real_div_18_cast = real_div(x = sub_36_cast, y = sqrt_18_cast)[name = tensor("real_div_18_cast")]; tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([2, 1280, 32, 32])]; - tensor reshape_73 = reshape(shape = reshape_73_shape_0, x = real_div_18)[name = tensor("reshape_73")]; - tensor add_37_gamma_0 = const()[name = tensor("add_37_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268398976)))]; - tensor add_37_beta_0 = const()[name = tensor("add_37_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268404160)))]; - tensor add_37_epsilon_0 = const()[name = tensor("add_37_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_37 = batch_norm(beta = add_37_beta_0, epsilon = add_37_epsilon_0, gamma = add_37_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_73)[name = tensor("add_37")]; - tensor var_5028 = const()[name = tensor("op_5028"), val = tensor([1, 1])]; - tensor var_5030 = const()[name = tensor("op_5030"), val = tensor([1, 1])]; + tensor reshape_73_cast = reshape(shape = reshape_73_shape_0, x = real_div_18_cast)[name = tensor("reshape_73_cast")]; + tensor add_37_gamma_0_to_fp16 = const()[name = tensor("add_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722824320)))]; + tensor add_37_beta_0_to_fp16 = const()[name = tensor("add_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722826944)))]; + tensor add_37_epsilon_0_to_fp16 = const()[name = tensor("add_37_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_37_cast = batch_norm(beta = add_37_beta_0_to_fp16, epsilon = add_37_epsilon_0_to_fp16, gamma = add_37_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_73_cast)[name = tensor("add_37_cast")]; + tensor var_4948 = const()[name = tensor("op_4948"), val = tensor([1, 1])]; + tensor var_4950 = const()[name = tensor("op_4950"), val = tensor([1, 1])]; tensor hidden_states_207_pad_type_0 = const()[name = tensor("hidden_states_207_pad_type_0"), val = tensor("custom")]; tensor hidden_states_207_pad_0 = const()[name = tensor("hidden_states_207_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_207 = conv(bias = mid_block_attentions_0_proj_in_bias, dilations = var_5030, groups = var_4950, pad = hidden_states_207_pad_0, pad_type = hidden_states_207_pad_type_0, strides = var_5028, weight = mid_block_attentions_0_proj_in_weight, x = add_37)[name = tensor("hidden_states_207")]; - tensor var_5035 = const()[name = tensor("op_5035"), val = tensor([2, 1280, 1, 1024])]; - tensor inputs_145 = reshape(shape = var_5035, x = hidden_states_207)[name = tensor("inputs_145")]; - tensor var_5045 = const()[name = tensor("op_5045"), val = tensor([1])]; - tensor channels_mean_145 = reduce_mean(axes = var_5045, keep_dims = var_4945, x = inputs_145)[name = tensor("channels_mean_145")]; - tensor zero_mean_145 = sub(x = inputs_145, y = channels_mean_145)[name = tensor("zero_mean_145")]; - tensor zero_mean_sq_145 = mul(x = zero_mean_145, y = zero_mean_145)[name = tensor("zero_mean_sq_145")]; - tensor var_5049 = const()[name = tensor("op_5049"), val = tensor([1])]; - tensor var_5050 = reduce_mean(axes = var_5049, keep_dims = var_4945, x = zero_mean_sq_145)[name = tensor("op_5050")]; - tensor var_5051 = const()[name = tensor("op_5051"), val = tensor(0x1.4f8b58p-17)]; - tensor var_5052 = add(x = var_5050, y = var_5051)[name = tensor("op_5052")]; - tensor denom_145_epsilon_0 = const()[name = tensor("denom_145_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_145 = rsqrt(epsilon = denom_145_epsilon_0, x = var_5052)[name = tensor("denom_145")]; - tensor out_145 = mul(x = zero_mean_145, y = denom_145)[name = tensor("out_145")]; - tensor var_5056 = const()[name = tensor("op_5056"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268409344)))]; - tensor var_5057 = add(x = out_145, y = var_5056)[name = tensor("op_5057")]; - tensor var_5059 = const()[name = tensor("op_5059"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268414528)))]; - tensor hidden_states_209 = mul(x = var_5057, y = var_5059)[name = tensor("hidden_states_209")]; - tensor var_5066 = const()[name = tensor("op_5066"), val = tensor([1, 1])]; - tensor var_5068 = const()[name = tensor("op_5068"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1722829568)))]; + tensor unet_mid_block_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726106432)))]; + tensor hidden_states_207_cast = conv(bias = unet_mid_block_attentions_0_proj_in_bias_to_fp16, dilations = var_4950, groups = var_31, pad = hidden_states_207_pad_0, pad_type = hidden_states_207_pad_type_0, strides = var_4948, weight = unet_mid_block_attentions_0_proj_in_weight_to_fp16, x = add_37_cast)[name = tensor("hidden_states_207_cast")]; + tensor var_4955 = const()[name = tensor("op_4955"), val = tensor([2, 1280, 1, 1024])]; + tensor inputs_145_cast = reshape(shape = var_4955, x = hidden_states_207_cast)[name = tensor("inputs_145_cast")]; + tensor var_4965 = const()[name = tensor("op_4965"), val = tensor([1])]; + tensor channels_mean_145_cast = reduce_mean(axes = var_4965, keep_dims = var_23, x = inputs_145_cast)[name = tensor("channels_mean_145_cast")]; + tensor zero_mean_145_cast = sub(x = inputs_145_cast, y = channels_mean_145_cast)[name = tensor("zero_mean_145_cast")]; + tensor zero_mean_sq_145_cast = mul(x = zero_mean_145_cast, y = zero_mean_145_cast)[name = tensor("zero_mean_sq_145_cast")]; + tensor var_4969 = const()[name = tensor("op_4969"), val = tensor([1])]; + tensor var_4970_cast = reduce_mean(axes = var_4969, keep_dims = var_23, x = zero_mean_sq_145_cast)[name = tensor("op_4970_cast")]; + tensor var_4971_to_fp16 = const()[name = tensor("op_4971_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4972_cast = add(x = var_4970_cast, y = var_4971_to_fp16)[name = tensor("op_4972_cast")]; + tensor denom_145_epsilon_0_to_fp16 = const()[name = tensor("denom_145_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_145_cast = rsqrt(epsilon = denom_145_epsilon_0_to_fp16, x = var_4972_cast)[name = tensor("denom_145_cast")]; + tensor out_145_cast = mul(x = zero_mean_145_cast, y = denom_145_cast)[name = tensor("out_145_cast")]; + tensor var_4976_to_fp16 = const()[name = tensor("op_4976_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726109056)))]; + tensor var_4977_cast = add(x = out_145_cast, y = var_4976_to_fp16)[name = tensor("op_4977_cast")]; + tensor var_4979_to_fp16 = const()[name = tensor("op_4979_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726111680)))]; + tensor hidden_states_209_cast = mul(x = var_4977_cast, y = var_4979_to_fp16)[name = tensor("hidden_states_209_cast")]; + tensor var_4986 = const()[name = tensor("op_4986"), val = tensor([1, 1])]; + tensor var_4988 = const()[name = tensor("op_4988"), val = tensor([1, 1])]; tensor q_97_pad_type_0 = const()[name = tensor("q_97_pad_type_0"), val = tensor("custom")]; tensor q_97_pad_0 = const()[name = tensor("q_97_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_97 = conv(dilations = var_5068, groups = var_4950, pad = q_97_pad_0, pad_type = q_97_pad_type_0, strides = var_5066, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight, x = hidden_states_209)[name = tensor("q_97")]; - tensor var_5072 = const()[name = tensor("op_5072"), val = tensor([1, 1])]; - tensor var_5074 = const()[name = tensor("op_5074"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1726114304)))]; + tensor q_97_cast = conv(dilations = var_4988, groups = var_31, pad = q_97_pad_0, pad_type = q_97_pad_type_0, strides = var_4986, weight = unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_209_cast)[name = tensor("q_97_cast")]; + tensor var_4992 = const()[name = tensor("op_4992"), val = tensor([1, 1])]; + tensor var_4994 = const()[name = tensor("op_4994"), val = tensor([1, 1])]; tensor k_97_pad_type_0 = const()[name = tensor("k_97_pad_type_0"), val = tensor("custom")]; tensor k_97_pad_0 = const()[name = tensor("k_97_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_97 = conv(dilations = var_5074, groups = var_4950, pad = k_97_pad_0, pad_type = k_97_pad_type_0, strides = var_5072, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight, x = hidden_states_209)[name = tensor("k_97")]; - tensor var_5078 = const()[name = tensor("op_5078"), val = tensor([1, 1])]; - tensor var_5080 = const()[name = tensor("op_5080"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1729391168)))]; + tensor k_97_cast = conv(dilations = var_4994, groups = var_31, pad = k_97_pad_0, pad_type = k_97_pad_type_0, strides = var_4992, weight = unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_209_cast)[name = tensor("k_97_cast")]; + tensor var_4998 = const()[name = tensor("op_4998"), val = tensor([1, 1])]; + tensor var_5000 = const()[name = tensor("op_5000"), val = tensor([1, 1])]; tensor v_97_pad_type_0 = const()[name = tensor("v_97_pad_type_0"), val = tensor("custom")]; tensor v_97_pad_0 = const()[name = tensor("v_97_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_97 = conv(dilations = var_5080, groups = var_4950, pad = v_97_pad_0, pad_type = v_97_pad_type_0, strides = var_5078, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight, x = hidden_states_209)[name = tensor("v_97")]; - tensor var_5084 = const()[name = tensor("op_5084"), val = tensor([2, 20, 64, -1])]; - tensor var_5085 = reshape(shape = var_5084, x = q_97)[name = tensor("op_5085")]; - tensor var_5086 = const()[name = tensor("op_5086"), val = tensor([2, 20, 64, -1])]; - tensor var_5087 = reshape(shape = var_5086, x = k_97)[name = tensor("op_5087")]; - tensor var_5088 = const()[name = tensor("op_5088"), val = tensor([2, 20, 64, -1])]; - tensor var_5089 = reshape(shape = var_5088, x = v_97)[name = tensor("op_5089")]; + tensor unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1732668032)))]; + tensor v_97_cast = conv(dilations = var_5000, groups = var_31, pad = v_97_pad_0, pad_type = v_97_pad_type_0, strides = var_4998, weight = unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_209_cast)[name = tensor("v_97_cast")]; + tensor var_5004 = const()[name = tensor("op_5004"), val = tensor([2, 20, 64, -1])]; + tensor var_5005_cast = reshape(shape = var_5004, x = q_97_cast)[name = tensor("op_5005_cast")]; + tensor var_5006 = const()[name = tensor("op_5006"), val = tensor([2, 20, 64, -1])]; + tensor var_5007_cast = reshape(shape = var_5006, x = k_97_cast)[name = tensor("op_5007_cast")]; + tensor var_5008 = const()[name = tensor("op_5008"), val = tensor([2, 20, 64, -1])]; + tensor var_5009_cast = reshape(shape = var_5008, x = v_97_cast)[name = tensor("op_5009_cast")]; tensor attn_weights_193_transpose_x_0 = const()[name = tensor("attn_weights_193_transpose_x_0"), val = tensor(true)]; tensor attn_weights_193_transpose_y_0 = const()[name = tensor("attn_weights_193_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_193 = matmul(transpose_x = attn_weights_193_transpose_x_0, transpose_y = attn_weights_193_transpose_y_0, x = var_5085, y = var_5087)[name = tensor("attn_weights_193")]; - tensor attn_weights_195 = mul(x = attn_weights_193, y = var_4941)[name = tensor("attn_weights_195")]; - tensor var_5093 = softmax(axis = var_4934, x = attn_weights_195)[name = tensor("op_5093")]; + tensor attn_weights_193_cast = matmul(transpose_x = attn_weights_193_transpose_x_0, transpose_y = attn_weights_193_transpose_y_0, x = var_5005_cast, y = var_5007_cast)[name = tensor("attn_weights_193_cast")]; + tensor attn_weights_195_cast = mul(x = attn_weights_193_cast, y = var_12_to_fp16)[name = tensor("attn_weights_195_cast")]; + tensor var_5013_cast = softmax(axis = var_18, x = attn_weights_195_cast)[name = tensor("op_5013_cast")]; tensor attn_97_transpose_x_0 = const()[name = tensor("attn_97_transpose_x_0"), val = tensor(false)]; tensor attn_97_transpose_y_0 = const()[name = tensor("attn_97_transpose_y_0"), val = tensor(true)]; - tensor attn_97 = matmul(transpose_x = attn_97_transpose_x_0, transpose_y = attn_97_transpose_y_0, x = var_5089, y = var_5093)[name = tensor("attn_97")]; - tensor var_5097 = const()[name = tensor("op_5097"), val = tensor([2, 1280, 1, -1])]; - tensor input_327 = reshape(shape = var_5097, x = attn_97)[name = tensor("input_327")]; - tensor var_5102 = const()[name = tensor("op_5102"), val = tensor([1, 1])]; - tensor var_5104 = const()[name = tensor("op_5104"), val = tensor([1, 1])]; - tensor var_5106_pad_type_0 = const()[name = tensor("op_5106_pad_type_0"), val = tensor("custom")]; - tensor var_5106_pad_0 = const()[name = tensor("op_5106_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5106 = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias, dilations = var_5104, groups = var_4950, pad = var_5106_pad_0, pad_type = var_5106_pad_type_0, strides = var_5102, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight, x = input_327)[name = tensor("op_5106")]; - tensor inputs_147 = add(x = var_5106, y = inputs_145)[name = tensor("inputs_147")]; - tensor var_5110 = const()[name = tensor("op_5110"), val = tensor([1])]; - tensor channels_mean_147 = reduce_mean(axes = var_5110, keep_dims = var_4945, x = inputs_147)[name = tensor("channels_mean_147")]; - tensor zero_mean_147 = sub(x = inputs_147, y = channels_mean_147)[name = tensor("zero_mean_147")]; - tensor zero_mean_sq_147 = mul(x = zero_mean_147, y = zero_mean_147)[name = tensor("zero_mean_sq_147")]; - tensor var_5114 = const()[name = tensor("op_5114"), val = tensor([1])]; - tensor var_5115 = reduce_mean(axes = var_5114, keep_dims = var_4945, x = zero_mean_sq_147)[name = tensor("op_5115")]; - tensor var_5116 = const()[name = tensor("op_5116"), val = tensor(0x1.4f8b58p-17)]; - tensor var_5117 = add(x = var_5115, y = var_5116)[name = tensor("op_5117")]; - tensor denom_147_epsilon_0 = const()[name = tensor("denom_147_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_147 = rsqrt(epsilon = denom_147_epsilon_0, x = var_5117)[name = tensor("denom_147")]; - tensor out_147 = mul(x = zero_mean_147, y = denom_147)[name = tensor("out_147")]; - tensor var_5121 = const()[name = tensor("op_5121"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268419712)))]; - tensor var_5122 = add(x = out_147, y = var_5121)[name = tensor("op_5122")]; - tensor var_5124 = const()[name = tensor("op_5124"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268424896)))]; - tensor hidden_states_211 = mul(x = var_5122, y = var_5124)[name = tensor("hidden_states_211")]; - tensor var_5131 = const()[name = tensor("op_5131"), val = tensor([1, 1])]; - tensor var_5133 = const()[name = tensor("op_5133"), val = tensor([1, 1])]; + tensor attn_97_cast = matmul(transpose_x = attn_97_transpose_x_0, transpose_y = attn_97_transpose_y_0, x = var_5009_cast, y = var_5013_cast)[name = tensor("attn_97_cast")]; + tensor var_5017 = const()[name = tensor("op_5017"), val = tensor([2, 1280, 1, -1])]; + tensor input_327_cast = reshape(shape = var_5017, x = attn_97_cast)[name = tensor("input_327_cast")]; + tensor var_5022 = const()[name = tensor("op_5022"), val = tensor([1, 1])]; + tensor var_5024 = const()[name = tensor("op_5024"), val = tensor([1, 1])]; + tensor var_5026_pad_type_0 = const()[name = tensor("op_5026_pad_type_0"), val = tensor("custom")]; + tensor var_5026_pad_0 = const()[name = tensor("op_5026_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1735944896)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1739221760)))]; + tensor var_5026_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_5024, groups = var_31, pad = var_5026_pad_0, pad_type = var_5026_pad_type_0, strides = var_5022, weight = unet_mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_327_cast)[name = tensor("op_5026_cast")]; + tensor inputs_147_cast = add(x = var_5026_cast, y = inputs_145_cast)[name = tensor("inputs_147_cast")]; + tensor var_5030 = const()[name = tensor("op_5030"), val = tensor([1])]; + tensor channels_mean_147_cast = reduce_mean(axes = var_5030, keep_dims = var_23, x = inputs_147_cast)[name = tensor("channels_mean_147_cast")]; + tensor zero_mean_147_cast = sub(x = inputs_147_cast, y = channels_mean_147_cast)[name = tensor("zero_mean_147_cast")]; + tensor zero_mean_sq_147_cast = mul(x = zero_mean_147_cast, y = zero_mean_147_cast)[name = tensor("zero_mean_sq_147_cast")]; + tensor var_5034 = const()[name = tensor("op_5034"), val = tensor([1])]; + tensor var_5035_cast = reduce_mean(axes = var_5034, keep_dims = var_23, x = zero_mean_sq_147_cast)[name = tensor("op_5035_cast")]; + tensor var_5036_to_fp16 = const()[name = tensor("op_5036_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5037_cast = add(x = var_5035_cast, y = var_5036_to_fp16)[name = tensor("op_5037_cast")]; + tensor denom_147_epsilon_0_to_fp16 = const()[name = tensor("denom_147_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_147_cast = rsqrt(epsilon = denom_147_epsilon_0_to_fp16, x = var_5037_cast)[name = tensor("denom_147_cast")]; + tensor out_147_cast = mul(x = zero_mean_147_cast, y = denom_147_cast)[name = tensor("out_147_cast")]; + tensor var_5041_to_fp16 = const()[name = tensor("op_5041_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1739224384)))]; + tensor var_5042_cast = add(x = out_147_cast, y = var_5041_to_fp16)[name = tensor("op_5042_cast")]; + tensor var_5044_to_fp16 = const()[name = tensor("op_5044_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1739227008)))]; + tensor hidden_states_211_cast = mul(x = var_5042_cast, y = var_5044_to_fp16)[name = tensor("hidden_states_211_cast")]; + tensor var_5051 = const()[name = tensor("op_5051"), val = tensor([1, 1])]; + tensor var_5053 = const()[name = tensor("op_5053"), val = tensor([1, 1])]; tensor q_99_pad_type_0 = const()[name = tensor("q_99_pad_type_0"), val = tensor("custom")]; tensor q_99_pad_0 = const()[name = tensor("q_99_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_99 = conv(dilations = var_5133, groups = var_4950, pad = q_99_pad_0, pad_type = q_99_pad_type_0, strides = var_5131, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight, x = hidden_states_211)[name = tensor("q_99")]; - tensor var_5137 = const()[name = tensor("op_5137"), val = tensor([1, 1])]; - tensor var_5139 = const()[name = tensor("op_5139"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1739229632)))]; + tensor q_99_cast = conv(dilations = var_5053, groups = var_31, pad = q_99_pad_0, pad_type = q_99_pad_type_0, strides = var_5051, weight = unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_211_cast)[name = tensor("q_99_cast")]; + tensor var_5057 = const()[name = tensor("op_5057"), val = tensor([1, 1])]; + tensor var_5059 = const()[name = tensor("op_5059"), val = tensor([1, 1])]; tensor k_99_pad_type_0 = const()[name = tensor("k_99_pad_type_0"), val = tensor("custom")]; tensor k_99_pad_0 = const()[name = tensor("k_99_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_99 = conv(dilations = var_5139, groups = var_4950, pad = k_99_pad_0, pad_type = k_99_pad_type_0, strides = var_5137, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_99")]; - tensor var_5143 = const()[name = tensor("op_5143"), val = tensor([1, 1])]; - tensor var_5145 = const()[name = tensor("op_5145"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1742506496)))]; + tensor k_99_cast = conv(dilations = var_5059, groups = var_31, pad = k_99_pad_0, pad_type = k_99_pad_type_0, strides = var_5057, weight = unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_99_cast")]; + tensor var_5063 = const()[name = tensor("op_5063"), val = tensor([1, 1])]; + tensor var_5065 = const()[name = tensor("op_5065"), val = tensor([1, 1])]; tensor v_99_pad_type_0 = const()[name = tensor("v_99_pad_type_0"), val = tensor("custom")]; tensor v_99_pad_0 = const()[name = tensor("v_99_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_99 = conv(dilations = var_5145, groups = var_4950, pad = v_99_pad_0, pad_type = v_99_pad_type_0, strides = var_5143, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_99")]; - tensor var_5149 = const()[name = tensor("op_5149"), val = tensor([2, 20, 64, -1])]; - tensor var_5150 = reshape(shape = var_5149, x = q_99)[name = tensor("op_5150")]; - tensor var_5151 = const()[name = tensor("op_5151"), val = tensor([2, 20, 64, -1])]; - tensor var_5152 = reshape(shape = var_5151, x = k_99)[name = tensor("op_5152")]; - tensor var_5153 = const()[name = tensor("op_5153"), val = tensor([2, 20, 64, -1])]; - tensor var_5154 = reshape(shape = var_5153, x = v_99)[name = tensor("op_5154")]; + tensor unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1747749440)))]; + tensor v_99_cast = conv(dilations = var_5065, groups = var_31, pad = v_99_pad_0, pad_type = v_99_pad_type_0, strides = var_5063, weight = unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_99_cast")]; + tensor var_5069 = const()[name = tensor("op_5069"), val = tensor([2, 20, 64, -1])]; + tensor var_5070_cast = reshape(shape = var_5069, x = q_99_cast)[name = tensor("op_5070_cast")]; + tensor var_5071 = const()[name = tensor("op_5071"), val = tensor([2, 20, 64, -1])]; + tensor var_5072_cast = reshape(shape = var_5071, x = k_99_cast)[name = tensor("op_5072_cast")]; + tensor var_5073 = const()[name = tensor("op_5073"), val = tensor([2, 20, 64, -1])]; + tensor var_5074_cast = reshape(shape = var_5073, x = v_99_cast)[name = tensor("op_5074_cast")]; tensor attn_weights_197_transpose_x_0 = const()[name = tensor("attn_weights_197_transpose_x_0"), val = tensor(true)]; tensor attn_weights_197_transpose_y_0 = const()[name = tensor("attn_weights_197_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_197 = matmul(transpose_x = attn_weights_197_transpose_x_0, transpose_y = attn_weights_197_transpose_y_0, x = var_5150, y = var_5152)[name = tensor("attn_weights_197")]; - tensor attn_weights_199 = mul(x = attn_weights_197, y = var_4941)[name = tensor("attn_weights_199")]; - tensor var_5158 = softmax(axis = var_4934, x = attn_weights_199)[name = tensor("op_5158")]; + tensor attn_weights_197_cast = matmul(transpose_x = attn_weights_197_transpose_x_0, transpose_y = attn_weights_197_transpose_y_0, x = var_5070_cast, y = var_5072_cast)[name = tensor("attn_weights_197_cast")]; + tensor attn_weights_199_cast = mul(x = attn_weights_197_cast, y = var_12_to_fp16)[name = tensor("attn_weights_199_cast")]; + tensor var_5078_cast = softmax(axis = var_18, x = attn_weights_199_cast)[name = tensor("op_5078_cast")]; tensor attn_99_transpose_x_0 = const()[name = tensor("attn_99_transpose_x_0"), val = tensor(false)]; tensor attn_99_transpose_y_0 = const()[name = tensor("attn_99_transpose_y_0"), val = tensor(true)]; - tensor attn_99 = matmul(transpose_x = attn_99_transpose_x_0, transpose_y = attn_99_transpose_y_0, x = var_5154, y = var_5158)[name = tensor("attn_99")]; - tensor var_5162 = const()[name = tensor("op_5162"), val = tensor([2, 1280, 1, -1])]; - tensor input_329 = reshape(shape = var_5162, x = attn_99)[name = tensor("input_329")]; - tensor var_5167 = const()[name = tensor("op_5167"), val = tensor([1, 1])]; - tensor var_5169 = const()[name = tensor("op_5169"), val = tensor([1, 1])]; - tensor var_5171_pad_type_0 = const()[name = tensor("op_5171_pad_type_0"), val = tensor("custom")]; - tensor var_5171_pad_0 = const()[name = tensor("op_5171_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5171 = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias, dilations = var_5169, groups = var_4950, pad = var_5171_pad_0, pad_type = var_5171_pad_type_0, strides = var_5167, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight, x = input_329)[name = tensor("op_5171")]; - tensor inputs_149 = add(x = var_5171, y = inputs_147)[name = tensor("inputs_149")]; - tensor var_5175 = const()[name = tensor("op_5175"), val = tensor([1])]; - tensor channels_mean_149 = reduce_mean(axes = var_5175, keep_dims = var_4945, x = inputs_149)[name = tensor("channels_mean_149")]; - tensor zero_mean_149 = sub(x = inputs_149, y = channels_mean_149)[name = tensor("zero_mean_149")]; - tensor zero_mean_sq_149 = mul(x = zero_mean_149, y = zero_mean_149)[name = tensor("zero_mean_sq_149")]; - tensor var_5179 = const()[name = tensor("op_5179"), val = tensor([1])]; - tensor var_5180 = reduce_mean(axes = var_5179, keep_dims = var_4945, x = zero_mean_sq_149)[name = tensor("op_5180")]; - tensor var_5181 = const()[name = tensor("op_5181"), val = tensor(0x1.4f8b58p-17)]; - tensor var_5182 = add(x = var_5180, y = var_5181)[name = tensor("op_5182")]; - tensor denom_149_epsilon_0 = const()[name = tensor("denom_149_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_149 = rsqrt(epsilon = denom_149_epsilon_0, x = var_5182)[name = tensor("denom_149")]; - tensor out_149 = mul(x = zero_mean_149, y = denom_149)[name = tensor("out_149")]; - tensor var_5186 = const()[name = tensor("op_5186"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268430080)))]; - tensor var_5187 = add(x = out_149, y = var_5186)[name = tensor("op_5187")]; - tensor var_5189 = const()[name = tensor("op_5189"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268435264)))]; - tensor input_331 = mul(x = var_5187, y = var_5189)[name = tensor("input_331")]; - tensor var_5197 = const()[name = tensor("op_5197"), val = tensor([1, 1])]; - tensor var_5199 = const()[name = tensor("op_5199"), val = tensor([1, 1])]; - tensor var_5201_pad_type_0 = const()[name = tensor("op_5201_pad_type_0"), val = tensor("custom")]; - tensor var_5201_pad_0 = const()[name = tensor("op_5201_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5201 = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias, dilations = var_5199, groups = var_4950, pad = var_5201_pad_0, pad_type = var_5201_pad_type_0, strides = var_5197, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight, x = input_331)[name = tensor("op_5201")]; - tensor var_5202_split_sizes_0 = const()[name = tensor("op_5202_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_5202_axis_0 = const()[name = tensor("op_5202_axis_0"), val = tensor(1)]; - tensor var_5202_0, tensor var_5202_1 = split(axis = var_5202_axis_0, split_sizes = var_5202_split_sizes_0, x = var_5201)[name = tensor("op_5202")]; - tensor var_5204_mode_0 = const()[name = tensor("op_5204_mode_0"), val = tensor("EXACT")]; - tensor var_5204 = gelu(mode = var_5204_mode_0, x = var_5202_1)[name = tensor("op_5204")]; - tensor input_333 = mul(x = var_5202_0, y = var_5204)[name = tensor("input_333")]; - tensor var_5208 = const()[name = tensor("op_5208"), val = tensor([1, 1])]; - tensor var_5210 = const()[name = tensor("op_5210"), val = tensor([1, 1])]; - tensor var_5212_pad_type_0 = const()[name = tensor("op_5212_pad_type_0"), val = tensor("custom")]; - tensor var_5212_pad_0 = const()[name = tensor("op_5212_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5212 = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias, dilations = var_5210, groups = var_4950, pad = var_5212_pad_0, pad_type = var_5212_pad_type_0, strides = var_5208, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight, x = input_333)[name = tensor("op_5212")]; - tensor inputs_151 = add(x = var_5212, y = inputs_149)[name = tensor("inputs_151")]; - tensor var_5222 = const()[name = tensor("op_5222"), val = tensor([1])]; - tensor channels_mean_151 = reduce_mean(axes = var_5222, keep_dims = var_4945, x = inputs_151)[name = tensor("channels_mean_151")]; - tensor zero_mean_151 = sub(x = inputs_151, y = channels_mean_151)[name = tensor("zero_mean_151")]; - tensor zero_mean_sq_151 = mul(x = zero_mean_151, y = zero_mean_151)[name = tensor("zero_mean_sq_151")]; - tensor var_5226 = const()[name = tensor("op_5226"), val = tensor([1])]; - tensor var_5227 = reduce_mean(axes = var_5226, keep_dims = var_4945, x = zero_mean_sq_151)[name = tensor("op_5227")]; - tensor var_5228 = const()[name = tensor("op_5228"), val = tensor(0x1.4f8b58p-17)]; - tensor var_5229 = add(x = var_5227, y = var_5228)[name = tensor("op_5229")]; - tensor denom_151_epsilon_0 = const()[name = tensor("denom_151_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_151 = rsqrt(epsilon = denom_151_epsilon_0, x = var_5229)[name = tensor("denom_151")]; - tensor out_151 = mul(x = zero_mean_151, y = denom_151)[name = tensor("out_151")]; - tensor var_5233 = const()[name = tensor("op_5233"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268440448)))]; - tensor var_5234 = add(x = out_151, y = var_5233)[name = tensor("op_5234")]; - tensor var_5236 = const()[name = tensor("op_5236"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268445632)))]; - tensor hidden_states_215 = mul(x = var_5234, y = var_5236)[name = tensor("hidden_states_215")]; - tensor var_5243 = const()[name = tensor("op_5243"), val = tensor([1, 1])]; - tensor var_5245 = const()[name = tensor("op_5245"), val = tensor([1, 1])]; + tensor attn_99_cast = matmul(transpose_x = attn_99_transpose_x_0, transpose_y = attn_99_transpose_y_0, x = var_5074_cast, y = var_5078_cast)[name = tensor("attn_99_cast")]; + tensor var_5082 = const()[name = tensor("op_5082"), val = tensor([2, 1280, 1, -1])]; + tensor input_329_cast = reshape(shape = var_5082, x = attn_99_cast)[name = tensor("input_329_cast")]; + tensor var_5087 = const()[name = tensor("op_5087"), val = tensor([1, 1])]; + tensor var_5089 = const()[name = tensor("op_5089"), val = tensor([1, 1])]; + tensor var_5091_pad_type_0 = const()[name = tensor("op_5091_pad_type_0"), val = tensor("custom")]; + tensor var_5091_pad_0 = const()[name = tensor("op_5091_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1752992384)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1756269248)))]; + tensor var_5091_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_5089, groups = var_31, pad = var_5091_pad_0, pad_type = var_5091_pad_type_0, strides = var_5087, weight = unet_mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_329_cast)[name = tensor("op_5091_cast")]; + tensor inputs_149_cast = add(x = var_5091_cast, y = inputs_147_cast)[name = tensor("inputs_149_cast")]; + tensor var_5095 = const()[name = tensor("op_5095"), val = tensor([1])]; + tensor channels_mean_149_cast = reduce_mean(axes = var_5095, keep_dims = var_23, x = inputs_149_cast)[name = tensor("channels_mean_149_cast")]; + tensor zero_mean_149_cast = sub(x = inputs_149_cast, y = channels_mean_149_cast)[name = tensor("zero_mean_149_cast")]; + tensor zero_mean_sq_149_cast = mul(x = zero_mean_149_cast, y = zero_mean_149_cast)[name = tensor("zero_mean_sq_149_cast")]; + tensor var_5099 = const()[name = tensor("op_5099"), val = tensor([1])]; + tensor var_5100_cast = reduce_mean(axes = var_5099, keep_dims = var_23, x = zero_mean_sq_149_cast)[name = tensor("op_5100_cast")]; + tensor var_5101_to_fp16 = const()[name = tensor("op_5101_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5102_cast = add(x = var_5100_cast, y = var_5101_to_fp16)[name = tensor("op_5102_cast")]; + tensor denom_149_epsilon_0_to_fp16 = const()[name = tensor("denom_149_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_149_cast = rsqrt(epsilon = denom_149_epsilon_0_to_fp16, x = var_5102_cast)[name = tensor("denom_149_cast")]; + tensor out_149_cast = mul(x = zero_mean_149_cast, y = denom_149_cast)[name = tensor("out_149_cast")]; + tensor var_5106_to_fp16 = const()[name = tensor("op_5106_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1756271872)))]; + tensor var_5107_cast = add(x = out_149_cast, y = var_5106_to_fp16)[name = tensor("op_5107_cast")]; + tensor var_5109_to_fp16 = const()[name = tensor("op_5109_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1756274496)))]; + tensor input_331_cast = mul(x = var_5107_cast, y = var_5109_to_fp16)[name = tensor("input_331_cast")]; + tensor var_5117 = const()[name = tensor("op_5117"), val = tensor([1, 1])]; + tensor var_5119 = const()[name = tensor("op_5119"), val = tensor([1, 1])]; + tensor var_5121_pad_type_0 = const()[name = tensor("op_5121_pad_type_0"), val = tensor("custom")]; + tensor var_5121_pad_0 = const()[name = tensor("op_5121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1756277120)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1782491584)))]; + tensor var_5121_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_5119, groups = var_31, pad = var_5121_pad_0, pad_type = var_5121_pad_type_0, strides = var_5117, weight = unet_mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_331_cast)[name = tensor("op_5121_cast")]; + tensor var_5122_split_sizes_0 = const()[name = tensor("op_5122_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5122_axis_0 = const()[name = tensor("op_5122_axis_0"), val = tensor(1)]; + tensor var_5122_cast_0, tensor var_5122_cast_1 = split(axis = var_5122_axis_0, split_sizes = var_5122_split_sizes_0, x = var_5121_cast)[name = tensor("op_5122_cast")]; + tensor var_5124_mode_0 = const()[name = tensor("op_5124_mode_0"), val = tensor("EXACT")]; + tensor var_5124_cast = gelu(mode = var_5124_mode_0, x = var_5122_cast_1)[name = tensor("op_5124_cast")]; + tensor input_333_cast = mul(x = var_5122_cast_0, y = var_5124_cast)[name = tensor("input_333_cast")]; + tensor var_5128 = const()[name = tensor("op_5128"), val = tensor([1, 1])]; + tensor var_5130 = const()[name = tensor("op_5130"), val = tensor([1, 1])]; + tensor var_5132_pad_type_0 = const()[name = tensor("op_5132_pad_type_0"), val = tensor("custom")]; + tensor var_5132_pad_0 = const()[name = tensor("op_5132_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1782512128)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1795619392)))]; + tensor var_5132_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_5130, groups = var_31, pad = var_5132_pad_0, pad_type = var_5132_pad_type_0, strides = var_5128, weight = unet_mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_333_cast)[name = tensor("op_5132_cast")]; + tensor inputs_151_cast = add(x = var_5132_cast, y = inputs_149_cast)[name = tensor("inputs_151_cast")]; + tensor var_5142 = const()[name = tensor("op_5142"), val = tensor([1])]; + tensor channels_mean_151_cast = reduce_mean(axes = var_5142, keep_dims = var_23, x = inputs_151_cast)[name = tensor("channels_mean_151_cast")]; + tensor zero_mean_151_cast = sub(x = inputs_151_cast, y = channels_mean_151_cast)[name = tensor("zero_mean_151_cast")]; + tensor zero_mean_sq_151_cast = mul(x = zero_mean_151_cast, y = zero_mean_151_cast)[name = tensor("zero_mean_sq_151_cast")]; + tensor var_5146 = const()[name = tensor("op_5146"), val = tensor([1])]; + tensor var_5147_cast = reduce_mean(axes = var_5146, keep_dims = var_23, x = zero_mean_sq_151_cast)[name = tensor("op_5147_cast")]; + tensor var_5148_to_fp16 = const()[name = tensor("op_5148_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5149_cast = add(x = var_5147_cast, y = var_5148_to_fp16)[name = tensor("op_5149_cast")]; + tensor denom_151_epsilon_0_to_fp16 = const()[name = tensor("denom_151_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_151_cast = rsqrt(epsilon = denom_151_epsilon_0_to_fp16, x = var_5149_cast)[name = tensor("denom_151_cast")]; + tensor out_151_cast = mul(x = zero_mean_151_cast, y = denom_151_cast)[name = tensor("out_151_cast")]; + tensor var_5153_to_fp16 = const()[name = tensor("op_5153_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1795622016)))]; + tensor var_5154_cast = add(x = out_151_cast, y = var_5153_to_fp16)[name = tensor("op_5154_cast")]; + tensor var_5156_to_fp16 = const()[name = tensor("op_5156_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1795624640)))]; + tensor hidden_states_215_cast = mul(x = var_5154_cast, y = var_5156_to_fp16)[name = tensor("hidden_states_215_cast")]; + tensor var_5163 = const()[name = tensor("op_5163"), val = tensor([1, 1])]; + tensor var_5165 = const()[name = tensor("op_5165"), val = tensor([1, 1])]; tensor q_101_pad_type_0 = const()[name = tensor("q_101_pad_type_0"), val = tensor("custom")]; tensor q_101_pad_0 = const()[name = tensor("q_101_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_101 = conv(dilations = var_5245, groups = var_4950, pad = q_101_pad_0, pad_type = q_101_pad_type_0, strides = var_5243, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight, x = hidden_states_215)[name = tensor("q_101")]; - tensor var_5249 = const()[name = tensor("op_5249"), val = tensor([1, 1])]; - tensor var_5251 = const()[name = tensor("op_5251"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1795627264)))]; + tensor q_101_cast = conv(dilations = var_5165, groups = var_31, pad = q_101_pad_0, pad_type = q_101_pad_type_0, strides = var_5163, weight = unet_mid_block_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_215_cast)[name = tensor("q_101_cast")]; + tensor var_5169 = const()[name = tensor("op_5169"), val = tensor([1, 1])]; + tensor var_5171 = const()[name = tensor("op_5171"), val = tensor([1, 1])]; tensor k_101_pad_type_0 = const()[name = tensor("k_101_pad_type_0"), val = tensor("custom")]; tensor k_101_pad_0 = const()[name = tensor("k_101_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_101 = conv(dilations = var_5251, groups = var_4950, pad = k_101_pad_0, pad_type = k_101_pad_type_0, strides = var_5249, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight, x = hidden_states_215)[name = tensor("k_101")]; - tensor var_5255 = const()[name = tensor("op_5255"), val = tensor([1, 1])]; - tensor var_5257 = const()[name = tensor("op_5257"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1798904128)))]; + tensor k_101_cast = conv(dilations = var_5171, groups = var_31, pad = k_101_pad_0, pad_type = k_101_pad_type_0, strides = var_5169, weight = unet_mid_block_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_215_cast)[name = tensor("k_101_cast")]; + tensor var_5175 = const()[name = tensor("op_5175"), val = tensor([1, 1])]; + tensor var_5177 = const()[name = tensor("op_5177"), val = tensor([1, 1])]; tensor v_101_pad_type_0 = const()[name = tensor("v_101_pad_type_0"), val = tensor("custom")]; tensor v_101_pad_0 = const()[name = tensor("v_101_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_101 = conv(dilations = var_5257, groups = var_4950, pad = v_101_pad_0, pad_type = v_101_pad_type_0, strides = var_5255, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight, x = hidden_states_215)[name = tensor("v_101")]; - tensor var_5261 = const()[name = tensor("op_5261"), val = tensor([2, 20, 64, -1])]; - tensor var_5262 = reshape(shape = var_5261, x = q_101)[name = tensor("op_5262")]; - tensor var_5263 = const()[name = tensor("op_5263"), val = tensor([2, 20, 64, -1])]; - tensor var_5264 = reshape(shape = var_5263, x = k_101)[name = tensor("op_5264")]; - tensor var_5265 = const()[name = tensor("op_5265"), val = tensor([2, 20, 64, -1])]; - tensor var_5266 = reshape(shape = var_5265, x = v_101)[name = tensor("op_5266")]; + tensor unet_mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1802180992)))]; + tensor v_101_cast = conv(dilations = var_5177, groups = var_31, pad = v_101_pad_0, pad_type = v_101_pad_type_0, strides = var_5175, weight = unet_mid_block_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_215_cast)[name = tensor("v_101_cast")]; + tensor var_5181 = const()[name = tensor("op_5181"), val = tensor([2, 20, 64, -1])]; + tensor var_5182_cast = reshape(shape = var_5181, x = q_101_cast)[name = tensor("op_5182_cast")]; + tensor var_5183 = const()[name = tensor("op_5183"), val = tensor([2, 20, 64, -1])]; + tensor var_5184_cast = reshape(shape = var_5183, x = k_101_cast)[name = tensor("op_5184_cast")]; + tensor var_5185 = const()[name = tensor("op_5185"), val = tensor([2, 20, 64, -1])]; + tensor var_5186_cast = reshape(shape = var_5185, x = v_101_cast)[name = tensor("op_5186_cast")]; tensor attn_weights_201_transpose_x_0 = const()[name = tensor("attn_weights_201_transpose_x_0"), val = tensor(true)]; tensor attn_weights_201_transpose_y_0 = const()[name = tensor("attn_weights_201_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_201 = matmul(transpose_x = attn_weights_201_transpose_x_0, transpose_y = attn_weights_201_transpose_y_0, x = var_5262, y = var_5264)[name = tensor("attn_weights_201")]; - tensor attn_weights_203 = mul(x = attn_weights_201, y = var_4941)[name = tensor("attn_weights_203")]; - tensor var_5270 = softmax(axis = var_4934, x = attn_weights_203)[name = tensor("op_5270")]; + tensor attn_weights_201_cast = matmul(transpose_x = attn_weights_201_transpose_x_0, transpose_y = attn_weights_201_transpose_y_0, x = var_5182_cast, y = var_5184_cast)[name = tensor("attn_weights_201_cast")]; + tensor attn_weights_203_cast = mul(x = attn_weights_201_cast, y = var_12_to_fp16)[name = tensor("attn_weights_203_cast")]; + tensor var_5190_cast = softmax(axis = var_18, x = attn_weights_203_cast)[name = tensor("op_5190_cast")]; tensor attn_101_transpose_x_0 = const()[name = tensor("attn_101_transpose_x_0"), val = tensor(false)]; tensor attn_101_transpose_y_0 = const()[name = tensor("attn_101_transpose_y_0"), val = tensor(true)]; - tensor attn_101 = matmul(transpose_x = attn_101_transpose_x_0, transpose_y = attn_101_transpose_y_0, x = var_5266, y = var_5270)[name = tensor("attn_101")]; - tensor var_5274 = const()[name = tensor("op_5274"), val = tensor([2, 1280, 1, -1])]; - tensor input_335 = reshape(shape = var_5274, x = attn_101)[name = tensor("input_335")]; - tensor var_5279 = const()[name = tensor("op_5279"), val = tensor([1, 1])]; - tensor var_5281 = const()[name = tensor("op_5281"), val = tensor([1, 1])]; - tensor var_5283_pad_type_0 = const()[name = tensor("op_5283_pad_type_0"), val = tensor("custom")]; - tensor var_5283_pad_0 = const()[name = tensor("op_5283_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5283 = conv(bias = mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias, dilations = var_5281, groups = var_4950, pad = var_5283_pad_0, pad_type = var_5283_pad_type_0, strides = var_5279, weight = mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight, x = input_335)[name = tensor("op_5283")]; - tensor inputs_153 = add(x = var_5283, y = inputs_151)[name = tensor("inputs_153")]; - tensor var_5287 = const()[name = tensor("op_5287"), val = tensor([1])]; - tensor channels_mean_153 = reduce_mean(axes = var_5287, keep_dims = var_4945, x = inputs_153)[name = tensor("channels_mean_153")]; - tensor zero_mean_153 = sub(x = inputs_153, y = channels_mean_153)[name = tensor("zero_mean_153")]; - tensor zero_mean_sq_153 = mul(x = zero_mean_153, y = zero_mean_153)[name = tensor("zero_mean_sq_153")]; - tensor var_5291 = const()[name = tensor("op_5291"), val = tensor([1])]; - tensor var_5292 = reduce_mean(axes = var_5291, keep_dims = var_4945, x = zero_mean_sq_153)[name = tensor("op_5292")]; - tensor var_5293 = const()[name = tensor("op_5293"), val = tensor(0x1.4f8b58p-17)]; - tensor var_5294 = add(x = var_5292, y = var_5293)[name = tensor("op_5294")]; - tensor denom_153_epsilon_0 = const()[name = tensor("denom_153_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_153 = rsqrt(epsilon = denom_153_epsilon_0, x = var_5294)[name = tensor("denom_153")]; - tensor out_153 = mul(x = zero_mean_153, y = denom_153)[name = tensor("out_153")]; - tensor var_5298 = const()[name = tensor("op_5298"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268450816)))]; - tensor var_5299 = add(x = out_153, y = var_5298)[name = tensor("op_5299")]; - tensor var_5301 = const()[name = tensor("op_5301"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268456000)))]; - tensor hidden_states_217 = mul(x = var_5299, y = var_5301)[name = tensor("hidden_states_217")]; - tensor var_5308 = const()[name = tensor("op_5308"), val = tensor([1, 1])]; - tensor var_5310 = const()[name = tensor("op_5310"), val = tensor([1, 1])]; + tensor attn_101_cast = matmul(transpose_x = attn_101_transpose_x_0, transpose_y = attn_101_transpose_y_0, x = var_5186_cast, y = var_5190_cast)[name = tensor("attn_101_cast")]; + tensor var_5194 = const()[name = tensor("op_5194"), val = tensor([2, 1280, 1, -1])]; + tensor input_335_cast = reshape(shape = var_5194, x = attn_101_cast)[name = tensor("input_335_cast")]; + tensor var_5199 = const()[name = tensor("op_5199"), val = tensor([1, 1])]; + tensor var_5201 = const()[name = tensor("op_5201"), val = tensor([1, 1])]; + tensor var_5203_pad_type_0 = const()[name = tensor("op_5203_pad_type_0"), val = tensor("custom")]; + tensor var_5203_pad_0 = const()[name = tensor("op_5203_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1805457856)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1808734720)))]; + tensor var_5203_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_5201, groups = var_31, pad = var_5203_pad_0, pad_type = var_5203_pad_type_0, strides = var_5199, weight = unet_mid_block_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_335_cast)[name = tensor("op_5203_cast")]; + tensor inputs_153_cast = add(x = var_5203_cast, y = inputs_151_cast)[name = tensor("inputs_153_cast")]; + tensor var_5207 = const()[name = tensor("op_5207"), val = tensor([1])]; + tensor channels_mean_153_cast = reduce_mean(axes = var_5207, keep_dims = var_23, x = inputs_153_cast)[name = tensor("channels_mean_153_cast")]; + tensor zero_mean_153_cast = sub(x = inputs_153_cast, y = channels_mean_153_cast)[name = tensor("zero_mean_153_cast")]; + tensor zero_mean_sq_153_cast = mul(x = zero_mean_153_cast, y = zero_mean_153_cast)[name = tensor("zero_mean_sq_153_cast")]; + tensor var_5211 = const()[name = tensor("op_5211"), val = tensor([1])]; + tensor var_5212_cast = reduce_mean(axes = var_5211, keep_dims = var_23, x = zero_mean_sq_153_cast)[name = tensor("op_5212_cast")]; + tensor var_5213_to_fp16 = const()[name = tensor("op_5213_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5214_cast = add(x = var_5212_cast, y = var_5213_to_fp16)[name = tensor("op_5214_cast")]; + tensor denom_153_epsilon_0_to_fp16 = const()[name = tensor("denom_153_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_153_cast = rsqrt(epsilon = denom_153_epsilon_0_to_fp16, x = var_5214_cast)[name = tensor("denom_153_cast")]; + tensor out_153_cast = mul(x = zero_mean_153_cast, y = denom_153_cast)[name = tensor("out_153_cast")]; + tensor var_5218_to_fp16 = const()[name = tensor("op_5218_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1808737344)))]; + tensor var_5219_cast = add(x = out_153_cast, y = var_5218_to_fp16)[name = tensor("op_5219_cast")]; + tensor var_5221_to_fp16 = const()[name = tensor("op_5221_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1808739968)))]; + tensor hidden_states_217_cast = mul(x = var_5219_cast, y = var_5221_to_fp16)[name = tensor("hidden_states_217_cast")]; + tensor var_5228 = const()[name = tensor("op_5228"), val = tensor([1, 1])]; + tensor var_5230 = const()[name = tensor("op_5230"), val = tensor([1, 1])]; tensor q_103_pad_type_0 = const()[name = tensor("q_103_pad_type_0"), val = tensor("custom")]; tensor q_103_pad_0 = const()[name = tensor("q_103_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_103 = conv(dilations = var_5310, groups = var_4950, pad = q_103_pad_0, pad_type = q_103_pad_type_0, strides = var_5308, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight, x = hidden_states_217)[name = tensor("q_103")]; - tensor var_5314 = const()[name = tensor("op_5314"), val = tensor([1, 1])]; - tensor var_5316 = const()[name = tensor("op_5316"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1808742592)))]; + tensor q_103_cast = conv(dilations = var_5230, groups = var_31, pad = q_103_pad_0, pad_type = q_103_pad_type_0, strides = var_5228, weight = unet_mid_block_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_217_cast)[name = tensor("q_103_cast")]; + tensor var_5234 = const()[name = tensor("op_5234"), val = tensor([1, 1])]; + tensor var_5236 = const()[name = tensor("op_5236"), val = tensor([1, 1])]; tensor k_103_pad_type_0 = const()[name = tensor("k_103_pad_type_0"), val = tensor("custom")]; tensor k_103_pad_0 = const()[name = tensor("k_103_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_103 = conv(dilations = var_5316, groups = var_4950, pad = k_103_pad_0, pad_type = k_103_pad_type_0, strides = var_5314, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_103")]; - tensor var_5320 = const()[name = tensor("op_5320"), val = tensor([1, 1])]; - tensor var_5322 = const()[name = tensor("op_5322"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1812019456)))]; + tensor k_103_cast = conv(dilations = var_5236, groups = var_31, pad = k_103_pad_0, pad_type = k_103_pad_type_0, strides = var_5234, weight = unet_mid_block_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_103_cast")]; + tensor var_5240 = const()[name = tensor("op_5240"), val = tensor([1, 1])]; + tensor var_5242 = const()[name = tensor("op_5242"), val = tensor([1, 1])]; tensor v_103_pad_type_0 = const()[name = tensor("v_103_pad_type_0"), val = tensor("custom")]; tensor v_103_pad_0 = const()[name = tensor("v_103_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_103 = conv(dilations = var_5322, groups = var_4950, pad = v_103_pad_0, pad_type = v_103_pad_type_0, strides = var_5320, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_103")]; - tensor var_5326 = const()[name = tensor("op_5326"), val = tensor([2, 20, 64, -1])]; - tensor var_5327 = reshape(shape = var_5326, x = q_103)[name = tensor("op_5327")]; - tensor var_5328 = const()[name = tensor("op_5328"), val = tensor([2, 20, 64, -1])]; - tensor var_5329 = reshape(shape = var_5328, x = k_103)[name = tensor("op_5329")]; - tensor var_5330 = const()[name = tensor("op_5330"), val = tensor([2, 20, 64, -1])]; - tensor var_5331 = reshape(shape = var_5330, x = v_103)[name = tensor("op_5331")]; + tensor unet_mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1817262400)))]; + tensor v_103_cast = conv(dilations = var_5242, groups = var_31, pad = v_103_pad_0, pad_type = v_103_pad_type_0, strides = var_5240, weight = unet_mid_block_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_103_cast")]; + tensor var_5246 = const()[name = tensor("op_5246"), val = tensor([2, 20, 64, -1])]; + tensor var_5247_cast = reshape(shape = var_5246, x = q_103_cast)[name = tensor("op_5247_cast")]; + tensor var_5248 = const()[name = tensor("op_5248"), val = tensor([2, 20, 64, -1])]; + tensor var_5249_cast = reshape(shape = var_5248, x = k_103_cast)[name = tensor("op_5249_cast")]; + tensor var_5250 = const()[name = tensor("op_5250"), val = tensor([2, 20, 64, -1])]; + tensor var_5251_cast = reshape(shape = var_5250, x = v_103_cast)[name = tensor("op_5251_cast")]; tensor attn_weights_205_transpose_x_0 = const()[name = tensor("attn_weights_205_transpose_x_0"), val = tensor(true)]; tensor attn_weights_205_transpose_y_0 = const()[name = tensor("attn_weights_205_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_205 = matmul(transpose_x = attn_weights_205_transpose_x_0, transpose_y = attn_weights_205_transpose_y_0, x = var_5327, y = var_5329)[name = tensor("attn_weights_205")]; - tensor attn_weights_207 = mul(x = attn_weights_205, y = var_4941)[name = tensor("attn_weights_207")]; - tensor var_5335 = softmax(axis = var_4934, x = attn_weights_207)[name = tensor("op_5335")]; + tensor attn_weights_205_cast = matmul(transpose_x = attn_weights_205_transpose_x_0, transpose_y = attn_weights_205_transpose_y_0, x = var_5247_cast, y = var_5249_cast)[name = tensor("attn_weights_205_cast")]; + tensor attn_weights_207_cast = mul(x = attn_weights_205_cast, y = var_12_to_fp16)[name = tensor("attn_weights_207_cast")]; + tensor var_5255_cast = softmax(axis = var_18, x = attn_weights_207_cast)[name = tensor("op_5255_cast")]; tensor attn_103_transpose_x_0 = const()[name = tensor("attn_103_transpose_x_0"), val = tensor(false)]; tensor attn_103_transpose_y_0 = const()[name = tensor("attn_103_transpose_y_0"), val = tensor(true)]; - tensor attn_103 = matmul(transpose_x = attn_103_transpose_x_0, transpose_y = attn_103_transpose_y_0, x = var_5331, y = var_5335)[name = tensor("attn_103")]; - tensor var_5339 = const()[name = tensor("op_5339"), val = tensor([2, 1280, 1, -1])]; - tensor input_337 = reshape(shape = var_5339, x = attn_103)[name = tensor("input_337")]; - tensor var_5344 = const()[name = tensor("op_5344"), val = tensor([1, 1])]; - tensor var_5346 = const()[name = tensor("op_5346"), val = tensor([1, 1])]; - tensor var_5348_pad_type_0 = const()[name = tensor("op_5348_pad_type_0"), val = tensor("custom")]; - tensor var_5348_pad_0 = const()[name = tensor("op_5348_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5348 = conv(bias = mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias, dilations = var_5346, groups = var_4950, pad = var_5348_pad_0, pad_type = var_5348_pad_type_0, strides = var_5344, weight = mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight, x = input_337)[name = tensor("op_5348")]; - tensor inputs_155 = add(x = var_5348, y = inputs_153)[name = tensor("inputs_155")]; - tensor var_5352 = const()[name = tensor("op_5352"), val = tensor([1])]; - tensor channels_mean_155 = reduce_mean(axes = var_5352, keep_dims = var_4945, x = inputs_155)[name = tensor("channels_mean_155")]; - tensor zero_mean_155 = sub(x = inputs_155, y = channels_mean_155)[name = tensor("zero_mean_155")]; - tensor zero_mean_sq_155 = mul(x = zero_mean_155, y = zero_mean_155)[name = tensor("zero_mean_sq_155")]; - tensor var_5356 = const()[name = tensor("op_5356"), val = tensor([1])]; - tensor var_5357 = reduce_mean(axes = var_5356, keep_dims = var_4945, x = zero_mean_sq_155)[name = tensor("op_5357")]; - tensor var_5358 = const()[name = tensor("op_5358"), val = tensor(0x1.4f8b58p-17)]; - tensor var_5359 = add(x = var_5357, y = var_5358)[name = tensor("op_5359")]; - tensor denom_155_epsilon_0 = const()[name = tensor("denom_155_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_155 = rsqrt(epsilon = denom_155_epsilon_0, x = var_5359)[name = tensor("denom_155")]; - tensor out_155 = mul(x = zero_mean_155, y = denom_155)[name = tensor("out_155")]; - tensor var_5363 = const()[name = tensor("op_5363"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268461184)))]; - tensor var_5364 = add(x = out_155, y = var_5363)[name = tensor("op_5364")]; - tensor var_5366 = const()[name = tensor("op_5366"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268466368)))]; - tensor input_339 = mul(x = var_5364, y = var_5366)[name = tensor("input_339")]; - tensor var_5374 = const()[name = tensor("op_5374"), val = tensor([1, 1])]; - tensor var_5376 = const()[name = tensor("op_5376"), val = tensor([1, 1])]; - tensor var_5378_pad_type_0 = const()[name = tensor("op_5378_pad_type_0"), val = tensor("custom")]; - tensor var_5378_pad_0 = const()[name = tensor("op_5378_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5378 = conv(bias = mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias, dilations = var_5376, groups = var_4950, pad = var_5378_pad_0, pad_type = var_5378_pad_type_0, strides = var_5374, weight = mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight, x = input_339)[name = tensor("op_5378")]; - tensor var_5379_split_sizes_0 = const()[name = tensor("op_5379_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_5379_axis_0 = const()[name = tensor("op_5379_axis_0"), val = tensor(1)]; - tensor var_5379_0, tensor var_5379_1 = split(axis = var_5379_axis_0, split_sizes = var_5379_split_sizes_0, x = var_5378)[name = tensor("op_5379")]; - tensor var_5381_mode_0 = const()[name = tensor("op_5381_mode_0"), val = tensor("EXACT")]; - tensor var_5381 = gelu(mode = var_5381_mode_0, x = var_5379_1)[name = tensor("op_5381")]; - tensor input_341 = mul(x = var_5379_0, y = var_5381)[name = tensor("input_341")]; - tensor var_5385 = const()[name = tensor("op_5385"), val = tensor([1, 1])]; - tensor var_5387 = const()[name = tensor("op_5387"), val = tensor([1, 1])]; - tensor var_5389_pad_type_0 = const()[name = tensor("op_5389_pad_type_0"), val = tensor("custom")]; - tensor var_5389_pad_0 = const()[name = tensor("op_5389_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5389 = conv(bias = mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias, dilations = var_5387, groups = var_4950, pad = var_5389_pad_0, pad_type = var_5389_pad_type_0, strides = var_5385, weight = mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight, x = input_341)[name = tensor("op_5389")]; - tensor inputs_157 = add(x = var_5389, y = inputs_155)[name = tensor("inputs_157")]; - tensor var_5399 = const()[name = tensor("op_5399"), val = tensor([1])]; - tensor channels_mean_157 = reduce_mean(axes = var_5399, keep_dims = var_4945, x = inputs_157)[name = tensor("channels_mean_157")]; - tensor zero_mean_157 = sub(x = inputs_157, y = channels_mean_157)[name = tensor("zero_mean_157")]; - tensor zero_mean_sq_157 = mul(x = zero_mean_157, y = zero_mean_157)[name = tensor("zero_mean_sq_157")]; - tensor var_5403 = const()[name = tensor("op_5403"), val = tensor([1])]; - tensor var_5404 = reduce_mean(axes = var_5403, keep_dims = var_4945, x = zero_mean_sq_157)[name = tensor("op_5404")]; - tensor var_5405 = const()[name = tensor("op_5405"), val = tensor(0x1.4f8b58p-17)]; - tensor var_5406 = add(x = var_5404, y = var_5405)[name = tensor("op_5406")]; - tensor denom_157_epsilon_0 = const()[name = tensor("denom_157_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_157 = rsqrt(epsilon = denom_157_epsilon_0, x = var_5406)[name = tensor("denom_157")]; - tensor out_157 = mul(x = zero_mean_157, y = denom_157)[name = tensor("out_157")]; - tensor var_5410 = const()[name = tensor("op_5410"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268471552)))]; - tensor var_5411 = add(x = out_157, y = var_5410)[name = tensor("op_5411")]; - tensor var_5413 = const()[name = tensor("op_5413"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268476736)))]; - tensor hidden_states_221 = mul(x = var_5411, y = var_5413)[name = tensor("hidden_states_221")]; - tensor var_5420 = const()[name = tensor("op_5420"), val = tensor([1, 1])]; - tensor var_5422 = const()[name = tensor("op_5422"), val = tensor([1, 1])]; + tensor attn_103_cast = matmul(transpose_x = attn_103_transpose_x_0, transpose_y = attn_103_transpose_y_0, x = var_5251_cast, y = var_5255_cast)[name = tensor("attn_103_cast")]; + tensor var_5259 = const()[name = tensor("op_5259"), val = tensor([2, 1280, 1, -1])]; + tensor input_337_cast = reshape(shape = var_5259, x = attn_103_cast)[name = tensor("input_337_cast")]; + tensor var_5264 = const()[name = tensor("op_5264"), val = tensor([1, 1])]; + tensor var_5266 = const()[name = tensor("op_5266"), val = tensor([1, 1])]; + tensor var_5268_pad_type_0 = const()[name = tensor("op_5268_pad_type_0"), val = tensor("custom")]; + tensor var_5268_pad_0 = const()[name = tensor("op_5268_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1822505344)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1825782208)))]; + tensor var_5268_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_5266, groups = var_31, pad = var_5268_pad_0, pad_type = var_5268_pad_type_0, strides = var_5264, weight = unet_mid_block_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_337_cast)[name = tensor("op_5268_cast")]; + tensor inputs_155_cast = add(x = var_5268_cast, y = inputs_153_cast)[name = tensor("inputs_155_cast")]; + tensor var_5272 = const()[name = tensor("op_5272"), val = tensor([1])]; + tensor channels_mean_155_cast = reduce_mean(axes = var_5272, keep_dims = var_23, x = inputs_155_cast)[name = tensor("channels_mean_155_cast")]; + tensor zero_mean_155_cast = sub(x = inputs_155_cast, y = channels_mean_155_cast)[name = tensor("zero_mean_155_cast")]; + tensor zero_mean_sq_155_cast = mul(x = zero_mean_155_cast, y = zero_mean_155_cast)[name = tensor("zero_mean_sq_155_cast")]; + tensor var_5276 = const()[name = tensor("op_5276"), val = tensor([1])]; + tensor var_5277_cast = reduce_mean(axes = var_5276, keep_dims = var_23, x = zero_mean_sq_155_cast)[name = tensor("op_5277_cast")]; + tensor var_5278_to_fp16 = const()[name = tensor("op_5278_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5279_cast = add(x = var_5277_cast, y = var_5278_to_fp16)[name = tensor("op_5279_cast")]; + tensor denom_155_epsilon_0_to_fp16 = const()[name = tensor("denom_155_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_155_cast = rsqrt(epsilon = denom_155_epsilon_0_to_fp16, x = var_5279_cast)[name = tensor("denom_155_cast")]; + tensor out_155_cast = mul(x = zero_mean_155_cast, y = denom_155_cast)[name = tensor("out_155_cast")]; + tensor var_5283_to_fp16 = const()[name = tensor("op_5283_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1825784832)))]; + tensor var_5284_cast = add(x = out_155_cast, y = var_5283_to_fp16)[name = tensor("op_5284_cast")]; + tensor var_5286_to_fp16 = const()[name = tensor("op_5286_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1825787456)))]; + tensor input_339_cast = mul(x = var_5284_cast, y = var_5286_to_fp16)[name = tensor("input_339_cast")]; + tensor var_5294 = const()[name = tensor("op_5294"), val = tensor([1, 1])]; + tensor var_5296 = const()[name = tensor("op_5296"), val = tensor([1, 1])]; + tensor var_5298_pad_type_0 = const()[name = tensor("op_5298_pad_type_0"), val = tensor("custom")]; + tensor var_5298_pad_0 = const()[name = tensor("op_5298_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1825790080)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852004544)))]; + tensor var_5298_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_5296, groups = var_31, pad = var_5298_pad_0, pad_type = var_5298_pad_type_0, strides = var_5294, weight = unet_mid_block_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_339_cast)[name = tensor("op_5298_cast")]; + tensor var_5299_split_sizes_0 = const()[name = tensor("op_5299_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5299_axis_0 = const()[name = tensor("op_5299_axis_0"), val = tensor(1)]; + tensor var_5299_cast_0, tensor var_5299_cast_1 = split(axis = var_5299_axis_0, split_sizes = var_5299_split_sizes_0, x = var_5298_cast)[name = tensor("op_5299_cast")]; + tensor var_5301_mode_0 = const()[name = tensor("op_5301_mode_0"), val = tensor("EXACT")]; + tensor var_5301_cast = gelu(mode = var_5301_mode_0, x = var_5299_cast_1)[name = tensor("op_5301_cast")]; + tensor input_341_cast = mul(x = var_5299_cast_0, y = var_5301_cast)[name = tensor("input_341_cast")]; + tensor var_5305 = const()[name = tensor("op_5305"), val = tensor([1, 1])]; + tensor var_5307 = const()[name = tensor("op_5307"), val = tensor([1, 1])]; + tensor var_5309_pad_type_0 = const()[name = tensor("op_5309_pad_type_0"), val = tensor("custom")]; + tensor var_5309_pad_0 = const()[name = tensor("op_5309_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1852025088)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1865132352)))]; + tensor var_5309_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_5307, groups = var_31, pad = var_5309_pad_0, pad_type = var_5309_pad_type_0, strides = var_5305, weight = unet_mid_block_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_341_cast)[name = tensor("op_5309_cast")]; + tensor inputs_157_cast = add(x = var_5309_cast, y = inputs_155_cast)[name = tensor("inputs_157_cast")]; + tensor var_5319 = const()[name = tensor("op_5319"), val = tensor([1])]; + tensor channels_mean_157_cast = reduce_mean(axes = var_5319, keep_dims = var_23, x = inputs_157_cast)[name = tensor("channels_mean_157_cast")]; + tensor zero_mean_157_cast = sub(x = inputs_157_cast, y = channels_mean_157_cast)[name = tensor("zero_mean_157_cast")]; + tensor zero_mean_sq_157_cast = mul(x = zero_mean_157_cast, y = zero_mean_157_cast)[name = tensor("zero_mean_sq_157_cast")]; + tensor var_5323 = const()[name = tensor("op_5323"), val = tensor([1])]; + tensor var_5324_cast = reduce_mean(axes = var_5323, keep_dims = var_23, x = zero_mean_sq_157_cast)[name = tensor("op_5324_cast")]; + tensor var_5325_to_fp16 = const()[name = tensor("op_5325_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5326_cast = add(x = var_5324_cast, y = var_5325_to_fp16)[name = tensor("op_5326_cast")]; + tensor denom_157_epsilon_0_to_fp16 = const()[name = tensor("denom_157_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_157_cast = rsqrt(epsilon = denom_157_epsilon_0_to_fp16, x = var_5326_cast)[name = tensor("denom_157_cast")]; + tensor out_157_cast = mul(x = zero_mean_157_cast, y = denom_157_cast)[name = tensor("out_157_cast")]; + tensor var_5330_to_fp16 = const()[name = tensor("op_5330_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1865134976)))]; + tensor var_5331_cast = add(x = out_157_cast, y = var_5330_to_fp16)[name = tensor("op_5331_cast")]; + tensor var_5333_to_fp16 = const()[name = tensor("op_5333_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1865137600)))]; + tensor hidden_states_221_cast = mul(x = var_5331_cast, y = var_5333_to_fp16)[name = tensor("hidden_states_221_cast")]; + tensor var_5340 = const()[name = tensor("op_5340"), val = tensor([1, 1])]; + tensor var_5342 = const()[name = tensor("op_5342"), val = tensor([1, 1])]; tensor q_105_pad_type_0 = const()[name = tensor("q_105_pad_type_0"), val = tensor("custom")]; tensor q_105_pad_0 = const()[name = tensor("q_105_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_105 = conv(dilations = var_5422, groups = var_4950, pad = q_105_pad_0, pad_type = q_105_pad_type_0, strides = var_5420, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight, x = hidden_states_221)[name = tensor("q_105")]; - tensor var_5426 = const()[name = tensor("op_5426"), val = tensor([1, 1])]; - tensor var_5428 = const()[name = tensor("op_5428"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1865140224)))]; + tensor q_105_cast = conv(dilations = var_5342, groups = var_31, pad = q_105_pad_0, pad_type = q_105_pad_type_0, strides = var_5340, weight = unet_mid_block_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_221_cast)[name = tensor("q_105_cast")]; + tensor var_5346 = const()[name = tensor("op_5346"), val = tensor([1, 1])]; + tensor var_5348 = const()[name = tensor("op_5348"), val = tensor([1, 1])]; tensor k_105_pad_type_0 = const()[name = tensor("k_105_pad_type_0"), val = tensor("custom")]; tensor k_105_pad_0 = const()[name = tensor("k_105_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_105 = conv(dilations = var_5428, groups = var_4950, pad = k_105_pad_0, pad_type = k_105_pad_type_0, strides = var_5426, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight, x = hidden_states_221)[name = tensor("k_105")]; - tensor var_5432 = const()[name = tensor("op_5432"), val = tensor([1, 1])]; - tensor var_5434 = const()[name = tensor("op_5434"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1868417088)))]; + tensor k_105_cast = conv(dilations = var_5348, groups = var_31, pad = k_105_pad_0, pad_type = k_105_pad_type_0, strides = var_5346, weight = unet_mid_block_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_221_cast)[name = tensor("k_105_cast")]; + tensor var_5352 = const()[name = tensor("op_5352"), val = tensor([1, 1])]; + tensor var_5354 = const()[name = tensor("op_5354"), val = tensor([1, 1])]; tensor v_105_pad_type_0 = const()[name = tensor("v_105_pad_type_0"), val = tensor("custom")]; tensor v_105_pad_0 = const()[name = tensor("v_105_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_105 = conv(dilations = var_5434, groups = var_4950, pad = v_105_pad_0, pad_type = v_105_pad_type_0, strides = var_5432, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight, x = hidden_states_221)[name = tensor("v_105")]; - tensor var_5438 = const()[name = tensor("op_5438"), val = tensor([2, 20, 64, -1])]; - tensor var_5439 = reshape(shape = var_5438, x = q_105)[name = tensor("op_5439")]; - tensor var_5440 = const()[name = tensor("op_5440"), val = tensor([2, 20, 64, -1])]; - tensor var_5441 = reshape(shape = var_5440, x = k_105)[name = tensor("op_5441")]; - tensor var_5442 = const()[name = tensor("op_5442"), val = tensor([2, 20, 64, -1])]; - tensor var_5443 = reshape(shape = var_5442, x = v_105)[name = tensor("op_5443")]; + tensor unet_mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1871693952)))]; + tensor v_105_cast = conv(dilations = var_5354, groups = var_31, pad = v_105_pad_0, pad_type = v_105_pad_type_0, strides = var_5352, weight = unet_mid_block_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_221_cast)[name = tensor("v_105_cast")]; + tensor var_5358 = const()[name = tensor("op_5358"), val = tensor([2, 20, 64, -1])]; + tensor var_5359_cast = reshape(shape = var_5358, x = q_105_cast)[name = tensor("op_5359_cast")]; + tensor var_5360 = const()[name = tensor("op_5360"), val = tensor([2, 20, 64, -1])]; + tensor var_5361_cast = reshape(shape = var_5360, x = k_105_cast)[name = tensor("op_5361_cast")]; + tensor var_5362 = const()[name = tensor("op_5362"), val = tensor([2, 20, 64, -1])]; + tensor var_5363_cast = reshape(shape = var_5362, x = v_105_cast)[name = tensor("op_5363_cast")]; tensor attn_weights_209_transpose_x_0 = const()[name = tensor("attn_weights_209_transpose_x_0"), val = tensor(true)]; tensor attn_weights_209_transpose_y_0 = const()[name = tensor("attn_weights_209_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_209 = matmul(transpose_x = attn_weights_209_transpose_x_0, transpose_y = attn_weights_209_transpose_y_0, x = var_5439, y = var_5441)[name = tensor("attn_weights_209")]; - tensor attn_weights_211 = mul(x = attn_weights_209, y = var_4941)[name = tensor("attn_weights_211")]; - tensor var_5447 = softmax(axis = var_4934, x = attn_weights_211)[name = tensor("op_5447")]; + tensor attn_weights_209_cast = matmul(transpose_x = attn_weights_209_transpose_x_0, transpose_y = attn_weights_209_transpose_y_0, x = var_5359_cast, y = var_5361_cast)[name = tensor("attn_weights_209_cast")]; + tensor attn_weights_211_cast = mul(x = attn_weights_209_cast, y = var_12_to_fp16)[name = tensor("attn_weights_211_cast")]; + tensor var_5367_cast = softmax(axis = var_18, x = attn_weights_211_cast)[name = tensor("op_5367_cast")]; tensor attn_105_transpose_x_0 = const()[name = tensor("attn_105_transpose_x_0"), val = tensor(false)]; tensor attn_105_transpose_y_0 = const()[name = tensor("attn_105_transpose_y_0"), val = tensor(true)]; - tensor attn_105 = matmul(transpose_x = attn_105_transpose_x_0, transpose_y = attn_105_transpose_y_0, x = var_5443, y = var_5447)[name = tensor("attn_105")]; - tensor var_5451 = const()[name = tensor("op_5451"), val = tensor([2, 1280, 1, -1])]; - tensor input_343 = reshape(shape = var_5451, x = attn_105)[name = tensor("input_343")]; - tensor var_5456 = const()[name = tensor("op_5456"), val = tensor([1, 1])]; - tensor var_5458 = const()[name = tensor("op_5458"), val = tensor([1, 1])]; - tensor var_5460_pad_type_0 = const()[name = tensor("op_5460_pad_type_0"), val = tensor("custom")]; - tensor var_5460_pad_0 = const()[name = tensor("op_5460_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5460 = conv(bias = mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias, dilations = var_5458, groups = var_4950, pad = var_5460_pad_0, pad_type = var_5460_pad_type_0, strides = var_5456, weight = mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight, x = input_343)[name = tensor("op_5460")]; - tensor inputs_159 = add(x = var_5460, y = inputs_157)[name = tensor("inputs_159")]; - tensor var_5464 = const()[name = tensor("op_5464"), val = tensor([1])]; - tensor channels_mean_159 = reduce_mean(axes = var_5464, keep_dims = var_4945, x = inputs_159)[name = tensor("channels_mean_159")]; - tensor zero_mean_159 = sub(x = inputs_159, y = channels_mean_159)[name = tensor("zero_mean_159")]; - tensor zero_mean_sq_159 = mul(x = zero_mean_159, y = zero_mean_159)[name = tensor("zero_mean_sq_159")]; - tensor var_5468 = const()[name = tensor("op_5468"), val = tensor([1])]; - tensor var_5469 = reduce_mean(axes = var_5468, keep_dims = var_4945, x = zero_mean_sq_159)[name = tensor("op_5469")]; - tensor var_5470 = const()[name = tensor("op_5470"), val = tensor(0x1.4f8b58p-17)]; - tensor var_5471 = add(x = var_5469, y = var_5470)[name = tensor("op_5471")]; - tensor denom_159_epsilon_0 = const()[name = tensor("denom_159_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_159 = rsqrt(epsilon = denom_159_epsilon_0, x = var_5471)[name = tensor("denom_159")]; - tensor out_159 = mul(x = zero_mean_159, y = denom_159)[name = tensor("out_159")]; - tensor var_5475 = const()[name = tensor("op_5475"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268481920)))]; - tensor var_5476 = add(x = out_159, y = var_5475)[name = tensor("op_5476")]; - tensor var_5478 = const()[name = tensor("op_5478"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268487104)))]; - tensor hidden_states_223 = mul(x = var_5476, y = var_5478)[name = tensor("hidden_states_223")]; - tensor var_5485 = const()[name = tensor("op_5485"), val = tensor([1, 1])]; - tensor var_5487 = const()[name = tensor("op_5487"), val = tensor([1, 1])]; + tensor attn_105_cast = matmul(transpose_x = attn_105_transpose_x_0, transpose_y = attn_105_transpose_y_0, x = var_5363_cast, y = var_5367_cast)[name = tensor("attn_105_cast")]; + tensor var_5371 = const()[name = tensor("op_5371"), val = tensor([2, 1280, 1, -1])]; + tensor input_343_cast = reshape(shape = var_5371, x = attn_105_cast)[name = tensor("input_343_cast")]; + tensor var_5376 = const()[name = tensor("op_5376"), val = tensor([1, 1])]; + tensor var_5378 = const()[name = tensor("op_5378"), val = tensor([1, 1])]; + tensor var_5380_pad_type_0 = const()[name = tensor("op_5380_pad_type_0"), val = tensor("custom")]; + tensor var_5380_pad_0 = const()[name = tensor("op_5380_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1874970816)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878247680)))]; + tensor var_5380_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_5378, groups = var_31, pad = var_5380_pad_0, pad_type = var_5380_pad_type_0, strides = var_5376, weight = unet_mid_block_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_343_cast)[name = tensor("op_5380_cast")]; + tensor inputs_159_cast = add(x = var_5380_cast, y = inputs_157_cast)[name = tensor("inputs_159_cast")]; + tensor var_5384 = const()[name = tensor("op_5384"), val = tensor([1])]; + tensor channels_mean_159_cast = reduce_mean(axes = var_5384, keep_dims = var_23, x = inputs_159_cast)[name = tensor("channels_mean_159_cast")]; + tensor zero_mean_159_cast = sub(x = inputs_159_cast, y = channels_mean_159_cast)[name = tensor("zero_mean_159_cast")]; + tensor zero_mean_sq_159_cast = mul(x = zero_mean_159_cast, y = zero_mean_159_cast)[name = tensor("zero_mean_sq_159_cast")]; + tensor var_5388 = const()[name = tensor("op_5388"), val = tensor([1])]; + tensor var_5389_cast = reduce_mean(axes = var_5388, keep_dims = var_23, x = zero_mean_sq_159_cast)[name = tensor("op_5389_cast")]; + tensor var_5390_to_fp16 = const()[name = tensor("op_5390_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5391_cast = add(x = var_5389_cast, y = var_5390_to_fp16)[name = tensor("op_5391_cast")]; + tensor denom_159_epsilon_0_to_fp16 = const()[name = tensor("denom_159_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_159_cast = rsqrt(epsilon = denom_159_epsilon_0_to_fp16, x = var_5391_cast)[name = tensor("denom_159_cast")]; + tensor out_159_cast = mul(x = zero_mean_159_cast, y = denom_159_cast)[name = tensor("out_159_cast")]; + tensor var_5395_to_fp16 = const()[name = tensor("op_5395_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878250304)))]; + tensor var_5396_cast = add(x = out_159_cast, y = var_5395_to_fp16)[name = tensor("op_5396_cast")]; + tensor var_5398_to_fp16 = const()[name = tensor("op_5398_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878252928)))]; + tensor hidden_states_223_cast = mul(x = var_5396_cast, y = var_5398_to_fp16)[name = tensor("hidden_states_223_cast")]; + tensor var_5405 = const()[name = tensor("op_5405"), val = tensor([1, 1])]; + tensor var_5407 = const()[name = tensor("op_5407"), val = tensor([1, 1])]; tensor q_107_pad_type_0 = const()[name = tensor("q_107_pad_type_0"), val = tensor("custom")]; tensor q_107_pad_0 = const()[name = tensor("q_107_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_107 = conv(dilations = var_5487, groups = var_4950, pad = q_107_pad_0, pad_type = q_107_pad_type_0, strides = var_5485, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight, x = hidden_states_223)[name = tensor("q_107")]; - tensor var_5491 = const()[name = tensor("op_5491"), val = tensor([1, 1])]; - tensor var_5493 = const()[name = tensor("op_5493"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1878255552)))]; + tensor q_107_cast = conv(dilations = var_5407, groups = var_31, pad = q_107_pad_0, pad_type = q_107_pad_type_0, strides = var_5405, weight = unet_mid_block_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_223_cast)[name = tensor("q_107_cast")]; + tensor var_5411 = const()[name = tensor("op_5411"), val = tensor([1, 1])]; + tensor var_5413 = const()[name = tensor("op_5413"), val = tensor([1, 1])]; tensor k_107_pad_type_0 = const()[name = tensor("k_107_pad_type_0"), val = tensor("custom")]; tensor k_107_pad_0 = const()[name = tensor("k_107_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_107 = conv(dilations = var_5493, groups = var_4950, pad = k_107_pad_0, pad_type = k_107_pad_type_0, strides = var_5491, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_107")]; - tensor var_5497 = const()[name = tensor("op_5497"), val = tensor([1, 1])]; - tensor var_5499 = const()[name = tensor("op_5499"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1881532416)))]; + tensor k_107_cast = conv(dilations = var_5413, groups = var_31, pad = k_107_pad_0, pad_type = k_107_pad_type_0, strides = var_5411, weight = unet_mid_block_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_107_cast")]; + tensor var_5417 = const()[name = tensor("op_5417"), val = tensor([1, 1])]; + tensor var_5419 = const()[name = tensor("op_5419"), val = tensor([1, 1])]; tensor v_107_pad_type_0 = const()[name = tensor("v_107_pad_type_0"), val = tensor("custom")]; tensor v_107_pad_0 = const()[name = tensor("v_107_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_107 = conv(dilations = var_5499, groups = var_4950, pad = v_107_pad_0, pad_type = v_107_pad_type_0, strides = var_5497, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_107")]; - tensor var_5503 = const()[name = tensor("op_5503"), val = tensor([2, 20, 64, -1])]; - tensor var_5504 = reshape(shape = var_5503, x = q_107)[name = tensor("op_5504")]; - tensor var_5505 = const()[name = tensor("op_5505"), val = tensor([2, 20, 64, -1])]; - tensor var_5506 = reshape(shape = var_5505, x = k_107)[name = tensor("op_5506")]; - tensor var_5507 = const()[name = tensor("op_5507"), val = tensor([2, 20, 64, -1])]; - tensor var_5508 = reshape(shape = var_5507, x = v_107)[name = tensor("op_5508")]; + tensor unet_mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1886775360)))]; + tensor v_107_cast = conv(dilations = var_5419, groups = var_31, pad = v_107_pad_0, pad_type = v_107_pad_type_0, strides = var_5417, weight = unet_mid_block_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_107_cast")]; + tensor var_5423 = const()[name = tensor("op_5423"), val = tensor([2, 20, 64, -1])]; + tensor var_5424_cast = reshape(shape = var_5423, x = q_107_cast)[name = tensor("op_5424_cast")]; + tensor var_5425 = const()[name = tensor("op_5425"), val = tensor([2, 20, 64, -1])]; + tensor var_5426_cast = reshape(shape = var_5425, x = k_107_cast)[name = tensor("op_5426_cast")]; + tensor var_5427 = const()[name = tensor("op_5427"), val = tensor([2, 20, 64, -1])]; + tensor var_5428_cast = reshape(shape = var_5427, x = v_107_cast)[name = tensor("op_5428_cast")]; tensor attn_weights_213_transpose_x_0 = const()[name = tensor("attn_weights_213_transpose_x_0"), val = tensor(true)]; tensor attn_weights_213_transpose_y_0 = const()[name = tensor("attn_weights_213_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_213 = matmul(transpose_x = attn_weights_213_transpose_x_0, transpose_y = attn_weights_213_transpose_y_0, x = var_5504, y = var_5506)[name = tensor("attn_weights_213")]; - tensor attn_weights_215 = mul(x = attn_weights_213, y = var_4941)[name = tensor("attn_weights_215")]; - tensor var_5512 = softmax(axis = var_4934, x = attn_weights_215)[name = tensor("op_5512")]; + tensor attn_weights_213_cast = matmul(transpose_x = attn_weights_213_transpose_x_0, transpose_y = attn_weights_213_transpose_y_0, x = var_5424_cast, y = var_5426_cast)[name = tensor("attn_weights_213_cast")]; + tensor attn_weights_215_cast = mul(x = attn_weights_213_cast, y = var_12_to_fp16)[name = tensor("attn_weights_215_cast")]; + tensor var_5432_cast = softmax(axis = var_18, x = attn_weights_215_cast)[name = tensor("op_5432_cast")]; tensor attn_107_transpose_x_0 = const()[name = tensor("attn_107_transpose_x_0"), val = tensor(false)]; tensor attn_107_transpose_y_0 = const()[name = tensor("attn_107_transpose_y_0"), val = tensor(true)]; - tensor attn_107 = matmul(transpose_x = attn_107_transpose_x_0, transpose_y = attn_107_transpose_y_0, x = var_5508, y = var_5512)[name = tensor("attn_107")]; - tensor var_5516 = const()[name = tensor("op_5516"), val = tensor([2, 1280, 1, -1])]; - tensor input_345 = reshape(shape = var_5516, x = attn_107)[name = tensor("input_345")]; - tensor var_5521 = const()[name = tensor("op_5521"), val = tensor([1, 1])]; - tensor var_5523 = const()[name = tensor("op_5523"), val = tensor([1, 1])]; - tensor var_5525_pad_type_0 = const()[name = tensor("op_5525_pad_type_0"), val = tensor("custom")]; - tensor var_5525_pad_0 = const()[name = tensor("op_5525_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5525 = conv(bias = mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias, dilations = var_5523, groups = var_4950, pad = var_5525_pad_0, pad_type = var_5525_pad_type_0, strides = var_5521, weight = mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight, x = input_345)[name = tensor("op_5525")]; - tensor inputs_161 = add(x = var_5525, y = inputs_159)[name = tensor("inputs_161")]; - tensor var_5529 = const()[name = tensor("op_5529"), val = tensor([1])]; - tensor channels_mean_161 = reduce_mean(axes = var_5529, keep_dims = var_4945, x = inputs_161)[name = tensor("channels_mean_161")]; - tensor zero_mean_161 = sub(x = inputs_161, y = channels_mean_161)[name = tensor("zero_mean_161")]; - tensor zero_mean_sq_161 = mul(x = zero_mean_161, y = zero_mean_161)[name = tensor("zero_mean_sq_161")]; - tensor var_5533 = const()[name = tensor("op_5533"), val = tensor([1])]; - tensor var_5534 = reduce_mean(axes = var_5533, keep_dims = var_4945, x = zero_mean_sq_161)[name = tensor("op_5534")]; - tensor var_5535 = const()[name = tensor("op_5535"), val = tensor(0x1.4f8b58p-17)]; - tensor var_5536 = add(x = var_5534, y = var_5535)[name = tensor("op_5536")]; - tensor denom_161_epsilon_0 = const()[name = tensor("denom_161_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_161 = rsqrt(epsilon = denom_161_epsilon_0, x = var_5536)[name = tensor("denom_161")]; - tensor out_161 = mul(x = zero_mean_161, y = denom_161)[name = tensor("out_161")]; - tensor var_5540 = const()[name = tensor("op_5540"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268492288)))]; - tensor var_5541 = add(x = out_161, y = var_5540)[name = tensor("op_5541")]; - tensor var_5543 = const()[name = tensor("op_5543"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268497472)))]; - tensor input_347 = mul(x = var_5541, y = var_5543)[name = tensor("input_347")]; - tensor var_5551 = const()[name = tensor("op_5551"), val = tensor([1, 1])]; - tensor var_5553 = const()[name = tensor("op_5553"), val = tensor([1, 1])]; - tensor var_5555_pad_type_0 = const()[name = tensor("op_5555_pad_type_0"), val = tensor("custom")]; - tensor var_5555_pad_0 = const()[name = tensor("op_5555_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5555 = conv(bias = mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias, dilations = var_5553, groups = var_4950, pad = var_5555_pad_0, pad_type = var_5555_pad_type_0, strides = var_5551, weight = mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight, x = input_347)[name = tensor("op_5555")]; - tensor var_5556_split_sizes_0 = const()[name = tensor("op_5556_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_5556_axis_0 = const()[name = tensor("op_5556_axis_0"), val = tensor(1)]; - tensor var_5556_0, tensor var_5556_1 = split(axis = var_5556_axis_0, split_sizes = var_5556_split_sizes_0, x = var_5555)[name = tensor("op_5556")]; - tensor var_5558_mode_0 = const()[name = tensor("op_5558_mode_0"), val = tensor("EXACT")]; - tensor var_5558 = gelu(mode = var_5558_mode_0, x = var_5556_1)[name = tensor("op_5558")]; - tensor input_349 = mul(x = var_5556_0, y = var_5558)[name = tensor("input_349")]; - tensor var_5562 = const()[name = tensor("op_5562"), val = tensor([1, 1])]; - tensor var_5564 = const()[name = tensor("op_5564"), val = tensor([1, 1])]; - tensor var_5566_pad_type_0 = const()[name = tensor("op_5566_pad_type_0"), val = tensor("custom")]; - tensor var_5566_pad_0 = const()[name = tensor("op_5566_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5566 = conv(bias = mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias, dilations = var_5564, groups = var_4950, pad = var_5566_pad_0, pad_type = var_5566_pad_type_0, strides = var_5562, weight = mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight, x = input_349)[name = tensor("op_5566")]; - tensor inputs_163 = add(x = var_5566, y = inputs_161)[name = tensor("inputs_163")]; - tensor var_5576 = const()[name = tensor("op_5576"), val = tensor([1])]; - tensor channels_mean_163 = reduce_mean(axes = var_5576, keep_dims = var_4945, x = inputs_163)[name = tensor("channels_mean_163")]; - tensor zero_mean_163 = sub(x = inputs_163, y = channels_mean_163)[name = tensor("zero_mean_163")]; - tensor zero_mean_sq_163 = mul(x = zero_mean_163, y = zero_mean_163)[name = tensor("zero_mean_sq_163")]; - tensor var_5580 = const()[name = tensor("op_5580"), val = tensor([1])]; - tensor var_5581 = reduce_mean(axes = var_5580, keep_dims = var_4945, x = zero_mean_sq_163)[name = tensor("op_5581")]; - tensor var_5582 = const()[name = tensor("op_5582"), val = tensor(0x1.4f8b58p-17)]; - tensor var_5583 = add(x = var_5581, y = var_5582)[name = tensor("op_5583")]; - tensor denom_163_epsilon_0 = const()[name = tensor("denom_163_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_163 = rsqrt(epsilon = denom_163_epsilon_0, x = var_5583)[name = tensor("denom_163")]; - tensor out_163 = mul(x = zero_mean_163, y = denom_163)[name = tensor("out_163")]; - tensor var_5587 = const()[name = tensor("op_5587"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268502656)))]; - tensor var_5588 = add(x = out_163, y = var_5587)[name = tensor("op_5588")]; - tensor var_5590 = const()[name = tensor("op_5590"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268507840)))]; - tensor hidden_states_227 = mul(x = var_5588, y = var_5590)[name = tensor("hidden_states_227")]; - tensor var_5597 = const()[name = tensor("op_5597"), val = tensor([1, 1])]; - tensor var_5599 = const()[name = tensor("op_5599"), val = tensor([1, 1])]; + tensor attn_107_cast = matmul(transpose_x = attn_107_transpose_x_0, transpose_y = attn_107_transpose_y_0, x = var_5428_cast, y = var_5432_cast)[name = tensor("attn_107_cast")]; + tensor var_5436 = const()[name = tensor("op_5436"), val = tensor([2, 1280, 1, -1])]; + tensor input_345_cast = reshape(shape = var_5436, x = attn_107_cast)[name = tensor("input_345_cast")]; + tensor var_5441 = const()[name = tensor("op_5441"), val = tensor([1, 1])]; + tensor var_5443 = const()[name = tensor("op_5443"), val = tensor([1, 1])]; + tensor var_5445_pad_type_0 = const()[name = tensor("op_5445_pad_type_0"), val = tensor("custom")]; + tensor var_5445_pad_0 = const()[name = tensor("op_5445_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1892018304)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1895295168)))]; + tensor var_5445_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_5443, groups = var_31, pad = var_5445_pad_0, pad_type = var_5445_pad_type_0, strides = var_5441, weight = unet_mid_block_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_345_cast)[name = tensor("op_5445_cast")]; + tensor inputs_161_cast = add(x = var_5445_cast, y = inputs_159_cast)[name = tensor("inputs_161_cast")]; + tensor var_5449 = const()[name = tensor("op_5449"), val = tensor([1])]; + tensor channels_mean_161_cast = reduce_mean(axes = var_5449, keep_dims = var_23, x = inputs_161_cast)[name = tensor("channels_mean_161_cast")]; + tensor zero_mean_161_cast = sub(x = inputs_161_cast, y = channels_mean_161_cast)[name = tensor("zero_mean_161_cast")]; + tensor zero_mean_sq_161_cast = mul(x = zero_mean_161_cast, y = zero_mean_161_cast)[name = tensor("zero_mean_sq_161_cast")]; + tensor var_5453 = const()[name = tensor("op_5453"), val = tensor([1])]; + tensor var_5454_cast = reduce_mean(axes = var_5453, keep_dims = var_23, x = zero_mean_sq_161_cast)[name = tensor("op_5454_cast")]; + tensor var_5455_to_fp16 = const()[name = tensor("op_5455_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5456_cast = add(x = var_5454_cast, y = var_5455_to_fp16)[name = tensor("op_5456_cast")]; + tensor denom_161_epsilon_0_to_fp16 = const()[name = tensor("denom_161_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_161_cast = rsqrt(epsilon = denom_161_epsilon_0_to_fp16, x = var_5456_cast)[name = tensor("denom_161_cast")]; + tensor out_161_cast = mul(x = zero_mean_161_cast, y = denom_161_cast)[name = tensor("out_161_cast")]; + tensor var_5460_to_fp16 = const()[name = tensor("op_5460_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1895297792)))]; + tensor var_5461_cast = add(x = out_161_cast, y = var_5460_to_fp16)[name = tensor("op_5461_cast")]; + tensor var_5463_to_fp16 = const()[name = tensor("op_5463_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1895300416)))]; + tensor input_347_cast = mul(x = var_5461_cast, y = var_5463_to_fp16)[name = tensor("input_347_cast")]; + tensor var_5471 = const()[name = tensor("op_5471"), val = tensor([1, 1])]; + tensor var_5473 = const()[name = tensor("op_5473"), val = tensor([1, 1])]; + tensor var_5475_pad_type_0 = const()[name = tensor("op_5475_pad_type_0"), val = tensor("custom")]; + tensor var_5475_pad_0 = const()[name = tensor("op_5475_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1895303040)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921517504)))]; + tensor var_5475_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_5473, groups = var_31, pad = var_5475_pad_0, pad_type = var_5475_pad_type_0, strides = var_5471, weight = unet_mid_block_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_347_cast)[name = tensor("op_5475_cast")]; + tensor var_5476_split_sizes_0 = const()[name = tensor("op_5476_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5476_axis_0 = const()[name = tensor("op_5476_axis_0"), val = tensor(1)]; + tensor var_5476_cast_0, tensor var_5476_cast_1 = split(axis = var_5476_axis_0, split_sizes = var_5476_split_sizes_0, x = var_5475_cast)[name = tensor("op_5476_cast")]; + tensor var_5478_mode_0 = const()[name = tensor("op_5478_mode_0"), val = tensor("EXACT")]; + tensor var_5478_cast = gelu(mode = var_5478_mode_0, x = var_5476_cast_1)[name = tensor("op_5478_cast")]; + tensor input_349_cast = mul(x = var_5476_cast_0, y = var_5478_cast)[name = tensor("input_349_cast")]; + tensor var_5482 = const()[name = tensor("op_5482"), val = tensor([1, 1])]; + tensor var_5484 = const()[name = tensor("op_5484"), val = tensor([1, 1])]; + tensor var_5486_pad_type_0 = const()[name = tensor("op_5486_pad_type_0"), val = tensor("custom")]; + tensor var_5486_pad_0 = const()[name = tensor("op_5486_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1921538048)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1934645312)))]; + tensor var_5486_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_5484, groups = var_31, pad = var_5486_pad_0, pad_type = var_5486_pad_type_0, strides = var_5482, weight = unet_mid_block_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_349_cast)[name = tensor("op_5486_cast")]; + tensor inputs_163_cast = add(x = var_5486_cast, y = inputs_161_cast)[name = tensor("inputs_163_cast")]; + tensor var_5496 = const()[name = tensor("op_5496"), val = tensor([1])]; + tensor channels_mean_163_cast = reduce_mean(axes = var_5496, keep_dims = var_23, x = inputs_163_cast)[name = tensor("channels_mean_163_cast")]; + tensor zero_mean_163_cast = sub(x = inputs_163_cast, y = channels_mean_163_cast)[name = tensor("zero_mean_163_cast")]; + tensor zero_mean_sq_163_cast = mul(x = zero_mean_163_cast, y = zero_mean_163_cast)[name = tensor("zero_mean_sq_163_cast")]; + tensor var_5500 = const()[name = tensor("op_5500"), val = tensor([1])]; + tensor var_5501_cast = reduce_mean(axes = var_5500, keep_dims = var_23, x = zero_mean_sq_163_cast)[name = tensor("op_5501_cast")]; + tensor var_5502_to_fp16 = const()[name = tensor("op_5502_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5503_cast = add(x = var_5501_cast, y = var_5502_to_fp16)[name = tensor("op_5503_cast")]; + tensor denom_163_epsilon_0_to_fp16 = const()[name = tensor("denom_163_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_163_cast = rsqrt(epsilon = denom_163_epsilon_0_to_fp16, x = var_5503_cast)[name = tensor("denom_163_cast")]; + tensor out_163_cast = mul(x = zero_mean_163_cast, y = denom_163_cast)[name = tensor("out_163_cast")]; + tensor var_5507_to_fp16 = const()[name = tensor("op_5507_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1934647936)))]; + tensor var_5508_cast = add(x = out_163_cast, y = var_5507_to_fp16)[name = tensor("op_5508_cast")]; + tensor var_5510_to_fp16 = const()[name = tensor("op_5510_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1934650560)))]; + tensor hidden_states_227_cast = mul(x = var_5508_cast, y = var_5510_to_fp16)[name = tensor("hidden_states_227_cast")]; + tensor var_5517 = const()[name = tensor("op_5517"), val = tensor([1, 1])]; + tensor var_5519 = const()[name = tensor("op_5519"), val = tensor([1, 1])]; tensor q_109_pad_type_0 = const()[name = tensor("q_109_pad_type_0"), val = tensor("custom")]; tensor q_109_pad_0 = const()[name = tensor("q_109_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_109 = conv(dilations = var_5599, groups = var_4950, pad = q_109_pad_0, pad_type = q_109_pad_type_0, strides = var_5597, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight, x = hidden_states_227)[name = tensor("q_109")]; - tensor var_5603 = const()[name = tensor("op_5603"), val = tensor([1, 1])]; - tensor var_5605 = const()[name = tensor("op_5605"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1934653184)))]; + tensor q_109_cast = conv(dilations = var_5519, groups = var_31, pad = q_109_pad_0, pad_type = q_109_pad_type_0, strides = var_5517, weight = unet_mid_block_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_227_cast)[name = tensor("q_109_cast")]; + tensor var_5523 = const()[name = tensor("op_5523"), val = tensor([1, 1])]; + tensor var_5525 = const()[name = tensor("op_5525"), val = tensor([1, 1])]; tensor k_109_pad_type_0 = const()[name = tensor("k_109_pad_type_0"), val = tensor("custom")]; tensor k_109_pad_0 = const()[name = tensor("k_109_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_109 = conv(dilations = var_5605, groups = var_4950, pad = k_109_pad_0, pad_type = k_109_pad_type_0, strides = var_5603, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight, x = hidden_states_227)[name = tensor("k_109")]; - tensor var_5609 = const()[name = tensor("op_5609"), val = tensor([1, 1])]; - tensor var_5611 = const()[name = tensor("op_5611"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1937930048)))]; + tensor k_109_cast = conv(dilations = var_5525, groups = var_31, pad = k_109_pad_0, pad_type = k_109_pad_type_0, strides = var_5523, weight = unet_mid_block_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_227_cast)[name = tensor("k_109_cast")]; + tensor var_5529 = const()[name = tensor("op_5529"), val = tensor([1, 1])]; + tensor var_5531 = const()[name = tensor("op_5531"), val = tensor([1, 1])]; tensor v_109_pad_type_0 = const()[name = tensor("v_109_pad_type_0"), val = tensor("custom")]; tensor v_109_pad_0 = const()[name = tensor("v_109_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_109 = conv(dilations = var_5611, groups = var_4950, pad = v_109_pad_0, pad_type = v_109_pad_type_0, strides = var_5609, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight, x = hidden_states_227)[name = tensor("v_109")]; - tensor var_5615 = const()[name = tensor("op_5615"), val = tensor([2, 20, 64, -1])]; - tensor var_5616 = reshape(shape = var_5615, x = q_109)[name = tensor("op_5616")]; - tensor var_5617 = const()[name = tensor("op_5617"), val = tensor([2, 20, 64, -1])]; - tensor var_5618 = reshape(shape = var_5617, x = k_109)[name = tensor("op_5618")]; - tensor var_5619 = const()[name = tensor("op_5619"), val = tensor([2, 20, 64, -1])]; - tensor var_5620 = reshape(shape = var_5619, x = v_109)[name = tensor("op_5620")]; + tensor unet_mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1941206912)))]; + tensor v_109_cast = conv(dilations = var_5531, groups = var_31, pad = v_109_pad_0, pad_type = v_109_pad_type_0, strides = var_5529, weight = unet_mid_block_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_227_cast)[name = tensor("v_109_cast")]; + tensor var_5535 = const()[name = tensor("op_5535"), val = tensor([2, 20, 64, -1])]; + tensor var_5536_cast = reshape(shape = var_5535, x = q_109_cast)[name = tensor("op_5536_cast")]; + tensor var_5537 = const()[name = tensor("op_5537"), val = tensor([2, 20, 64, -1])]; + tensor var_5538_cast = reshape(shape = var_5537, x = k_109_cast)[name = tensor("op_5538_cast")]; + tensor var_5539 = const()[name = tensor("op_5539"), val = tensor([2, 20, 64, -1])]; + tensor var_5540_cast = reshape(shape = var_5539, x = v_109_cast)[name = tensor("op_5540_cast")]; tensor attn_weights_217_transpose_x_0 = const()[name = tensor("attn_weights_217_transpose_x_0"), val = tensor(true)]; tensor attn_weights_217_transpose_y_0 = const()[name = tensor("attn_weights_217_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_217 = matmul(transpose_x = attn_weights_217_transpose_x_0, transpose_y = attn_weights_217_transpose_y_0, x = var_5616, y = var_5618)[name = tensor("attn_weights_217")]; - tensor attn_weights_219 = mul(x = attn_weights_217, y = var_4941)[name = tensor("attn_weights_219")]; - tensor var_5624 = softmax(axis = var_4934, x = attn_weights_219)[name = tensor("op_5624")]; + tensor attn_weights_217_cast = matmul(transpose_x = attn_weights_217_transpose_x_0, transpose_y = attn_weights_217_transpose_y_0, x = var_5536_cast, y = var_5538_cast)[name = tensor("attn_weights_217_cast")]; + tensor attn_weights_219_cast = mul(x = attn_weights_217_cast, y = var_12_to_fp16)[name = tensor("attn_weights_219_cast")]; + tensor var_5544_cast = softmax(axis = var_18, x = attn_weights_219_cast)[name = tensor("op_5544_cast")]; tensor attn_109_transpose_x_0 = const()[name = tensor("attn_109_transpose_x_0"), val = tensor(false)]; tensor attn_109_transpose_y_0 = const()[name = tensor("attn_109_transpose_y_0"), val = tensor(true)]; - tensor attn_109 = matmul(transpose_x = attn_109_transpose_x_0, transpose_y = attn_109_transpose_y_0, x = var_5620, y = var_5624)[name = tensor("attn_109")]; - tensor var_5628 = const()[name = tensor("op_5628"), val = tensor([2, 1280, 1, -1])]; - tensor input_351 = reshape(shape = var_5628, x = attn_109)[name = tensor("input_351")]; - tensor var_5633 = const()[name = tensor("op_5633"), val = tensor([1, 1])]; - tensor var_5635 = const()[name = tensor("op_5635"), val = tensor([1, 1])]; - tensor var_5637_pad_type_0 = const()[name = tensor("op_5637_pad_type_0"), val = tensor("custom")]; - tensor var_5637_pad_0 = const()[name = tensor("op_5637_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5637 = conv(bias = mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias, dilations = var_5635, groups = var_4950, pad = var_5637_pad_0, pad_type = var_5637_pad_type_0, strides = var_5633, weight = mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight, x = input_351)[name = tensor("op_5637")]; - tensor inputs_165 = add(x = var_5637, y = inputs_163)[name = tensor("inputs_165")]; - tensor var_5641 = const()[name = tensor("op_5641"), val = tensor([1])]; - tensor channels_mean_165 = reduce_mean(axes = var_5641, keep_dims = var_4945, x = inputs_165)[name = tensor("channels_mean_165")]; - tensor zero_mean_165 = sub(x = inputs_165, y = channels_mean_165)[name = tensor("zero_mean_165")]; - tensor zero_mean_sq_165 = mul(x = zero_mean_165, y = zero_mean_165)[name = tensor("zero_mean_sq_165")]; - tensor var_5645 = const()[name = tensor("op_5645"), val = tensor([1])]; - tensor var_5646 = reduce_mean(axes = var_5645, keep_dims = var_4945, x = zero_mean_sq_165)[name = tensor("op_5646")]; - tensor var_5647 = const()[name = tensor("op_5647"), val = tensor(0x1.4f8b58p-17)]; - tensor var_5648 = add(x = var_5646, y = var_5647)[name = tensor("op_5648")]; - tensor denom_165_epsilon_0 = const()[name = tensor("denom_165_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_165 = rsqrt(epsilon = denom_165_epsilon_0, x = var_5648)[name = tensor("denom_165")]; - tensor out_165 = mul(x = zero_mean_165, y = denom_165)[name = tensor("out_165")]; - tensor var_5652 = const()[name = tensor("op_5652"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268513024)))]; - tensor var_5653 = add(x = out_165, y = var_5652)[name = tensor("op_5653")]; - tensor var_5655 = const()[name = tensor("op_5655"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268518208)))]; - tensor hidden_states_229 = mul(x = var_5653, y = var_5655)[name = tensor("hidden_states_229")]; - tensor var_5662 = const()[name = tensor("op_5662"), val = tensor([1, 1])]; - tensor var_5664 = const()[name = tensor("op_5664"), val = tensor([1, 1])]; + tensor attn_109_cast = matmul(transpose_x = attn_109_transpose_x_0, transpose_y = attn_109_transpose_y_0, x = var_5540_cast, y = var_5544_cast)[name = tensor("attn_109_cast")]; + tensor var_5548 = const()[name = tensor("op_5548"), val = tensor([2, 1280, 1, -1])]; + tensor input_351_cast = reshape(shape = var_5548, x = attn_109_cast)[name = tensor("input_351_cast")]; + tensor var_5553 = const()[name = tensor("op_5553"), val = tensor([1, 1])]; + tensor var_5555 = const()[name = tensor("op_5555"), val = tensor([1, 1])]; + tensor var_5557_pad_type_0 = const()[name = tensor("op_5557_pad_type_0"), val = tensor("custom")]; + tensor var_5557_pad_0 = const()[name = tensor("op_5557_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1944483776)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1947760640)))]; + tensor var_5557_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_5555, groups = var_31, pad = var_5557_pad_0, pad_type = var_5557_pad_type_0, strides = var_5553, weight = unet_mid_block_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_351_cast)[name = tensor("op_5557_cast")]; + tensor inputs_165_cast = add(x = var_5557_cast, y = inputs_163_cast)[name = tensor("inputs_165_cast")]; + tensor var_5561 = const()[name = tensor("op_5561"), val = tensor([1])]; + tensor channels_mean_165_cast = reduce_mean(axes = var_5561, keep_dims = var_23, x = inputs_165_cast)[name = tensor("channels_mean_165_cast")]; + tensor zero_mean_165_cast = sub(x = inputs_165_cast, y = channels_mean_165_cast)[name = tensor("zero_mean_165_cast")]; + tensor zero_mean_sq_165_cast = mul(x = zero_mean_165_cast, y = zero_mean_165_cast)[name = tensor("zero_mean_sq_165_cast")]; + tensor var_5565 = const()[name = tensor("op_5565"), val = tensor([1])]; + tensor var_5566_cast = reduce_mean(axes = var_5565, keep_dims = var_23, x = zero_mean_sq_165_cast)[name = tensor("op_5566_cast")]; + tensor var_5567_to_fp16 = const()[name = tensor("op_5567_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5568_cast = add(x = var_5566_cast, y = var_5567_to_fp16)[name = tensor("op_5568_cast")]; + tensor denom_165_epsilon_0_to_fp16 = const()[name = tensor("denom_165_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_165_cast = rsqrt(epsilon = denom_165_epsilon_0_to_fp16, x = var_5568_cast)[name = tensor("denom_165_cast")]; + tensor out_165_cast = mul(x = zero_mean_165_cast, y = denom_165_cast)[name = tensor("out_165_cast")]; + tensor var_5572_to_fp16 = const()[name = tensor("op_5572_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1947763264)))]; + tensor var_5573_cast = add(x = out_165_cast, y = var_5572_to_fp16)[name = tensor("op_5573_cast")]; + tensor var_5575_to_fp16 = const()[name = tensor("op_5575_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1947765888)))]; + tensor hidden_states_229_cast = mul(x = var_5573_cast, y = var_5575_to_fp16)[name = tensor("hidden_states_229_cast")]; + tensor var_5582 = const()[name = tensor("op_5582"), val = tensor([1, 1])]; + tensor var_5584 = const()[name = tensor("op_5584"), val = tensor([1, 1])]; tensor q_111_pad_type_0 = const()[name = tensor("q_111_pad_type_0"), val = tensor("custom")]; tensor q_111_pad_0 = const()[name = tensor("q_111_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_111 = conv(dilations = var_5664, groups = var_4950, pad = q_111_pad_0, pad_type = q_111_pad_type_0, strides = var_5662, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight, x = hidden_states_229)[name = tensor("q_111")]; - tensor var_5668 = const()[name = tensor("op_5668"), val = tensor([1, 1])]; - tensor var_5670 = const()[name = tensor("op_5670"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1947768512)))]; + tensor q_111_cast = conv(dilations = var_5584, groups = var_31, pad = q_111_pad_0, pad_type = q_111_pad_type_0, strides = var_5582, weight = unet_mid_block_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_229_cast)[name = tensor("q_111_cast")]; + tensor var_5588 = const()[name = tensor("op_5588"), val = tensor([1, 1])]; + tensor var_5590 = const()[name = tensor("op_5590"), val = tensor([1, 1])]; tensor k_111_pad_type_0 = const()[name = tensor("k_111_pad_type_0"), val = tensor("custom")]; tensor k_111_pad_0 = const()[name = tensor("k_111_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_111 = conv(dilations = var_5670, groups = var_4950, pad = k_111_pad_0, pad_type = k_111_pad_type_0, strides = var_5668, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_111")]; - tensor var_5674 = const()[name = tensor("op_5674"), val = tensor([1, 1])]; - tensor var_5676 = const()[name = tensor("op_5676"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1951045376)))]; + tensor k_111_cast = conv(dilations = var_5590, groups = var_31, pad = k_111_pad_0, pad_type = k_111_pad_type_0, strides = var_5588, weight = unet_mid_block_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_111_cast")]; + tensor var_5594 = const()[name = tensor("op_5594"), val = tensor([1, 1])]; + tensor var_5596 = const()[name = tensor("op_5596"), val = tensor([1, 1])]; tensor v_111_pad_type_0 = const()[name = tensor("v_111_pad_type_0"), val = tensor("custom")]; tensor v_111_pad_0 = const()[name = tensor("v_111_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_111 = conv(dilations = var_5676, groups = var_4950, pad = v_111_pad_0, pad_type = v_111_pad_type_0, strides = var_5674, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_111")]; - tensor var_5680 = const()[name = tensor("op_5680"), val = tensor([2, 20, 64, -1])]; - tensor var_5681 = reshape(shape = var_5680, x = q_111)[name = tensor("op_5681")]; - tensor var_5682 = const()[name = tensor("op_5682"), val = tensor([2, 20, 64, -1])]; - tensor var_5683 = reshape(shape = var_5682, x = k_111)[name = tensor("op_5683")]; - tensor var_5684 = const()[name = tensor("op_5684"), val = tensor([2, 20, 64, -1])]; - tensor var_5685 = reshape(shape = var_5684, x = v_111)[name = tensor("op_5685")]; + tensor unet_mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1956288320)))]; + tensor v_111_cast = conv(dilations = var_5596, groups = var_31, pad = v_111_pad_0, pad_type = v_111_pad_type_0, strides = var_5594, weight = unet_mid_block_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_111_cast")]; + tensor var_5600 = const()[name = tensor("op_5600"), val = tensor([2, 20, 64, -1])]; + tensor var_5601_cast = reshape(shape = var_5600, x = q_111_cast)[name = tensor("op_5601_cast")]; + tensor var_5602 = const()[name = tensor("op_5602"), val = tensor([2, 20, 64, -1])]; + tensor var_5603_cast = reshape(shape = var_5602, x = k_111_cast)[name = tensor("op_5603_cast")]; + tensor var_5604 = const()[name = tensor("op_5604"), val = tensor([2, 20, 64, -1])]; + tensor var_5605_cast = reshape(shape = var_5604, x = v_111_cast)[name = tensor("op_5605_cast")]; tensor attn_weights_221_transpose_x_0 = const()[name = tensor("attn_weights_221_transpose_x_0"), val = tensor(true)]; tensor attn_weights_221_transpose_y_0 = const()[name = tensor("attn_weights_221_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_221 = matmul(transpose_x = attn_weights_221_transpose_x_0, transpose_y = attn_weights_221_transpose_y_0, x = var_5681, y = var_5683)[name = tensor("attn_weights_221")]; - tensor attn_weights_223 = mul(x = attn_weights_221, y = var_4941)[name = tensor("attn_weights_223")]; - tensor var_5689 = softmax(axis = var_4934, x = attn_weights_223)[name = tensor("op_5689")]; + tensor attn_weights_221_cast = matmul(transpose_x = attn_weights_221_transpose_x_0, transpose_y = attn_weights_221_transpose_y_0, x = var_5601_cast, y = var_5603_cast)[name = tensor("attn_weights_221_cast")]; + tensor attn_weights_223_cast = mul(x = attn_weights_221_cast, y = var_12_to_fp16)[name = tensor("attn_weights_223_cast")]; + tensor var_5609_cast = softmax(axis = var_18, x = attn_weights_223_cast)[name = tensor("op_5609_cast")]; tensor attn_111_transpose_x_0 = const()[name = tensor("attn_111_transpose_x_0"), val = tensor(false)]; tensor attn_111_transpose_y_0 = const()[name = tensor("attn_111_transpose_y_0"), val = tensor(true)]; - tensor attn_111 = matmul(transpose_x = attn_111_transpose_x_0, transpose_y = attn_111_transpose_y_0, x = var_5685, y = var_5689)[name = tensor("attn_111")]; - tensor var_5693 = const()[name = tensor("op_5693"), val = tensor([2, 1280, 1, -1])]; - tensor input_353 = reshape(shape = var_5693, x = attn_111)[name = tensor("input_353")]; - tensor var_5698 = const()[name = tensor("op_5698"), val = tensor([1, 1])]; - tensor var_5700 = const()[name = tensor("op_5700"), val = tensor([1, 1])]; - tensor var_5702_pad_type_0 = const()[name = tensor("op_5702_pad_type_0"), val = tensor("custom")]; - tensor var_5702_pad_0 = const()[name = tensor("op_5702_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5702 = conv(bias = mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias, dilations = var_5700, groups = var_4950, pad = var_5702_pad_0, pad_type = var_5702_pad_type_0, strides = var_5698, weight = mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight, x = input_353)[name = tensor("op_5702")]; - tensor inputs_167 = add(x = var_5702, y = inputs_165)[name = tensor("inputs_167")]; - tensor var_5706 = const()[name = tensor("op_5706"), val = tensor([1])]; - tensor channels_mean_167 = reduce_mean(axes = var_5706, keep_dims = var_4945, x = inputs_167)[name = tensor("channels_mean_167")]; - tensor zero_mean_167 = sub(x = inputs_167, y = channels_mean_167)[name = tensor("zero_mean_167")]; - tensor zero_mean_sq_167 = mul(x = zero_mean_167, y = zero_mean_167)[name = tensor("zero_mean_sq_167")]; - tensor var_5710 = const()[name = tensor("op_5710"), val = tensor([1])]; - tensor var_5711 = reduce_mean(axes = var_5710, keep_dims = var_4945, x = zero_mean_sq_167)[name = tensor("op_5711")]; - tensor var_5712 = const()[name = tensor("op_5712"), val = tensor(0x1.4f8b58p-17)]; - tensor var_5713 = add(x = var_5711, y = var_5712)[name = tensor("op_5713")]; - tensor denom_167_epsilon_0 = const()[name = tensor("denom_167_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_167 = rsqrt(epsilon = denom_167_epsilon_0, x = var_5713)[name = tensor("denom_167")]; - tensor out_167 = mul(x = zero_mean_167, y = denom_167)[name = tensor("out_167")]; - tensor var_5717 = const()[name = tensor("op_5717"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268523392)))]; - tensor var_5718 = add(x = out_167, y = var_5717)[name = tensor("op_5718")]; - tensor var_5720 = const()[name = tensor("op_5720"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268528576)))]; - tensor input_355 = mul(x = var_5718, y = var_5720)[name = tensor("input_355")]; - tensor var_5728 = const()[name = tensor("op_5728"), val = tensor([1, 1])]; - tensor var_5730 = const()[name = tensor("op_5730"), val = tensor([1, 1])]; - tensor var_5732_pad_type_0 = const()[name = tensor("op_5732_pad_type_0"), val = tensor("custom")]; - tensor var_5732_pad_0 = const()[name = tensor("op_5732_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5732 = conv(bias = mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias, dilations = var_5730, groups = var_4950, pad = var_5732_pad_0, pad_type = var_5732_pad_type_0, strides = var_5728, weight = mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight, x = input_355)[name = tensor("op_5732")]; - tensor var_5733_split_sizes_0 = const()[name = tensor("op_5733_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_5733_axis_0 = const()[name = tensor("op_5733_axis_0"), val = tensor(1)]; - tensor var_5733_0, tensor var_5733_1 = split(axis = var_5733_axis_0, split_sizes = var_5733_split_sizes_0, x = var_5732)[name = tensor("op_5733")]; - tensor var_5735_mode_0 = const()[name = tensor("op_5735_mode_0"), val = tensor("EXACT")]; - tensor var_5735 = gelu(mode = var_5735_mode_0, x = var_5733_1)[name = tensor("op_5735")]; - tensor input_357 = mul(x = var_5733_0, y = var_5735)[name = tensor("input_357")]; - tensor var_5739 = const()[name = tensor("op_5739"), val = tensor([1, 1])]; - tensor var_5741 = const()[name = tensor("op_5741"), val = tensor([1, 1])]; - tensor var_5743_pad_type_0 = const()[name = tensor("op_5743_pad_type_0"), val = tensor("custom")]; - tensor var_5743_pad_0 = const()[name = tensor("op_5743_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5743 = conv(bias = mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias, dilations = var_5741, groups = var_4950, pad = var_5743_pad_0, pad_type = var_5743_pad_type_0, strides = var_5739, weight = mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight, x = input_357)[name = tensor("op_5743")]; - tensor inputs_169 = add(x = var_5743, y = inputs_167)[name = tensor("inputs_169")]; - tensor var_5753 = const()[name = tensor("op_5753"), val = tensor([1])]; - tensor channels_mean_169 = reduce_mean(axes = var_5753, keep_dims = var_4945, x = inputs_169)[name = tensor("channels_mean_169")]; - tensor zero_mean_169 = sub(x = inputs_169, y = channels_mean_169)[name = tensor("zero_mean_169")]; - tensor zero_mean_sq_169 = mul(x = zero_mean_169, y = zero_mean_169)[name = tensor("zero_mean_sq_169")]; - tensor var_5757 = const()[name = tensor("op_5757"), val = tensor([1])]; - tensor var_5758 = reduce_mean(axes = var_5757, keep_dims = var_4945, x = zero_mean_sq_169)[name = tensor("op_5758")]; - tensor var_5759 = const()[name = tensor("op_5759"), val = tensor(0x1.4f8b58p-17)]; - tensor var_5760 = add(x = var_5758, y = var_5759)[name = tensor("op_5760")]; - tensor denom_169_epsilon_0 = const()[name = tensor("denom_169_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_169 = rsqrt(epsilon = denom_169_epsilon_0, x = var_5760)[name = tensor("denom_169")]; - tensor out_169 = mul(x = zero_mean_169, y = denom_169)[name = tensor("out_169")]; - tensor var_5764 = const()[name = tensor("op_5764"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268533760)))]; - tensor var_5765 = add(x = out_169, y = var_5764)[name = tensor("op_5765")]; - tensor var_5767 = const()[name = tensor("op_5767"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268538944)))]; - tensor hidden_states_233 = mul(x = var_5765, y = var_5767)[name = tensor("hidden_states_233")]; - tensor var_5774 = const()[name = tensor("op_5774"), val = tensor([1, 1])]; - tensor var_5776 = const()[name = tensor("op_5776"), val = tensor([1, 1])]; + tensor attn_111_cast = matmul(transpose_x = attn_111_transpose_x_0, transpose_y = attn_111_transpose_y_0, x = var_5605_cast, y = var_5609_cast)[name = tensor("attn_111_cast")]; + tensor var_5613 = const()[name = tensor("op_5613"), val = tensor([2, 1280, 1, -1])]; + tensor input_353_cast = reshape(shape = var_5613, x = attn_111_cast)[name = tensor("input_353_cast")]; + tensor var_5618 = const()[name = tensor("op_5618"), val = tensor([1, 1])]; + tensor var_5620 = const()[name = tensor("op_5620"), val = tensor([1, 1])]; + tensor var_5622_pad_type_0 = const()[name = tensor("op_5622_pad_type_0"), val = tensor("custom")]; + tensor var_5622_pad_0 = const()[name = tensor("op_5622_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1961531264)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1964808128)))]; + tensor var_5622_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_5620, groups = var_31, pad = var_5622_pad_0, pad_type = var_5622_pad_type_0, strides = var_5618, weight = unet_mid_block_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_353_cast)[name = tensor("op_5622_cast")]; + tensor inputs_167_cast = add(x = var_5622_cast, y = inputs_165_cast)[name = tensor("inputs_167_cast")]; + tensor var_5626 = const()[name = tensor("op_5626"), val = tensor([1])]; + tensor channels_mean_167_cast = reduce_mean(axes = var_5626, keep_dims = var_23, x = inputs_167_cast)[name = tensor("channels_mean_167_cast")]; + tensor zero_mean_167_cast = sub(x = inputs_167_cast, y = channels_mean_167_cast)[name = tensor("zero_mean_167_cast")]; + tensor zero_mean_sq_167_cast = mul(x = zero_mean_167_cast, y = zero_mean_167_cast)[name = tensor("zero_mean_sq_167_cast")]; + tensor var_5630 = const()[name = tensor("op_5630"), val = tensor([1])]; + tensor var_5631_cast = reduce_mean(axes = var_5630, keep_dims = var_23, x = zero_mean_sq_167_cast)[name = tensor("op_5631_cast")]; + tensor var_5632_to_fp16 = const()[name = tensor("op_5632_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5633_cast = add(x = var_5631_cast, y = var_5632_to_fp16)[name = tensor("op_5633_cast")]; + tensor denom_167_epsilon_0_to_fp16 = const()[name = tensor("denom_167_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_167_cast = rsqrt(epsilon = denom_167_epsilon_0_to_fp16, x = var_5633_cast)[name = tensor("denom_167_cast")]; + tensor out_167_cast = mul(x = zero_mean_167_cast, y = denom_167_cast)[name = tensor("out_167_cast")]; + tensor var_5637_to_fp16 = const()[name = tensor("op_5637_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1964810752)))]; + tensor var_5638_cast = add(x = out_167_cast, y = var_5637_to_fp16)[name = tensor("op_5638_cast")]; + tensor var_5640_to_fp16 = const()[name = tensor("op_5640_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1964813376)))]; + tensor input_355_cast = mul(x = var_5638_cast, y = var_5640_to_fp16)[name = tensor("input_355_cast")]; + tensor var_5648 = const()[name = tensor("op_5648"), val = tensor([1, 1])]; + tensor var_5650 = const()[name = tensor("op_5650"), val = tensor([1, 1])]; + tensor var_5652_pad_type_0 = const()[name = tensor("op_5652_pad_type_0"), val = tensor("custom")]; + tensor var_5652_pad_0 = const()[name = tensor("op_5652_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1964816000)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1991030464)))]; + tensor var_5652_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_5650, groups = var_31, pad = var_5652_pad_0, pad_type = var_5652_pad_type_0, strides = var_5648, weight = unet_mid_block_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_355_cast)[name = tensor("op_5652_cast")]; + tensor var_5653_split_sizes_0 = const()[name = tensor("op_5653_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5653_axis_0 = const()[name = tensor("op_5653_axis_0"), val = tensor(1)]; + tensor var_5653_cast_0, tensor var_5653_cast_1 = split(axis = var_5653_axis_0, split_sizes = var_5653_split_sizes_0, x = var_5652_cast)[name = tensor("op_5653_cast")]; + tensor var_5655_mode_0 = const()[name = tensor("op_5655_mode_0"), val = tensor("EXACT")]; + tensor var_5655_cast = gelu(mode = var_5655_mode_0, x = var_5653_cast_1)[name = tensor("op_5655_cast")]; + tensor input_357_cast = mul(x = var_5653_cast_0, y = var_5655_cast)[name = tensor("input_357_cast")]; + tensor var_5659 = const()[name = tensor("op_5659"), val = tensor([1, 1])]; + tensor var_5661 = const()[name = tensor("op_5661"), val = tensor([1, 1])]; + tensor var_5663_pad_type_0 = const()[name = tensor("op_5663_pad_type_0"), val = tensor("custom")]; + tensor var_5663_pad_0 = const()[name = tensor("op_5663_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1991051008)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2004158272)))]; + tensor var_5663_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_5661, groups = var_31, pad = var_5663_pad_0, pad_type = var_5663_pad_type_0, strides = var_5659, weight = unet_mid_block_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_357_cast)[name = tensor("op_5663_cast")]; + tensor inputs_169_cast = add(x = var_5663_cast, y = inputs_167_cast)[name = tensor("inputs_169_cast")]; + tensor var_5673 = const()[name = tensor("op_5673"), val = tensor([1])]; + tensor channels_mean_169_cast = reduce_mean(axes = var_5673, keep_dims = var_23, x = inputs_169_cast)[name = tensor("channels_mean_169_cast")]; + tensor zero_mean_169_cast = sub(x = inputs_169_cast, y = channels_mean_169_cast)[name = tensor("zero_mean_169_cast")]; + tensor zero_mean_sq_169_cast = mul(x = zero_mean_169_cast, y = zero_mean_169_cast)[name = tensor("zero_mean_sq_169_cast")]; + tensor var_5677 = const()[name = tensor("op_5677"), val = tensor([1])]; + tensor var_5678_cast = reduce_mean(axes = var_5677, keep_dims = var_23, x = zero_mean_sq_169_cast)[name = tensor("op_5678_cast")]; + tensor var_5679_to_fp16 = const()[name = tensor("op_5679_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5680_cast = add(x = var_5678_cast, y = var_5679_to_fp16)[name = tensor("op_5680_cast")]; + tensor denom_169_epsilon_0_to_fp16 = const()[name = tensor("denom_169_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_169_cast = rsqrt(epsilon = denom_169_epsilon_0_to_fp16, x = var_5680_cast)[name = tensor("denom_169_cast")]; + tensor out_169_cast = mul(x = zero_mean_169_cast, y = denom_169_cast)[name = tensor("out_169_cast")]; + tensor var_5684_to_fp16 = const()[name = tensor("op_5684_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2004160896)))]; + tensor var_5685_cast = add(x = out_169_cast, y = var_5684_to_fp16)[name = tensor("op_5685_cast")]; + tensor var_5687_to_fp16 = const()[name = tensor("op_5687_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2004163520)))]; + tensor hidden_states_233_cast = mul(x = var_5685_cast, y = var_5687_to_fp16)[name = tensor("hidden_states_233_cast")]; + tensor var_5694 = const()[name = tensor("op_5694"), val = tensor([1, 1])]; + tensor var_5696 = const()[name = tensor("op_5696"), val = tensor([1, 1])]; tensor q_113_pad_type_0 = const()[name = tensor("q_113_pad_type_0"), val = tensor("custom")]; tensor q_113_pad_0 = const()[name = tensor("q_113_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_113 = conv(dilations = var_5776, groups = var_4950, pad = q_113_pad_0, pad_type = q_113_pad_type_0, strides = var_5774, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_q_weight, x = hidden_states_233)[name = tensor("q_113")]; - tensor var_5780 = const()[name = tensor("op_5780"), val = tensor([1, 1])]; - tensor var_5782 = const()[name = tensor("op_5782"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2004166144)))]; + tensor q_113_cast = conv(dilations = var_5696, groups = var_31, pad = q_113_pad_0, pad_type = q_113_pad_type_0, strides = var_5694, weight = unet_mid_block_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16, x = hidden_states_233_cast)[name = tensor("q_113_cast")]; + tensor var_5700 = const()[name = tensor("op_5700"), val = tensor([1, 1])]; + tensor var_5702 = const()[name = tensor("op_5702"), val = tensor([1, 1])]; tensor k_113_pad_type_0 = const()[name = tensor("k_113_pad_type_0"), val = tensor("custom")]; tensor k_113_pad_0 = const()[name = tensor("k_113_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_113 = conv(dilations = var_5782, groups = var_4950, pad = k_113_pad_0, pad_type = k_113_pad_type_0, strides = var_5780, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_k_weight, x = hidden_states_233)[name = tensor("k_113")]; - tensor var_5786 = const()[name = tensor("op_5786"), val = tensor([1, 1])]; - tensor var_5788 = const()[name = tensor("op_5788"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2007443008)))]; + tensor k_113_cast = conv(dilations = var_5702, groups = var_31, pad = k_113_pad_0, pad_type = k_113_pad_type_0, strides = var_5700, weight = unet_mid_block_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16, x = hidden_states_233_cast)[name = tensor("k_113_cast")]; + tensor var_5706 = const()[name = tensor("op_5706"), val = tensor([1, 1])]; + tensor var_5708 = const()[name = tensor("op_5708"), val = tensor([1, 1])]; tensor v_113_pad_type_0 = const()[name = tensor("v_113_pad_type_0"), val = tensor("custom")]; tensor v_113_pad_0 = const()[name = tensor("v_113_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_113 = conv(dilations = var_5788, groups = var_4950, pad = v_113_pad_0, pad_type = v_113_pad_type_0, strides = var_5786, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_v_weight, x = hidden_states_233)[name = tensor("v_113")]; - tensor var_5792 = const()[name = tensor("op_5792"), val = tensor([2, 20, 64, -1])]; - tensor var_5793 = reshape(shape = var_5792, x = q_113)[name = tensor("op_5793")]; - tensor var_5794 = const()[name = tensor("op_5794"), val = tensor([2, 20, 64, -1])]; - tensor var_5795 = reshape(shape = var_5794, x = k_113)[name = tensor("op_5795")]; - tensor var_5796 = const()[name = tensor("op_5796"), val = tensor([2, 20, 64, -1])]; - tensor var_5797 = reshape(shape = var_5796, x = v_113)[name = tensor("op_5797")]; + tensor unet_mid_block_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2010719872)))]; + tensor v_113_cast = conv(dilations = var_5708, groups = var_31, pad = v_113_pad_0, pad_type = v_113_pad_type_0, strides = var_5706, weight = unet_mid_block_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16, x = hidden_states_233_cast)[name = tensor("v_113_cast")]; + tensor var_5712 = const()[name = tensor("op_5712"), val = tensor([2, 20, 64, -1])]; + tensor var_5713_cast = reshape(shape = var_5712, x = q_113_cast)[name = tensor("op_5713_cast")]; + tensor var_5714 = const()[name = tensor("op_5714"), val = tensor([2, 20, 64, -1])]; + tensor var_5715_cast = reshape(shape = var_5714, x = k_113_cast)[name = tensor("op_5715_cast")]; + tensor var_5716 = const()[name = tensor("op_5716"), val = tensor([2, 20, 64, -1])]; + tensor var_5717_cast = reshape(shape = var_5716, x = v_113_cast)[name = tensor("op_5717_cast")]; tensor attn_weights_225_transpose_x_0 = const()[name = tensor("attn_weights_225_transpose_x_0"), val = tensor(true)]; tensor attn_weights_225_transpose_y_0 = const()[name = tensor("attn_weights_225_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_225 = matmul(transpose_x = attn_weights_225_transpose_x_0, transpose_y = attn_weights_225_transpose_y_0, x = var_5793, y = var_5795)[name = tensor("attn_weights_225")]; - tensor attn_weights_227 = mul(x = attn_weights_225, y = var_4941)[name = tensor("attn_weights_227")]; - tensor var_5801 = softmax(axis = var_4934, x = attn_weights_227)[name = tensor("op_5801")]; + tensor attn_weights_225_cast = matmul(transpose_x = attn_weights_225_transpose_x_0, transpose_y = attn_weights_225_transpose_y_0, x = var_5713_cast, y = var_5715_cast)[name = tensor("attn_weights_225_cast")]; + tensor attn_weights_227_cast = mul(x = attn_weights_225_cast, y = var_12_to_fp16)[name = tensor("attn_weights_227_cast")]; + tensor var_5721_cast = softmax(axis = var_18, x = attn_weights_227_cast)[name = tensor("op_5721_cast")]; tensor attn_113_transpose_x_0 = const()[name = tensor("attn_113_transpose_x_0"), val = tensor(false)]; tensor attn_113_transpose_y_0 = const()[name = tensor("attn_113_transpose_y_0"), val = tensor(true)]; - tensor attn_113 = matmul(transpose_x = attn_113_transpose_x_0, transpose_y = attn_113_transpose_y_0, x = var_5797, y = var_5801)[name = tensor("attn_113")]; - tensor var_5805 = const()[name = tensor("op_5805"), val = tensor([2, 1280, 1, -1])]; - tensor input_359 = reshape(shape = var_5805, x = attn_113)[name = tensor("input_359")]; - tensor var_5810 = const()[name = tensor("op_5810"), val = tensor([1, 1])]; - tensor var_5812 = const()[name = tensor("op_5812"), val = tensor([1, 1])]; - tensor var_5814_pad_type_0 = const()[name = tensor("op_5814_pad_type_0"), val = tensor("custom")]; - tensor var_5814_pad_0 = const()[name = tensor("op_5814_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5814 = conv(bias = mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_bias, dilations = var_5812, groups = var_4950, pad = var_5814_pad_0, pad_type = var_5814_pad_type_0, strides = var_5810, weight = mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_weight, x = input_359)[name = tensor("op_5814")]; - tensor inputs_171 = add(x = var_5814, y = inputs_169)[name = tensor("inputs_171")]; - tensor var_5818 = const()[name = tensor("op_5818"), val = tensor([1])]; - tensor channels_mean_171 = reduce_mean(axes = var_5818, keep_dims = var_4945, x = inputs_171)[name = tensor("channels_mean_171")]; - tensor zero_mean_171 = sub(x = inputs_171, y = channels_mean_171)[name = tensor("zero_mean_171")]; - tensor zero_mean_sq_171 = mul(x = zero_mean_171, y = zero_mean_171)[name = tensor("zero_mean_sq_171")]; - tensor var_5822 = const()[name = tensor("op_5822"), val = tensor([1])]; - tensor var_5823 = reduce_mean(axes = var_5822, keep_dims = var_4945, x = zero_mean_sq_171)[name = tensor("op_5823")]; - tensor var_5824 = const()[name = tensor("op_5824"), val = tensor(0x1.4f8b58p-17)]; - tensor var_5825 = add(x = var_5823, y = var_5824)[name = tensor("op_5825")]; - tensor denom_171_epsilon_0 = const()[name = tensor("denom_171_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_171 = rsqrt(epsilon = denom_171_epsilon_0, x = var_5825)[name = tensor("denom_171")]; - tensor out_171 = mul(x = zero_mean_171, y = denom_171)[name = tensor("out_171")]; - tensor var_5829 = const()[name = tensor("op_5829"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268544128)))]; - tensor var_5830 = add(x = out_171, y = var_5829)[name = tensor("op_5830")]; - tensor var_5832 = const()[name = tensor("op_5832"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268549312)))]; - tensor hidden_states_235 = mul(x = var_5830, y = var_5832)[name = tensor("hidden_states_235")]; - tensor var_5839 = const()[name = tensor("op_5839"), val = tensor([1, 1])]; - tensor var_5841 = const()[name = tensor("op_5841"), val = tensor([1, 1])]; + tensor attn_113_cast = matmul(transpose_x = attn_113_transpose_x_0, transpose_y = attn_113_transpose_y_0, x = var_5717_cast, y = var_5721_cast)[name = tensor("attn_113_cast")]; + tensor var_5725 = const()[name = tensor("op_5725"), val = tensor([2, 1280, 1, -1])]; + tensor input_359_cast = reshape(shape = var_5725, x = attn_113_cast)[name = tensor("input_359_cast")]; + tensor var_5730 = const()[name = tensor("op_5730"), val = tensor([1, 1])]; + tensor var_5732 = const()[name = tensor("op_5732"), val = tensor([1, 1])]; + tensor var_5734_pad_type_0 = const()[name = tensor("op_5734_pad_type_0"), val = tensor("custom")]; + tensor var_5734_pad_0 = const()[name = tensor("op_5734_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2013996736)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2017273600)))]; + tensor var_5734_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_5732, groups = var_31, pad = var_5734_pad_0, pad_type = var_5734_pad_type_0, strides = var_5730, weight = unet_mid_block_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16, x = input_359_cast)[name = tensor("op_5734_cast")]; + tensor inputs_171_cast = add(x = var_5734_cast, y = inputs_169_cast)[name = tensor("inputs_171_cast")]; + tensor var_5738 = const()[name = tensor("op_5738"), val = tensor([1])]; + tensor channels_mean_171_cast = reduce_mean(axes = var_5738, keep_dims = var_23, x = inputs_171_cast)[name = tensor("channels_mean_171_cast")]; + tensor zero_mean_171_cast = sub(x = inputs_171_cast, y = channels_mean_171_cast)[name = tensor("zero_mean_171_cast")]; + tensor zero_mean_sq_171_cast = mul(x = zero_mean_171_cast, y = zero_mean_171_cast)[name = tensor("zero_mean_sq_171_cast")]; + tensor var_5742 = const()[name = tensor("op_5742"), val = tensor([1])]; + tensor var_5743_cast = reduce_mean(axes = var_5742, keep_dims = var_23, x = zero_mean_sq_171_cast)[name = tensor("op_5743_cast")]; + tensor var_5744_to_fp16 = const()[name = tensor("op_5744_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5745_cast = add(x = var_5743_cast, y = var_5744_to_fp16)[name = tensor("op_5745_cast")]; + tensor denom_171_epsilon_0_to_fp16 = const()[name = tensor("denom_171_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_171_cast = rsqrt(epsilon = denom_171_epsilon_0_to_fp16, x = var_5745_cast)[name = tensor("denom_171_cast")]; + tensor out_171_cast = mul(x = zero_mean_171_cast, y = denom_171_cast)[name = tensor("out_171_cast")]; + tensor var_5749_to_fp16 = const()[name = tensor("op_5749_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2017276224)))]; + tensor var_5750_cast = add(x = out_171_cast, y = var_5749_to_fp16)[name = tensor("op_5750_cast")]; + tensor var_5752_to_fp16 = const()[name = tensor("op_5752_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2017278848)))]; + tensor hidden_states_235_cast = mul(x = var_5750_cast, y = var_5752_to_fp16)[name = tensor("hidden_states_235_cast")]; + tensor var_5759 = const()[name = tensor("op_5759"), val = tensor([1, 1])]; + tensor var_5761 = const()[name = tensor("op_5761"), val = tensor([1, 1])]; tensor q_115_pad_type_0 = const()[name = tensor("q_115_pad_type_0"), val = tensor("custom")]; tensor q_115_pad_0 = const()[name = tensor("q_115_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_115 = conv(dilations = var_5841, groups = var_4950, pad = q_115_pad_0, pad_type = q_115_pad_type_0, strides = var_5839, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_q_weight, x = hidden_states_235)[name = tensor("q_115")]; - tensor var_5845 = const()[name = tensor("op_5845"), val = tensor([1, 1])]; - tensor var_5847 = const()[name = tensor("op_5847"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2017281472)))]; + tensor q_115_cast = conv(dilations = var_5761, groups = var_31, pad = q_115_pad_0, pad_type = q_115_pad_type_0, strides = var_5759, weight = unet_mid_block_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16, x = hidden_states_235_cast)[name = tensor("q_115_cast")]; + tensor var_5765 = const()[name = tensor("op_5765"), val = tensor([1, 1])]; + tensor var_5767 = const()[name = tensor("op_5767"), val = tensor([1, 1])]; tensor k_115_pad_type_0 = const()[name = tensor("k_115_pad_type_0"), val = tensor("custom")]; tensor k_115_pad_0 = const()[name = tensor("k_115_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_115 = conv(dilations = var_5847, groups = var_4950, pad = k_115_pad_0, pad_type = k_115_pad_type_0, strides = var_5845, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_115")]; - tensor var_5851 = const()[name = tensor("op_5851"), val = tensor([1, 1])]; - tensor var_5853 = const()[name = tensor("op_5853"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2020558336)))]; + tensor k_115_cast = conv(dilations = var_5767, groups = var_31, pad = k_115_pad_0, pad_type = k_115_pad_type_0, strides = var_5765, weight = unet_mid_block_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_115_cast")]; + tensor var_5771 = const()[name = tensor("op_5771"), val = tensor([1, 1])]; + tensor var_5773 = const()[name = tensor("op_5773"), val = tensor([1, 1])]; tensor v_115_pad_type_0 = const()[name = tensor("v_115_pad_type_0"), val = tensor("custom")]; tensor v_115_pad_0 = const()[name = tensor("v_115_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_115 = conv(dilations = var_5853, groups = var_4950, pad = v_115_pad_0, pad_type = v_115_pad_type_0, strides = var_5851, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_115")]; - tensor var_5857 = const()[name = tensor("op_5857"), val = tensor([2, 20, 64, -1])]; - tensor var_5858 = reshape(shape = var_5857, x = q_115)[name = tensor("op_5858")]; - tensor var_5859 = const()[name = tensor("op_5859"), val = tensor([2, 20, 64, -1])]; - tensor var_5860 = reshape(shape = var_5859, x = k_115)[name = tensor("op_5860")]; - tensor var_5861 = const()[name = tensor("op_5861"), val = tensor([2, 20, 64, -1])]; - tensor var_5862 = reshape(shape = var_5861, x = v_115)[name = tensor("op_5862")]; + tensor unet_mid_block_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2025801280)))]; + tensor v_115_cast = conv(dilations = var_5773, groups = var_31, pad = v_115_pad_0, pad_type = v_115_pad_type_0, strides = var_5771, weight = unet_mid_block_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_115_cast")]; + tensor var_5777 = const()[name = tensor("op_5777"), val = tensor([2, 20, 64, -1])]; + tensor var_5778_cast = reshape(shape = var_5777, x = q_115_cast)[name = tensor("op_5778_cast")]; + tensor var_5779 = const()[name = tensor("op_5779"), val = tensor([2, 20, 64, -1])]; + tensor var_5780_cast = reshape(shape = var_5779, x = k_115_cast)[name = tensor("op_5780_cast")]; + tensor var_5781 = const()[name = tensor("op_5781"), val = tensor([2, 20, 64, -1])]; + tensor var_5782_cast = reshape(shape = var_5781, x = v_115_cast)[name = tensor("op_5782_cast")]; tensor attn_weights_229_transpose_x_0 = const()[name = tensor("attn_weights_229_transpose_x_0"), val = tensor(true)]; tensor attn_weights_229_transpose_y_0 = const()[name = tensor("attn_weights_229_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_229 = matmul(transpose_x = attn_weights_229_transpose_x_0, transpose_y = attn_weights_229_transpose_y_0, x = var_5858, y = var_5860)[name = tensor("attn_weights_229")]; - tensor attn_weights_231 = mul(x = attn_weights_229, y = var_4941)[name = tensor("attn_weights_231")]; - tensor var_5866 = softmax(axis = var_4934, x = attn_weights_231)[name = tensor("op_5866")]; + tensor attn_weights_229_cast = matmul(transpose_x = attn_weights_229_transpose_x_0, transpose_y = attn_weights_229_transpose_y_0, x = var_5778_cast, y = var_5780_cast)[name = tensor("attn_weights_229_cast")]; + tensor attn_weights_231_cast = mul(x = attn_weights_229_cast, y = var_12_to_fp16)[name = tensor("attn_weights_231_cast")]; + tensor var_5786_cast = softmax(axis = var_18, x = attn_weights_231_cast)[name = tensor("op_5786_cast")]; tensor attn_115_transpose_x_0 = const()[name = tensor("attn_115_transpose_x_0"), val = tensor(false)]; tensor attn_115_transpose_y_0 = const()[name = tensor("attn_115_transpose_y_0"), val = tensor(true)]; - tensor attn_115 = matmul(transpose_x = attn_115_transpose_x_0, transpose_y = attn_115_transpose_y_0, x = var_5862, y = var_5866)[name = tensor("attn_115")]; - tensor var_5870 = const()[name = tensor("op_5870"), val = tensor([2, 1280, 1, -1])]; - tensor input_361 = reshape(shape = var_5870, x = attn_115)[name = tensor("input_361")]; - tensor var_5875 = const()[name = tensor("op_5875"), val = tensor([1, 1])]; - tensor var_5877 = const()[name = tensor("op_5877"), val = tensor([1, 1])]; - tensor var_5879_pad_type_0 = const()[name = tensor("op_5879_pad_type_0"), val = tensor("custom")]; - tensor var_5879_pad_0 = const()[name = tensor("op_5879_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5879 = conv(bias = mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_bias, dilations = var_5877, groups = var_4950, pad = var_5879_pad_0, pad_type = var_5879_pad_type_0, strides = var_5875, weight = mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_weight, x = input_361)[name = tensor("op_5879")]; - tensor inputs_173 = add(x = var_5879, y = inputs_171)[name = tensor("inputs_173")]; - tensor var_5883 = const()[name = tensor("op_5883"), val = tensor([1])]; - tensor channels_mean_173 = reduce_mean(axes = var_5883, keep_dims = var_4945, x = inputs_173)[name = tensor("channels_mean_173")]; - tensor zero_mean_173 = sub(x = inputs_173, y = channels_mean_173)[name = tensor("zero_mean_173")]; - tensor zero_mean_sq_173 = mul(x = zero_mean_173, y = zero_mean_173)[name = tensor("zero_mean_sq_173")]; - tensor var_5887 = const()[name = tensor("op_5887"), val = tensor([1])]; - tensor var_5888 = reduce_mean(axes = var_5887, keep_dims = var_4945, x = zero_mean_sq_173)[name = tensor("op_5888")]; - tensor var_5889 = const()[name = tensor("op_5889"), val = tensor(0x1.4f8b58p-17)]; - tensor var_5890 = add(x = var_5888, y = var_5889)[name = tensor("op_5890")]; - tensor denom_173_epsilon_0 = const()[name = tensor("denom_173_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_173 = rsqrt(epsilon = denom_173_epsilon_0, x = var_5890)[name = tensor("denom_173")]; - tensor out_173 = mul(x = zero_mean_173, y = denom_173)[name = tensor("out_173")]; - tensor var_5894 = const()[name = tensor("op_5894"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268554496)))]; - tensor var_5895 = add(x = out_173, y = var_5894)[name = tensor("op_5895")]; - tensor var_5897 = const()[name = tensor("op_5897"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268559680)))]; - tensor input_363 = mul(x = var_5895, y = var_5897)[name = tensor("input_363")]; - tensor var_5905 = const()[name = tensor("op_5905"), val = tensor([1, 1])]; - tensor var_5907 = const()[name = tensor("op_5907"), val = tensor([1, 1])]; - tensor var_5909_pad_type_0 = const()[name = tensor("op_5909_pad_type_0"), val = tensor("custom")]; - tensor var_5909_pad_0 = const()[name = tensor("op_5909_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5909 = conv(bias = mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_bias, dilations = var_5907, groups = var_4950, pad = var_5909_pad_0, pad_type = var_5909_pad_type_0, strides = var_5905, weight = mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_weight, x = input_363)[name = tensor("op_5909")]; - tensor var_5910_split_sizes_0 = const()[name = tensor("op_5910_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_5910_axis_0 = const()[name = tensor("op_5910_axis_0"), val = tensor(1)]; - tensor var_5910_0, tensor var_5910_1 = split(axis = var_5910_axis_0, split_sizes = var_5910_split_sizes_0, x = var_5909)[name = tensor("op_5910")]; - tensor var_5912_mode_0 = const()[name = tensor("op_5912_mode_0"), val = tensor("EXACT")]; - tensor var_5912 = gelu(mode = var_5912_mode_0, x = var_5910_1)[name = tensor("op_5912")]; - tensor input_365 = mul(x = var_5910_0, y = var_5912)[name = tensor("input_365")]; - tensor var_5916 = const()[name = tensor("op_5916"), val = tensor([1, 1])]; - tensor var_5918 = const()[name = tensor("op_5918"), val = tensor([1, 1])]; - tensor var_5920_pad_type_0 = const()[name = tensor("op_5920_pad_type_0"), val = tensor("custom")]; - tensor var_5920_pad_0 = const()[name = tensor("op_5920_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5920 = conv(bias = mid_block_attentions_0_transformer_blocks_4_ff_net_2_bias, dilations = var_5918, groups = var_4950, pad = var_5920_pad_0, pad_type = var_5920_pad_type_0, strides = var_5916, weight = mid_block_attentions_0_transformer_blocks_4_ff_net_2_weight, x = input_365)[name = tensor("op_5920")]; - tensor inputs_175 = add(x = var_5920, y = inputs_173)[name = tensor("inputs_175")]; - tensor var_5930 = const()[name = tensor("op_5930"), val = tensor([1])]; - tensor channels_mean_175 = reduce_mean(axes = var_5930, keep_dims = var_4945, x = inputs_175)[name = tensor("channels_mean_175")]; - tensor zero_mean_175 = sub(x = inputs_175, y = channels_mean_175)[name = tensor("zero_mean_175")]; - tensor zero_mean_sq_175 = mul(x = zero_mean_175, y = zero_mean_175)[name = tensor("zero_mean_sq_175")]; - tensor var_5934 = const()[name = tensor("op_5934"), val = tensor([1])]; - tensor var_5935 = reduce_mean(axes = var_5934, keep_dims = var_4945, x = zero_mean_sq_175)[name = tensor("op_5935")]; - tensor var_5936 = const()[name = tensor("op_5936"), val = tensor(0x1.4f8b58p-17)]; - tensor var_5937 = add(x = var_5935, y = var_5936)[name = tensor("op_5937")]; - tensor denom_175_epsilon_0 = const()[name = tensor("denom_175_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_175 = rsqrt(epsilon = denom_175_epsilon_0, x = var_5937)[name = tensor("denom_175")]; - tensor out_175 = mul(x = zero_mean_175, y = denom_175)[name = tensor("out_175")]; - tensor var_5941 = const()[name = tensor("op_5941"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268564864)))]; - tensor var_5942 = add(x = out_175, y = var_5941)[name = tensor("op_5942")]; - tensor var_5944 = const()[name = tensor("op_5944"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268570048)))]; - tensor hidden_states_239 = mul(x = var_5942, y = var_5944)[name = tensor("hidden_states_239")]; - tensor var_5951 = const()[name = tensor("op_5951"), val = tensor([1, 1])]; - tensor var_5953 = const()[name = tensor("op_5953"), val = tensor([1, 1])]; + tensor attn_115_cast = matmul(transpose_x = attn_115_transpose_x_0, transpose_y = attn_115_transpose_y_0, x = var_5782_cast, y = var_5786_cast)[name = tensor("attn_115_cast")]; + tensor var_5790 = const()[name = tensor("op_5790"), val = tensor([2, 1280, 1, -1])]; + tensor input_361_cast = reshape(shape = var_5790, x = attn_115_cast)[name = tensor("input_361_cast")]; + tensor var_5795 = const()[name = tensor("op_5795"), val = tensor([1, 1])]; + tensor var_5797 = const()[name = tensor("op_5797"), val = tensor([1, 1])]; + tensor var_5799_pad_type_0 = const()[name = tensor("op_5799_pad_type_0"), val = tensor("custom")]; + tensor var_5799_pad_0 = const()[name = tensor("op_5799_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2031044224)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2034321088)))]; + tensor var_5799_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_5797, groups = var_31, pad = var_5799_pad_0, pad_type = var_5799_pad_type_0, strides = var_5795, weight = unet_mid_block_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16, x = input_361_cast)[name = tensor("op_5799_cast")]; + tensor inputs_173_cast = add(x = var_5799_cast, y = inputs_171_cast)[name = tensor("inputs_173_cast")]; + tensor var_5803 = const()[name = tensor("op_5803"), val = tensor([1])]; + tensor channels_mean_173_cast = reduce_mean(axes = var_5803, keep_dims = var_23, x = inputs_173_cast)[name = tensor("channels_mean_173_cast")]; + tensor zero_mean_173_cast = sub(x = inputs_173_cast, y = channels_mean_173_cast)[name = tensor("zero_mean_173_cast")]; + tensor zero_mean_sq_173_cast = mul(x = zero_mean_173_cast, y = zero_mean_173_cast)[name = tensor("zero_mean_sq_173_cast")]; + tensor var_5807 = const()[name = tensor("op_5807"), val = tensor([1])]; + tensor var_5808_cast = reduce_mean(axes = var_5807, keep_dims = var_23, x = zero_mean_sq_173_cast)[name = tensor("op_5808_cast")]; + tensor var_5809_to_fp16 = const()[name = tensor("op_5809_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5810_cast = add(x = var_5808_cast, y = var_5809_to_fp16)[name = tensor("op_5810_cast")]; + tensor denom_173_epsilon_0_to_fp16 = const()[name = tensor("denom_173_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_173_cast = rsqrt(epsilon = denom_173_epsilon_0_to_fp16, x = var_5810_cast)[name = tensor("denom_173_cast")]; + tensor out_173_cast = mul(x = zero_mean_173_cast, y = denom_173_cast)[name = tensor("out_173_cast")]; + tensor var_5814_to_fp16 = const()[name = tensor("op_5814_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2034323712)))]; + tensor var_5815_cast = add(x = out_173_cast, y = var_5814_to_fp16)[name = tensor("op_5815_cast")]; + tensor var_5817_to_fp16 = const()[name = tensor("op_5817_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2034326336)))]; + tensor input_363_cast = mul(x = var_5815_cast, y = var_5817_to_fp16)[name = tensor("input_363_cast")]; + tensor var_5825 = const()[name = tensor("op_5825"), val = tensor([1, 1])]; + tensor var_5827 = const()[name = tensor("op_5827"), val = tensor([1, 1])]; + tensor var_5829_pad_type_0 = const()[name = tensor("op_5829_pad_type_0"), val = tensor("custom")]; + tensor var_5829_pad_0 = const()[name = tensor("op_5829_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2034328960)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2060543424)))]; + tensor var_5829_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_5827, groups = var_31, pad = var_5829_pad_0, pad_type = var_5829_pad_type_0, strides = var_5825, weight = unet_mid_block_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16, x = input_363_cast)[name = tensor("op_5829_cast")]; + tensor var_5830_split_sizes_0 = const()[name = tensor("op_5830_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_5830_axis_0 = const()[name = tensor("op_5830_axis_0"), val = tensor(1)]; + tensor var_5830_cast_0, tensor var_5830_cast_1 = split(axis = var_5830_axis_0, split_sizes = var_5830_split_sizes_0, x = var_5829_cast)[name = tensor("op_5830_cast")]; + tensor var_5832_mode_0 = const()[name = tensor("op_5832_mode_0"), val = tensor("EXACT")]; + tensor var_5832_cast = gelu(mode = var_5832_mode_0, x = var_5830_cast_1)[name = tensor("op_5832_cast")]; + tensor input_365_cast = mul(x = var_5830_cast_0, y = var_5832_cast)[name = tensor("input_365_cast")]; + tensor var_5836 = const()[name = tensor("op_5836"), val = tensor([1, 1])]; + tensor var_5838 = const()[name = tensor("op_5838"), val = tensor([1, 1])]; + tensor var_5840_pad_type_0 = const()[name = tensor("op_5840_pad_type_0"), val = tensor("custom")]; + tensor var_5840_pad_0 = const()[name = tensor("op_5840_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2060563968)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2073671232)))]; + tensor var_5840_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_5838, groups = var_31, pad = var_5840_pad_0, pad_type = var_5840_pad_type_0, strides = var_5836, weight = unet_mid_block_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16, x = input_365_cast)[name = tensor("op_5840_cast")]; + tensor inputs_175_cast = add(x = var_5840_cast, y = inputs_173_cast)[name = tensor("inputs_175_cast")]; + tensor var_5850 = const()[name = tensor("op_5850"), val = tensor([1])]; + tensor channels_mean_175_cast = reduce_mean(axes = var_5850, keep_dims = var_23, x = inputs_175_cast)[name = tensor("channels_mean_175_cast")]; + tensor zero_mean_175_cast = sub(x = inputs_175_cast, y = channels_mean_175_cast)[name = tensor("zero_mean_175_cast")]; + tensor zero_mean_sq_175_cast = mul(x = zero_mean_175_cast, y = zero_mean_175_cast)[name = tensor("zero_mean_sq_175_cast")]; + tensor var_5854 = const()[name = tensor("op_5854"), val = tensor([1])]; + tensor var_5855_cast = reduce_mean(axes = var_5854, keep_dims = var_23, x = zero_mean_sq_175_cast)[name = tensor("op_5855_cast")]; + tensor var_5856_to_fp16 = const()[name = tensor("op_5856_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5857_cast = add(x = var_5855_cast, y = var_5856_to_fp16)[name = tensor("op_5857_cast")]; + tensor denom_175_epsilon_0_to_fp16 = const()[name = tensor("denom_175_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_175_cast = rsqrt(epsilon = denom_175_epsilon_0_to_fp16, x = var_5857_cast)[name = tensor("denom_175_cast")]; + tensor out_175_cast = mul(x = zero_mean_175_cast, y = denom_175_cast)[name = tensor("out_175_cast")]; + tensor var_5861_to_fp16 = const()[name = tensor("op_5861_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2073673856)))]; + tensor var_5862_cast = add(x = out_175_cast, y = var_5861_to_fp16)[name = tensor("op_5862_cast")]; + tensor var_5864_to_fp16 = const()[name = tensor("op_5864_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2073676480)))]; + tensor hidden_states_239_cast = mul(x = var_5862_cast, y = var_5864_to_fp16)[name = tensor("hidden_states_239_cast")]; + tensor var_5871 = const()[name = tensor("op_5871"), val = tensor([1, 1])]; + tensor var_5873 = const()[name = tensor("op_5873"), val = tensor([1, 1])]; tensor q_117_pad_type_0 = const()[name = tensor("q_117_pad_type_0"), val = tensor("custom")]; tensor q_117_pad_0 = const()[name = tensor("q_117_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_117 = conv(dilations = var_5953, groups = var_4950, pad = q_117_pad_0, pad_type = q_117_pad_type_0, strides = var_5951, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_q_weight, x = hidden_states_239)[name = tensor("q_117")]; - tensor var_5957 = const()[name = tensor("op_5957"), val = tensor([1, 1])]; - tensor var_5959 = const()[name = tensor("op_5959"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2073679104)))]; + tensor q_117_cast = conv(dilations = var_5873, groups = var_31, pad = q_117_pad_0, pad_type = q_117_pad_type_0, strides = var_5871, weight = unet_mid_block_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16, x = hidden_states_239_cast)[name = tensor("q_117_cast")]; + tensor var_5877 = const()[name = tensor("op_5877"), val = tensor([1, 1])]; + tensor var_5879 = const()[name = tensor("op_5879"), val = tensor([1, 1])]; tensor k_117_pad_type_0 = const()[name = tensor("k_117_pad_type_0"), val = tensor("custom")]; tensor k_117_pad_0 = const()[name = tensor("k_117_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_117 = conv(dilations = var_5959, groups = var_4950, pad = k_117_pad_0, pad_type = k_117_pad_type_0, strides = var_5957, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_k_weight, x = hidden_states_239)[name = tensor("k_117")]; - tensor var_5963 = const()[name = tensor("op_5963"), val = tensor([1, 1])]; - tensor var_5965 = const()[name = tensor("op_5965"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2076955968)))]; + tensor k_117_cast = conv(dilations = var_5879, groups = var_31, pad = k_117_pad_0, pad_type = k_117_pad_type_0, strides = var_5877, weight = unet_mid_block_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16, x = hidden_states_239_cast)[name = tensor("k_117_cast")]; + tensor var_5883 = const()[name = tensor("op_5883"), val = tensor([1, 1])]; + tensor var_5885 = const()[name = tensor("op_5885"), val = tensor([1, 1])]; tensor v_117_pad_type_0 = const()[name = tensor("v_117_pad_type_0"), val = tensor("custom")]; tensor v_117_pad_0 = const()[name = tensor("v_117_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_117 = conv(dilations = var_5965, groups = var_4950, pad = v_117_pad_0, pad_type = v_117_pad_type_0, strides = var_5963, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_v_weight, x = hidden_states_239)[name = tensor("v_117")]; - tensor var_5969 = const()[name = tensor("op_5969"), val = tensor([2, 20, 64, -1])]; - tensor var_5970 = reshape(shape = var_5969, x = q_117)[name = tensor("op_5970")]; - tensor var_5971 = const()[name = tensor("op_5971"), val = tensor([2, 20, 64, -1])]; - tensor var_5972 = reshape(shape = var_5971, x = k_117)[name = tensor("op_5972")]; - tensor var_5973 = const()[name = tensor("op_5973"), val = tensor([2, 20, 64, -1])]; - tensor var_5974 = reshape(shape = var_5973, x = v_117)[name = tensor("op_5974")]; + tensor unet_mid_block_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2080232832)))]; + tensor v_117_cast = conv(dilations = var_5885, groups = var_31, pad = v_117_pad_0, pad_type = v_117_pad_type_0, strides = var_5883, weight = unet_mid_block_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16, x = hidden_states_239_cast)[name = tensor("v_117_cast")]; + tensor var_5889 = const()[name = tensor("op_5889"), val = tensor([2, 20, 64, -1])]; + tensor var_5890_cast = reshape(shape = var_5889, x = q_117_cast)[name = tensor("op_5890_cast")]; + tensor var_5891 = const()[name = tensor("op_5891"), val = tensor([2, 20, 64, -1])]; + tensor var_5892_cast = reshape(shape = var_5891, x = k_117_cast)[name = tensor("op_5892_cast")]; + tensor var_5893 = const()[name = tensor("op_5893"), val = tensor([2, 20, 64, -1])]; + tensor var_5894_cast = reshape(shape = var_5893, x = v_117_cast)[name = tensor("op_5894_cast")]; tensor attn_weights_233_transpose_x_0 = const()[name = tensor("attn_weights_233_transpose_x_0"), val = tensor(true)]; tensor attn_weights_233_transpose_y_0 = const()[name = tensor("attn_weights_233_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_233 = matmul(transpose_x = attn_weights_233_transpose_x_0, transpose_y = attn_weights_233_transpose_y_0, x = var_5970, y = var_5972)[name = tensor("attn_weights_233")]; - tensor attn_weights_235 = mul(x = attn_weights_233, y = var_4941)[name = tensor("attn_weights_235")]; - tensor var_5978 = softmax(axis = var_4934, x = attn_weights_235)[name = tensor("op_5978")]; + tensor attn_weights_233_cast = matmul(transpose_x = attn_weights_233_transpose_x_0, transpose_y = attn_weights_233_transpose_y_0, x = var_5890_cast, y = var_5892_cast)[name = tensor("attn_weights_233_cast")]; + tensor attn_weights_235_cast = mul(x = attn_weights_233_cast, y = var_12_to_fp16)[name = tensor("attn_weights_235_cast")]; + tensor var_5898_cast = softmax(axis = var_18, x = attn_weights_235_cast)[name = tensor("op_5898_cast")]; tensor attn_117_transpose_x_0 = const()[name = tensor("attn_117_transpose_x_0"), val = tensor(false)]; tensor attn_117_transpose_y_0 = const()[name = tensor("attn_117_transpose_y_0"), val = tensor(true)]; - tensor attn_117 = matmul(transpose_x = attn_117_transpose_x_0, transpose_y = attn_117_transpose_y_0, x = var_5974, y = var_5978)[name = tensor("attn_117")]; - tensor var_5982 = const()[name = tensor("op_5982"), val = tensor([2, 1280, 1, -1])]; - tensor input_367 = reshape(shape = var_5982, x = attn_117)[name = tensor("input_367")]; - tensor var_5987 = const()[name = tensor("op_5987"), val = tensor([1, 1])]; - tensor var_5989 = const()[name = tensor("op_5989"), val = tensor([1, 1])]; - tensor var_5991_pad_type_0 = const()[name = tensor("op_5991_pad_type_0"), val = tensor("custom")]; - tensor var_5991_pad_0 = const()[name = tensor("op_5991_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_5991 = conv(bias = mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_bias, dilations = var_5989, groups = var_4950, pad = var_5991_pad_0, pad_type = var_5991_pad_type_0, strides = var_5987, weight = mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_weight, x = input_367)[name = tensor("op_5991")]; - tensor inputs_177 = add(x = var_5991, y = inputs_175)[name = tensor("inputs_177")]; - tensor var_5995 = const()[name = tensor("op_5995"), val = tensor([1])]; - tensor channels_mean_177 = reduce_mean(axes = var_5995, keep_dims = var_4945, x = inputs_177)[name = tensor("channels_mean_177")]; - tensor zero_mean_177 = sub(x = inputs_177, y = channels_mean_177)[name = tensor("zero_mean_177")]; - tensor zero_mean_sq_177 = mul(x = zero_mean_177, y = zero_mean_177)[name = tensor("zero_mean_sq_177")]; - tensor var_5999 = const()[name = tensor("op_5999"), val = tensor([1])]; - tensor var_6000 = reduce_mean(axes = var_5999, keep_dims = var_4945, x = zero_mean_sq_177)[name = tensor("op_6000")]; - tensor var_6001 = const()[name = tensor("op_6001"), val = tensor(0x1.4f8b58p-17)]; - tensor var_6002 = add(x = var_6000, y = var_6001)[name = tensor("op_6002")]; - tensor denom_177_epsilon_0 = const()[name = tensor("denom_177_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_177 = rsqrt(epsilon = denom_177_epsilon_0, x = var_6002)[name = tensor("denom_177")]; - tensor out_177 = mul(x = zero_mean_177, y = denom_177)[name = tensor("out_177")]; - tensor var_6006 = const()[name = tensor("op_6006"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268575232)))]; - tensor var_6007 = add(x = out_177, y = var_6006)[name = tensor("op_6007")]; - tensor var_6009 = const()[name = tensor("op_6009"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268580416)))]; - tensor hidden_states_241 = mul(x = var_6007, y = var_6009)[name = tensor("hidden_states_241")]; - tensor var_6016 = const()[name = tensor("op_6016"), val = tensor([1, 1])]; - tensor var_6018 = const()[name = tensor("op_6018"), val = tensor([1, 1])]; + tensor attn_117_cast = matmul(transpose_x = attn_117_transpose_x_0, transpose_y = attn_117_transpose_y_0, x = var_5894_cast, y = var_5898_cast)[name = tensor("attn_117_cast")]; + tensor var_5902 = const()[name = tensor("op_5902"), val = tensor([2, 1280, 1, -1])]; + tensor input_367_cast = reshape(shape = var_5902, x = attn_117_cast)[name = tensor("input_367_cast")]; + tensor var_5907 = const()[name = tensor("op_5907"), val = tensor([1, 1])]; + tensor var_5909 = const()[name = tensor("op_5909"), val = tensor([1, 1])]; + tensor var_5911_pad_type_0 = const()[name = tensor("op_5911_pad_type_0"), val = tensor("custom")]; + tensor var_5911_pad_0 = const()[name = tensor("op_5911_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2083509696)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2086786560)))]; + tensor var_5911_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_5909, groups = var_31, pad = var_5911_pad_0, pad_type = var_5911_pad_type_0, strides = var_5907, weight = unet_mid_block_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16, x = input_367_cast)[name = tensor("op_5911_cast")]; + tensor inputs_177_cast = add(x = var_5911_cast, y = inputs_175_cast)[name = tensor("inputs_177_cast")]; + tensor var_5915 = const()[name = tensor("op_5915"), val = tensor([1])]; + tensor channels_mean_177_cast = reduce_mean(axes = var_5915, keep_dims = var_23, x = inputs_177_cast)[name = tensor("channels_mean_177_cast")]; + tensor zero_mean_177_cast = sub(x = inputs_177_cast, y = channels_mean_177_cast)[name = tensor("zero_mean_177_cast")]; + tensor zero_mean_sq_177_cast = mul(x = zero_mean_177_cast, y = zero_mean_177_cast)[name = tensor("zero_mean_sq_177_cast")]; + tensor var_5919 = const()[name = tensor("op_5919"), val = tensor([1])]; + tensor var_5920_cast = reduce_mean(axes = var_5919, keep_dims = var_23, x = zero_mean_sq_177_cast)[name = tensor("op_5920_cast")]; + tensor var_5921_to_fp16 = const()[name = tensor("op_5921_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5922_cast = add(x = var_5920_cast, y = var_5921_to_fp16)[name = tensor("op_5922_cast")]; + tensor denom_177_epsilon_0_to_fp16 = const()[name = tensor("denom_177_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_177_cast = rsqrt(epsilon = denom_177_epsilon_0_to_fp16, x = var_5922_cast)[name = tensor("denom_177_cast")]; + tensor out_177_cast = mul(x = zero_mean_177_cast, y = denom_177_cast)[name = tensor("out_177_cast")]; + tensor var_5926_to_fp16 = const()[name = tensor("op_5926_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2086789184)))]; + tensor var_5927_cast = add(x = out_177_cast, y = var_5926_to_fp16)[name = tensor("op_5927_cast")]; + tensor var_5929_to_fp16 = const()[name = tensor("op_5929_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2086791808)))]; + tensor hidden_states_241_cast = mul(x = var_5927_cast, y = var_5929_to_fp16)[name = tensor("hidden_states_241_cast")]; + tensor var_5936 = const()[name = tensor("op_5936"), val = tensor([1, 1])]; + tensor var_5938 = const()[name = tensor("op_5938"), val = tensor([1, 1])]; tensor q_119_pad_type_0 = const()[name = tensor("q_119_pad_type_0"), val = tensor("custom")]; tensor q_119_pad_0 = const()[name = tensor("q_119_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_119 = conv(dilations = var_6018, groups = var_4950, pad = q_119_pad_0, pad_type = q_119_pad_type_0, strides = var_6016, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_q_weight, x = hidden_states_241)[name = tensor("q_119")]; - tensor var_6022 = const()[name = tensor("op_6022"), val = tensor([1, 1])]; - tensor var_6024 = const()[name = tensor("op_6024"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2086794432)))]; + tensor q_119_cast = conv(dilations = var_5938, groups = var_31, pad = q_119_pad_0, pad_type = q_119_pad_type_0, strides = var_5936, weight = unet_mid_block_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16, x = hidden_states_241_cast)[name = tensor("q_119_cast")]; + tensor var_5942 = const()[name = tensor("op_5942"), val = tensor([1, 1])]; + tensor var_5944 = const()[name = tensor("op_5944"), val = tensor([1, 1])]; tensor k_119_pad_type_0 = const()[name = tensor("k_119_pad_type_0"), val = tensor("custom")]; tensor k_119_pad_0 = const()[name = tensor("k_119_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_119 = conv(dilations = var_6024, groups = var_4950, pad = k_119_pad_0, pad_type = k_119_pad_type_0, strides = var_6022, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_119")]; - tensor var_6028 = const()[name = tensor("op_6028"), val = tensor([1, 1])]; - tensor var_6030 = const()[name = tensor("op_6030"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2090071296)))]; + tensor k_119_cast = conv(dilations = var_5944, groups = var_31, pad = k_119_pad_0, pad_type = k_119_pad_type_0, strides = var_5942, weight = unet_mid_block_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_119_cast")]; + tensor var_5948 = const()[name = tensor("op_5948"), val = tensor([1, 1])]; + tensor var_5950 = const()[name = tensor("op_5950"), val = tensor([1, 1])]; tensor v_119_pad_type_0 = const()[name = tensor("v_119_pad_type_0"), val = tensor("custom")]; tensor v_119_pad_0 = const()[name = tensor("v_119_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_119 = conv(dilations = var_6030, groups = var_4950, pad = v_119_pad_0, pad_type = v_119_pad_type_0, strides = var_6028, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_119")]; - tensor var_6034 = const()[name = tensor("op_6034"), val = tensor([2, 20, 64, -1])]; - tensor var_6035 = reshape(shape = var_6034, x = q_119)[name = tensor("op_6035")]; - tensor var_6036 = const()[name = tensor("op_6036"), val = tensor([2, 20, 64, -1])]; - tensor var_6037 = reshape(shape = var_6036, x = k_119)[name = tensor("op_6037")]; - tensor var_6038 = const()[name = tensor("op_6038"), val = tensor([2, 20, 64, -1])]; - tensor var_6039 = reshape(shape = var_6038, x = v_119)[name = tensor("op_6039")]; + tensor unet_mid_block_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2095314240)))]; + tensor v_119_cast = conv(dilations = var_5950, groups = var_31, pad = v_119_pad_0, pad_type = v_119_pad_type_0, strides = var_5948, weight = unet_mid_block_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_119_cast")]; + tensor var_5954 = const()[name = tensor("op_5954"), val = tensor([2, 20, 64, -1])]; + tensor var_5955_cast = reshape(shape = var_5954, x = q_119_cast)[name = tensor("op_5955_cast")]; + tensor var_5956 = const()[name = tensor("op_5956"), val = tensor([2, 20, 64, -1])]; + tensor var_5957_cast = reshape(shape = var_5956, x = k_119_cast)[name = tensor("op_5957_cast")]; + tensor var_5958 = const()[name = tensor("op_5958"), val = tensor([2, 20, 64, -1])]; + tensor var_5959_cast = reshape(shape = var_5958, x = v_119_cast)[name = tensor("op_5959_cast")]; tensor attn_weights_237_transpose_x_0 = const()[name = tensor("attn_weights_237_transpose_x_0"), val = tensor(true)]; tensor attn_weights_237_transpose_y_0 = const()[name = tensor("attn_weights_237_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_237 = matmul(transpose_x = attn_weights_237_transpose_x_0, transpose_y = attn_weights_237_transpose_y_0, x = var_6035, y = var_6037)[name = tensor("attn_weights_237")]; - tensor attn_weights_239 = mul(x = attn_weights_237, y = var_4941)[name = tensor("attn_weights_239")]; - tensor var_6043 = softmax(axis = var_4934, x = attn_weights_239)[name = tensor("op_6043")]; + tensor attn_weights_237_cast = matmul(transpose_x = attn_weights_237_transpose_x_0, transpose_y = attn_weights_237_transpose_y_0, x = var_5955_cast, y = var_5957_cast)[name = tensor("attn_weights_237_cast")]; + tensor attn_weights_239_cast = mul(x = attn_weights_237_cast, y = var_12_to_fp16)[name = tensor("attn_weights_239_cast")]; + tensor var_5963_cast = softmax(axis = var_18, x = attn_weights_239_cast)[name = tensor("op_5963_cast")]; tensor attn_119_transpose_x_0 = const()[name = tensor("attn_119_transpose_x_0"), val = tensor(false)]; tensor attn_119_transpose_y_0 = const()[name = tensor("attn_119_transpose_y_0"), val = tensor(true)]; - tensor attn_119 = matmul(transpose_x = attn_119_transpose_x_0, transpose_y = attn_119_transpose_y_0, x = var_6039, y = var_6043)[name = tensor("attn_119")]; - tensor var_6047 = const()[name = tensor("op_6047"), val = tensor([2, 1280, 1, -1])]; - tensor input_369 = reshape(shape = var_6047, x = attn_119)[name = tensor("input_369")]; - tensor var_6052 = const()[name = tensor("op_6052"), val = tensor([1, 1])]; - tensor var_6054 = const()[name = tensor("op_6054"), val = tensor([1, 1])]; - tensor var_6056_pad_type_0 = const()[name = tensor("op_6056_pad_type_0"), val = tensor("custom")]; - tensor var_6056_pad_0 = const()[name = tensor("op_6056_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6056 = conv(bias = mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_bias, dilations = var_6054, groups = var_4950, pad = var_6056_pad_0, pad_type = var_6056_pad_type_0, strides = var_6052, weight = mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_weight, x = input_369)[name = tensor("op_6056")]; - tensor inputs_179 = add(x = var_6056, y = inputs_177)[name = tensor("inputs_179")]; - tensor var_6060 = const()[name = tensor("op_6060"), val = tensor([1])]; - tensor channels_mean_179 = reduce_mean(axes = var_6060, keep_dims = var_4945, x = inputs_179)[name = tensor("channels_mean_179")]; - tensor zero_mean_179 = sub(x = inputs_179, y = channels_mean_179)[name = tensor("zero_mean_179")]; - tensor zero_mean_sq_179 = mul(x = zero_mean_179, y = zero_mean_179)[name = tensor("zero_mean_sq_179")]; - tensor var_6064 = const()[name = tensor("op_6064"), val = tensor([1])]; - tensor var_6065 = reduce_mean(axes = var_6064, keep_dims = var_4945, x = zero_mean_sq_179)[name = tensor("op_6065")]; - tensor var_6066 = const()[name = tensor("op_6066"), val = tensor(0x1.4f8b58p-17)]; - tensor var_6067 = add(x = var_6065, y = var_6066)[name = tensor("op_6067")]; - tensor denom_179_epsilon_0 = const()[name = tensor("denom_179_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_179 = rsqrt(epsilon = denom_179_epsilon_0, x = var_6067)[name = tensor("denom_179")]; - tensor out_179 = mul(x = zero_mean_179, y = denom_179)[name = tensor("out_179")]; - tensor var_6071 = const()[name = tensor("op_6071"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268585600)))]; - tensor var_6072 = add(x = out_179, y = var_6071)[name = tensor("op_6072")]; - tensor var_6074 = const()[name = tensor("op_6074"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268590784)))]; - tensor input_371 = mul(x = var_6072, y = var_6074)[name = tensor("input_371")]; - tensor var_6082 = const()[name = tensor("op_6082"), val = tensor([1, 1])]; - tensor var_6084 = const()[name = tensor("op_6084"), val = tensor([1, 1])]; - tensor var_6086_pad_type_0 = const()[name = tensor("op_6086_pad_type_0"), val = tensor("custom")]; - tensor var_6086_pad_0 = const()[name = tensor("op_6086_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6086 = conv(bias = mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_bias, dilations = var_6084, groups = var_4950, pad = var_6086_pad_0, pad_type = var_6086_pad_type_0, strides = var_6082, weight = mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_weight, x = input_371)[name = tensor("op_6086")]; - tensor var_6087_split_sizes_0 = const()[name = tensor("op_6087_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_6087_axis_0 = const()[name = tensor("op_6087_axis_0"), val = tensor(1)]; - tensor var_6087_0, tensor var_6087_1 = split(axis = var_6087_axis_0, split_sizes = var_6087_split_sizes_0, x = var_6086)[name = tensor("op_6087")]; - tensor var_6089_mode_0 = const()[name = tensor("op_6089_mode_0"), val = tensor("EXACT")]; - tensor var_6089 = gelu(mode = var_6089_mode_0, x = var_6087_1)[name = tensor("op_6089")]; - tensor input_373 = mul(x = var_6087_0, y = var_6089)[name = tensor("input_373")]; - tensor var_6093 = const()[name = tensor("op_6093"), val = tensor([1, 1])]; - tensor var_6095 = const()[name = tensor("op_6095"), val = tensor([1, 1])]; - tensor var_6097_pad_type_0 = const()[name = tensor("op_6097_pad_type_0"), val = tensor("custom")]; - tensor var_6097_pad_0 = const()[name = tensor("op_6097_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6097 = conv(bias = mid_block_attentions_0_transformer_blocks_5_ff_net_2_bias, dilations = var_6095, groups = var_4950, pad = var_6097_pad_0, pad_type = var_6097_pad_type_0, strides = var_6093, weight = mid_block_attentions_0_transformer_blocks_5_ff_net_2_weight, x = input_373)[name = tensor("op_6097")]; - tensor inputs_181 = add(x = var_6097, y = inputs_179)[name = tensor("inputs_181")]; - tensor var_6107 = const()[name = tensor("op_6107"), val = tensor([1])]; - tensor channels_mean_181 = reduce_mean(axes = var_6107, keep_dims = var_4945, x = inputs_181)[name = tensor("channels_mean_181")]; - tensor zero_mean_181 = sub(x = inputs_181, y = channels_mean_181)[name = tensor("zero_mean_181")]; - tensor zero_mean_sq_181 = mul(x = zero_mean_181, y = zero_mean_181)[name = tensor("zero_mean_sq_181")]; - tensor var_6111 = const()[name = tensor("op_6111"), val = tensor([1])]; - tensor var_6112 = reduce_mean(axes = var_6111, keep_dims = var_4945, x = zero_mean_sq_181)[name = tensor("op_6112")]; - tensor var_6113 = const()[name = tensor("op_6113"), val = tensor(0x1.4f8b58p-17)]; - tensor var_6114 = add(x = var_6112, y = var_6113)[name = tensor("op_6114")]; - tensor denom_181_epsilon_0 = const()[name = tensor("denom_181_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_181 = rsqrt(epsilon = denom_181_epsilon_0, x = var_6114)[name = tensor("denom_181")]; - tensor out_181 = mul(x = zero_mean_181, y = denom_181)[name = tensor("out_181")]; - tensor var_6118 = const()[name = tensor("op_6118"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268595968)))]; - tensor var_6119 = add(x = out_181, y = var_6118)[name = tensor("op_6119")]; - tensor var_6121 = const()[name = tensor("op_6121"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268601152)))]; - tensor hidden_states_245 = mul(x = var_6119, y = var_6121)[name = tensor("hidden_states_245")]; - tensor var_6128 = const()[name = tensor("op_6128"), val = tensor([1, 1])]; - tensor var_6130 = const()[name = tensor("op_6130"), val = tensor([1, 1])]; + tensor attn_119_cast = matmul(transpose_x = attn_119_transpose_x_0, transpose_y = attn_119_transpose_y_0, x = var_5959_cast, y = var_5963_cast)[name = tensor("attn_119_cast")]; + tensor var_5967 = const()[name = tensor("op_5967"), val = tensor([2, 1280, 1, -1])]; + tensor input_369_cast = reshape(shape = var_5967, x = attn_119_cast)[name = tensor("input_369_cast")]; + tensor var_5972 = const()[name = tensor("op_5972"), val = tensor([1, 1])]; + tensor var_5974 = const()[name = tensor("op_5974"), val = tensor([1, 1])]; + tensor var_5976_pad_type_0 = const()[name = tensor("op_5976_pad_type_0"), val = tensor("custom")]; + tensor var_5976_pad_0 = const()[name = tensor("op_5976_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2100557184)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2103834048)))]; + tensor var_5976_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_5974, groups = var_31, pad = var_5976_pad_0, pad_type = var_5976_pad_type_0, strides = var_5972, weight = unet_mid_block_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16, x = input_369_cast)[name = tensor("op_5976_cast")]; + tensor inputs_179_cast = add(x = var_5976_cast, y = inputs_177_cast)[name = tensor("inputs_179_cast")]; + tensor var_5980 = const()[name = tensor("op_5980"), val = tensor([1])]; + tensor channels_mean_179_cast = reduce_mean(axes = var_5980, keep_dims = var_23, x = inputs_179_cast)[name = tensor("channels_mean_179_cast")]; + tensor zero_mean_179_cast = sub(x = inputs_179_cast, y = channels_mean_179_cast)[name = tensor("zero_mean_179_cast")]; + tensor zero_mean_sq_179_cast = mul(x = zero_mean_179_cast, y = zero_mean_179_cast)[name = tensor("zero_mean_sq_179_cast")]; + tensor var_5984 = const()[name = tensor("op_5984"), val = tensor([1])]; + tensor var_5985_cast = reduce_mean(axes = var_5984, keep_dims = var_23, x = zero_mean_sq_179_cast)[name = tensor("op_5985_cast")]; + tensor var_5986_to_fp16 = const()[name = tensor("op_5986_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_5987_cast = add(x = var_5985_cast, y = var_5986_to_fp16)[name = tensor("op_5987_cast")]; + tensor denom_179_epsilon_0_to_fp16 = const()[name = tensor("denom_179_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_179_cast = rsqrt(epsilon = denom_179_epsilon_0_to_fp16, x = var_5987_cast)[name = tensor("denom_179_cast")]; + tensor out_179_cast = mul(x = zero_mean_179_cast, y = denom_179_cast)[name = tensor("out_179_cast")]; + tensor var_5991_to_fp16 = const()[name = tensor("op_5991_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2103836672)))]; + tensor var_5992_cast = add(x = out_179_cast, y = var_5991_to_fp16)[name = tensor("op_5992_cast")]; + tensor var_5994_to_fp16 = const()[name = tensor("op_5994_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2103839296)))]; + tensor input_371_cast = mul(x = var_5992_cast, y = var_5994_to_fp16)[name = tensor("input_371_cast")]; + tensor var_6002 = const()[name = tensor("op_6002"), val = tensor([1, 1])]; + tensor var_6004 = const()[name = tensor("op_6004"), val = tensor([1, 1])]; + tensor var_6006_pad_type_0 = const()[name = tensor("op_6006_pad_type_0"), val = tensor("custom")]; + tensor var_6006_pad_0 = const()[name = tensor("op_6006_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2103841920)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2130056384)))]; + tensor var_6006_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_6004, groups = var_31, pad = var_6006_pad_0, pad_type = var_6006_pad_type_0, strides = var_6002, weight = unet_mid_block_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16, x = input_371_cast)[name = tensor("op_6006_cast")]; + tensor var_6007_split_sizes_0 = const()[name = tensor("op_6007_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6007_axis_0 = const()[name = tensor("op_6007_axis_0"), val = tensor(1)]; + tensor var_6007_cast_0, tensor var_6007_cast_1 = split(axis = var_6007_axis_0, split_sizes = var_6007_split_sizes_0, x = var_6006_cast)[name = tensor("op_6007_cast")]; + tensor var_6009_mode_0 = const()[name = tensor("op_6009_mode_0"), val = tensor("EXACT")]; + tensor var_6009_cast = gelu(mode = var_6009_mode_0, x = var_6007_cast_1)[name = tensor("op_6009_cast")]; + tensor input_373_cast = mul(x = var_6007_cast_0, y = var_6009_cast)[name = tensor("input_373_cast")]; + tensor var_6013 = const()[name = tensor("op_6013"), val = tensor([1, 1])]; + tensor var_6015 = const()[name = tensor("op_6015"), val = tensor([1, 1])]; + tensor var_6017_pad_type_0 = const()[name = tensor("op_6017_pad_type_0"), val = tensor("custom")]; + tensor var_6017_pad_0 = const()[name = tensor("op_6017_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2130076928)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2143184192)))]; + tensor var_6017_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_6015, groups = var_31, pad = var_6017_pad_0, pad_type = var_6017_pad_type_0, strides = var_6013, weight = unet_mid_block_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16, x = input_373_cast)[name = tensor("op_6017_cast")]; + tensor inputs_181_cast = add(x = var_6017_cast, y = inputs_179_cast)[name = tensor("inputs_181_cast")]; + tensor var_6027 = const()[name = tensor("op_6027"), val = tensor([1])]; + tensor channels_mean_181_cast = reduce_mean(axes = var_6027, keep_dims = var_23, x = inputs_181_cast)[name = tensor("channels_mean_181_cast")]; + tensor zero_mean_181_cast = sub(x = inputs_181_cast, y = channels_mean_181_cast)[name = tensor("zero_mean_181_cast")]; + tensor zero_mean_sq_181_cast = mul(x = zero_mean_181_cast, y = zero_mean_181_cast)[name = tensor("zero_mean_sq_181_cast")]; + tensor var_6031 = const()[name = tensor("op_6031"), val = tensor([1])]; + tensor var_6032_cast = reduce_mean(axes = var_6031, keep_dims = var_23, x = zero_mean_sq_181_cast)[name = tensor("op_6032_cast")]; + tensor var_6033_to_fp16 = const()[name = tensor("op_6033_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6034_cast = add(x = var_6032_cast, y = var_6033_to_fp16)[name = tensor("op_6034_cast")]; + tensor denom_181_epsilon_0_to_fp16 = const()[name = tensor("denom_181_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_181_cast = rsqrt(epsilon = denom_181_epsilon_0_to_fp16, x = var_6034_cast)[name = tensor("denom_181_cast")]; + tensor out_181_cast = mul(x = zero_mean_181_cast, y = denom_181_cast)[name = tensor("out_181_cast")]; + tensor var_6038_to_fp16 = const()[name = tensor("op_6038_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2143186816)))]; + tensor var_6039_cast = add(x = out_181_cast, y = var_6038_to_fp16)[name = tensor("op_6039_cast")]; + tensor var_6041_to_fp16 = const()[name = tensor("op_6041_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2143189440)))]; + tensor hidden_states_245_cast = mul(x = var_6039_cast, y = var_6041_to_fp16)[name = tensor("hidden_states_245_cast")]; + tensor var_6048 = const()[name = tensor("op_6048"), val = tensor([1, 1])]; + tensor var_6050 = const()[name = tensor("op_6050"), val = tensor([1, 1])]; tensor q_121_pad_type_0 = const()[name = tensor("q_121_pad_type_0"), val = tensor("custom")]; tensor q_121_pad_0 = const()[name = tensor("q_121_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_121 = conv(dilations = var_6130, groups = var_4950, pad = q_121_pad_0, pad_type = q_121_pad_type_0, strides = var_6128, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_q_weight, x = hidden_states_245)[name = tensor("q_121")]; - tensor var_6134 = const()[name = tensor("op_6134"), val = tensor([1, 1])]; - tensor var_6136 = const()[name = tensor("op_6136"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2143192064)))]; + tensor q_121_cast = conv(dilations = var_6050, groups = var_31, pad = q_121_pad_0, pad_type = q_121_pad_type_0, strides = var_6048, weight = unet_mid_block_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16, x = hidden_states_245_cast)[name = tensor("q_121_cast")]; + tensor var_6054 = const()[name = tensor("op_6054"), val = tensor([1, 1])]; + tensor var_6056 = const()[name = tensor("op_6056"), val = tensor([1, 1])]; tensor k_121_pad_type_0 = const()[name = tensor("k_121_pad_type_0"), val = tensor("custom")]; tensor k_121_pad_0 = const()[name = tensor("k_121_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_121 = conv(dilations = var_6136, groups = var_4950, pad = k_121_pad_0, pad_type = k_121_pad_type_0, strides = var_6134, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_k_weight, x = hidden_states_245)[name = tensor("k_121")]; - tensor var_6140 = const()[name = tensor("op_6140"), val = tensor([1, 1])]; - tensor var_6142 = const()[name = tensor("op_6142"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2146468928)))]; + tensor k_121_cast = conv(dilations = var_6056, groups = var_31, pad = k_121_pad_0, pad_type = k_121_pad_type_0, strides = var_6054, weight = unet_mid_block_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16, x = hidden_states_245_cast)[name = tensor("k_121_cast")]; + tensor var_6060 = const()[name = tensor("op_6060"), val = tensor([1, 1])]; + tensor var_6062 = const()[name = tensor("op_6062"), val = tensor([1, 1])]; tensor v_121_pad_type_0 = const()[name = tensor("v_121_pad_type_0"), val = tensor("custom")]; tensor v_121_pad_0 = const()[name = tensor("v_121_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_121 = conv(dilations = var_6142, groups = var_4950, pad = v_121_pad_0, pad_type = v_121_pad_type_0, strides = var_6140, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_v_weight, x = hidden_states_245)[name = tensor("v_121")]; - tensor var_6146 = const()[name = tensor("op_6146"), val = tensor([2, 20, 64, -1])]; - tensor var_6147 = reshape(shape = var_6146, x = q_121)[name = tensor("op_6147")]; - tensor var_6148 = const()[name = tensor("op_6148"), val = tensor([2, 20, 64, -1])]; - tensor var_6149 = reshape(shape = var_6148, x = k_121)[name = tensor("op_6149")]; - tensor var_6150 = const()[name = tensor("op_6150"), val = tensor([2, 20, 64, -1])]; - tensor var_6151 = reshape(shape = var_6150, x = v_121)[name = tensor("op_6151")]; + tensor unet_mid_block_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2149745792)))]; + tensor v_121_cast = conv(dilations = var_6062, groups = var_31, pad = v_121_pad_0, pad_type = v_121_pad_type_0, strides = var_6060, weight = unet_mid_block_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16, x = hidden_states_245_cast)[name = tensor("v_121_cast")]; + tensor var_6066 = const()[name = tensor("op_6066"), val = tensor([2, 20, 64, -1])]; + tensor var_6067_cast = reshape(shape = var_6066, x = q_121_cast)[name = tensor("op_6067_cast")]; + tensor var_6068 = const()[name = tensor("op_6068"), val = tensor([2, 20, 64, -1])]; + tensor var_6069_cast = reshape(shape = var_6068, x = k_121_cast)[name = tensor("op_6069_cast")]; + tensor var_6070 = const()[name = tensor("op_6070"), val = tensor([2, 20, 64, -1])]; + tensor var_6071_cast = reshape(shape = var_6070, x = v_121_cast)[name = tensor("op_6071_cast")]; tensor attn_weights_241_transpose_x_0 = const()[name = tensor("attn_weights_241_transpose_x_0"), val = tensor(true)]; tensor attn_weights_241_transpose_y_0 = const()[name = tensor("attn_weights_241_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_241 = matmul(transpose_x = attn_weights_241_transpose_x_0, transpose_y = attn_weights_241_transpose_y_0, x = var_6147, y = var_6149)[name = tensor("attn_weights_241")]; - tensor attn_weights_243 = mul(x = attn_weights_241, y = var_4941)[name = tensor("attn_weights_243")]; - tensor var_6155 = softmax(axis = var_4934, x = attn_weights_243)[name = tensor("op_6155")]; + tensor attn_weights_241_cast = matmul(transpose_x = attn_weights_241_transpose_x_0, transpose_y = attn_weights_241_transpose_y_0, x = var_6067_cast, y = var_6069_cast)[name = tensor("attn_weights_241_cast")]; + tensor attn_weights_243_cast = mul(x = attn_weights_241_cast, y = var_12_to_fp16)[name = tensor("attn_weights_243_cast")]; + tensor var_6075_cast = softmax(axis = var_18, x = attn_weights_243_cast)[name = tensor("op_6075_cast")]; tensor attn_121_transpose_x_0 = const()[name = tensor("attn_121_transpose_x_0"), val = tensor(false)]; tensor attn_121_transpose_y_0 = const()[name = tensor("attn_121_transpose_y_0"), val = tensor(true)]; - tensor attn_121 = matmul(transpose_x = attn_121_transpose_x_0, transpose_y = attn_121_transpose_y_0, x = var_6151, y = var_6155)[name = tensor("attn_121")]; - tensor var_6159 = const()[name = tensor("op_6159"), val = tensor([2, 1280, 1, -1])]; - tensor input_375 = reshape(shape = var_6159, x = attn_121)[name = tensor("input_375")]; - tensor var_6164 = const()[name = tensor("op_6164"), val = tensor([1, 1])]; - tensor var_6166 = const()[name = tensor("op_6166"), val = tensor([1, 1])]; - tensor var_6168_pad_type_0 = const()[name = tensor("op_6168_pad_type_0"), val = tensor("custom")]; - tensor var_6168_pad_0 = const()[name = tensor("op_6168_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6168 = conv(bias = mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_bias, dilations = var_6166, groups = var_4950, pad = var_6168_pad_0, pad_type = var_6168_pad_type_0, strides = var_6164, weight = mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_weight, x = input_375)[name = tensor("op_6168")]; - tensor inputs_183 = add(x = var_6168, y = inputs_181)[name = tensor("inputs_183")]; - tensor var_6172 = const()[name = tensor("op_6172"), val = tensor([1])]; - tensor channels_mean_183 = reduce_mean(axes = var_6172, keep_dims = var_4945, x = inputs_183)[name = tensor("channels_mean_183")]; - tensor zero_mean_183 = sub(x = inputs_183, y = channels_mean_183)[name = tensor("zero_mean_183")]; - tensor zero_mean_sq_183 = mul(x = zero_mean_183, y = zero_mean_183)[name = tensor("zero_mean_sq_183")]; - tensor var_6176 = const()[name = tensor("op_6176"), val = tensor([1])]; - tensor var_6177 = reduce_mean(axes = var_6176, keep_dims = var_4945, x = zero_mean_sq_183)[name = tensor("op_6177")]; - tensor var_6178 = const()[name = tensor("op_6178"), val = tensor(0x1.4f8b58p-17)]; - tensor var_6179 = add(x = var_6177, y = var_6178)[name = tensor("op_6179")]; - tensor denom_183_epsilon_0 = const()[name = tensor("denom_183_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_183 = rsqrt(epsilon = denom_183_epsilon_0, x = var_6179)[name = tensor("denom_183")]; - tensor out_183 = mul(x = zero_mean_183, y = denom_183)[name = tensor("out_183")]; - tensor var_6183 = const()[name = tensor("op_6183"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268606336)))]; - tensor var_6184 = add(x = out_183, y = var_6183)[name = tensor("op_6184")]; - tensor var_6186 = const()[name = tensor("op_6186"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268611520)))]; - tensor hidden_states_247 = mul(x = var_6184, y = var_6186)[name = tensor("hidden_states_247")]; - tensor var_6193 = const()[name = tensor("op_6193"), val = tensor([1, 1])]; - tensor var_6195 = const()[name = tensor("op_6195"), val = tensor([1, 1])]; + tensor attn_121_cast = matmul(transpose_x = attn_121_transpose_x_0, transpose_y = attn_121_transpose_y_0, x = var_6071_cast, y = var_6075_cast)[name = tensor("attn_121_cast")]; + tensor var_6079 = const()[name = tensor("op_6079"), val = tensor([2, 1280, 1, -1])]; + tensor input_375_cast = reshape(shape = var_6079, x = attn_121_cast)[name = tensor("input_375_cast")]; + tensor var_6084 = const()[name = tensor("op_6084"), val = tensor([1, 1])]; + tensor var_6086 = const()[name = tensor("op_6086"), val = tensor([1, 1])]; + tensor var_6088_pad_type_0 = const()[name = tensor("op_6088_pad_type_0"), val = tensor("custom")]; + tensor var_6088_pad_0 = const()[name = tensor("op_6088_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2153022656)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2156299520)))]; + tensor var_6088_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_6086, groups = var_31, pad = var_6088_pad_0, pad_type = var_6088_pad_type_0, strides = var_6084, weight = unet_mid_block_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16, x = input_375_cast)[name = tensor("op_6088_cast")]; + tensor inputs_183_cast = add(x = var_6088_cast, y = inputs_181_cast)[name = tensor("inputs_183_cast")]; + tensor var_6092 = const()[name = tensor("op_6092"), val = tensor([1])]; + tensor channels_mean_183_cast = reduce_mean(axes = var_6092, keep_dims = var_23, x = inputs_183_cast)[name = tensor("channels_mean_183_cast")]; + tensor zero_mean_183_cast = sub(x = inputs_183_cast, y = channels_mean_183_cast)[name = tensor("zero_mean_183_cast")]; + tensor zero_mean_sq_183_cast = mul(x = zero_mean_183_cast, y = zero_mean_183_cast)[name = tensor("zero_mean_sq_183_cast")]; + tensor var_6096 = const()[name = tensor("op_6096"), val = tensor([1])]; + tensor var_6097_cast = reduce_mean(axes = var_6096, keep_dims = var_23, x = zero_mean_sq_183_cast)[name = tensor("op_6097_cast")]; + tensor var_6098_to_fp16 = const()[name = tensor("op_6098_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6099_cast = add(x = var_6097_cast, y = var_6098_to_fp16)[name = tensor("op_6099_cast")]; + tensor denom_183_epsilon_0_to_fp16 = const()[name = tensor("denom_183_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_183_cast = rsqrt(epsilon = denom_183_epsilon_0_to_fp16, x = var_6099_cast)[name = tensor("denom_183_cast")]; + tensor out_183_cast = mul(x = zero_mean_183_cast, y = denom_183_cast)[name = tensor("out_183_cast")]; + tensor var_6103_to_fp16 = const()[name = tensor("op_6103_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2156302144)))]; + tensor var_6104_cast = add(x = out_183_cast, y = var_6103_to_fp16)[name = tensor("op_6104_cast")]; + tensor var_6106_to_fp16 = const()[name = tensor("op_6106_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2156304768)))]; + tensor hidden_states_247_cast = mul(x = var_6104_cast, y = var_6106_to_fp16)[name = tensor("hidden_states_247_cast")]; + tensor var_6113 = const()[name = tensor("op_6113"), val = tensor([1, 1])]; + tensor var_6115 = const()[name = tensor("op_6115"), val = tensor([1, 1])]; tensor q_123_pad_type_0 = const()[name = tensor("q_123_pad_type_0"), val = tensor("custom")]; tensor q_123_pad_0 = const()[name = tensor("q_123_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_123 = conv(dilations = var_6195, groups = var_4950, pad = q_123_pad_0, pad_type = q_123_pad_type_0, strides = var_6193, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_q_weight, x = hidden_states_247)[name = tensor("q_123")]; - tensor var_6199 = const()[name = tensor("op_6199"), val = tensor([1, 1])]; - tensor var_6201 = const()[name = tensor("op_6201"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2156307392)))]; + tensor q_123_cast = conv(dilations = var_6115, groups = var_31, pad = q_123_pad_0, pad_type = q_123_pad_type_0, strides = var_6113, weight = unet_mid_block_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16, x = hidden_states_247_cast)[name = tensor("q_123_cast")]; + tensor var_6119 = const()[name = tensor("op_6119"), val = tensor([1, 1])]; + tensor var_6121 = const()[name = tensor("op_6121"), val = tensor([1, 1])]; tensor k_123_pad_type_0 = const()[name = tensor("k_123_pad_type_0"), val = tensor("custom")]; tensor k_123_pad_0 = const()[name = tensor("k_123_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_123 = conv(dilations = var_6201, groups = var_4950, pad = k_123_pad_0, pad_type = k_123_pad_type_0, strides = var_6199, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_123")]; - tensor var_6205 = const()[name = tensor("op_6205"), val = tensor([1, 1])]; - tensor var_6207 = const()[name = tensor("op_6207"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2159584256)))]; + tensor k_123_cast = conv(dilations = var_6121, groups = var_31, pad = k_123_pad_0, pad_type = k_123_pad_type_0, strides = var_6119, weight = unet_mid_block_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_123_cast")]; + tensor var_6125 = const()[name = tensor("op_6125"), val = tensor([1, 1])]; + tensor var_6127 = const()[name = tensor("op_6127"), val = tensor([1, 1])]; tensor v_123_pad_type_0 = const()[name = tensor("v_123_pad_type_0"), val = tensor("custom")]; tensor v_123_pad_0 = const()[name = tensor("v_123_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_123 = conv(dilations = var_6207, groups = var_4950, pad = v_123_pad_0, pad_type = v_123_pad_type_0, strides = var_6205, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_123")]; - tensor var_6211 = const()[name = tensor("op_6211"), val = tensor([2, 20, 64, -1])]; - tensor var_6212 = reshape(shape = var_6211, x = q_123)[name = tensor("op_6212")]; - tensor var_6213 = const()[name = tensor("op_6213"), val = tensor([2, 20, 64, -1])]; - tensor var_6214 = reshape(shape = var_6213, x = k_123)[name = tensor("op_6214")]; - tensor var_6215 = const()[name = tensor("op_6215"), val = tensor([2, 20, 64, -1])]; - tensor var_6216 = reshape(shape = var_6215, x = v_123)[name = tensor("op_6216")]; + tensor unet_mid_block_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2164827200)))]; + tensor v_123_cast = conv(dilations = var_6127, groups = var_31, pad = v_123_pad_0, pad_type = v_123_pad_type_0, strides = var_6125, weight = unet_mid_block_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_123_cast")]; + tensor var_6131 = const()[name = tensor("op_6131"), val = tensor([2, 20, 64, -1])]; + tensor var_6132_cast = reshape(shape = var_6131, x = q_123_cast)[name = tensor("op_6132_cast")]; + tensor var_6133 = const()[name = tensor("op_6133"), val = tensor([2, 20, 64, -1])]; + tensor var_6134_cast = reshape(shape = var_6133, x = k_123_cast)[name = tensor("op_6134_cast")]; + tensor var_6135 = const()[name = tensor("op_6135"), val = tensor([2, 20, 64, -1])]; + tensor var_6136_cast = reshape(shape = var_6135, x = v_123_cast)[name = tensor("op_6136_cast")]; tensor attn_weights_245_transpose_x_0 = const()[name = tensor("attn_weights_245_transpose_x_0"), val = tensor(true)]; tensor attn_weights_245_transpose_y_0 = const()[name = tensor("attn_weights_245_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_245 = matmul(transpose_x = attn_weights_245_transpose_x_0, transpose_y = attn_weights_245_transpose_y_0, x = var_6212, y = var_6214)[name = tensor("attn_weights_245")]; - tensor attn_weights_247 = mul(x = attn_weights_245, y = var_4941)[name = tensor("attn_weights_247")]; - tensor var_6220 = softmax(axis = var_4934, x = attn_weights_247)[name = tensor("op_6220")]; + tensor attn_weights_245_cast = matmul(transpose_x = attn_weights_245_transpose_x_0, transpose_y = attn_weights_245_transpose_y_0, x = var_6132_cast, y = var_6134_cast)[name = tensor("attn_weights_245_cast")]; + tensor attn_weights_247_cast = mul(x = attn_weights_245_cast, y = var_12_to_fp16)[name = tensor("attn_weights_247_cast")]; + tensor var_6140_cast = softmax(axis = var_18, x = attn_weights_247_cast)[name = tensor("op_6140_cast")]; tensor attn_123_transpose_x_0 = const()[name = tensor("attn_123_transpose_x_0"), val = tensor(false)]; tensor attn_123_transpose_y_0 = const()[name = tensor("attn_123_transpose_y_0"), val = tensor(true)]; - tensor attn_123 = matmul(transpose_x = attn_123_transpose_x_0, transpose_y = attn_123_transpose_y_0, x = var_6216, y = var_6220)[name = tensor("attn_123")]; - tensor var_6224 = const()[name = tensor("op_6224"), val = tensor([2, 1280, 1, -1])]; - tensor input_377 = reshape(shape = var_6224, x = attn_123)[name = tensor("input_377")]; - tensor var_6229 = const()[name = tensor("op_6229"), val = tensor([1, 1])]; - tensor var_6231 = const()[name = tensor("op_6231"), val = tensor([1, 1])]; - tensor var_6233_pad_type_0 = const()[name = tensor("op_6233_pad_type_0"), val = tensor("custom")]; - tensor var_6233_pad_0 = const()[name = tensor("op_6233_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6233 = conv(bias = mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_bias, dilations = var_6231, groups = var_4950, pad = var_6233_pad_0, pad_type = var_6233_pad_type_0, strides = var_6229, weight = mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_weight, x = input_377)[name = tensor("op_6233")]; - tensor inputs_185 = add(x = var_6233, y = inputs_183)[name = tensor("inputs_185")]; - tensor var_6237 = const()[name = tensor("op_6237"), val = tensor([1])]; - tensor channels_mean_185 = reduce_mean(axes = var_6237, keep_dims = var_4945, x = inputs_185)[name = tensor("channels_mean_185")]; - tensor zero_mean_185 = sub(x = inputs_185, y = channels_mean_185)[name = tensor("zero_mean_185")]; - tensor zero_mean_sq_185 = mul(x = zero_mean_185, y = zero_mean_185)[name = tensor("zero_mean_sq_185")]; - tensor var_6241 = const()[name = tensor("op_6241"), val = tensor([1])]; - tensor var_6242 = reduce_mean(axes = var_6241, keep_dims = var_4945, x = zero_mean_sq_185)[name = tensor("op_6242")]; - tensor var_6243 = const()[name = tensor("op_6243"), val = tensor(0x1.4f8b58p-17)]; - tensor var_6244 = add(x = var_6242, y = var_6243)[name = tensor("op_6244")]; - tensor denom_185_epsilon_0 = const()[name = tensor("denom_185_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_185 = rsqrt(epsilon = denom_185_epsilon_0, x = var_6244)[name = tensor("denom_185")]; - tensor out_185 = mul(x = zero_mean_185, y = denom_185)[name = tensor("out_185")]; - tensor var_6248 = const()[name = tensor("op_6248"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268616704)))]; - tensor var_6249 = add(x = out_185, y = var_6248)[name = tensor("op_6249")]; - tensor var_6251 = const()[name = tensor("op_6251"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268621888)))]; - tensor input_379 = mul(x = var_6249, y = var_6251)[name = tensor("input_379")]; - tensor var_6259 = const()[name = tensor("op_6259"), val = tensor([1, 1])]; - tensor var_6261 = const()[name = tensor("op_6261"), val = tensor([1, 1])]; - tensor var_6263_pad_type_0 = const()[name = tensor("op_6263_pad_type_0"), val = tensor("custom")]; - tensor var_6263_pad_0 = const()[name = tensor("op_6263_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6263 = conv(bias = mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_bias, dilations = var_6261, groups = var_4950, pad = var_6263_pad_0, pad_type = var_6263_pad_type_0, strides = var_6259, weight = mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_weight, x = input_379)[name = tensor("op_6263")]; - tensor var_6264_split_sizes_0 = const()[name = tensor("op_6264_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_6264_axis_0 = const()[name = tensor("op_6264_axis_0"), val = tensor(1)]; - tensor var_6264_0, tensor var_6264_1 = split(axis = var_6264_axis_0, split_sizes = var_6264_split_sizes_0, x = var_6263)[name = tensor("op_6264")]; - tensor var_6266_mode_0 = const()[name = tensor("op_6266_mode_0"), val = tensor("EXACT")]; - tensor var_6266 = gelu(mode = var_6266_mode_0, x = var_6264_1)[name = tensor("op_6266")]; - tensor input_381 = mul(x = var_6264_0, y = var_6266)[name = tensor("input_381")]; - tensor var_6270 = const()[name = tensor("op_6270"), val = tensor([1, 1])]; - tensor var_6272 = const()[name = tensor("op_6272"), val = tensor([1, 1])]; - tensor var_6274_pad_type_0 = const()[name = tensor("op_6274_pad_type_0"), val = tensor("custom")]; - tensor var_6274_pad_0 = const()[name = tensor("op_6274_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6274 = conv(bias = mid_block_attentions_0_transformer_blocks_6_ff_net_2_bias, dilations = var_6272, groups = var_4950, pad = var_6274_pad_0, pad_type = var_6274_pad_type_0, strides = var_6270, weight = mid_block_attentions_0_transformer_blocks_6_ff_net_2_weight, x = input_381)[name = tensor("op_6274")]; - tensor inputs_187 = add(x = var_6274, y = inputs_185)[name = tensor("inputs_187")]; - tensor var_6284 = const()[name = tensor("op_6284"), val = tensor([1])]; - tensor channels_mean_187 = reduce_mean(axes = var_6284, keep_dims = var_4945, x = inputs_187)[name = tensor("channels_mean_187")]; - tensor zero_mean_187 = sub(x = inputs_187, y = channels_mean_187)[name = tensor("zero_mean_187")]; - tensor zero_mean_sq_187 = mul(x = zero_mean_187, y = zero_mean_187)[name = tensor("zero_mean_sq_187")]; - tensor var_6288 = const()[name = tensor("op_6288"), val = tensor([1])]; - tensor var_6289 = reduce_mean(axes = var_6288, keep_dims = var_4945, x = zero_mean_sq_187)[name = tensor("op_6289")]; - tensor var_6290 = const()[name = tensor("op_6290"), val = tensor(0x1.4f8b58p-17)]; - tensor var_6291 = add(x = var_6289, y = var_6290)[name = tensor("op_6291")]; - tensor denom_187_epsilon_0 = const()[name = tensor("denom_187_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_187 = rsqrt(epsilon = denom_187_epsilon_0, x = var_6291)[name = tensor("denom_187")]; - tensor out_187 = mul(x = zero_mean_187, y = denom_187)[name = tensor("out_187")]; - tensor var_6295 = const()[name = tensor("op_6295"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268627072)))]; - tensor var_6296 = add(x = out_187, y = var_6295)[name = tensor("op_6296")]; - tensor var_6298 = const()[name = tensor("op_6298"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268632256)))]; - tensor hidden_states_251 = mul(x = var_6296, y = var_6298)[name = tensor("hidden_states_251")]; - tensor var_6305 = const()[name = tensor("op_6305"), val = tensor([1, 1])]; - tensor var_6307 = const()[name = tensor("op_6307"), val = tensor([1, 1])]; + tensor attn_123_cast = matmul(transpose_x = attn_123_transpose_x_0, transpose_y = attn_123_transpose_y_0, x = var_6136_cast, y = var_6140_cast)[name = tensor("attn_123_cast")]; + tensor var_6144 = const()[name = tensor("op_6144"), val = tensor([2, 1280, 1, -1])]; + tensor input_377_cast = reshape(shape = var_6144, x = attn_123_cast)[name = tensor("input_377_cast")]; + tensor var_6149 = const()[name = tensor("op_6149"), val = tensor([1, 1])]; + tensor var_6151 = const()[name = tensor("op_6151"), val = tensor([1, 1])]; + tensor var_6153_pad_type_0 = const()[name = tensor("op_6153_pad_type_0"), val = tensor("custom")]; + tensor var_6153_pad_0 = const()[name = tensor("op_6153_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2170070144)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2173347008)))]; + tensor var_6153_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_6151, groups = var_31, pad = var_6153_pad_0, pad_type = var_6153_pad_type_0, strides = var_6149, weight = unet_mid_block_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16, x = input_377_cast)[name = tensor("op_6153_cast")]; + tensor inputs_185_cast = add(x = var_6153_cast, y = inputs_183_cast)[name = tensor("inputs_185_cast")]; + tensor var_6157 = const()[name = tensor("op_6157"), val = tensor([1])]; + tensor channels_mean_185_cast = reduce_mean(axes = var_6157, keep_dims = var_23, x = inputs_185_cast)[name = tensor("channels_mean_185_cast")]; + tensor zero_mean_185_cast = sub(x = inputs_185_cast, y = channels_mean_185_cast)[name = tensor("zero_mean_185_cast")]; + tensor zero_mean_sq_185_cast = mul(x = zero_mean_185_cast, y = zero_mean_185_cast)[name = tensor("zero_mean_sq_185_cast")]; + tensor var_6161 = const()[name = tensor("op_6161"), val = tensor([1])]; + tensor var_6162_cast = reduce_mean(axes = var_6161, keep_dims = var_23, x = zero_mean_sq_185_cast)[name = tensor("op_6162_cast")]; + tensor var_6163_to_fp16 = const()[name = tensor("op_6163_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6164_cast = add(x = var_6162_cast, y = var_6163_to_fp16)[name = tensor("op_6164_cast")]; + tensor denom_185_epsilon_0_to_fp16 = const()[name = tensor("denom_185_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_185_cast = rsqrt(epsilon = denom_185_epsilon_0_to_fp16, x = var_6164_cast)[name = tensor("denom_185_cast")]; + tensor out_185_cast = mul(x = zero_mean_185_cast, y = denom_185_cast)[name = tensor("out_185_cast")]; + tensor var_6168_to_fp16 = const()[name = tensor("op_6168_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2173349632)))]; + tensor var_6169_cast = add(x = out_185_cast, y = var_6168_to_fp16)[name = tensor("op_6169_cast")]; + tensor var_6171_to_fp16 = const()[name = tensor("op_6171_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2173352256)))]; + tensor input_379_cast = mul(x = var_6169_cast, y = var_6171_to_fp16)[name = tensor("input_379_cast")]; + tensor var_6179 = const()[name = tensor("op_6179"), val = tensor([1, 1])]; + tensor var_6181 = const()[name = tensor("op_6181"), val = tensor([1, 1])]; + tensor var_6183_pad_type_0 = const()[name = tensor("op_6183_pad_type_0"), val = tensor("custom")]; + tensor var_6183_pad_0 = const()[name = tensor("op_6183_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2173354880)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2199569344)))]; + tensor var_6183_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_6181, groups = var_31, pad = var_6183_pad_0, pad_type = var_6183_pad_type_0, strides = var_6179, weight = unet_mid_block_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16, x = input_379_cast)[name = tensor("op_6183_cast")]; + tensor var_6184_split_sizes_0 = const()[name = tensor("op_6184_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6184_axis_0 = const()[name = tensor("op_6184_axis_0"), val = tensor(1)]; + tensor var_6184_cast_0, tensor var_6184_cast_1 = split(axis = var_6184_axis_0, split_sizes = var_6184_split_sizes_0, x = var_6183_cast)[name = tensor("op_6184_cast")]; + tensor var_6186_mode_0 = const()[name = tensor("op_6186_mode_0"), val = tensor("EXACT")]; + tensor var_6186_cast = gelu(mode = var_6186_mode_0, x = var_6184_cast_1)[name = tensor("op_6186_cast")]; + tensor input_381_cast = mul(x = var_6184_cast_0, y = var_6186_cast)[name = tensor("input_381_cast")]; + tensor var_6190 = const()[name = tensor("op_6190"), val = tensor([1, 1])]; + tensor var_6192 = const()[name = tensor("op_6192"), val = tensor([1, 1])]; + tensor var_6194_pad_type_0 = const()[name = tensor("op_6194_pad_type_0"), val = tensor("custom")]; + tensor var_6194_pad_0 = const()[name = tensor("op_6194_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2199589888)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2212697152)))]; + tensor var_6194_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_6192, groups = var_31, pad = var_6194_pad_0, pad_type = var_6194_pad_type_0, strides = var_6190, weight = unet_mid_block_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16, x = input_381_cast)[name = tensor("op_6194_cast")]; + tensor inputs_187_cast = add(x = var_6194_cast, y = inputs_185_cast)[name = tensor("inputs_187_cast")]; + tensor var_6204 = const()[name = tensor("op_6204"), val = tensor([1])]; + tensor channels_mean_187_cast = reduce_mean(axes = var_6204, keep_dims = var_23, x = inputs_187_cast)[name = tensor("channels_mean_187_cast")]; + tensor zero_mean_187_cast = sub(x = inputs_187_cast, y = channels_mean_187_cast)[name = tensor("zero_mean_187_cast")]; + tensor zero_mean_sq_187_cast = mul(x = zero_mean_187_cast, y = zero_mean_187_cast)[name = tensor("zero_mean_sq_187_cast")]; + tensor var_6208 = const()[name = tensor("op_6208"), val = tensor([1])]; + tensor var_6209_cast = reduce_mean(axes = var_6208, keep_dims = var_23, x = zero_mean_sq_187_cast)[name = tensor("op_6209_cast")]; + tensor var_6210_to_fp16 = const()[name = tensor("op_6210_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6211_cast = add(x = var_6209_cast, y = var_6210_to_fp16)[name = tensor("op_6211_cast")]; + tensor denom_187_epsilon_0_to_fp16 = const()[name = tensor("denom_187_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_187_cast = rsqrt(epsilon = denom_187_epsilon_0_to_fp16, x = var_6211_cast)[name = tensor("denom_187_cast")]; + tensor out_187_cast = mul(x = zero_mean_187_cast, y = denom_187_cast)[name = tensor("out_187_cast")]; + tensor var_6215_to_fp16 = const()[name = tensor("op_6215_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2212699776)))]; + tensor var_6216_cast = add(x = out_187_cast, y = var_6215_to_fp16)[name = tensor("op_6216_cast")]; + tensor var_6218_to_fp16 = const()[name = tensor("op_6218_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2212702400)))]; + tensor hidden_states_251_cast = mul(x = var_6216_cast, y = var_6218_to_fp16)[name = tensor("hidden_states_251_cast")]; + tensor var_6225 = const()[name = tensor("op_6225"), val = tensor([1, 1])]; + tensor var_6227 = const()[name = tensor("op_6227"), val = tensor([1, 1])]; tensor q_125_pad_type_0 = const()[name = tensor("q_125_pad_type_0"), val = tensor("custom")]; tensor q_125_pad_0 = const()[name = tensor("q_125_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_125 = conv(dilations = var_6307, groups = var_4950, pad = q_125_pad_0, pad_type = q_125_pad_type_0, strides = var_6305, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_q_weight, x = hidden_states_251)[name = tensor("q_125")]; - tensor var_6311 = const()[name = tensor("op_6311"), val = tensor([1, 1])]; - tensor var_6313 = const()[name = tensor("op_6313"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2212705024)))]; + tensor q_125_cast = conv(dilations = var_6227, groups = var_31, pad = q_125_pad_0, pad_type = q_125_pad_type_0, strides = var_6225, weight = unet_mid_block_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16, x = hidden_states_251_cast)[name = tensor("q_125_cast")]; + tensor var_6231 = const()[name = tensor("op_6231"), val = tensor([1, 1])]; + tensor var_6233 = const()[name = tensor("op_6233"), val = tensor([1, 1])]; tensor k_125_pad_type_0 = const()[name = tensor("k_125_pad_type_0"), val = tensor("custom")]; tensor k_125_pad_0 = const()[name = tensor("k_125_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_125 = conv(dilations = var_6313, groups = var_4950, pad = k_125_pad_0, pad_type = k_125_pad_type_0, strides = var_6311, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_k_weight, x = hidden_states_251)[name = tensor("k_125")]; - tensor var_6317 = const()[name = tensor("op_6317"), val = tensor([1, 1])]; - tensor var_6319 = const()[name = tensor("op_6319"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2215981888)))]; + tensor k_125_cast = conv(dilations = var_6233, groups = var_31, pad = k_125_pad_0, pad_type = k_125_pad_type_0, strides = var_6231, weight = unet_mid_block_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16, x = hidden_states_251_cast)[name = tensor("k_125_cast")]; + tensor var_6237 = const()[name = tensor("op_6237"), val = tensor([1, 1])]; + tensor var_6239 = const()[name = tensor("op_6239"), val = tensor([1, 1])]; tensor v_125_pad_type_0 = const()[name = tensor("v_125_pad_type_0"), val = tensor("custom")]; tensor v_125_pad_0 = const()[name = tensor("v_125_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_125 = conv(dilations = var_6319, groups = var_4950, pad = v_125_pad_0, pad_type = v_125_pad_type_0, strides = var_6317, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_v_weight, x = hidden_states_251)[name = tensor("v_125")]; - tensor var_6323 = const()[name = tensor("op_6323"), val = tensor([2, 20, 64, -1])]; - tensor var_6324 = reshape(shape = var_6323, x = q_125)[name = tensor("op_6324")]; - tensor var_6325 = const()[name = tensor("op_6325"), val = tensor([2, 20, 64, -1])]; - tensor var_6326 = reshape(shape = var_6325, x = k_125)[name = tensor("op_6326")]; - tensor var_6327 = const()[name = tensor("op_6327"), val = tensor([2, 20, 64, -1])]; - tensor var_6328 = reshape(shape = var_6327, x = v_125)[name = tensor("op_6328")]; + tensor unet_mid_block_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2219258752)))]; + tensor v_125_cast = conv(dilations = var_6239, groups = var_31, pad = v_125_pad_0, pad_type = v_125_pad_type_0, strides = var_6237, weight = unet_mid_block_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16, x = hidden_states_251_cast)[name = tensor("v_125_cast")]; + tensor var_6243 = const()[name = tensor("op_6243"), val = tensor([2, 20, 64, -1])]; + tensor var_6244_cast = reshape(shape = var_6243, x = q_125_cast)[name = tensor("op_6244_cast")]; + tensor var_6245 = const()[name = tensor("op_6245"), val = tensor([2, 20, 64, -1])]; + tensor var_6246_cast = reshape(shape = var_6245, x = k_125_cast)[name = tensor("op_6246_cast")]; + tensor var_6247 = const()[name = tensor("op_6247"), val = tensor([2, 20, 64, -1])]; + tensor var_6248_cast = reshape(shape = var_6247, x = v_125_cast)[name = tensor("op_6248_cast")]; tensor attn_weights_249_transpose_x_0 = const()[name = tensor("attn_weights_249_transpose_x_0"), val = tensor(true)]; tensor attn_weights_249_transpose_y_0 = const()[name = tensor("attn_weights_249_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_249 = matmul(transpose_x = attn_weights_249_transpose_x_0, transpose_y = attn_weights_249_transpose_y_0, x = var_6324, y = var_6326)[name = tensor("attn_weights_249")]; - tensor attn_weights_251 = mul(x = attn_weights_249, y = var_4941)[name = tensor("attn_weights_251")]; - tensor var_6332 = softmax(axis = var_4934, x = attn_weights_251)[name = tensor("op_6332")]; + tensor attn_weights_249_cast = matmul(transpose_x = attn_weights_249_transpose_x_0, transpose_y = attn_weights_249_transpose_y_0, x = var_6244_cast, y = var_6246_cast)[name = tensor("attn_weights_249_cast")]; + tensor attn_weights_251_cast = mul(x = attn_weights_249_cast, y = var_12_to_fp16)[name = tensor("attn_weights_251_cast")]; + tensor var_6252_cast = softmax(axis = var_18, x = attn_weights_251_cast)[name = tensor("op_6252_cast")]; tensor attn_125_transpose_x_0 = const()[name = tensor("attn_125_transpose_x_0"), val = tensor(false)]; tensor attn_125_transpose_y_0 = const()[name = tensor("attn_125_transpose_y_0"), val = tensor(true)]; - tensor attn_125 = matmul(transpose_x = attn_125_transpose_x_0, transpose_y = attn_125_transpose_y_0, x = var_6328, y = var_6332)[name = tensor("attn_125")]; - tensor var_6336 = const()[name = tensor("op_6336"), val = tensor([2, 1280, 1, -1])]; - tensor input_383 = reshape(shape = var_6336, x = attn_125)[name = tensor("input_383")]; - tensor var_6341 = const()[name = tensor("op_6341"), val = tensor([1, 1])]; - tensor var_6343 = const()[name = tensor("op_6343"), val = tensor([1, 1])]; - tensor var_6345_pad_type_0 = const()[name = tensor("op_6345_pad_type_0"), val = tensor("custom")]; - tensor var_6345_pad_0 = const()[name = tensor("op_6345_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6345 = conv(bias = mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_bias, dilations = var_6343, groups = var_4950, pad = var_6345_pad_0, pad_type = var_6345_pad_type_0, strides = var_6341, weight = mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_weight, x = input_383)[name = tensor("op_6345")]; - tensor inputs_189 = add(x = var_6345, y = inputs_187)[name = tensor("inputs_189")]; - tensor var_6349 = const()[name = tensor("op_6349"), val = tensor([1])]; - tensor channels_mean_189 = reduce_mean(axes = var_6349, keep_dims = var_4945, x = inputs_189)[name = tensor("channels_mean_189")]; - tensor zero_mean_189 = sub(x = inputs_189, y = channels_mean_189)[name = tensor("zero_mean_189")]; - tensor zero_mean_sq_189 = mul(x = zero_mean_189, y = zero_mean_189)[name = tensor("zero_mean_sq_189")]; - tensor var_6353 = const()[name = tensor("op_6353"), val = tensor([1])]; - tensor var_6354 = reduce_mean(axes = var_6353, keep_dims = var_4945, x = zero_mean_sq_189)[name = tensor("op_6354")]; - tensor var_6355 = const()[name = tensor("op_6355"), val = tensor(0x1.4f8b58p-17)]; - tensor var_6356 = add(x = var_6354, y = var_6355)[name = tensor("op_6356")]; - tensor denom_189_epsilon_0 = const()[name = tensor("denom_189_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_189 = rsqrt(epsilon = denom_189_epsilon_0, x = var_6356)[name = tensor("denom_189")]; - tensor out_189 = mul(x = zero_mean_189, y = denom_189)[name = tensor("out_189")]; - tensor var_6360 = const()[name = tensor("op_6360"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268637440)))]; - tensor var_6361 = add(x = out_189, y = var_6360)[name = tensor("op_6361")]; - tensor var_6363 = const()[name = tensor("op_6363"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268642624)))]; - tensor hidden_states_253 = mul(x = var_6361, y = var_6363)[name = tensor("hidden_states_253")]; - tensor var_6370 = const()[name = tensor("op_6370"), val = tensor([1, 1])]; - tensor var_6372 = const()[name = tensor("op_6372"), val = tensor([1, 1])]; + tensor attn_125_cast = matmul(transpose_x = attn_125_transpose_x_0, transpose_y = attn_125_transpose_y_0, x = var_6248_cast, y = var_6252_cast)[name = tensor("attn_125_cast")]; + tensor var_6256 = const()[name = tensor("op_6256"), val = tensor([2, 1280, 1, -1])]; + tensor input_383_cast = reshape(shape = var_6256, x = attn_125_cast)[name = tensor("input_383_cast")]; + tensor var_6261 = const()[name = tensor("op_6261"), val = tensor([1, 1])]; + tensor var_6263 = const()[name = tensor("op_6263"), val = tensor([1, 1])]; + tensor var_6265_pad_type_0 = const()[name = tensor("op_6265_pad_type_0"), val = tensor("custom")]; + tensor var_6265_pad_0 = const()[name = tensor("op_6265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2222535616)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2225812480)))]; + tensor var_6265_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_6263, groups = var_31, pad = var_6265_pad_0, pad_type = var_6265_pad_type_0, strides = var_6261, weight = unet_mid_block_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16, x = input_383_cast)[name = tensor("op_6265_cast")]; + tensor inputs_189_cast = add(x = var_6265_cast, y = inputs_187_cast)[name = tensor("inputs_189_cast")]; + tensor var_6269 = const()[name = tensor("op_6269"), val = tensor([1])]; + tensor channels_mean_189_cast = reduce_mean(axes = var_6269, keep_dims = var_23, x = inputs_189_cast)[name = tensor("channels_mean_189_cast")]; + tensor zero_mean_189_cast = sub(x = inputs_189_cast, y = channels_mean_189_cast)[name = tensor("zero_mean_189_cast")]; + tensor zero_mean_sq_189_cast = mul(x = zero_mean_189_cast, y = zero_mean_189_cast)[name = tensor("zero_mean_sq_189_cast")]; + tensor var_6273 = const()[name = tensor("op_6273"), val = tensor([1])]; + tensor var_6274_cast = reduce_mean(axes = var_6273, keep_dims = var_23, x = zero_mean_sq_189_cast)[name = tensor("op_6274_cast")]; + tensor var_6275_to_fp16 = const()[name = tensor("op_6275_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6276_cast = add(x = var_6274_cast, y = var_6275_to_fp16)[name = tensor("op_6276_cast")]; + tensor denom_189_epsilon_0_to_fp16 = const()[name = tensor("denom_189_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_189_cast = rsqrt(epsilon = denom_189_epsilon_0_to_fp16, x = var_6276_cast)[name = tensor("denom_189_cast")]; + tensor out_189_cast = mul(x = zero_mean_189_cast, y = denom_189_cast)[name = tensor("out_189_cast")]; + tensor var_6280_to_fp16 = const()[name = tensor("op_6280_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2225815104)))]; + tensor var_6281_cast = add(x = out_189_cast, y = var_6280_to_fp16)[name = tensor("op_6281_cast")]; + tensor var_6283_to_fp16 = const()[name = tensor("op_6283_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2225817728)))]; + tensor hidden_states_253_cast = mul(x = var_6281_cast, y = var_6283_to_fp16)[name = tensor("hidden_states_253_cast")]; + tensor var_6290 = const()[name = tensor("op_6290"), val = tensor([1, 1])]; + tensor var_6292 = const()[name = tensor("op_6292"), val = tensor([1, 1])]; tensor q_127_pad_type_0 = const()[name = tensor("q_127_pad_type_0"), val = tensor("custom")]; tensor q_127_pad_0 = const()[name = tensor("q_127_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_127 = conv(dilations = var_6372, groups = var_4950, pad = q_127_pad_0, pad_type = q_127_pad_type_0, strides = var_6370, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_q_weight, x = hidden_states_253)[name = tensor("q_127")]; - tensor var_6376 = const()[name = tensor("op_6376"), val = tensor([1, 1])]; - tensor var_6378 = const()[name = tensor("op_6378"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2225820352)))]; + tensor q_127_cast = conv(dilations = var_6292, groups = var_31, pad = q_127_pad_0, pad_type = q_127_pad_type_0, strides = var_6290, weight = unet_mid_block_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16, x = hidden_states_253_cast)[name = tensor("q_127_cast")]; + tensor var_6296 = const()[name = tensor("op_6296"), val = tensor([1, 1])]; + tensor var_6298 = const()[name = tensor("op_6298"), val = tensor([1, 1])]; tensor k_127_pad_type_0 = const()[name = tensor("k_127_pad_type_0"), val = tensor("custom")]; tensor k_127_pad_0 = const()[name = tensor("k_127_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_127 = conv(dilations = var_6378, groups = var_4950, pad = k_127_pad_0, pad_type = k_127_pad_type_0, strides = var_6376, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_127")]; - tensor var_6382 = const()[name = tensor("op_6382"), val = tensor([1, 1])]; - tensor var_6384 = const()[name = tensor("op_6384"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2229097216)))]; + tensor k_127_cast = conv(dilations = var_6298, groups = var_31, pad = k_127_pad_0, pad_type = k_127_pad_type_0, strides = var_6296, weight = unet_mid_block_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_127_cast")]; + tensor var_6302 = const()[name = tensor("op_6302"), val = tensor([1, 1])]; + tensor var_6304 = const()[name = tensor("op_6304"), val = tensor([1, 1])]; tensor v_127_pad_type_0 = const()[name = tensor("v_127_pad_type_0"), val = tensor("custom")]; tensor v_127_pad_0 = const()[name = tensor("v_127_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_127 = conv(dilations = var_6384, groups = var_4950, pad = v_127_pad_0, pad_type = v_127_pad_type_0, strides = var_6382, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_127")]; - tensor var_6388 = const()[name = tensor("op_6388"), val = tensor([2, 20, 64, -1])]; - tensor var_6389 = reshape(shape = var_6388, x = q_127)[name = tensor("op_6389")]; - tensor var_6390 = const()[name = tensor("op_6390"), val = tensor([2, 20, 64, -1])]; - tensor var_6391 = reshape(shape = var_6390, x = k_127)[name = tensor("op_6391")]; - tensor var_6392 = const()[name = tensor("op_6392"), val = tensor([2, 20, 64, -1])]; - tensor var_6393 = reshape(shape = var_6392, x = v_127)[name = tensor("op_6393")]; + tensor unet_mid_block_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2234340160)))]; + tensor v_127_cast = conv(dilations = var_6304, groups = var_31, pad = v_127_pad_0, pad_type = v_127_pad_type_0, strides = var_6302, weight = unet_mid_block_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_127_cast")]; + tensor var_6308 = const()[name = tensor("op_6308"), val = tensor([2, 20, 64, -1])]; + tensor var_6309_cast = reshape(shape = var_6308, x = q_127_cast)[name = tensor("op_6309_cast")]; + tensor var_6310 = const()[name = tensor("op_6310"), val = tensor([2, 20, 64, -1])]; + tensor var_6311_cast = reshape(shape = var_6310, x = k_127_cast)[name = tensor("op_6311_cast")]; + tensor var_6312 = const()[name = tensor("op_6312"), val = tensor([2, 20, 64, -1])]; + tensor var_6313_cast = reshape(shape = var_6312, x = v_127_cast)[name = tensor("op_6313_cast")]; tensor attn_weights_253_transpose_x_0 = const()[name = tensor("attn_weights_253_transpose_x_0"), val = tensor(true)]; tensor attn_weights_253_transpose_y_0 = const()[name = tensor("attn_weights_253_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_253 = matmul(transpose_x = attn_weights_253_transpose_x_0, transpose_y = attn_weights_253_transpose_y_0, x = var_6389, y = var_6391)[name = tensor("attn_weights_253")]; - tensor attn_weights_255 = mul(x = attn_weights_253, y = var_4941)[name = tensor("attn_weights_255")]; - tensor var_6397 = softmax(axis = var_4934, x = attn_weights_255)[name = tensor("op_6397")]; + tensor attn_weights_253_cast = matmul(transpose_x = attn_weights_253_transpose_x_0, transpose_y = attn_weights_253_transpose_y_0, x = var_6309_cast, y = var_6311_cast)[name = tensor("attn_weights_253_cast")]; + tensor attn_weights_255_cast = mul(x = attn_weights_253_cast, y = var_12_to_fp16)[name = tensor("attn_weights_255_cast")]; + tensor var_6317_cast = softmax(axis = var_18, x = attn_weights_255_cast)[name = tensor("op_6317_cast")]; tensor attn_127_transpose_x_0 = const()[name = tensor("attn_127_transpose_x_0"), val = tensor(false)]; tensor attn_127_transpose_y_0 = const()[name = tensor("attn_127_transpose_y_0"), val = tensor(true)]; - tensor attn_127 = matmul(transpose_x = attn_127_transpose_x_0, transpose_y = attn_127_transpose_y_0, x = var_6393, y = var_6397)[name = tensor("attn_127")]; - tensor var_6401 = const()[name = tensor("op_6401"), val = tensor([2, 1280, 1, -1])]; - tensor input_385 = reshape(shape = var_6401, x = attn_127)[name = tensor("input_385")]; - tensor var_6406 = const()[name = tensor("op_6406"), val = tensor([1, 1])]; - tensor var_6408 = const()[name = tensor("op_6408"), val = tensor([1, 1])]; - tensor var_6410_pad_type_0 = const()[name = tensor("op_6410_pad_type_0"), val = tensor("custom")]; - tensor var_6410_pad_0 = const()[name = tensor("op_6410_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6410 = conv(bias = mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_bias, dilations = var_6408, groups = var_4950, pad = var_6410_pad_0, pad_type = var_6410_pad_type_0, strides = var_6406, weight = mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_weight, x = input_385)[name = tensor("op_6410")]; - tensor inputs_191 = add(x = var_6410, y = inputs_189)[name = tensor("inputs_191")]; - tensor var_6414 = const()[name = tensor("op_6414"), val = tensor([1])]; - tensor channels_mean_191 = reduce_mean(axes = var_6414, keep_dims = var_4945, x = inputs_191)[name = tensor("channels_mean_191")]; - tensor zero_mean_191 = sub(x = inputs_191, y = channels_mean_191)[name = tensor("zero_mean_191")]; - tensor zero_mean_sq_191 = mul(x = zero_mean_191, y = zero_mean_191)[name = tensor("zero_mean_sq_191")]; - tensor var_6418 = const()[name = tensor("op_6418"), val = tensor([1])]; - tensor var_6419 = reduce_mean(axes = var_6418, keep_dims = var_4945, x = zero_mean_sq_191)[name = tensor("op_6419")]; - tensor var_6420 = const()[name = tensor("op_6420"), val = tensor(0x1.4f8b58p-17)]; - tensor var_6421 = add(x = var_6419, y = var_6420)[name = tensor("op_6421")]; - tensor denom_191_epsilon_0 = const()[name = tensor("denom_191_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_191 = rsqrt(epsilon = denom_191_epsilon_0, x = var_6421)[name = tensor("denom_191")]; - tensor out_191 = mul(x = zero_mean_191, y = denom_191)[name = tensor("out_191")]; - tensor var_6425 = const()[name = tensor("op_6425"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268647808)))]; - tensor var_6426 = add(x = out_191, y = var_6425)[name = tensor("op_6426")]; - tensor var_6428 = const()[name = tensor("op_6428"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268652992)))]; - tensor input_387 = mul(x = var_6426, y = var_6428)[name = tensor("input_387")]; - tensor var_6436 = const()[name = tensor("op_6436"), val = tensor([1, 1])]; - tensor var_6438 = const()[name = tensor("op_6438"), val = tensor([1, 1])]; - tensor var_6440_pad_type_0 = const()[name = tensor("op_6440_pad_type_0"), val = tensor("custom")]; - tensor var_6440_pad_0 = const()[name = tensor("op_6440_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6440 = conv(bias = mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_bias, dilations = var_6438, groups = var_4950, pad = var_6440_pad_0, pad_type = var_6440_pad_type_0, strides = var_6436, weight = mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_weight, x = input_387)[name = tensor("op_6440")]; - tensor var_6441_split_sizes_0 = const()[name = tensor("op_6441_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_6441_axis_0 = const()[name = tensor("op_6441_axis_0"), val = tensor(1)]; - tensor var_6441_0, tensor var_6441_1 = split(axis = var_6441_axis_0, split_sizes = var_6441_split_sizes_0, x = var_6440)[name = tensor("op_6441")]; - tensor var_6443_mode_0 = const()[name = tensor("op_6443_mode_0"), val = tensor("EXACT")]; - tensor var_6443 = gelu(mode = var_6443_mode_0, x = var_6441_1)[name = tensor("op_6443")]; - tensor input_389 = mul(x = var_6441_0, y = var_6443)[name = tensor("input_389")]; - tensor var_6447 = const()[name = tensor("op_6447"), val = tensor([1, 1])]; - tensor var_6449 = const()[name = tensor("op_6449"), val = tensor([1, 1])]; - tensor var_6451_pad_type_0 = const()[name = tensor("op_6451_pad_type_0"), val = tensor("custom")]; - tensor var_6451_pad_0 = const()[name = tensor("op_6451_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6451 = conv(bias = mid_block_attentions_0_transformer_blocks_7_ff_net_2_bias, dilations = var_6449, groups = var_4950, pad = var_6451_pad_0, pad_type = var_6451_pad_type_0, strides = var_6447, weight = mid_block_attentions_0_transformer_blocks_7_ff_net_2_weight, x = input_389)[name = tensor("op_6451")]; - tensor inputs_193 = add(x = var_6451, y = inputs_191)[name = tensor("inputs_193")]; - tensor var_6461 = const()[name = tensor("op_6461"), val = tensor([1])]; - tensor channels_mean_193 = reduce_mean(axes = var_6461, keep_dims = var_4945, x = inputs_193)[name = tensor("channels_mean_193")]; - tensor zero_mean_193 = sub(x = inputs_193, y = channels_mean_193)[name = tensor("zero_mean_193")]; - tensor zero_mean_sq_193 = mul(x = zero_mean_193, y = zero_mean_193)[name = tensor("zero_mean_sq_193")]; - tensor var_6465 = const()[name = tensor("op_6465"), val = tensor([1])]; - tensor var_6466 = reduce_mean(axes = var_6465, keep_dims = var_4945, x = zero_mean_sq_193)[name = tensor("op_6466")]; - tensor var_6467 = const()[name = tensor("op_6467"), val = tensor(0x1.4f8b58p-17)]; - tensor var_6468 = add(x = var_6466, y = var_6467)[name = tensor("op_6468")]; - tensor denom_193_epsilon_0 = const()[name = tensor("denom_193_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_193 = rsqrt(epsilon = denom_193_epsilon_0, x = var_6468)[name = tensor("denom_193")]; - tensor out_193 = mul(x = zero_mean_193, y = denom_193)[name = tensor("out_193")]; - tensor var_6472 = const()[name = tensor("op_6472"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268658176)))]; - tensor var_6473 = add(x = out_193, y = var_6472)[name = tensor("op_6473")]; - tensor var_6475 = const()[name = tensor("op_6475"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268663360)))]; - tensor hidden_states_257 = mul(x = var_6473, y = var_6475)[name = tensor("hidden_states_257")]; - tensor var_6482 = const()[name = tensor("op_6482"), val = tensor([1, 1])]; - tensor var_6484 = const()[name = tensor("op_6484"), val = tensor([1, 1])]; + tensor attn_127_cast = matmul(transpose_x = attn_127_transpose_x_0, transpose_y = attn_127_transpose_y_0, x = var_6313_cast, y = var_6317_cast)[name = tensor("attn_127_cast")]; + tensor var_6321 = const()[name = tensor("op_6321"), val = tensor([2, 1280, 1, -1])]; + tensor input_385_cast = reshape(shape = var_6321, x = attn_127_cast)[name = tensor("input_385_cast")]; + tensor var_6326 = const()[name = tensor("op_6326"), val = tensor([1, 1])]; + tensor var_6328 = const()[name = tensor("op_6328"), val = tensor([1, 1])]; + tensor var_6330_pad_type_0 = const()[name = tensor("op_6330_pad_type_0"), val = tensor("custom")]; + tensor var_6330_pad_0 = const()[name = tensor("op_6330_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2239583104)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2242859968)))]; + tensor var_6330_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_6328, groups = var_31, pad = var_6330_pad_0, pad_type = var_6330_pad_type_0, strides = var_6326, weight = unet_mid_block_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16, x = input_385_cast)[name = tensor("op_6330_cast")]; + tensor inputs_191_cast = add(x = var_6330_cast, y = inputs_189_cast)[name = tensor("inputs_191_cast")]; + tensor var_6334 = const()[name = tensor("op_6334"), val = tensor([1])]; + tensor channels_mean_191_cast = reduce_mean(axes = var_6334, keep_dims = var_23, x = inputs_191_cast)[name = tensor("channels_mean_191_cast")]; + tensor zero_mean_191_cast = sub(x = inputs_191_cast, y = channels_mean_191_cast)[name = tensor("zero_mean_191_cast")]; + tensor zero_mean_sq_191_cast = mul(x = zero_mean_191_cast, y = zero_mean_191_cast)[name = tensor("zero_mean_sq_191_cast")]; + tensor var_6338 = const()[name = tensor("op_6338"), val = tensor([1])]; + tensor var_6339_cast = reduce_mean(axes = var_6338, keep_dims = var_23, x = zero_mean_sq_191_cast)[name = tensor("op_6339_cast")]; + tensor var_6340_to_fp16 = const()[name = tensor("op_6340_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6341_cast = add(x = var_6339_cast, y = var_6340_to_fp16)[name = tensor("op_6341_cast")]; + tensor denom_191_epsilon_0_to_fp16 = const()[name = tensor("denom_191_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_191_cast = rsqrt(epsilon = denom_191_epsilon_0_to_fp16, x = var_6341_cast)[name = tensor("denom_191_cast")]; + tensor out_191_cast = mul(x = zero_mean_191_cast, y = denom_191_cast)[name = tensor("out_191_cast")]; + tensor var_6345_to_fp16 = const()[name = tensor("op_6345_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2242862592)))]; + tensor var_6346_cast = add(x = out_191_cast, y = var_6345_to_fp16)[name = tensor("op_6346_cast")]; + tensor var_6348_to_fp16 = const()[name = tensor("op_6348_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2242865216)))]; + tensor input_387_cast = mul(x = var_6346_cast, y = var_6348_to_fp16)[name = tensor("input_387_cast")]; + tensor var_6356 = const()[name = tensor("op_6356"), val = tensor([1, 1])]; + tensor var_6358 = const()[name = tensor("op_6358"), val = tensor([1, 1])]; + tensor var_6360_pad_type_0 = const()[name = tensor("op_6360_pad_type_0"), val = tensor("custom")]; + tensor var_6360_pad_0 = const()[name = tensor("op_6360_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2242867840)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2269082304)))]; + tensor var_6360_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_6358, groups = var_31, pad = var_6360_pad_0, pad_type = var_6360_pad_type_0, strides = var_6356, weight = unet_mid_block_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16, x = input_387_cast)[name = tensor("op_6360_cast")]; + tensor var_6361_split_sizes_0 = const()[name = tensor("op_6361_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6361_axis_0 = const()[name = tensor("op_6361_axis_0"), val = tensor(1)]; + tensor var_6361_cast_0, tensor var_6361_cast_1 = split(axis = var_6361_axis_0, split_sizes = var_6361_split_sizes_0, x = var_6360_cast)[name = tensor("op_6361_cast")]; + tensor var_6363_mode_0 = const()[name = tensor("op_6363_mode_0"), val = tensor("EXACT")]; + tensor var_6363_cast = gelu(mode = var_6363_mode_0, x = var_6361_cast_1)[name = tensor("op_6363_cast")]; + tensor input_389_cast = mul(x = var_6361_cast_0, y = var_6363_cast)[name = tensor("input_389_cast")]; + tensor var_6367 = const()[name = tensor("op_6367"), val = tensor([1, 1])]; + tensor var_6369 = const()[name = tensor("op_6369"), val = tensor([1, 1])]; + tensor var_6371_pad_type_0 = const()[name = tensor("op_6371_pad_type_0"), val = tensor("custom")]; + tensor var_6371_pad_0 = const()[name = tensor("op_6371_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2269102848)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2282210112)))]; + tensor var_6371_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_6369, groups = var_31, pad = var_6371_pad_0, pad_type = var_6371_pad_type_0, strides = var_6367, weight = unet_mid_block_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16, x = input_389_cast)[name = tensor("op_6371_cast")]; + tensor inputs_193_cast = add(x = var_6371_cast, y = inputs_191_cast)[name = tensor("inputs_193_cast")]; + tensor var_6381 = const()[name = tensor("op_6381"), val = tensor([1])]; + tensor channels_mean_193_cast = reduce_mean(axes = var_6381, keep_dims = var_23, x = inputs_193_cast)[name = tensor("channels_mean_193_cast")]; + tensor zero_mean_193_cast = sub(x = inputs_193_cast, y = channels_mean_193_cast)[name = tensor("zero_mean_193_cast")]; + tensor zero_mean_sq_193_cast = mul(x = zero_mean_193_cast, y = zero_mean_193_cast)[name = tensor("zero_mean_sq_193_cast")]; + tensor var_6385 = const()[name = tensor("op_6385"), val = tensor([1])]; + tensor var_6386_cast = reduce_mean(axes = var_6385, keep_dims = var_23, x = zero_mean_sq_193_cast)[name = tensor("op_6386_cast")]; + tensor var_6387_to_fp16 = const()[name = tensor("op_6387_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6388_cast = add(x = var_6386_cast, y = var_6387_to_fp16)[name = tensor("op_6388_cast")]; + tensor denom_193_epsilon_0_to_fp16 = const()[name = tensor("denom_193_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_193_cast = rsqrt(epsilon = denom_193_epsilon_0_to_fp16, x = var_6388_cast)[name = tensor("denom_193_cast")]; + tensor out_193_cast = mul(x = zero_mean_193_cast, y = denom_193_cast)[name = tensor("out_193_cast")]; + tensor var_6392_to_fp16 = const()[name = tensor("op_6392_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2282212736)))]; + tensor var_6393_cast = add(x = out_193_cast, y = var_6392_to_fp16)[name = tensor("op_6393_cast")]; + tensor var_6395_to_fp16 = const()[name = tensor("op_6395_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2282215360)))]; + tensor hidden_states_257_cast = mul(x = var_6393_cast, y = var_6395_to_fp16)[name = tensor("hidden_states_257_cast")]; + tensor var_6402 = const()[name = tensor("op_6402"), val = tensor([1, 1])]; + tensor var_6404 = const()[name = tensor("op_6404"), val = tensor([1, 1])]; tensor q_129_pad_type_0 = const()[name = tensor("q_129_pad_type_0"), val = tensor("custom")]; tensor q_129_pad_0 = const()[name = tensor("q_129_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_129 = conv(dilations = var_6484, groups = var_4950, pad = q_129_pad_0, pad_type = q_129_pad_type_0, strides = var_6482, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_q_weight, x = hidden_states_257)[name = tensor("q_129")]; - tensor var_6488 = const()[name = tensor("op_6488"), val = tensor([1, 1])]; - tensor var_6490 = const()[name = tensor("op_6490"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2282217984)))]; + tensor q_129_cast = conv(dilations = var_6404, groups = var_31, pad = q_129_pad_0, pad_type = q_129_pad_type_0, strides = var_6402, weight = unet_mid_block_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16, x = hidden_states_257_cast)[name = tensor("q_129_cast")]; + tensor var_6408 = const()[name = tensor("op_6408"), val = tensor([1, 1])]; + tensor var_6410 = const()[name = tensor("op_6410"), val = tensor([1, 1])]; tensor k_129_pad_type_0 = const()[name = tensor("k_129_pad_type_0"), val = tensor("custom")]; tensor k_129_pad_0 = const()[name = tensor("k_129_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_129 = conv(dilations = var_6490, groups = var_4950, pad = k_129_pad_0, pad_type = k_129_pad_type_0, strides = var_6488, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_k_weight, x = hidden_states_257)[name = tensor("k_129")]; - tensor var_6494 = const()[name = tensor("op_6494"), val = tensor([1, 1])]; - tensor var_6496 = const()[name = tensor("op_6496"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2285494848)))]; + tensor k_129_cast = conv(dilations = var_6410, groups = var_31, pad = k_129_pad_0, pad_type = k_129_pad_type_0, strides = var_6408, weight = unet_mid_block_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16, x = hidden_states_257_cast)[name = tensor("k_129_cast")]; + tensor var_6414 = const()[name = tensor("op_6414"), val = tensor([1, 1])]; + tensor var_6416 = const()[name = tensor("op_6416"), val = tensor([1, 1])]; tensor v_129_pad_type_0 = const()[name = tensor("v_129_pad_type_0"), val = tensor("custom")]; tensor v_129_pad_0 = const()[name = tensor("v_129_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_129 = conv(dilations = var_6496, groups = var_4950, pad = v_129_pad_0, pad_type = v_129_pad_type_0, strides = var_6494, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_v_weight, x = hidden_states_257)[name = tensor("v_129")]; - tensor var_6500 = const()[name = tensor("op_6500"), val = tensor([2, 20, 64, -1])]; - tensor var_6501 = reshape(shape = var_6500, x = q_129)[name = tensor("op_6501")]; - tensor var_6502 = const()[name = tensor("op_6502"), val = tensor([2, 20, 64, -1])]; - tensor var_6503 = reshape(shape = var_6502, x = k_129)[name = tensor("op_6503")]; - tensor var_6504 = const()[name = tensor("op_6504"), val = tensor([2, 20, 64, -1])]; - tensor var_6505 = reshape(shape = var_6504, x = v_129)[name = tensor("op_6505")]; + tensor unet_mid_block_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2288771712)))]; + tensor v_129_cast = conv(dilations = var_6416, groups = var_31, pad = v_129_pad_0, pad_type = v_129_pad_type_0, strides = var_6414, weight = unet_mid_block_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16, x = hidden_states_257_cast)[name = tensor("v_129_cast")]; + tensor var_6420 = const()[name = tensor("op_6420"), val = tensor([2, 20, 64, -1])]; + tensor var_6421_cast = reshape(shape = var_6420, x = q_129_cast)[name = tensor("op_6421_cast")]; + tensor var_6422 = const()[name = tensor("op_6422"), val = tensor([2, 20, 64, -1])]; + tensor var_6423_cast = reshape(shape = var_6422, x = k_129_cast)[name = tensor("op_6423_cast")]; + tensor var_6424 = const()[name = tensor("op_6424"), val = tensor([2, 20, 64, -1])]; + tensor var_6425_cast = reshape(shape = var_6424, x = v_129_cast)[name = tensor("op_6425_cast")]; tensor attn_weights_257_transpose_x_0 = const()[name = tensor("attn_weights_257_transpose_x_0"), val = tensor(true)]; tensor attn_weights_257_transpose_y_0 = const()[name = tensor("attn_weights_257_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_257 = matmul(transpose_x = attn_weights_257_transpose_x_0, transpose_y = attn_weights_257_transpose_y_0, x = var_6501, y = var_6503)[name = tensor("attn_weights_257")]; - tensor attn_weights_259 = mul(x = attn_weights_257, y = var_4941)[name = tensor("attn_weights_259")]; - tensor var_6509 = softmax(axis = var_4934, x = attn_weights_259)[name = tensor("op_6509")]; + tensor attn_weights_257_cast = matmul(transpose_x = attn_weights_257_transpose_x_0, transpose_y = attn_weights_257_transpose_y_0, x = var_6421_cast, y = var_6423_cast)[name = tensor("attn_weights_257_cast")]; + tensor attn_weights_259_cast = mul(x = attn_weights_257_cast, y = var_12_to_fp16)[name = tensor("attn_weights_259_cast")]; + tensor var_6429_cast = softmax(axis = var_18, x = attn_weights_259_cast)[name = tensor("op_6429_cast")]; tensor attn_129_transpose_x_0 = const()[name = tensor("attn_129_transpose_x_0"), val = tensor(false)]; tensor attn_129_transpose_y_0 = const()[name = tensor("attn_129_transpose_y_0"), val = tensor(true)]; - tensor attn_129 = matmul(transpose_x = attn_129_transpose_x_0, transpose_y = attn_129_transpose_y_0, x = var_6505, y = var_6509)[name = tensor("attn_129")]; - tensor var_6513 = const()[name = tensor("op_6513"), val = tensor([2, 1280, 1, -1])]; - tensor input_391 = reshape(shape = var_6513, x = attn_129)[name = tensor("input_391")]; - tensor var_6518 = const()[name = tensor("op_6518"), val = tensor([1, 1])]; - tensor var_6520 = const()[name = tensor("op_6520"), val = tensor([1, 1])]; - tensor var_6522_pad_type_0 = const()[name = tensor("op_6522_pad_type_0"), val = tensor("custom")]; - tensor var_6522_pad_0 = const()[name = tensor("op_6522_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6522 = conv(bias = mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_bias, dilations = var_6520, groups = var_4950, pad = var_6522_pad_0, pad_type = var_6522_pad_type_0, strides = var_6518, weight = mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_weight, x = input_391)[name = tensor("op_6522")]; - tensor inputs_195 = add(x = var_6522, y = inputs_193)[name = tensor("inputs_195")]; - tensor var_6526 = const()[name = tensor("op_6526"), val = tensor([1])]; - tensor channels_mean_195 = reduce_mean(axes = var_6526, keep_dims = var_4945, x = inputs_195)[name = tensor("channels_mean_195")]; - tensor zero_mean_195 = sub(x = inputs_195, y = channels_mean_195)[name = tensor("zero_mean_195")]; - tensor zero_mean_sq_195 = mul(x = zero_mean_195, y = zero_mean_195)[name = tensor("zero_mean_sq_195")]; - tensor var_6530 = const()[name = tensor("op_6530"), val = tensor([1])]; - tensor var_6531 = reduce_mean(axes = var_6530, keep_dims = var_4945, x = zero_mean_sq_195)[name = tensor("op_6531")]; - tensor var_6532 = const()[name = tensor("op_6532"), val = tensor(0x1.4f8b58p-17)]; - tensor var_6533 = add(x = var_6531, y = var_6532)[name = tensor("op_6533")]; - tensor denom_195_epsilon_0 = const()[name = tensor("denom_195_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_195 = rsqrt(epsilon = denom_195_epsilon_0, x = var_6533)[name = tensor("denom_195")]; - tensor out_195 = mul(x = zero_mean_195, y = denom_195)[name = tensor("out_195")]; - tensor var_6537 = const()[name = tensor("op_6537"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268668544)))]; - tensor var_6538 = add(x = out_195, y = var_6537)[name = tensor("op_6538")]; - tensor var_6540 = const()[name = tensor("op_6540"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268673728)))]; - tensor hidden_states_259 = mul(x = var_6538, y = var_6540)[name = tensor("hidden_states_259")]; - tensor var_6547 = const()[name = tensor("op_6547"), val = tensor([1, 1])]; - tensor var_6549 = const()[name = tensor("op_6549"), val = tensor([1, 1])]; + tensor attn_129_cast = matmul(transpose_x = attn_129_transpose_x_0, transpose_y = attn_129_transpose_y_0, x = var_6425_cast, y = var_6429_cast)[name = tensor("attn_129_cast")]; + tensor var_6433 = const()[name = tensor("op_6433"), val = tensor([2, 1280, 1, -1])]; + tensor input_391_cast = reshape(shape = var_6433, x = attn_129_cast)[name = tensor("input_391_cast")]; + tensor var_6438 = const()[name = tensor("op_6438"), val = tensor([1, 1])]; + tensor var_6440 = const()[name = tensor("op_6440"), val = tensor([1, 1])]; + tensor var_6442_pad_type_0 = const()[name = tensor("op_6442_pad_type_0"), val = tensor("custom")]; + tensor var_6442_pad_0 = const()[name = tensor("op_6442_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2292048576)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2295325440)))]; + tensor var_6442_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_6440, groups = var_31, pad = var_6442_pad_0, pad_type = var_6442_pad_type_0, strides = var_6438, weight = unet_mid_block_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16, x = input_391_cast)[name = tensor("op_6442_cast")]; + tensor inputs_195_cast = add(x = var_6442_cast, y = inputs_193_cast)[name = tensor("inputs_195_cast")]; + tensor var_6446 = const()[name = tensor("op_6446"), val = tensor([1])]; + tensor channels_mean_195_cast = reduce_mean(axes = var_6446, keep_dims = var_23, x = inputs_195_cast)[name = tensor("channels_mean_195_cast")]; + tensor zero_mean_195_cast = sub(x = inputs_195_cast, y = channels_mean_195_cast)[name = tensor("zero_mean_195_cast")]; + tensor zero_mean_sq_195_cast = mul(x = zero_mean_195_cast, y = zero_mean_195_cast)[name = tensor("zero_mean_sq_195_cast")]; + tensor var_6450 = const()[name = tensor("op_6450"), val = tensor([1])]; + tensor var_6451_cast = reduce_mean(axes = var_6450, keep_dims = var_23, x = zero_mean_sq_195_cast)[name = tensor("op_6451_cast")]; + tensor var_6452_to_fp16 = const()[name = tensor("op_6452_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6453_cast = add(x = var_6451_cast, y = var_6452_to_fp16)[name = tensor("op_6453_cast")]; + tensor denom_195_epsilon_0_to_fp16 = const()[name = tensor("denom_195_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_195_cast = rsqrt(epsilon = denom_195_epsilon_0_to_fp16, x = var_6453_cast)[name = tensor("denom_195_cast")]; + tensor out_195_cast = mul(x = zero_mean_195_cast, y = denom_195_cast)[name = tensor("out_195_cast")]; + tensor var_6457_to_fp16 = const()[name = tensor("op_6457_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2295328064)))]; + tensor var_6458_cast = add(x = out_195_cast, y = var_6457_to_fp16)[name = tensor("op_6458_cast")]; + tensor var_6460_to_fp16 = const()[name = tensor("op_6460_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2295330688)))]; + tensor hidden_states_259_cast = mul(x = var_6458_cast, y = var_6460_to_fp16)[name = tensor("hidden_states_259_cast")]; + tensor var_6467 = const()[name = tensor("op_6467"), val = tensor([1, 1])]; + tensor var_6469 = const()[name = tensor("op_6469"), val = tensor([1, 1])]; tensor q_131_pad_type_0 = const()[name = tensor("q_131_pad_type_0"), val = tensor("custom")]; tensor q_131_pad_0 = const()[name = tensor("q_131_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_131 = conv(dilations = var_6549, groups = var_4950, pad = q_131_pad_0, pad_type = q_131_pad_type_0, strides = var_6547, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_q_weight, x = hidden_states_259)[name = tensor("q_131")]; - tensor var_6553 = const()[name = tensor("op_6553"), val = tensor([1, 1])]; - tensor var_6555 = const()[name = tensor("op_6555"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2295333312)))]; + tensor q_131_cast = conv(dilations = var_6469, groups = var_31, pad = q_131_pad_0, pad_type = q_131_pad_type_0, strides = var_6467, weight = unet_mid_block_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16, x = hidden_states_259_cast)[name = tensor("q_131_cast")]; + tensor var_6473 = const()[name = tensor("op_6473"), val = tensor([1, 1])]; + tensor var_6475 = const()[name = tensor("op_6475"), val = tensor([1, 1])]; tensor k_131_pad_type_0 = const()[name = tensor("k_131_pad_type_0"), val = tensor("custom")]; tensor k_131_pad_0 = const()[name = tensor("k_131_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_131 = conv(dilations = var_6555, groups = var_4950, pad = k_131_pad_0, pad_type = k_131_pad_type_0, strides = var_6553, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_131")]; - tensor var_6559 = const()[name = tensor("op_6559"), val = tensor([1, 1])]; - tensor var_6561 = const()[name = tensor("op_6561"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2298610176)))]; + tensor k_131_cast = conv(dilations = var_6475, groups = var_31, pad = k_131_pad_0, pad_type = k_131_pad_type_0, strides = var_6473, weight = unet_mid_block_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_131_cast")]; + tensor var_6479 = const()[name = tensor("op_6479"), val = tensor([1, 1])]; + tensor var_6481 = const()[name = tensor("op_6481"), val = tensor([1, 1])]; tensor v_131_pad_type_0 = const()[name = tensor("v_131_pad_type_0"), val = tensor("custom")]; tensor v_131_pad_0 = const()[name = tensor("v_131_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_131 = conv(dilations = var_6561, groups = var_4950, pad = v_131_pad_0, pad_type = v_131_pad_type_0, strides = var_6559, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_131")]; - tensor var_6565 = const()[name = tensor("op_6565"), val = tensor([2, 20, 64, -1])]; - tensor var_6566 = reshape(shape = var_6565, x = q_131)[name = tensor("op_6566")]; - tensor var_6567 = const()[name = tensor("op_6567"), val = tensor([2, 20, 64, -1])]; - tensor var_6568 = reshape(shape = var_6567, x = k_131)[name = tensor("op_6568")]; - tensor var_6569 = const()[name = tensor("op_6569"), val = tensor([2, 20, 64, -1])]; - tensor var_6570 = reshape(shape = var_6569, x = v_131)[name = tensor("op_6570")]; + tensor unet_mid_block_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2303853120)))]; + tensor v_131_cast = conv(dilations = var_6481, groups = var_31, pad = v_131_pad_0, pad_type = v_131_pad_type_0, strides = var_6479, weight = unet_mid_block_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_131_cast")]; + tensor var_6485 = const()[name = tensor("op_6485"), val = tensor([2, 20, 64, -1])]; + tensor var_6486_cast = reshape(shape = var_6485, x = q_131_cast)[name = tensor("op_6486_cast")]; + tensor var_6487 = const()[name = tensor("op_6487"), val = tensor([2, 20, 64, -1])]; + tensor var_6488_cast = reshape(shape = var_6487, x = k_131_cast)[name = tensor("op_6488_cast")]; + tensor var_6489 = const()[name = tensor("op_6489"), val = tensor([2, 20, 64, -1])]; + tensor var_6490_cast = reshape(shape = var_6489, x = v_131_cast)[name = tensor("op_6490_cast")]; tensor attn_weights_261_transpose_x_0 = const()[name = tensor("attn_weights_261_transpose_x_0"), val = tensor(true)]; tensor attn_weights_261_transpose_y_0 = const()[name = tensor("attn_weights_261_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_261 = matmul(transpose_x = attn_weights_261_transpose_x_0, transpose_y = attn_weights_261_transpose_y_0, x = var_6566, y = var_6568)[name = tensor("attn_weights_261")]; - tensor attn_weights_263 = mul(x = attn_weights_261, y = var_4941)[name = tensor("attn_weights_263")]; - tensor var_6574 = softmax(axis = var_4934, x = attn_weights_263)[name = tensor("op_6574")]; + tensor attn_weights_261_cast = matmul(transpose_x = attn_weights_261_transpose_x_0, transpose_y = attn_weights_261_transpose_y_0, x = var_6486_cast, y = var_6488_cast)[name = tensor("attn_weights_261_cast")]; + tensor attn_weights_263_cast = mul(x = attn_weights_261_cast, y = var_12_to_fp16)[name = tensor("attn_weights_263_cast")]; + tensor var_6494_cast = softmax(axis = var_18, x = attn_weights_263_cast)[name = tensor("op_6494_cast")]; tensor attn_131_transpose_x_0 = const()[name = tensor("attn_131_transpose_x_0"), val = tensor(false)]; tensor attn_131_transpose_y_0 = const()[name = tensor("attn_131_transpose_y_0"), val = tensor(true)]; - tensor attn_131 = matmul(transpose_x = attn_131_transpose_x_0, transpose_y = attn_131_transpose_y_0, x = var_6570, y = var_6574)[name = tensor("attn_131")]; - tensor var_6578 = const()[name = tensor("op_6578"), val = tensor([2, 1280, 1, -1])]; - tensor input_393 = reshape(shape = var_6578, x = attn_131)[name = tensor("input_393")]; - tensor var_6583 = const()[name = tensor("op_6583"), val = tensor([1, 1])]; - tensor var_6585 = const()[name = tensor("op_6585"), val = tensor([1, 1])]; - tensor var_6587_pad_type_0 = const()[name = tensor("op_6587_pad_type_0"), val = tensor("custom")]; - tensor var_6587_pad_0 = const()[name = tensor("op_6587_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6587 = conv(bias = mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_bias, dilations = var_6585, groups = var_4950, pad = var_6587_pad_0, pad_type = var_6587_pad_type_0, strides = var_6583, weight = mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_weight, x = input_393)[name = tensor("op_6587")]; - tensor inputs_197 = add(x = var_6587, y = inputs_195)[name = tensor("inputs_197")]; - tensor var_6591 = const()[name = tensor("op_6591"), val = tensor([1])]; - tensor channels_mean_197 = reduce_mean(axes = var_6591, keep_dims = var_4945, x = inputs_197)[name = tensor("channels_mean_197")]; - tensor zero_mean_197 = sub(x = inputs_197, y = channels_mean_197)[name = tensor("zero_mean_197")]; - tensor zero_mean_sq_197 = mul(x = zero_mean_197, y = zero_mean_197)[name = tensor("zero_mean_sq_197")]; - tensor var_6595 = const()[name = tensor("op_6595"), val = tensor([1])]; - tensor var_6596 = reduce_mean(axes = var_6595, keep_dims = var_4945, x = zero_mean_sq_197)[name = tensor("op_6596")]; - tensor var_6597 = const()[name = tensor("op_6597"), val = tensor(0x1.4f8b58p-17)]; - tensor var_6598 = add(x = var_6596, y = var_6597)[name = tensor("op_6598")]; - tensor denom_197_epsilon_0 = const()[name = tensor("denom_197_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_197 = rsqrt(epsilon = denom_197_epsilon_0, x = var_6598)[name = tensor("denom_197")]; - tensor out_197 = mul(x = zero_mean_197, y = denom_197)[name = tensor("out_197")]; - tensor var_6602 = const()[name = tensor("op_6602"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268678912)))]; - tensor var_6603 = add(x = out_197, y = var_6602)[name = tensor("op_6603")]; - tensor var_6605 = const()[name = tensor("op_6605"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268684096)))]; - tensor input_395 = mul(x = var_6603, y = var_6605)[name = tensor("input_395")]; - tensor var_6613 = const()[name = tensor("op_6613"), val = tensor([1, 1])]; - tensor var_6615 = const()[name = tensor("op_6615"), val = tensor([1, 1])]; - tensor var_6617_pad_type_0 = const()[name = tensor("op_6617_pad_type_0"), val = tensor("custom")]; - tensor var_6617_pad_0 = const()[name = tensor("op_6617_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6617 = conv(bias = mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_bias, dilations = var_6615, groups = var_4950, pad = var_6617_pad_0, pad_type = var_6617_pad_type_0, strides = var_6613, weight = mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_weight, x = input_395)[name = tensor("op_6617")]; - tensor var_6618_split_sizes_0 = const()[name = tensor("op_6618_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_6618_axis_0 = const()[name = tensor("op_6618_axis_0"), val = tensor(1)]; - tensor var_6618_0, tensor var_6618_1 = split(axis = var_6618_axis_0, split_sizes = var_6618_split_sizes_0, x = var_6617)[name = tensor("op_6618")]; - tensor var_6620_mode_0 = const()[name = tensor("op_6620_mode_0"), val = tensor("EXACT")]; - tensor var_6620 = gelu(mode = var_6620_mode_0, x = var_6618_1)[name = tensor("op_6620")]; - tensor input_397 = mul(x = var_6618_0, y = var_6620)[name = tensor("input_397")]; - tensor var_6624 = const()[name = tensor("op_6624"), val = tensor([1, 1])]; - tensor var_6626 = const()[name = tensor("op_6626"), val = tensor([1, 1])]; - tensor var_6628_pad_type_0 = const()[name = tensor("op_6628_pad_type_0"), val = tensor("custom")]; - tensor var_6628_pad_0 = const()[name = tensor("op_6628_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6628 = conv(bias = mid_block_attentions_0_transformer_blocks_8_ff_net_2_bias, dilations = var_6626, groups = var_4950, pad = var_6628_pad_0, pad_type = var_6628_pad_type_0, strides = var_6624, weight = mid_block_attentions_0_transformer_blocks_8_ff_net_2_weight, x = input_397)[name = tensor("op_6628")]; - tensor inputs_199 = add(x = var_6628, y = inputs_197)[name = tensor("inputs_199")]; - tensor var_6638 = const()[name = tensor("op_6638"), val = tensor([1])]; - tensor channels_mean_199 = reduce_mean(axes = var_6638, keep_dims = var_4945, x = inputs_199)[name = tensor("channels_mean_199")]; - tensor zero_mean_199 = sub(x = inputs_199, y = channels_mean_199)[name = tensor("zero_mean_199")]; - tensor zero_mean_sq_199 = mul(x = zero_mean_199, y = zero_mean_199)[name = tensor("zero_mean_sq_199")]; - tensor var_6642 = const()[name = tensor("op_6642"), val = tensor([1])]; - tensor var_6643 = reduce_mean(axes = var_6642, keep_dims = var_4945, x = zero_mean_sq_199)[name = tensor("op_6643")]; - tensor var_6644 = const()[name = tensor("op_6644"), val = tensor(0x1.4f8b58p-17)]; - tensor var_6645 = add(x = var_6643, y = var_6644)[name = tensor("op_6645")]; - tensor denom_199_epsilon_0 = const()[name = tensor("denom_199_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_199 = rsqrt(epsilon = denom_199_epsilon_0, x = var_6645)[name = tensor("denom_199")]; - tensor out_199 = mul(x = zero_mean_199, y = denom_199)[name = tensor("out_199")]; - tensor var_6649 = const()[name = tensor("op_6649"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268689280)))]; - tensor var_6650 = add(x = out_199, y = var_6649)[name = tensor("op_6650")]; - tensor var_6652 = const()[name = tensor("op_6652"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268694464)))]; - tensor hidden_states_263 = mul(x = var_6650, y = var_6652)[name = tensor("hidden_states_263")]; - tensor var_6659 = const()[name = tensor("op_6659"), val = tensor([1, 1])]; - tensor var_6661 = const()[name = tensor("op_6661"), val = tensor([1, 1])]; + tensor attn_131_cast = matmul(transpose_x = attn_131_transpose_x_0, transpose_y = attn_131_transpose_y_0, x = var_6490_cast, y = var_6494_cast)[name = tensor("attn_131_cast")]; + tensor var_6498 = const()[name = tensor("op_6498"), val = tensor([2, 1280, 1, -1])]; + tensor input_393_cast = reshape(shape = var_6498, x = attn_131_cast)[name = tensor("input_393_cast")]; + tensor var_6503 = const()[name = tensor("op_6503"), val = tensor([1, 1])]; + tensor var_6505 = const()[name = tensor("op_6505"), val = tensor([1, 1])]; + tensor var_6507_pad_type_0 = const()[name = tensor("op_6507_pad_type_0"), val = tensor("custom")]; + tensor var_6507_pad_0 = const()[name = tensor("op_6507_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2309096064)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2312372928)))]; + tensor var_6507_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_6505, groups = var_31, pad = var_6507_pad_0, pad_type = var_6507_pad_type_0, strides = var_6503, weight = unet_mid_block_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16, x = input_393_cast)[name = tensor("op_6507_cast")]; + tensor inputs_197_cast = add(x = var_6507_cast, y = inputs_195_cast)[name = tensor("inputs_197_cast")]; + tensor var_6511 = const()[name = tensor("op_6511"), val = tensor([1])]; + tensor channels_mean_197_cast = reduce_mean(axes = var_6511, keep_dims = var_23, x = inputs_197_cast)[name = tensor("channels_mean_197_cast")]; + tensor zero_mean_197_cast = sub(x = inputs_197_cast, y = channels_mean_197_cast)[name = tensor("zero_mean_197_cast")]; + tensor zero_mean_sq_197_cast = mul(x = zero_mean_197_cast, y = zero_mean_197_cast)[name = tensor("zero_mean_sq_197_cast")]; + tensor var_6515 = const()[name = tensor("op_6515"), val = tensor([1])]; + tensor var_6516_cast = reduce_mean(axes = var_6515, keep_dims = var_23, x = zero_mean_sq_197_cast)[name = tensor("op_6516_cast")]; + tensor var_6517_to_fp16 = const()[name = tensor("op_6517_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6518_cast = add(x = var_6516_cast, y = var_6517_to_fp16)[name = tensor("op_6518_cast")]; + tensor denom_197_epsilon_0_to_fp16 = const()[name = tensor("denom_197_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_197_cast = rsqrt(epsilon = denom_197_epsilon_0_to_fp16, x = var_6518_cast)[name = tensor("denom_197_cast")]; + tensor out_197_cast = mul(x = zero_mean_197_cast, y = denom_197_cast)[name = tensor("out_197_cast")]; + tensor var_6522_to_fp16 = const()[name = tensor("op_6522_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2312375552)))]; + tensor var_6523_cast = add(x = out_197_cast, y = var_6522_to_fp16)[name = tensor("op_6523_cast")]; + tensor var_6525_to_fp16 = const()[name = tensor("op_6525_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2312378176)))]; + tensor input_395_cast = mul(x = var_6523_cast, y = var_6525_to_fp16)[name = tensor("input_395_cast")]; + tensor var_6533 = const()[name = tensor("op_6533"), val = tensor([1, 1])]; + tensor var_6535 = const()[name = tensor("op_6535"), val = tensor([1, 1])]; + tensor var_6537_pad_type_0 = const()[name = tensor("op_6537_pad_type_0"), val = tensor("custom")]; + tensor var_6537_pad_0 = const()[name = tensor("op_6537_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2312380800)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2338595264)))]; + tensor var_6537_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_6535, groups = var_31, pad = var_6537_pad_0, pad_type = var_6537_pad_type_0, strides = var_6533, weight = unet_mid_block_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16, x = input_395_cast)[name = tensor("op_6537_cast")]; + tensor var_6538_split_sizes_0 = const()[name = tensor("op_6538_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6538_axis_0 = const()[name = tensor("op_6538_axis_0"), val = tensor(1)]; + tensor var_6538_cast_0, tensor var_6538_cast_1 = split(axis = var_6538_axis_0, split_sizes = var_6538_split_sizes_0, x = var_6537_cast)[name = tensor("op_6538_cast")]; + tensor var_6540_mode_0 = const()[name = tensor("op_6540_mode_0"), val = tensor("EXACT")]; + tensor var_6540_cast = gelu(mode = var_6540_mode_0, x = var_6538_cast_1)[name = tensor("op_6540_cast")]; + tensor input_397_cast = mul(x = var_6538_cast_0, y = var_6540_cast)[name = tensor("input_397_cast")]; + tensor var_6544 = const()[name = tensor("op_6544"), val = tensor([1, 1])]; + tensor var_6546 = const()[name = tensor("op_6546"), val = tensor([1, 1])]; + tensor var_6548_pad_type_0 = const()[name = tensor("op_6548_pad_type_0"), val = tensor("custom")]; + tensor var_6548_pad_0 = const()[name = tensor("op_6548_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2338615808)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2351723072)))]; + tensor var_6548_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_6546, groups = var_31, pad = var_6548_pad_0, pad_type = var_6548_pad_type_0, strides = var_6544, weight = unet_mid_block_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16, x = input_397_cast)[name = tensor("op_6548_cast")]; + tensor inputs_199_cast = add(x = var_6548_cast, y = inputs_197_cast)[name = tensor("inputs_199_cast")]; + tensor var_6558 = const()[name = tensor("op_6558"), val = tensor([1])]; + tensor channels_mean_199_cast = reduce_mean(axes = var_6558, keep_dims = var_23, x = inputs_199_cast)[name = tensor("channels_mean_199_cast")]; + tensor zero_mean_199_cast = sub(x = inputs_199_cast, y = channels_mean_199_cast)[name = tensor("zero_mean_199_cast")]; + tensor zero_mean_sq_199_cast = mul(x = zero_mean_199_cast, y = zero_mean_199_cast)[name = tensor("zero_mean_sq_199_cast")]; + tensor var_6562 = const()[name = tensor("op_6562"), val = tensor([1])]; + tensor var_6563_cast = reduce_mean(axes = var_6562, keep_dims = var_23, x = zero_mean_sq_199_cast)[name = tensor("op_6563_cast")]; + tensor var_6564_to_fp16 = const()[name = tensor("op_6564_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6565_cast = add(x = var_6563_cast, y = var_6564_to_fp16)[name = tensor("op_6565_cast")]; + tensor denom_199_epsilon_0_to_fp16 = const()[name = tensor("denom_199_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_199_cast = rsqrt(epsilon = denom_199_epsilon_0_to_fp16, x = var_6565_cast)[name = tensor("denom_199_cast")]; + tensor out_199_cast = mul(x = zero_mean_199_cast, y = denom_199_cast)[name = tensor("out_199_cast")]; + tensor var_6569_to_fp16 = const()[name = tensor("op_6569_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2351725696)))]; + tensor var_6570_cast = add(x = out_199_cast, y = var_6569_to_fp16)[name = tensor("op_6570_cast")]; + tensor var_6572_to_fp16 = const()[name = tensor("op_6572_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2351728320)))]; + tensor hidden_states_263_cast = mul(x = var_6570_cast, y = var_6572_to_fp16)[name = tensor("hidden_states_263_cast")]; + tensor var_6579 = const()[name = tensor("op_6579"), val = tensor([1, 1])]; + tensor var_6581 = const()[name = tensor("op_6581"), val = tensor([1, 1])]; tensor q_133_pad_type_0 = const()[name = tensor("q_133_pad_type_0"), val = tensor("custom")]; tensor q_133_pad_0 = const()[name = tensor("q_133_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_133 = conv(dilations = var_6661, groups = var_4950, pad = q_133_pad_0, pad_type = q_133_pad_type_0, strides = var_6659, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_q_weight, x = hidden_states_263)[name = tensor("q_133")]; - tensor var_6665 = const()[name = tensor("op_6665"), val = tensor([1, 1])]; - tensor var_6667 = const()[name = tensor("op_6667"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2351730944)))]; + tensor q_133_cast = conv(dilations = var_6581, groups = var_31, pad = q_133_pad_0, pad_type = q_133_pad_type_0, strides = var_6579, weight = unet_mid_block_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16, x = hidden_states_263_cast)[name = tensor("q_133_cast")]; + tensor var_6585 = const()[name = tensor("op_6585"), val = tensor([1, 1])]; + tensor var_6587 = const()[name = tensor("op_6587"), val = tensor([1, 1])]; tensor k_133_pad_type_0 = const()[name = tensor("k_133_pad_type_0"), val = tensor("custom")]; tensor k_133_pad_0 = const()[name = tensor("k_133_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_133 = conv(dilations = var_6667, groups = var_4950, pad = k_133_pad_0, pad_type = k_133_pad_type_0, strides = var_6665, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_k_weight, x = hidden_states_263)[name = tensor("k_133")]; - tensor var_6671 = const()[name = tensor("op_6671"), val = tensor([1, 1])]; - tensor var_6673 = const()[name = tensor("op_6673"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2355007808)))]; + tensor k_133_cast = conv(dilations = var_6587, groups = var_31, pad = k_133_pad_0, pad_type = k_133_pad_type_0, strides = var_6585, weight = unet_mid_block_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16, x = hidden_states_263_cast)[name = tensor("k_133_cast")]; + tensor var_6591 = const()[name = tensor("op_6591"), val = tensor([1, 1])]; + tensor var_6593 = const()[name = tensor("op_6593"), val = tensor([1, 1])]; tensor v_133_pad_type_0 = const()[name = tensor("v_133_pad_type_0"), val = tensor("custom")]; tensor v_133_pad_0 = const()[name = tensor("v_133_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_133 = conv(dilations = var_6673, groups = var_4950, pad = v_133_pad_0, pad_type = v_133_pad_type_0, strides = var_6671, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_v_weight, x = hidden_states_263)[name = tensor("v_133")]; - tensor var_6677 = const()[name = tensor("op_6677"), val = tensor([2, 20, 64, -1])]; - tensor var_6678 = reshape(shape = var_6677, x = q_133)[name = tensor("op_6678")]; - tensor var_6679 = const()[name = tensor("op_6679"), val = tensor([2, 20, 64, -1])]; - tensor var_6680 = reshape(shape = var_6679, x = k_133)[name = tensor("op_6680")]; - tensor var_6681 = const()[name = tensor("op_6681"), val = tensor([2, 20, 64, -1])]; - tensor var_6682 = reshape(shape = var_6681, x = v_133)[name = tensor("op_6682")]; + tensor unet_mid_block_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2358284672)))]; + tensor v_133_cast = conv(dilations = var_6593, groups = var_31, pad = v_133_pad_0, pad_type = v_133_pad_type_0, strides = var_6591, weight = unet_mid_block_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16, x = hidden_states_263_cast)[name = tensor("v_133_cast")]; + tensor var_6597 = const()[name = tensor("op_6597"), val = tensor([2, 20, 64, -1])]; + tensor var_6598_cast = reshape(shape = var_6597, x = q_133_cast)[name = tensor("op_6598_cast")]; + tensor var_6599 = const()[name = tensor("op_6599"), val = tensor([2, 20, 64, -1])]; + tensor var_6600_cast = reshape(shape = var_6599, x = k_133_cast)[name = tensor("op_6600_cast")]; + tensor var_6601 = const()[name = tensor("op_6601"), val = tensor([2, 20, 64, -1])]; + tensor var_6602_cast = reshape(shape = var_6601, x = v_133_cast)[name = tensor("op_6602_cast")]; tensor attn_weights_265_transpose_x_0 = const()[name = tensor("attn_weights_265_transpose_x_0"), val = tensor(true)]; tensor attn_weights_265_transpose_y_0 = const()[name = tensor("attn_weights_265_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_265 = matmul(transpose_x = attn_weights_265_transpose_x_0, transpose_y = attn_weights_265_transpose_y_0, x = var_6678, y = var_6680)[name = tensor("attn_weights_265")]; - tensor attn_weights_267 = mul(x = attn_weights_265, y = var_4941)[name = tensor("attn_weights_267")]; - tensor var_6686 = softmax(axis = var_4934, x = attn_weights_267)[name = tensor("op_6686")]; + tensor attn_weights_265_cast = matmul(transpose_x = attn_weights_265_transpose_x_0, transpose_y = attn_weights_265_transpose_y_0, x = var_6598_cast, y = var_6600_cast)[name = tensor("attn_weights_265_cast")]; + tensor attn_weights_267_cast = mul(x = attn_weights_265_cast, y = var_12_to_fp16)[name = tensor("attn_weights_267_cast")]; + tensor var_6606_cast = softmax(axis = var_18, x = attn_weights_267_cast)[name = tensor("op_6606_cast")]; tensor attn_133_transpose_x_0 = const()[name = tensor("attn_133_transpose_x_0"), val = tensor(false)]; tensor attn_133_transpose_y_0 = const()[name = tensor("attn_133_transpose_y_0"), val = tensor(true)]; - tensor attn_133 = matmul(transpose_x = attn_133_transpose_x_0, transpose_y = attn_133_transpose_y_0, x = var_6682, y = var_6686)[name = tensor("attn_133")]; - tensor var_6690 = const()[name = tensor("op_6690"), val = tensor([2, 1280, 1, -1])]; - tensor input_399 = reshape(shape = var_6690, x = attn_133)[name = tensor("input_399")]; - tensor var_6695 = const()[name = tensor("op_6695"), val = tensor([1, 1])]; - tensor var_6697 = const()[name = tensor("op_6697"), val = tensor([1, 1])]; - tensor var_6699_pad_type_0 = const()[name = tensor("op_6699_pad_type_0"), val = tensor("custom")]; - tensor var_6699_pad_0 = const()[name = tensor("op_6699_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6699 = conv(bias = mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_bias, dilations = var_6697, groups = var_4950, pad = var_6699_pad_0, pad_type = var_6699_pad_type_0, strides = var_6695, weight = mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_weight, x = input_399)[name = tensor("op_6699")]; - tensor inputs_201 = add(x = var_6699, y = inputs_199)[name = tensor("inputs_201")]; - tensor var_6703 = const()[name = tensor("op_6703"), val = tensor([1])]; - tensor channels_mean_201 = reduce_mean(axes = var_6703, keep_dims = var_4945, x = inputs_201)[name = tensor("channels_mean_201")]; - tensor zero_mean_201 = sub(x = inputs_201, y = channels_mean_201)[name = tensor("zero_mean_201")]; - tensor zero_mean_sq_201 = mul(x = zero_mean_201, y = zero_mean_201)[name = tensor("zero_mean_sq_201")]; - tensor var_6707 = const()[name = tensor("op_6707"), val = tensor([1])]; - tensor var_6708 = reduce_mean(axes = var_6707, keep_dims = var_4945, x = zero_mean_sq_201)[name = tensor("op_6708")]; - tensor var_6709 = const()[name = tensor("op_6709"), val = tensor(0x1.4f8b58p-17)]; - tensor var_6710 = add(x = var_6708, y = var_6709)[name = tensor("op_6710")]; - tensor denom_201_epsilon_0 = const()[name = tensor("denom_201_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_201 = rsqrt(epsilon = denom_201_epsilon_0, x = var_6710)[name = tensor("denom_201")]; - tensor out_201 = mul(x = zero_mean_201, y = denom_201)[name = tensor("out_201")]; - tensor var_6714 = const()[name = tensor("op_6714"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268699648)))]; - tensor var_6715 = add(x = out_201, y = var_6714)[name = tensor("op_6715")]; - tensor var_6717 = const()[name = tensor("op_6717"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268704832)))]; - tensor hidden_states_265 = mul(x = var_6715, y = var_6717)[name = tensor("hidden_states_265")]; - tensor var_6724 = const()[name = tensor("op_6724"), val = tensor([1, 1])]; - tensor var_6726 = const()[name = tensor("op_6726"), val = tensor([1, 1])]; + tensor attn_133_cast = matmul(transpose_x = attn_133_transpose_x_0, transpose_y = attn_133_transpose_y_0, x = var_6602_cast, y = var_6606_cast)[name = tensor("attn_133_cast")]; + tensor var_6610 = const()[name = tensor("op_6610"), val = tensor([2, 1280, 1, -1])]; + tensor input_399_cast = reshape(shape = var_6610, x = attn_133_cast)[name = tensor("input_399_cast")]; + tensor var_6615 = const()[name = tensor("op_6615"), val = tensor([1, 1])]; + tensor var_6617 = const()[name = tensor("op_6617"), val = tensor([1, 1])]; + tensor var_6619_pad_type_0 = const()[name = tensor("op_6619_pad_type_0"), val = tensor("custom")]; + tensor var_6619_pad_0 = const()[name = tensor("op_6619_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2361561536)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2364838400)))]; + tensor var_6619_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_6617, groups = var_31, pad = var_6619_pad_0, pad_type = var_6619_pad_type_0, strides = var_6615, weight = unet_mid_block_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16, x = input_399_cast)[name = tensor("op_6619_cast")]; + tensor inputs_201_cast = add(x = var_6619_cast, y = inputs_199_cast)[name = tensor("inputs_201_cast")]; + tensor var_6623 = const()[name = tensor("op_6623"), val = tensor([1])]; + tensor channels_mean_201_cast = reduce_mean(axes = var_6623, keep_dims = var_23, x = inputs_201_cast)[name = tensor("channels_mean_201_cast")]; + tensor zero_mean_201_cast = sub(x = inputs_201_cast, y = channels_mean_201_cast)[name = tensor("zero_mean_201_cast")]; + tensor zero_mean_sq_201_cast = mul(x = zero_mean_201_cast, y = zero_mean_201_cast)[name = tensor("zero_mean_sq_201_cast")]; + tensor var_6627 = const()[name = tensor("op_6627"), val = tensor([1])]; + tensor var_6628_cast = reduce_mean(axes = var_6627, keep_dims = var_23, x = zero_mean_sq_201_cast)[name = tensor("op_6628_cast")]; + tensor var_6629_to_fp16 = const()[name = tensor("op_6629_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6630_cast = add(x = var_6628_cast, y = var_6629_to_fp16)[name = tensor("op_6630_cast")]; + tensor denom_201_epsilon_0_to_fp16 = const()[name = tensor("denom_201_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_201_cast = rsqrt(epsilon = denom_201_epsilon_0_to_fp16, x = var_6630_cast)[name = tensor("denom_201_cast")]; + tensor out_201_cast = mul(x = zero_mean_201_cast, y = denom_201_cast)[name = tensor("out_201_cast")]; + tensor var_6634_to_fp16 = const()[name = tensor("op_6634_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2364841024)))]; + tensor var_6635_cast = add(x = out_201_cast, y = var_6634_to_fp16)[name = tensor("op_6635_cast")]; + tensor var_6637_to_fp16 = const()[name = tensor("op_6637_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2364843648)))]; + tensor hidden_states_265_cast = mul(x = var_6635_cast, y = var_6637_to_fp16)[name = tensor("hidden_states_265_cast")]; + tensor var_6644 = const()[name = tensor("op_6644"), val = tensor([1, 1])]; + tensor var_6646 = const()[name = tensor("op_6646"), val = tensor([1, 1])]; tensor q_135_pad_type_0 = const()[name = tensor("q_135_pad_type_0"), val = tensor("custom")]; tensor q_135_pad_0 = const()[name = tensor("q_135_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_135 = conv(dilations = var_6726, groups = var_4950, pad = q_135_pad_0, pad_type = q_135_pad_type_0, strides = var_6724, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_q_weight, x = hidden_states_265)[name = tensor("q_135")]; - tensor var_6730 = const()[name = tensor("op_6730"), val = tensor([1, 1])]; - tensor var_6732 = const()[name = tensor("op_6732"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2364846272)))]; + tensor q_135_cast = conv(dilations = var_6646, groups = var_31, pad = q_135_pad_0, pad_type = q_135_pad_type_0, strides = var_6644, weight = unet_mid_block_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16, x = hidden_states_265_cast)[name = tensor("q_135_cast")]; + tensor var_6650 = const()[name = tensor("op_6650"), val = tensor([1, 1])]; + tensor var_6652 = const()[name = tensor("op_6652"), val = tensor([1, 1])]; tensor k_135_pad_type_0 = const()[name = tensor("k_135_pad_type_0"), val = tensor("custom")]; tensor k_135_pad_0 = const()[name = tensor("k_135_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_135 = conv(dilations = var_6732, groups = var_4950, pad = k_135_pad_0, pad_type = k_135_pad_type_0, strides = var_6730, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_135")]; - tensor var_6736 = const()[name = tensor("op_6736"), val = tensor([1, 1])]; - tensor var_6738 = const()[name = tensor("op_6738"), val = tensor([1, 1])]; + tensor unet_mid_block_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2368123136)))]; + tensor k_135_cast = conv(dilations = var_6652, groups = var_31, pad = k_135_pad_0, pad_type = k_135_pad_type_0, strides = var_6650, weight = unet_mid_block_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_135_cast")]; + tensor var_6656 = const()[name = tensor("op_6656"), val = tensor([1, 1])]; + tensor var_6658 = const()[name = tensor("op_6658"), val = tensor([1, 1])]; tensor v_135_pad_type_0 = const()[name = tensor("v_135_pad_type_0"), val = tensor("custom")]; tensor v_135_pad_0 = const()[name = tensor("v_135_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_135 = conv(dilations = var_6738, groups = var_4950, pad = v_135_pad_0, pad_type = v_135_pad_type_0, strides = var_6736, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_135")]; - tensor var_6742 = const()[name = tensor("op_6742"), val = tensor([2, 20, 64, -1])]; - tensor var_6743 = reshape(shape = var_6742, x = q_135)[name = tensor("op_6743")]; - tensor var_6744 = const()[name = tensor("op_6744"), val = tensor([2, 20, 64, -1])]; - tensor var_6745 = reshape(shape = var_6744, x = k_135)[name = tensor("op_6745")]; - tensor var_6746 = const()[name = tensor("op_6746"), val = tensor([2, 20, 64, -1])]; - tensor var_6747 = reshape(shape = var_6746, x = v_135)[name = tensor("op_6747")]; + tensor unet_mid_block_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2373366080)))]; + tensor v_135_cast = conv(dilations = var_6658, groups = var_31, pad = v_135_pad_0, pad_type = v_135_pad_type_0, strides = var_6656, weight = unet_mid_block_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_135_cast")]; + tensor var_6662 = const()[name = tensor("op_6662"), val = tensor([2, 20, 64, -1])]; + tensor var_6663_cast = reshape(shape = var_6662, x = q_135_cast)[name = tensor("op_6663_cast")]; + tensor var_6664 = const()[name = tensor("op_6664"), val = tensor([2, 20, 64, -1])]; + tensor var_6665_cast = reshape(shape = var_6664, x = k_135_cast)[name = tensor("op_6665_cast")]; + tensor var_6666 = const()[name = tensor("op_6666"), val = tensor([2, 20, 64, -1])]; + tensor var_6667_cast = reshape(shape = var_6666, x = v_135_cast)[name = tensor("op_6667_cast")]; tensor attn_weights_269_transpose_x_0 = const()[name = tensor("attn_weights_269_transpose_x_0"), val = tensor(true)]; tensor attn_weights_269_transpose_y_0 = const()[name = tensor("attn_weights_269_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_269 = matmul(transpose_x = attn_weights_269_transpose_x_0, transpose_y = attn_weights_269_transpose_y_0, x = var_6743, y = var_6745)[name = tensor("attn_weights_269")]; - tensor attn_weights_271 = mul(x = attn_weights_269, y = var_4941)[name = tensor("attn_weights_271")]; - tensor var_6751 = softmax(axis = var_4934, x = attn_weights_271)[name = tensor("op_6751")]; + tensor attn_weights_269_cast = matmul(transpose_x = attn_weights_269_transpose_x_0, transpose_y = attn_weights_269_transpose_y_0, x = var_6663_cast, y = var_6665_cast)[name = tensor("attn_weights_269_cast")]; + tensor attn_weights_271_cast = mul(x = attn_weights_269_cast, y = var_12_to_fp16)[name = tensor("attn_weights_271_cast")]; + tensor var_6671_cast = softmax(axis = var_18, x = attn_weights_271_cast)[name = tensor("op_6671_cast")]; tensor attn_135_transpose_x_0 = const()[name = tensor("attn_135_transpose_x_0"), val = tensor(false)]; tensor attn_135_transpose_y_0 = const()[name = tensor("attn_135_transpose_y_0"), val = tensor(true)]; - tensor attn_135 = matmul(transpose_x = attn_135_transpose_x_0, transpose_y = attn_135_transpose_y_0, x = var_6747, y = var_6751)[name = tensor("attn_135")]; - tensor var_6755 = const()[name = tensor("op_6755"), val = tensor([2, 1280, 1, -1])]; - tensor input_401 = reshape(shape = var_6755, x = attn_135)[name = tensor("input_401")]; - tensor var_6760 = const()[name = tensor("op_6760"), val = tensor([1, 1])]; - tensor var_6762 = const()[name = tensor("op_6762"), val = tensor([1, 1])]; - tensor var_6764_pad_type_0 = const()[name = tensor("op_6764_pad_type_0"), val = tensor("custom")]; - tensor var_6764_pad_0 = const()[name = tensor("op_6764_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6764 = conv(bias = mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_bias, dilations = var_6762, groups = var_4950, pad = var_6764_pad_0, pad_type = var_6764_pad_type_0, strides = var_6760, weight = mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_weight, x = input_401)[name = tensor("op_6764")]; - tensor inputs_203 = add(x = var_6764, y = inputs_201)[name = tensor("inputs_203")]; - tensor var_6768 = const()[name = tensor("op_6768"), val = tensor([1])]; - tensor channels_mean_203 = reduce_mean(axes = var_6768, keep_dims = var_4945, x = inputs_203)[name = tensor("channels_mean_203")]; - tensor zero_mean_203 = sub(x = inputs_203, y = channels_mean_203)[name = tensor("zero_mean_203")]; - tensor zero_mean_sq_203 = mul(x = zero_mean_203, y = zero_mean_203)[name = tensor("zero_mean_sq_203")]; - tensor var_6772 = const()[name = tensor("op_6772"), val = tensor([1])]; - tensor var_6773 = reduce_mean(axes = var_6772, keep_dims = var_4945, x = zero_mean_sq_203)[name = tensor("op_6773")]; - tensor var_6774 = const()[name = tensor("op_6774"), val = tensor(0x1.4f8b58p-17)]; - tensor var_6775 = add(x = var_6773, y = var_6774)[name = tensor("op_6775")]; - tensor denom_203_epsilon_0 = const()[name = tensor("denom_203_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_203 = rsqrt(epsilon = denom_203_epsilon_0, x = var_6775)[name = tensor("denom_203")]; - tensor out_203 = mul(x = zero_mean_203, y = denom_203)[name = tensor("out_203")]; - tensor var_6779 = const()[name = tensor("op_6779"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268710016)))]; - tensor var_6780 = add(x = out_203, y = var_6779)[name = tensor("op_6780")]; - tensor var_6782 = const()[name = tensor("op_6782"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268715200)))]; - tensor input_403 = mul(x = var_6780, y = var_6782)[name = tensor("input_403")]; - tensor var_6790 = const()[name = tensor("op_6790"), val = tensor([1, 1])]; - tensor var_6792 = const()[name = tensor("op_6792"), val = tensor([1, 1])]; - tensor var_6794_pad_type_0 = const()[name = tensor("op_6794_pad_type_0"), val = tensor("custom")]; - tensor var_6794_pad_0 = const()[name = tensor("op_6794_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6794 = conv(bias = mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_bias, dilations = var_6792, groups = var_4950, pad = var_6794_pad_0, pad_type = var_6794_pad_type_0, strides = var_6790, weight = mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight, x = input_403)[name = tensor("op_6794")]; - tensor var_6795_split_sizes_0 = const()[name = tensor("op_6795_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_6795_axis_0 = const()[name = tensor("op_6795_axis_0"), val = tensor(1)]; - tensor var_6795_0, tensor var_6795_1 = split(axis = var_6795_axis_0, split_sizes = var_6795_split_sizes_0, x = var_6794)[name = tensor("op_6795")]; - tensor var_6797_mode_0 = const()[name = tensor("op_6797_mode_0"), val = tensor("EXACT")]; - tensor var_6797 = gelu(mode = var_6797_mode_0, x = var_6795_1)[name = tensor("op_6797")]; - tensor input_405 = mul(x = var_6795_0, y = var_6797)[name = tensor("input_405")]; - tensor var_6801 = const()[name = tensor("op_6801"), val = tensor([1, 1])]; - tensor var_6803 = const()[name = tensor("op_6803"), val = tensor([1, 1])]; - tensor var_6805_pad_type_0 = const()[name = tensor("op_6805_pad_type_0"), val = tensor("custom")]; - tensor var_6805_pad_0 = const()[name = tensor("op_6805_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_6805 = conv(bias = mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias, dilations = var_6803, groups = var_4950, pad = var_6805_pad_0, pad_type = var_6805_pad_type_0, strides = var_6801, weight = mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight, x = input_405)[name = tensor("op_6805")]; - tensor hidden_states_269 = add(x = var_6805, y = inputs_203)[name = tensor("hidden_states_269")]; - tensor var_6807 = const()[name = tensor("op_6807"), val = tensor([2, 1280, 32, 32])]; - tensor input_407 = reshape(shape = var_6807, x = hidden_states_269)[name = tensor("input_407")]; - tensor var_6811 = const()[name = tensor("op_6811"), val = tensor([1, 1])]; - tensor var_6813 = const()[name = tensor("op_6813"), val = tensor([1, 1])]; + tensor attn_135_cast = matmul(transpose_x = attn_135_transpose_x_0, transpose_y = attn_135_transpose_y_0, x = var_6667_cast, y = var_6671_cast)[name = tensor("attn_135_cast")]; + tensor var_6675 = const()[name = tensor("op_6675"), val = tensor([2, 1280, 1, -1])]; + tensor input_401_cast = reshape(shape = var_6675, x = attn_135_cast)[name = tensor("input_401_cast")]; + tensor var_6680 = const()[name = tensor("op_6680"), val = tensor([1, 1])]; + tensor var_6682 = const()[name = tensor("op_6682"), val = tensor([1, 1])]; + tensor var_6684_pad_type_0 = const()[name = tensor("op_6684_pad_type_0"), val = tensor("custom")]; + tensor var_6684_pad_0 = const()[name = tensor("op_6684_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2378609024)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2381885888)))]; + tensor var_6684_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_6682, groups = var_31, pad = var_6684_pad_0, pad_type = var_6684_pad_type_0, strides = var_6680, weight = unet_mid_block_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16, x = input_401_cast)[name = tensor("op_6684_cast")]; + tensor inputs_203_cast = add(x = var_6684_cast, y = inputs_201_cast)[name = tensor("inputs_203_cast")]; + tensor var_6688 = const()[name = tensor("op_6688"), val = tensor([1])]; + tensor channels_mean_203_cast = reduce_mean(axes = var_6688, keep_dims = var_23, x = inputs_203_cast)[name = tensor("channels_mean_203_cast")]; + tensor zero_mean_203_cast = sub(x = inputs_203_cast, y = channels_mean_203_cast)[name = tensor("zero_mean_203_cast")]; + tensor zero_mean_sq_203_cast = mul(x = zero_mean_203_cast, y = zero_mean_203_cast)[name = tensor("zero_mean_sq_203_cast")]; + tensor var_6692 = const()[name = tensor("op_6692"), val = tensor([1])]; + tensor var_6693_cast = reduce_mean(axes = var_6692, keep_dims = var_23, x = zero_mean_sq_203_cast)[name = tensor("op_6693_cast")]; + tensor var_6694_to_fp16 = const()[name = tensor("op_6694_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6695_cast = add(x = var_6693_cast, y = var_6694_to_fp16)[name = tensor("op_6695_cast")]; + tensor denom_203_epsilon_0_to_fp16 = const()[name = tensor("denom_203_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_203_cast = rsqrt(epsilon = denom_203_epsilon_0_to_fp16, x = var_6695_cast)[name = tensor("denom_203_cast")]; + tensor out_203_cast = mul(x = zero_mean_203_cast, y = denom_203_cast)[name = tensor("out_203_cast")]; + tensor var_6699_to_fp16 = const()[name = tensor("op_6699_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2381888512)))]; + tensor var_6700_cast = add(x = out_203_cast, y = var_6699_to_fp16)[name = tensor("op_6700_cast")]; + tensor var_6702_to_fp16 = const()[name = tensor("op_6702_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2381891136)))]; + tensor input_403_cast = mul(x = var_6700_cast, y = var_6702_to_fp16)[name = tensor("input_403_cast")]; + tensor var_6710 = const()[name = tensor("op_6710"), val = tensor([1, 1])]; + tensor var_6712 = const()[name = tensor("op_6712"), val = tensor([1, 1])]; + tensor var_6714_pad_type_0 = const()[name = tensor("op_6714_pad_type_0"), val = tensor("custom")]; + tensor var_6714_pad_0 = const()[name = tensor("op_6714_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2381893760)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2408108224)))]; + tensor var_6714_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_6712, groups = var_31, pad = var_6714_pad_0, pad_type = var_6714_pad_type_0, strides = var_6710, weight = unet_mid_block_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16, x = input_403_cast)[name = tensor("op_6714_cast")]; + tensor var_6715_split_sizes_0 = const()[name = tensor("op_6715_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_6715_axis_0 = const()[name = tensor("op_6715_axis_0"), val = tensor(1)]; + tensor var_6715_cast_0, tensor var_6715_cast_1 = split(axis = var_6715_axis_0, split_sizes = var_6715_split_sizes_0, x = var_6714_cast)[name = tensor("op_6715_cast")]; + tensor var_6717_mode_0 = const()[name = tensor("op_6717_mode_0"), val = tensor("EXACT")]; + tensor var_6717_cast = gelu(mode = var_6717_mode_0, x = var_6715_cast_1)[name = tensor("op_6717_cast")]; + tensor input_405_cast = mul(x = var_6715_cast_0, y = var_6717_cast)[name = tensor("input_405_cast")]; + tensor var_6721 = const()[name = tensor("op_6721"), val = tensor([1, 1])]; + tensor var_6723 = const()[name = tensor("op_6723"), val = tensor([1, 1])]; + tensor var_6725_pad_type_0 = const()[name = tensor("op_6725_pad_type_0"), val = tensor("custom")]; + tensor var_6725_pad_0 = const()[name = tensor("op_6725_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2408128768)))]; + tensor unet_mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2421236032)))]; + tensor var_6725_cast = conv(bias = unet_mid_block_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_6723, groups = var_31, pad = var_6725_pad_0, pad_type = var_6725_pad_type_0, strides = var_6721, weight = unet_mid_block_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16, x = input_405_cast)[name = tensor("op_6725_cast")]; + tensor hidden_states_269_cast = add(x = var_6725_cast, y = inputs_203_cast)[name = tensor("hidden_states_269_cast")]; + tensor var_6727 = const()[name = tensor("op_6727"), val = tensor([2, 1280, 32, 32])]; + tensor input_407_cast = reshape(shape = var_6727, x = hidden_states_269_cast)[name = tensor("input_407_cast")]; + tensor var_6731 = const()[name = tensor("op_6731"), val = tensor([1, 1])]; + tensor var_6733 = const()[name = tensor("op_6733"), val = tensor([1, 1])]; tensor hidden_states_271_pad_type_0 = const()[name = tensor("hidden_states_271_pad_type_0"), val = tensor("custom")]; tensor hidden_states_271_pad_0 = const()[name = tensor("hidden_states_271_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_271 = conv(bias = mid_block_attentions_0_proj_out_bias, dilations = var_6813, groups = var_4950, pad = hidden_states_271_pad_0, pad_type = hidden_states_271_pad_type_0, strides = var_6811, weight = mid_block_attentions_0_proj_out_weight, x = input_407)[name = tensor("hidden_states_271")]; - tensor input_409 = add(x = hidden_states_271, y = hidden_states_205)[name = tensor("input_409")]; + tensor unet_mid_block_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2421238656)))]; + tensor unet_mid_block_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("unet_mid_block_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2424515520)))]; + tensor hidden_states_271_cast = conv(bias = unet_mid_block_attentions_0_proj_out_bias_to_fp16, dilations = var_6733, groups = var_31, pad = hidden_states_271_pad_0, pad_type = hidden_states_271_pad_type_0, strides = var_6731, weight = unet_mid_block_attentions_0_proj_out_weight_to_fp16, x = input_407_cast)[name = tensor("hidden_states_271_cast")]; + tensor input_409_cast = add(x = hidden_states_271_cast, y = hidden_states_205_cast)[name = tensor("input_409_cast")]; tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([2, 32, 40, 32, 32])]; - tensor reshape_76 = reshape(shape = reshape_76_shape_0, x = input_409)[name = tensor("reshape_76")]; + tensor reshape_76_cast = reshape(shape = reshape_76_shape_0, x = input_409_cast)[name = tensor("reshape_76_cast")]; tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_57 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76)[name = tensor("reduce_mean_57")]; - tensor sub_38 = sub(x = reshape_76, y = reduce_mean_57)[name = tensor("sub_38")]; - tensor square_19 = square(x = sub_38)[name = tensor("square_19")]; + tensor reduce_mean_57_cast = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast)[name = tensor("reduce_mean_57_cast")]; + tensor sub_38_cast = sub(x = reshape_76_cast, y = reduce_mean_57_cast)[name = tensor("sub_38_cast")]; + tensor square_19_cast = square(x = sub_38_cast)[name = tensor("square_19_cast")]; tensor reduce_mean_59_axes_0 = const()[name = tensor("reduce_mean_59_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_59_keep_dims_0 = const()[name = tensor("reduce_mean_59_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_59 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19)[name = tensor("reduce_mean_59")]; - tensor add_38_y_0 = const()[name = tensor("add_38_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_38 = add(x = reduce_mean_59, y = add_38_y_0)[name = tensor("add_38")]; - tensor sqrt_19 = sqrt(x = add_38)[name = tensor("sqrt_19")]; - tensor real_div_19 = real_div(x = sub_38, y = sqrt_19)[name = tensor("real_div_19")]; + tensor reduce_mean_59_cast = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast)[name = tensor("reduce_mean_59_cast")]; + tensor add_38_y_0_to_fp16 = const()[name = tensor("add_38_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_38_cast = add(x = reduce_mean_59_cast, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast")]; + tensor sqrt_19_cast = sqrt(x = add_38_cast)[name = tensor("sqrt_19_cast")]; + tensor real_div_19_cast = real_div(x = sub_38_cast, y = sqrt_19_cast)[name = tensor("real_div_19_cast")]; tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([2, 1280, 32, 32])]; - tensor reshape_77 = reshape(shape = reshape_77_shape_0, x = real_div_19)[name = tensor("reshape_77")]; - tensor add_39_gamma_0 = const()[name = tensor("add_39_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268720384)))]; - tensor add_39_beta_0 = const()[name = tensor("add_39_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268725568)))]; - tensor add_39_epsilon_0 = const()[name = tensor("add_39_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_39 = batch_norm(beta = add_39_beta_0, epsilon = add_39_epsilon_0, gamma = add_39_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_77)[name = tensor("add_39")]; - tensor input_413 = silu(x = add_39)[name = tensor("input_413")]; - tensor var_6828 = const()[name = tensor("op_6828"), val = tensor([1, 1])]; - tensor var_6830 = const()[name = tensor("op_6830"), val = tensor([1, 1])]; + tensor reshape_77_cast = reshape(shape = reshape_77_shape_0, x = real_div_19_cast)[name = tensor("reshape_77_cast")]; + tensor add_39_gamma_0_to_fp16 = const()[name = tensor("add_39_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2424518144)))]; + tensor add_39_beta_0_to_fp16 = const()[name = tensor("add_39_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2424520768)))]; + tensor add_39_epsilon_0_to_fp16 = const()[name = tensor("add_39_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_39_cast = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_77_cast)[name = tensor("add_39_cast")]; + tensor input_413_cast = silu(x = add_39_cast)[name = tensor("input_413_cast")]; + tensor var_6748 = const()[name = tensor("op_6748"), val = tensor([1, 1])]; + tensor var_6750 = const()[name = tensor("op_6750"), val = tensor([1, 1])]; tensor hidden_states_273_pad_type_0 = const()[name = tensor("hidden_states_273_pad_type_0"), val = tensor("custom")]; tensor hidden_states_273_pad_0 = const()[name = tensor("hidden_states_273_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_273 = conv(bias = mid_block_resnets_1_conv1_bias, dilations = var_6830, groups = var_4950, pad = hidden_states_273_pad_0, pad_type = hidden_states_273_pad_type_0, strides = var_6828, weight = mid_block_resnets_1_conv1_weight, x = input_413)[name = tensor("hidden_states_273")]; - tensor var_6836 = const()[name = tensor("op_6836"), val = tensor([1, 1])]; - tensor var_6838 = const()[name = tensor("op_6838"), val = tensor([1, 1])]; + tensor unet_mid_block_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("unet_mid_block_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2424523392)))]; + tensor unet_mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("unet_mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2454014656)))]; + tensor hidden_states_273_cast = conv(bias = unet_mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_6750, groups = var_31, pad = hidden_states_273_pad_0, pad_type = hidden_states_273_pad_type_0, strides = var_6748, weight = unet_mid_block_resnets_1_conv1_weight_to_fp16, x = input_413_cast)[name = tensor("hidden_states_273_cast")]; + tensor var_6756 = const()[name = tensor("op_6756"), val = tensor([1, 1])]; + tensor var_6758 = const()[name = tensor("op_6758"), val = tensor([1, 1])]; tensor temb_15_pad_type_0 = const()[name = tensor("temb_15_pad_type_0"), val = tensor("custom")]; tensor temb_15_pad_0 = const()[name = tensor("temb_15_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb_15 = conv(bias = mid_block_resnets_1_time_emb_proj_bias, dilations = var_6838, groups = var_4950, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = var_6836, weight = mid_block_resnets_1_time_emb_proj_weight, x = input_21)[name = tensor("temb_15")]; - tensor input_417 = add(x = hidden_states_273, y = temb_15)[name = tensor("input_417")]; + tensor unet_mid_block_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_mid_block_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2454017280)))]; + tensor unet_mid_block_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_mid_block_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2457294144)))]; + tensor temb_15_cast = conv(bias = unet_mid_block_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_6758, groups = var_31, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = var_6756, weight = unet_mid_block_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_15_cast")]; + tensor input_417_cast = add(x = hidden_states_273_cast, y = temb_15_cast)[name = tensor("input_417_cast")]; tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([2, 32, 40, 32, 32])]; - tensor reshape_80 = reshape(shape = reshape_80_shape_0, x = input_417)[name = tensor("reshape_80")]; + tensor reshape_80_cast = reshape(shape = reshape_80_shape_0, x = input_417_cast)[name = tensor("reshape_80_cast")]; tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_60_keep_dims_0 = const()[name = tensor("reduce_mean_60_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_60 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80)[name = tensor("reduce_mean_60")]; - tensor sub_40 = sub(x = reshape_80, y = reduce_mean_60)[name = tensor("sub_40")]; - tensor square_20 = square(x = sub_40)[name = tensor("square_20")]; + tensor reduce_mean_60_cast = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast)[name = tensor("reduce_mean_60_cast")]; + tensor sub_40_cast = sub(x = reshape_80_cast, y = reduce_mean_60_cast)[name = tensor("sub_40_cast")]; + tensor square_20_cast = square(x = sub_40_cast)[name = tensor("square_20_cast")]; tensor reduce_mean_62_axes_0 = const()[name = tensor("reduce_mean_62_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_62_keep_dims_0 = const()[name = tensor("reduce_mean_62_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_62 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20)[name = tensor("reduce_mean_62")]; - tensor add_40_y_0 = const()[name = tensor("add_40_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_40 = add(x = reduce_mean_62, y = add_40_y_0)[name = tensor("add_40")]; - tensor sqrt_20 = sqrt(x = add_40)[name = tensor("sqrt_20")]; - tensor real_div_20 = real_div(x = sub_40, y = sqrt_20)[name = tensor("real_div_20")]; + tensor reduce_mean_62_cast = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast)[name = tensor("reduce_mean_62_cast")]; + tensor add_40_y_0_to_fp16 = const()[name = tensor("add_40_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_40_cast = add(x = reduce_mean_62_cast, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast")]; + tensor sqrt_20_cast = sqrt(x = add_40_cast)[name = tensor("sqrt_20_cast")]; + tensor real_div_20_cast = real_div(x = sub_40_cast, y = sqrt_20_cast)[name = tensor("real_div_20_cast")]; tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([2, 1280, 32, 32])]; - tensor reshape_81 = reshape(shape = reshape_81_shape_0, x = real_div_20)[name = tensor("reshape_81")]; - tensor add_41_gamma_0 = const()[name = tensor("add_41_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268730752)))]; - tensor add_41_beta_0 = const()[name = tensor("add_41_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268735936)))]; - tensor add_41_epsilon_0 = const()[name = tensor("add_41_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_41 = batch_norm(beta = add_41_beta_0, epsilon = add_41_epsilon_0, gamma = add_41_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_81)[name = tensor("add_41")]; - tensor input_421 = silu(x = add_41)[name = tensor("input_421")]; - tensor var_6848 = const()[name = tensor("op_6848"), val = tensor([1, 1])]; - tensor var_6850 = const()[name = tensor("op_6850"), val = tensor([1, 1])]; + tensor reshape_81_cast = reshape(shape = reshape_81_shape_0, x = real_div_20_cast)[name = tensor("reshape_81_cast")]; + tensor add_41_gamma_0_to_fp16 = const()[name = tensor("add_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2457296768)))]; + tensor add_41_beta_0_to_fp16 = const()[name = tensor("add_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2457299392)))]; + tensor add_41_epsilon_0_to_fp16 = const()[name = tensor("add_41_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_41_cast = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_81_cast)[name = tensor("add_41_cast")]; + tensor input_421_cast = silu(x = add_41_cast)[name = tensor("input_421_cast")]; + tensor var_6768 = const()[name = tensor("op_6768"), val = tensor([1, 1])]; + tensor var_6770 = const()[name = tensor("op_6770"), val = tensor([1, 1])]; tensor hidden_states_275_pad_type_0 = const()[name = tensor("hidden_states_275_pad_type_0"), val = tensor("custom")]; tensor hidden_states_275_pad_0 = const()[name = tensor("hidden_states_275_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_275 = conv(bias = mid_block_resnets_1_conv2_bias, dilations = var_6850, groups = var_4950, pad = hidden_states_275_pad_0, pad_type = hidden_states_275_pad_type_0, strides = var_6848, weight = mid_block_resnets_1_conv2_weight, x = input_421)[name = tensor("hidden_states_275")]; - tensor hidden_states_277 = add(x = input_409, y = hidden_states_275)[name = tensor("hidden_states_277")]; - tensor var_6856 = const()[name = tensor("op_6856"), val = tensor(3)]; - tensor var_6863 = const()[name = tensor("op_6863"), val = tensor(0x1p-3)]; - tensor var_6867 = const()[name = tensor("op_6867"), val = tensor(true)]; - tensor var_6872 = const()[name = tensor("op_6872"), val = tensor(1)]; + tensor unet_mid_block_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("unet_mid_block_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2457302016)))]; + tensor unet_mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("unet_mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2486793280)))]; + tensor hidden_states_275_cast = conv(bias = unet_mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_6770, groups = var_31, pad = hidden_states_275_pad_0, pad_type = hidden_states_275_pad_type_0, strides = var_6768, weight = unet_mid_block_resnets_1_conv2_weight_to_fp16, x = input_421_cast)[name = tensor("hidden_states_275_cast")]; + tensor hidden_states_277_cast = add(x = input_409_cast, y = hidden_states_275_cast)[name = tensor("hidden_states_277_cast")]; tensor input_423_interleave_0 = const()[name = tensor("input_423_interleave_0"), val = tensor(false)]; - tensor input_423 = concat(axis = var_6872, interleave = input_423_interleave_0, values = (hidden_states_277, input_311))[name = tensor("input_423")]; + tensor input_423_cast = concat(axis = var_31, interleave = input_423_interleave_0, values = (hidden_states_277_cast, input_311_cast))[name = tensor("input_423_cast")]; tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([2, 32, 80, 32, 32])]; - tensor reshape_84 = reshape(shape = reshape_84_shape_0, x = input_423)[name = tensor("reshape_84")]; + tensor reshape_84_cast = reshape(shape = reshape_84_shape_0, x = input_423_cast)[name = tensor("reshape_84_cast")]; tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_63 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84)[name = tensor("reduce_mean_63")]; - tensor sub_42 = sub(x = reshape_84, y = reduce_mean_63)[name = tensor("sub_42")]; - tensor square_21 = square(x = sub_42)[name = tensor("square_21")]; + tensor reduce_mean_63_cast = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast)[name = tensor("reduce_mean_63_cast")]; + tensor sub_42_cast = sub(x = reshape_84_cast, y = reduce_mean_63_cast)[name = tensor("sub_42_cast")]; + tensor square_21_cast = square(x = sub_42_cast)[name = tensor("square_21_cast")]; tensor reduce_mean_65_axes_0 = const()[name = tensor("reduce_mean_65_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_65_keep_dims_0 = const()[name = tensor("reduce_mean_65_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_65 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21)[name = tensor("reduce_mean_65")]; - tensor add_42_y_0 = const()[name = tensor("add_42_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_42 = add(x = reduce_mean_65, y = add_42_y_0)[name = tensor("add_42")]; - tensor sqrt_21 = sqrt(x = add_42)[name = tensor("sqrt_21")]; - tensor real_div_21 = real_div(x = sub_42, y = sqrt_21)[name = tensor("real_div_21")]; + tensor reduce_mean_65_cast = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast)[name = tensor("reduce_mean_65_cast")]; + tensor add_42_y_0_to_fp16 = const()[name = tensor("add_42_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_42_cast = add(x = reduce_mean_65_cast, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast")]; + tensor sqrt_21_cast = sqrt(x = add_42_cast)[name = tensor("sqrt_21_cast")]; + tensor real_div_21_cast = real_div(x = sub_42_cast, y = sqrt_21_cast)[name = tensor("real_div_21_cast")]; tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([2, 2560, 32, 32])]; - tensor reshape_85 = reshape(shape = reshape_85_shape_0, x = real_div_21)[name = tensor("reshape_85")]; - tensor add_43_mean_0 = const()[name = tensor("add_43_mean_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268741120)))]; - tensor add_43_variance_0 = const()[name = tensor("add_43_variance_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268751424)))]; - tensor add_43_gamma_0 = const()[name = tensor("add_43_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268761728)))]; - tensor add_43_beta_0 = const()[name = tensor("add_43_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268772032)))]; - tensor add_43_epsilon_0 = const()[name = tensor("add_43_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_43 = batch_norm(beta = add_43_beta_0, epsilon = add_43_epsilon_0, gamma = add_43_gamma_0, mean = add_43_mean_0, variance = add_43_variance_0, x = reshape_85)[name = tensor("add_43")]; - tensor input_427 = silu(x = add_43)[name = tensor("input_427")]; - tensor var_6901 = const()[name = tensor("op_6901"), val = tensor([1, 1])]; - tensor var_6903 = const()[name = tensor("op_6903"), val = tensor([1, 1])]; + tensor reshape_85_cast = reshape(shape = reshape_85_shape_0, x = real_div_21_cast)[name = tensor("reshape_85_cast")]; + tensor add_43_mean_0_to_fp16 = const()[name = tensor("add_43_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2486795904)))]; + tensor add_43_variance_0_to_fp16 = const()[name = tensor("add_43_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2486801088)))]; + tensor add_43_gamma_0_to_fp16 = const()[name = tensor("add_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2486806272)))]; + tensor add_43_beta_0_to_fp16 = const()[name = tensor("add_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2486811456)))]; + tensor add_43_epsilon_0_to_fp16 = const()[name = tensor("add_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_43_cast = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_43_mean_0_to_fp16, variance = add_43_variance_0_to_fp16, x = reshape_85_cast)[name = tensor("add_43_cast")]; + tensor input_427_cast = silu(x = add_43_cast)[name = tensor("input_427_cast")]; + tensor var_6802 = const()[name = tensor("op_6802"), val = tensor([1, 1])]; + tensor var_6804 = const()[name = tensor("op_6804"), val = tensor([1, 1])]; tensor hidden_states_279_pad_type_0 = const()[name = tensor("hidden_states_279_pad_type_0"), val = tensor("custom")]; tensor hidden_states_279_pad_0 = const()[name = tensor("hidden_states_279_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_279 = conv(bias = up_blocks_0_resnets_0_conv1_bias, dilations = var_6903, groups = var_6872, pad = hidden_states_279_pad_0, pad_type = hidden_states_279_pad_type_0, strides = var_6901, weight = up_blocks_0_resnets_0_conv1_weight, x = input_427)[name = tensor("hidden_states_279")]; - tensor var_6909 = const()[name = tensor("op_6909"), val = tensor([1, 1])]; - tensor var_6911 = const()[name = tensor("op_6911"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2486816640)))]; + tensor unet_up_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2545799104)))]; + tensor hidden_states_279_cast = conv(bias = unet_up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_6804, groups = var_31, pad = hidden_states_279_pad_0, pad_type = hidden_states_279_pad_type_0, strides = var_6802, weight = unet_up_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_427_cast)[name = tensor("hidden_states_279_cast")]; + tensor var_6810 = const()[name = tensor("op_6810"), val = tensor([1, 1])]; + tensor var_6812 = const()[name = tensor("op_6812"), val = tensor([1, 1])]; tensor temb_17_pad_type_0 = const()[name = tensor("temb_17_pad_type_0"), val = tensor("custom")]; tensor temb_17_pad_0 = const()[name = tensor("temb_17_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb_17 = conv(bias = up_blocks_0_resnets_0_time_emb_proj_bias, dilations = var_6911, groups = var_6872, pad = temb_17_pad_0, pad_type = temb_17_pad_type_0, strides = var_6909, weight = up_blocks_0_resnets_0_time_emb_proj_weight, x = input_21)[name = tensor("temb_17")]; - tensor input_431 = add(x = hidden_states_279, y = temb_17)[name = tensor("input_431")]; + tensor unet_up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2545801728)))]; + tensor unet_up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2549078592)))]; + tensor temb_17_cast = conv(bias = unet_up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_6812, groups = var_31, pad = temb_17_pad_0, pad_type = temb_17_pad_type_0, strides = var_6810, weight = unet_up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_17_cast")]; + tensor input_431_cast = add(x = hidden_states_279_cast, y = temb_17_cast)[name = tensor("input_431_cast")]; tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([2, 32, 40, 32, 32])]; - tensor reshape_88 = reshape(shape = reshape_88_shape_0, x = input_431)[name = tensor("reshape_88")]; + tensor reshape_88_cast = reshape(shape = reshape_88_shape_0, x = input_431_cast)[name = tensor("reshape_88_cast")]; tensor reduce_mean_66_axes_0 = const()[name = tensor("reduce_mean_66_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_66_keep_dims_0 = const()[name = tensor("reduce_mean_66_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_66 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88)[name = tensor("reduce_mean_66")]; - tensor sub_44 = sub(x = reshape_88, y = reduce_mean_66)[name = tensor("sub_44")]; - tensor square_22 = square(x = sub_44)[name = tensor("square_22")]; + tensor reduce_mean_66_cast = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast)[name = tensor("reduce_mean_66_cast")]; + tensor sub_44_cast = sub(x = reshape_88_cast, y = reduce_mean_66_cast)[name = tensor("sub_44_cast")]; + tensor square_22_cast = square(x = sub_44_cast)[name = tensor("square_22_cast")]; tensor reduce_mean_68_axes_0 = const()[name = tensor("reduce_mean_68_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_68_keep_dims_0 = const()[name = tensor("reduce_mean_68_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_68 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22)[name = tensor("reduce_mean_68")]; - tensor add_44_y_0 = const()[name = tensor("add_44_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_44 = add(x = reduce_mean_68, y = add_44_y_0)[name = tensor("add_44")]; - tensor sqrt_22 = sqrt(x = add_44)[name = tensor("sqrt_22")]; - tensor real_div_22 = real_div(x = sub_44, y = sqrt_22)[name = tensor("real_div_22")]; + tensor reduce_mean_68_cast = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast)[name = tensor("reduce_mean_68_cast")]; + tensor add_44_y_0_to_fp16 = const()[name = tensor("add_44_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_44_cast = add(x = reduce_mean_68_cast, y = add_44_y_0_to_fp16)[name = tensor("add_44_cast")]; + tensor sqrt_22_cast = sqrt(x = add_44_cast)[name = tensor("sqrt_22_cast")]; + tensor real_div_22_cast = real_div(x = sub_44_cast, y = sqrt_22_cast)[name = tensor("real_div_22_cast")]; tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([2, 1280, 32, 32])]; - tensor reshape_89 = reshape(shape = reshape_89_shape_0, x = real_div_22)[name = tensor("reshape_89")]; - tensor add_45_gamma_0 = const()[name = tensor("add_45_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268782336)))]; - tensor add_45_beta_0 = const()[name = tensor("add_45_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268787520)))]; - tensor add_45_epsilon_0 = const()[name = tensor("add_45_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_45 = batch_norm(beta = add_45_beta_0, epsilon = add_45_epsilon_0, gamma = add_45_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_89)[name = tensor("add_45")]; - tensor input_435 = silu(x = add_45)[name = tensor("input_435")]; - tensor var_6921 = const()[name = tensor("op_6921"), val = tensor([1, 1])]; - tensor var_6923 = const()[name = tensor("op_6923"), val = tensor([1, 1])]; + tensor reshape_89_cast = reshape(shape = reshape_89_shape_0, x = real_div_22_cast)[name = tensor("reshape_89_cast")]; + tensor add_45_gamma_0_to_fp16 = const()[name = tensor("add_45_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2549081216)))]; + tensor add_45_beta_0_to_fp16 = const()[name = tensor("add_45_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2549083840)))]; + tensor add_45_epsilon_0_to_fp16 = const()[name = tensor("add_45_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_45_cast = batch_norm(beta = add_45_beta_0_to_fp16, epsilon = add_45_epsilon_0_to_fp16, gamma = add_45_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_89_cast)[name = tensor("add_45_cast")]; + tensor input_435_cast = silu(x = add_45_cast)[name = tensor("input_435_cast")]; + tensor var_6822 = const()[name = tensor("op_6822"), val = tensor([1, 1])]; + tensor var_6824 = const()[name = tensor("op_6824"), val = tensor([1, 1])]; tensor hidden_states_281_pad_type_0 = const()[name = tensor("hidden_states_281_pad_type_0"), val = tensor("custom")]; tensor hidden_states_281_pad_0 = const()[name = tensor("hidden_states_281_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_281 = conv(bias = up_blocks_0_resnets_0_conv2_bias, dilations = var_6923, groups = var_6872, pad = hidden_states_281_pad_0, pad_type = hidden_states_281_pad_type_0, strides = var_6921, weight = up_blocks_0_resnets_0_conv2_weight, x = input_435)[name = tensor("hidden_states_281")]; - tensor var_6928 = const()[name = tensor("op_6928"), val = tensor([1, 1])]; - tensor var_6930 = const()[name = tensor("op_6930"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2549086464)))]; + tensor unet_up_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2578577728)))]; + tensor hidden_states_281_cast = conv(bias = unet_up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_6824, groups = var_31, pad = hidden_states_281_pad_0, pad_type = hidden_states_281_pad_type_0, strides = var_6822, weight = unet_up_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_435_cast)[name = tensor("hidden_states_281_cast")]; + tensor var_6829 = const()[name = tensor("op_6829"), val = tensor([1, 1])]; + tensor var_6831 = const()[name = tensor("op_6831"), val = tensor([1, 1])]; tensor x_5_pad_type_0 = const()[name = tensor("x_5_pad_type_0"), val = tensor("custom")]; tensor x_5_pad_0 = const()[name = tensor("x_5_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor x_5 = conv(bias = up_blocks_0_resnets_0_conv_shortcut_bias, dilations = var_6930, groups = var_6872, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = var_6928, weight = up_blocks_0_resnets_0_conv_shortcut_weight, x = input_423)[name = tensor("x_5")]; - tensor hidden_states_283 = add(x = x_5, y = hidden_states_281)[name = tensor("hidden_states_283")]; + tensor unet_up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2578580352)))]; + tensor unet_up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2585134016)))]; + tensor x_5_cast = conv(bias = unet_up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_6831, groups = var_31, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = var_6829, weight = unet_up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16, x = input_423_cast)[name = tensor("x_5_cast")]; + tensor hidden_states_283_cast = add(x = x_5_cast, y = hidden_states_281_cast)[name = tensor("hidden_states_283_cast")]; tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([2, 32, 40, 32, 32])]; - tensor reshape_92 = reshape(shape = reshape_92_shape_0, x = hidden_states_283)[name = tensor("reshape_92")]; + tensor reshape_92_cast = reshape(shape = reshape_92_shape_0, x = hidden_states_283_cast)[name = tensor("reshape_92_cast")]; tensor reduce_mean_69_axes_0 = const()[name = tensor("reduce_mean_69_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_69_keep_dims_0 = const()[name = tensor("reduce_mean_69_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_69 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92)[name = tensor("reduce_mean_69")]; - tensor sub_46 = sub(x = reshape_92, y = reduce_mean_69)[name = tensor("sub_46")]; - tensor square_23 = square(x = sub_46)[name = tensor("square_23")]; + tensor reduce_mean_69_cast = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast)[name = tensor("reduce_mean_69_cast")]; + tensor sub_46_cast = sub(x = reshape_92_cast, y = reduce_mean_69_cast)[name = tensor("sub_46_cast")]; + tensor square_23_cast = square(x = sub_46_cast)[name = tensor("square_23_cast")]; tensor reduce_mean_71_axes_0 = const()[name = tensor("reduce_mean_71_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_71_keep_dims_0 = const()[name = tensor("reduce_mean_71_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_71 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23)[name = tensor("reduce_mean_71")]; - tensor add_46_y_0 = const()[name = tensor("add_46_y_0"), val = tensor(0x1.0c6f7ap-20)]; - tensor add_46 = add(x = reduce_mean_71, y = add_46_y_0)[name = tensor("add_46")]; - tensor sqrt_23 = sqrt(x = add_46)[name = tensor("sqrt_23")]; - tensor real_div_23 = real_div(x = sub_46, y = sqrt_23)[name = tensor("real_div_23")]; + tensor reduce_mean_71_cast = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast)[name = tensor("reduce_mean_71_cast")]; + tensor add_46_y_0_to_fp16 = const()[name = tensor("add_46_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_46_cast = add(x = reduce_mean_71_cast, y = add_46_y_0_to_fp16)[name = tensor("add_46_cast")]; + tensor sqrt_23_cast = sqrt(x = add_46_cast)[name = tensor("sqrt_23_cast")]; + tensor real_div_23_cast = real_div(x = sub_46_cast, y = sqrt_23_cast)[name = tensor("real_div_23_cast")]; tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([2, 1280, 32, 32])]; - tensor reshape_93 = reshape(shape = reshape_93_shape_0, x = real_div_23)[name = tensor("reshape_93")]; - tensor add_47_gamma_0 = const()[name = tensor("add_47_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268792704)))]; - tensor add_47_beta_0 = const()[name = tensor("add_47_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268797888)))]; - tensor add_47_epsilon_0 = const()[name = tensor("add_47_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_47 = batch_norm(beta = add_47_beta_0, epsilon = add_47_epsilon_0, gamma = add_47_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_93)[name = tensor("add_47")]; - tensor var_6968 = const()[name = tensor("op_6968"), val = tensor([1, 1])]; - tensor var_6970 = const()[name = tensor("op_6970"), val = tensor([1, 1])]; + tensor reshape_93_cast = reshape(shape = reshape_93_shape_0, x = real_div_23_cast)[name = tensor("reshape_93_cast")]; + tensor add_47_gamma_0_to_fp16 = const()[name = tensor("add_47_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2585136640)))]; + tensor add_47_beta_0_to_fp16 = const()[name = tensor("add_47_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2585139264)))]; + tensor add_47_epsilon_0_to_fp16 = const()[name = tensor("add_47_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_47_cast = batch_norm(beta = add_47_beta_0_to_fp16, epsilon = add_47_epsilon_0_to_fp16, gamma = add_47_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_93_cast)[name = tensor("add_47_cast")]; + tensor var_6869 = const()[name = tensor("op_6869"), val = tensor([1, 1])]; + tensor var_6871 = const()[name = tensor("op_6871"), val = tensor([1, 1])]; tensor hidden_states_285_pad_type_0 = const()[name = tensor("hidden_states_285_pad_type_0"), val = tensor("custom")]; tensor hidden_states_285_pad_0 = const()[name = tensor("hidden_states_285_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_285 = conv(bias = up_blocks_0_attentions_0_proj_in_bias, dilations = var_6970, groups = var_6872, pad = hidden_states_285_pad_0, pad_type = hidden_states_285_pad_type_0, strides = var_6968, weight = up_blocks_0_attentions_0_proj_in_weight, x = add_47)[name = tensor("hidden_states_285")]; - tensor var_6975 = const()[name = tensor("op_6975"), val = tensor([2, 1280, 1, 1024])]; - tensor inputs_205 = reshape(shape = var_6975, x = hidden_states_285)[name = tensor("inputs_205")]; - tensor var_6985 = const()[name = tensor("op_6985"), val = tensor([1])]; - tensor channels_mean_205 = reduce_mean(axes = var_6985, keep_dims = var_6867, x = inputs_205)[name = tensor("channels_mean_205")]; - tensor zero_mean_205 = sub(x = inputs_205, y = channels_mean_205)[name = tensor("zero_mean_205")]; - tensor zero_mean_sq_205 = mul(x = zero_mean_205, y = zero_mean_205)[name = tensor("zero_mean_sq_205")]; - tensor var_6989 = const()[name = tensor("op_6989"), val = tensor([1])]; - tensor var_6990 = reduce_mean(axes = var_6989, keep_dims = var_6867, x = zero_mean_sq_205)[name = tensor("op_6990")]; - tensor var_6991 = const()[name = tensor("op_6991"), val = tensor(0x1.4f8b58p-17)]; - tensor var_6992 = add(x = var_6990, y = var_6991)[name = tensor("op_6992")]; - tensor denom_205_epsilon_0 = const()[name = tensor("denom_205_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_205 = rsqrt(epsilon = denom_205_epsilon_0, x = var_6992)[name = tensor("denom_205")]; - tensor out_205 = mul(x = zero_mean_205, y = denom_205)[name = tensor("out_205")]; - tensor var_6996 = const()[name = tensor("op_6996"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268803072)))]; - tensor var_6997 = add(x = out_205, y = var_6996)[name = tensor("op_6997")]; - tensor var_6999 = const()[name = tensor("op_6999"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268808256)))]; - tensor hidden_states_287 = mul(x = var_6997, y = var_6999)[name = tensor("hidden_states_287")]; - tensor var_7006 = const()[name = tensor("op_7006"), val = tensor([1, 1])]; - tensor var_7008 = const()[name = tensor("op_7008"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2585141888)))]; + tensor unet_up_blocks_0_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2588418752)))]; + tensor hidden_states_285_cast = conv(bias = unet_up_blocks_0_attentions_0_proj_in_bias_to_fp16, dilations = var_6871, groups = var_31, pad = hidden_states_285_pad_0, pad_type = hidden_states_285_pad_type_0, strides = var_6869, weight = unet_up_blocks_0_attentions_0_proj_in_weight_to_fp16, x = add_47_cast)[name = tensor("hidden_states_285_cast")]; + tensor var_6876 = const()[name = tensor("op_6876"), val = tensor([2, 1280, 1, 1024])]; + tensor inputs_205_cast = reshape(shape = var_6876, x = hidden_states_285_cast)[name = tensor("inputs_205_cast")]; + tensor var_6886 = const()[name = tensor("op_6886"), val = tensor([1])]; + tensor channels_mean_205_cast = reduce_mean(axes = var_6886, keep_dims = var_23, x = inputs_205_cast)[name = tensor("channels_mean_205_cast")]; + tensor zero_mean_205_cast = sub(x = inputs_205_cast, y = channels_mean_205_cast)[name = tensor("zero_mean_205_cast")]; + tensor zero_mean_sq_205_cast = mul(x = zero_mean_205_cast, y = zero_mean_205_cast)[name = tensor("zero_mean_sq_205_cast")]; + tensor var_6890 = const()[name = tensor("op_6890"), val = tensor([1])]; + tensor var_6891_cast = reduce_mean(axes = var_6890, keep_dims = var_23, x = zero_mean_sq_205_cast)[name = tensor("op_6891_cast")]; + tensor var_6892_to_fp16 = const()[name = tensor("op_6892_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6893_cast = add(x = var_6891_cast, y = var_6892_to_fp16)[name = tensor("op_6893_cast")]; + tensor denom_205_epsilon_0_to_fp16 = const()[name = tensor("denom_205_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_205_cast = rsqrt(epsilon = denom_205_epsilon_0_to_fp16, x = var_6893_cast)[name = tensor("denom_205_cast")]; + tensor out_205_cast = mul(x = zero_mean_205_cast, y = denom_205_cast)[name = tensor("out_205_cast")]; + tensor var_6897_to_fp16 = const()[name = tensor("op_6897_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2588421376)))]; + tensor var_6898_cast = add(x = out_205_cast, y = var_6897_to_fp16)[name = tensor("op_6898_cast")]; + tensor var_6900_to_fp16 = const()[name = tensor("op_6900_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2588424000)))]; + tensor hidden_states_287_cast = mul(x = var_6898_cast, y = var_6900_to_fp16)[name = tensor("hidden_states_287_cast")]; + tensor var_6907 = const()[name = tensor("op_6907"), val = tensor([1, 1])]; + tensor var_6909 = const()[name = tensor("op_6909"), val = tensor([1, 1])]; tensor q_137_pad_type_0 = const()[name = tensor("q_137_pad_type_0"), val = tensor("custom")]; tensor q_137_pad_0 = const()[name = tensor("q_137_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_137 = conv(dilations = var_7008, groups = var_6872, pad = q_137_pad_0, pad_type = q_137_pad_type_0, strides = var_7006, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight, x = hidden_states_287)[name = tensor("q_137")]; - tensor var_7012 = const()[name = tensor("op_7012"), val = tensor([1, 1])]; - tensor var_7014 = const()[name = tensor("op_7014"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2588426624)))]; + tensor q_137_cast = conv(dilations = var_6909, groups = var_31, pad = q_137_pad_0, pad_type = q_137_pad_type_0, strides = var_6907, weight = unet_up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_287_cast)[name = tensor("q_137_cast")]; + tensor var_6913 = const()[name = tensor("op_6913"), val = tensor([1, 1])]; + tensor var_6915 = const()[name = tensor("op_6915"), val = tensor([1, 1])]; tensor k_137_pad_type_0 = const()[name = tensor("k_137_pad_type_0"), val = tensor("custom")]; tensor k_137_pad_0 = const()[name = tensor("k_137_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_137 = conv(dilations = var_7014, groups = var_6872, pad = k_137_pad_0, pad_type = k_137_pad_type_0, strides = var_7012, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight, x = hidden_states_287)[name = tensor("k_137")]; - tensor var_7018 = const()[name = tensor("op_7018"), val = tensor([1, 1])]; - tensor var_7020 = const()[name = tensor("op_7020"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2591703488)))]; + tensor k_137_cast = conv(dilations = var_6915, groups = var_31, pad = k_137_pad_0, pad_type = k_137_pad_type_0, strides = var_6913, weight = unet_up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_287_cast)[name = tensor("k_137_cast")]; + tensor var_6919 = const()[name = tensor("op_6919"), val = tensor([1, 1])]; + tensor var_6921 = const()[name = tensor("op_6921"), val = tensor([1, 1])]; tensor v_137_pad_type_0 = const()[name = tensor("v_137_pad_type_0"), val = tensor("custom")]; tensor v_137_pad_0 = const()[name = tensor("v_137_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_137 = conv(dilations = var_7020, groups = var_6872, pad = v_137_pad_0, pad_type = v_137_pad_type_0, strides = var_7018, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight, x = hidden_states_287)[name = tensor("v_137")]; - tensor var_7024 = const()[name = tensor("op_7024"), val = tensor([2, 20, 64, -1])]; - tensor var_7025 = reshape(shape = var_7024, x = q_137)[name = tensor("op_7025")]; - tensor var_7026 = const()[name = tensor("op_7026"), val = tensor([2, 20, 64, -1])]; - tensor var_7027 = reshape(shape = var_7026, x = k_137)[name = tensor("op_7027")]; - tensor var_7028 = const()[name = tensor("op_7028"), val = tensor([2, 20, 64, -1])]; - tensor var_7029 = reshape(shape = var_7028, x = v_137)[name = tensor("op_7029")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2594980352)))]; + tensor v_137_cast = conv(dilations = var_6921, groups = var_31, pad = v_137_pad_0, pad_type = v_137_pad_type_0, strides = var_6919, weight = unet_up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_287_cast)[name = tensor("v_137_cast")]; + tensor var_6925 = const()[name = tensor("op_6925"), val = tensor([2, 20, 64, -1])]; + tensor var_6926_cast = reshape(shape = var_6925, x = q_137_cast)[name = tensor("op_6926_cast")]; + tensor var_6927 = const()[name = tensor("op_6927"), val = tensor([2, 20, 64, -1])]; + tensor var_6928_cast = reshape(shape = var_6927, x = k_137_cast)[name = tensor("op_6928_cast")]; + tensor var_6929 = const()[name = tensor("op_6929"), val = tensor([2, 20, 64, -1])]; + tensor var_6930_cast = reshape(shape = var_6929, x = v_137_cast)[name = tensor("op_6930_cast")]; tensor attn_weights_273_transpose_x_0 = const()[name = tensor("attn_weights_273_transpose_x_0"), val = tensor(true)]; tensor attn_weights_273_transpose_y_0 = const()[name = tensor("attn_weights_273_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_273 = matmul(transpose_x = attn_weights_273_transpose_x_0, transpose_y = attn_weights_273_transpose_y_0, x = var_7025, y = var_7027)[name = tensor("attn_weights_273")]; - tensor attn_weights_275 = mul(x = attn_weights_273, y = var_6863)[name = tensor("attn_weights_275")]; - tensor var_7033 = softmax(axis = var_6856, x = attn_weights_275)[name = tensor("op_7033")]; + tensor attn_weights_273_cast = matmul(transpose_x = attn_weights_273_transpose_x_0, transpose_y = attn_weights_273_transpose_y_0, x = var_6926_cast, y = var_6928_cast)[name = tensor("attn_weights_273_cast")]; + tensor attn_weights_275_cast = mul(x = attn_weights_273_cast, y = var_12_to_fp16)[name = tensor("attn_weights_275_cast")]; + tensor var_6934_cast = softmax(axis = var_18, x = attn_weights_275_cast)[name = tensor("op_6934_cast")]; tensor attn_137_transpose_x_0 = const()[name = tensor("attn_137_transpose_x_0"), val = tensor(false)]; tensor attn_137_transpose_y_0 = const()[name = tensor("attn_137_transpose_y_0"), val = tensor(true)]; - tensor attn_137 = matmul(transpose_x = attn_137_transpose_x_0, transpose_y = attn_137_transpose_y_0, x = var_7029, y = var_7033)[name = tensor("attn_137")]; - tensor var_7037 = const()[name = tensor("op_7037"), val = tensor([2, 1280, 1, -1])]; - tensor input_439 = reshape(shape = var_7037, x = attn_137)[name = tensor("input_439")]; - tensor var_7042 = const()[name = tensor("op_7042"), val = tensor([1, 1])]; - tensor var_7044 = const()[name = tensor("op_7044"), val = tensor([1, 1])]; - tensor var_7046_pad_type_0 = const()[name = tensor("op_7046_pad_type_0"), val = tensor("custom")]; - tensor var_7046_pad_0 = const()[name = tensor("op_7046_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7046 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias, dilations = var_7044, groups = var_6872, pad = var_7046_pad_0, pad_type = var_7046_pad_type_0, strides = var_7042, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight, x = input_439)[name = tensor("op_7046")]; - tensor inputs_207 = add(x = var_7046, y = inputs_205)[name = tensor("inputs_207")]; - tensor var_7050 = const()[name = tensor("op_7050"), val = tensor([1])]; - tensor channels_mean_207 = reduce_mean(axes = var_7050, keep_dims = var_6867, x = inputs_207)[name = tensor("channels_mean_207")]; - tensor zero_mean_207 = sub(x = inputs_207, y = channels_mean_207)[name = tensor("zero_mean_207")]; - tensor zero_mean_sq_207 = mul(x = zero_mean_207, y = zero_mean_207)[name = tensor("zero_mean_sq_207")]; - tensor var_7054 = const()[name = tensor("op_7054"), val = tensor([1])]; - tensor var_7055 = reduce_mean(axes = var_7054, keep_dims = var_6867, x = zero_mean_sq_207)[name = tensor("op_7055")]; - tensor var_7056 = const()[name = tensor("op_7056"), val = tensor(0x1.4f8b58p-17)]; - tensor var_7057 = add(x = var_7055, y = var_7056)[name = tensor("op_7057")]; - tensor denom_207_epsilon_0 = const()[name = tensor("denom_207_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_207 = rsqrt(epsilon = denom_207_epsilon_0, x = var_7057)[name = tensor("denom_207")]; - tensor out_207 = mul(x = zero_mean_207, y = denom_207)[name = tensor("out_207")]; - tensor var_7061 = const()[name = tensor("op_7061"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268813440)))]; - tensor var_7062 = add(x = out_207, y = var_7061)[name = tensor("op_7062")]; - tensor var_7064 = const()[name = tensor("op_7064"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268818624)))]; - tensor hidden_states_289 = mul(x = var_7062, y = var_7064)[name = tensor("hidden_states_289")]; - tensor var_7071 = const()[name = tensor("op_7071"), val = tensor([1, 1])]; - tensor var_7073 = const()[name = tensor("op_7073"), val = tensor([1, 1])]; + tensor attn_137_cast = matmul(transpose_x = attn_137_transpose_x_0, transpose_y = attn_137_transpose_y_0, x = var_6930_cast, y = var_6934_cast)[name = tensor("attn_137_cast")]; + tensor var_6938 = const()[name = tensor("op_6938"), val = tensor([2, 1280, 1, -1])]; + tensor input_439_cast = reshape(shape = var_6938, x = attn_137_cast)[name = tensor("input_439_cast")]; + tensor var_6943 = const()[name = tensor("op_6943"), val = tensor([1, 1])]; + tensor var_6945 = const()[name = tensor("op_6945"), val = tensor([1, 1])]; + tensor var_6947_pad_type_0 = const()[name = tensor("op_6947_pad_type_0"), val = tensor("custom")]; + tensor var_6947_pad_0 = const()[name = tensor("op_6947_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2598257216)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2601534080)))]; + tensor var_6947_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_6945, groups = var_31, pad = var_6947_pad_0, pad_type = var_6947_pad_type_0, strides = var_6943, weight = unet_up_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_439_cast)[name = tensor("op_6947_cast")]; + tensor inputs_207_cast = add(x = var_6947_cast, y = inputs_205_cast)[name = tensor("inputs_207_cast")]; + tensor var_6951 = const()[name = tensor("op_6951"), val = tensor([1])]; + tensor channels_mean_207_cast = reduce_mean(axes = var_6951, keep_dims = var_23, x = inputs_207_cast)[name = tensor("channels_mean_207_cast")]; + tensor zero_mean_207_cast = sub(x = inputs_207_cast, y = channels_mean_207_cast)[name = tensor("zero_mean_207_cast")]; + tensor zero_mean_sq_207_cast = mul(x = zero_mean_207_cast, y = zero_mean_207_cast)[name = tensor("zero_mean_sq_207_cast")]; + tensor var_6955 = const()[name = tensor("op_6955"), val = tensor([1])]; + tensor var_6956_cast = reduce_mean(axes = var_6955, keep_dims = var_23, x = zero_mean_sq_207_cast)[name = tensor("op_6956_cast")]; + tensor var_6957_to_fp16 = const()[name = tensor("op_6957_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_6958_cast = add(x = var_6956_cast, y = var_6957_to_fp16)[name = tensor("op_6958_cast")]; + tensor denom_207_epsilon_0_to_fp16 = const()[name = tensor("denom_207_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_207_cast = rsqrt(epsilon = denom_207_epsilon_0_to_fp16, x = var_6958_cast)[name = tensor("denom_207_cast")]; + tensor out_207_cast = mul(x = zero_mean_207_cast, y = denom_207_cast)[name = tensor("out_207_cast")]; + tensor var_6962_to_fp16 = const()[name = tensor("op_6962_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2601536704)))]; + tensor var_6963_cast = add(x = out_207_cast, y = var_6962_to_fp16)[name = tensor("op_6963_cast")]; + tensor var_6965_to_fp16 = const()[name = tensor("op_6965_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2601539328)))]; + tensor hidden_states_289_cast = mul(x = var_6963_cast, y = var_6965_to_fp16)[name = tensor("hidden_states_289_cast")]; + tensor var_6972 = const()[name = tensor("op_6972"), val = tensor([1, 1])]; + tensor var_6974 = const()[name = tensor("op_6974"), val = tensor([1, 1])]; tensor q_139_pad_type_0 = const()[name = tensor("q_139_pad_type_0"), val = tensor("custom")]; tensor q_139_pad_0 = const()[name = tensor("q_139_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_139 = conv(dilations = var_7073, groups = var_6872, pad = q_139_pad_0, pad_type = q_139_pad_type_0, strides = var_7071, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight, x = hidden_states_289)[name = tensor("q_139")]; - tensor var_7077 = const()[name = tensor("op_7077"), val = tensor([1, 1])]; - tensor var_7079 = const()[name = tensor("op_7079"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2601541952)))]; + tensor q_139_cast = conv(dilations = var_6974, groups = var_31, pad = q_139_pad_0, pad_type = q_139_pad_type_0, strides = var_6972, weight = unet_up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_289_cast)[name = tensor("q_139_cast")]; + tensor var_6978 = const()[name = tensor("op_6978"), val = tensor([1, 1])]; + tensor var_6980 = const()[name = tensor("op_6980"), val = tensor([1, 1])]; tensor k_139_pad_type_0 = const()[name = tensor("k_139_pad_type_0"), val = tensor("custom")]; tensor k_139_pad_0 = const()[name = tensor("k_139_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_139 = conv(dilations = var_7079, groups = var_6872, pad = k_139_pad_0, pad_type = k_139_pad_type_0, strides = var_7077, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_139")]; - tensor var_7083 = const()[name = tensor("op_7083"), val = tensor([1, 1])]; - tensor var_7085 = const()[name = tensor("op_7085"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2604818816)))]; + tensor k_139_cast = conv(dilations = var_6980, groups = var_31, pad = k_139_pad_0, pad_type = k_139_pad_type_0, strides = var_6978, weight = unet_up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_139_cast")]; + tensor var_6984 = const()[name = tensor("op_6984"), val = tensor([1, 1])]; + tensor var_6986 = const()[name = tensor("op_6986"), val = tensor([1, 1])]; tensor v_139_pad_type_0 = const()[name = tensor("v_139_pad_type_0"), val = tensor("custom")]; tensor v_139_pad_0 = const()[name = tensor("v_139_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_139 = conv(dilations = var_7085, groups = var_6872, pad = v_139_pad_0, pad_type = v_139_pad_type_0, strides = var_7083, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_139")]; - tensor var_7089 = const()[name = tensor("op_7089"), val = tensor([2, 20, 64, -1])]; - tensor var_7090 = reshape(shape = var_7089, x = q_139)[name = tensor("op_7090")]; - tensor var_7091 = const()[name = tensor("op_7091"), val = tensor([2, 20, 64, -1])]; - tensor var_7092 = reshape(shape = var_7091, x = k_139)[name = tensor("op_7092")]; - tensor var_7093 = const()[name = tensor("op_7093"), val = tensor([2, 20, 64, -1])]; - tensor var_7094 = reshape(shape = var_7093, x = v_139)[name = tensor("op_7094")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2610061760)))]; + tensor v_139_cast = conv(dilations = var_6986, groups = var_31, pad = v_139_pad_0, pad_type = v_139_pad_type_0, strides = var_6984, weight = unet_up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_139_cast")]; + tensor var_6990 = const()[name = tensor("op_6990"), val = tensor([2, 20, 64, -1])]; + tensor var_6991_cast = reshape(shape = var_6990, x = q_139_cast)[name = tensor("op_6991_cast")]; + tensor var_6992 = const()[name = tensor("op_6992"), val = tensor([2, 20, 64, -1])]; + tensor var_6993_cast = reshape(shape = var_6992, x = k_139_cast)[name = tensor("op_6993_cast")]; + tensor var_6994 = const()[name = tensor("op_6994"), val = tensor([2, 20, 64, -1])]; + tensor var_6995_cast = reshape(shape = var_6994, x = v_139_cast)[name = tensor("op_6995_cast")]; tensor attn_weights_277_transpose_x_0 = const()[name = tensor("attn_weights_277_transpose_x_0"), val = tensor(true)]; tensor attn_weights_277_transpose_y_0 = const()[name = tensor("attn_weights_277_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_277 = matmul(transpose_x = attn_weights_277_transpose_x_0, transpose_y = attn_weights_277_transpose_y_0, x = var_7090, y = var_7092)[name = tensor("attn_weights_277")]; - tensor attn_weights_279 = mul(x = attn_weights_277, y = var_6863)[name = tensor("attn_weights_279")]; - tensor var_7098 = softmax(axis = var_6856, x = attn_weights_279)[name = tensor("op_7098")]; + tensor attn_weights_277_cast = matmul(transpose_x = attn_weights_277_transpose_x_0, transpose_y = attn_weights_277_transpose_y_0, x = var_6991_cast, y = var_6993_cast)[name = tensor("attn_weights_277_cast")]; + tensor attn_weights_279_cast = mul(x = attn_weights_277_cast, y = var_12_to_fp16)[name = tensor("attn_weights_279_cast")]; + tensor var_6999_cast = softmax(axis = var_18, x = attn_weights_279_cast)[name = tensor("op_6999_cast")]; tensor attn_139_transpose_x_0 = const()[name = tensor("attn_139_transpose_x_0"), val = tensor(false)]; tensor attn_139_transpose_y_0 = const()[name = tensor("attn_139_transpose_y_0"), val = tensor(true)]; - tensor attn_139 = matmul(transpose_x = attn_139_transpose_x_0, transpose_y = attn_139_transpose_y_0, x = var_7094, y = var_7098)[name = tensor("attn_139")]; - tensor var_7102 = const()[name = tensor("op_7102"), val = tensor([2, 1280, 1, -1])]; - tensor input_441 = reshape(shape = var_7102, x = attn_139)[name = tensor("input_441")]; - tensor var_7107 = const()[name = tensor("op_7107"), val = tensor([1, 1])]; - tensor var_7109 = const()[name = tensor("op_7109"), val = tensor([1, 1])]; - tensor var_7111_pad_type_0 = const()[name = tensor("op_7111_pad_type_0"), val = tensor("custom")]; - tensor var_7111_pad_0 = const()[name = tensor("op_7111_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7111 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias, dilations = var_7109, groups = var_6872, pad = var_7111_pad_0, pad_type = var_7111_pad_type_0, strides = var_7107, weight = up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight, x = input_441)[name = tensor("op_7111")]; - tensor inputs_209 = add(x = var_7111, y = inputs_207)[name = tensor("inputs_209")]; - tensor var_7115 = const()[name = tensor("op_7115"), val = tensor([1])]; - tensor channels_mean_209 = reduce_mean(axes = var_7115, keep_dims = var_6867, x = inputs_209)[name = tensor("channels_mean_209")]; - tensor zero_mean_209 = sub(x = inputs_209, y = channels_mean_209)[name = tensor("zero_mean_209")]; - tensor zero_mean_sq_209 = mul(x = zero_mean_209, y = zero_mean_209)[name = tensor("zero_mean_sq_209")]; - tensor var_7119 = const()[name = tensor("op_7119"), val = tensor([1])]; - tensor var_7120 = reduce_mean(axes = var_7119, keep_dims = var_6867, x = zero_mean_sq_209)[name = tensor("op_7120")]; - tensor var_7121 = const()[name = tensor("op_7121"), val = tensor(0x1.4f8b58p-17)]; - tensor var_7122 = add(x = var_7120, y = var_7121)[name = tensor("op_7122")]; - tensor denom_209_epsilon_0 = const()[name = tensor("denom_209_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_209 = rsqrt(epsilon = denom_209_epsilon_0, x = var_7122)[name = tensor("denom_209")]; - tensor out_209 = mul(x = zero_mean_209, y = denom_209)[name = tensor("out_209")]; - tensor var_7126 = const()[name = tensor("op_7126"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268823808)))]; - tensor var_7127 = add(x = out_209, y = var_7126)[name = tensor("op_7127")]; - tensor var_7129 = const()[name = tensor("op_7129"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268828992)))]; - tensor input_443 = mul(x = var_7127, y = var_7129)[name = tensor("input_443")]; - tensor var_7137 = const()[name = tensor("op_7137"), val = tensor([1, 1])]; - tensor var_7139 = const()[name = tensor("op_7139"), val = tensor([1, 1])]; - tensor var_7141_pad_type_0 = const()[name = tensor("op_7141_pad_type_0"), val = tensor("custom")]; - tensor var_7141_pad_0 = const()[name = tensor("op_7141_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7141 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias, dilations = var_7139, groups = var_6872, pad = var_7141_pad_0, pad_type = var_7141_pad_type_0, strides = var_7137, weight = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight, x = input_443)[name = tensor("op_7141")]; - tensor var_7142_split_sizes_0 = const()[name = tensor("op_7142_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_7142_axis_0 = const()[name = tensor("op_7142_axis_0"), val = tensor(1)]; - tensor var_7142_0, tensor var_7142_1 = split(axis = var_7142_axis_0, split_sizes = var_7142_split_sizes_0, x = var_7141)[name = tensor("op_7142")]; - tensor var_7144_mode_0 = const()[name = tensor("op_7144_mode_0"), val = tensor("EXACT")]; - tensor var_7144 = gelu(mode = var_7144_mode_0, x = var_7142_1)[name = tensor("op_7144")]; - tensor input_445 = mul(x = var_7142_0, y = var_7144)[name = tensor("input_445")]; - tensor var_7148 = const()[name = tensor("op_7148"), val = tensor([1, 1])]; - tensor var_7150 = const()[name = tensor("op_7150"), val = tensor([1, 1])]; - tensor var_7152_pad_type_0 = const()[name = tensor("op_7152_pad_type_0"), val = tensor("custom")]; - tensor var_7152_pad_0 = const()[name = tensor("op_7152_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7152 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias, dilations = var_7150, groups = var_6872, pad = var_7152_pad_0, pad_type = var_7152_pad_type_0, strides = var_7148, weight = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight, x = input_445)[name = tensor("op_7152")]; - tensor inputs_211 = add(x = var_7152, y = inputs_209)[name = tensor("inputs_211")]; - tensor var_7162 = const()[name = tensor("op_7162"), val = tensor([1])]; - tensor channels_mean_211 = reduce_mean(axes = var_7162, keep_dims = var_6867, x = inputs_211)[name = tensor("channels_mean_211")]; - tensor zero_mean_211 = sub(x = inputs_211, y = channels_mean_211)[name = tensor("zero_mean_211")]; - tensor zero_mean_sq_211 = mul(x = zero_mean_211, y = zero_mean_211)[name = tensor("zero_mean_sq_211")]; - tensor var_7166 = const()[name = tensor("op_7166"), val = tensor([1])]; - tensor var_7167 = reduce_mean(axes = var_7166, keep_dims = var_6867, x = zero_mean_sq_211)[name = tensor("op_7167")]; - tensor var_7168 = const()[name = tensor("op_7168"), val = tensor(0x1.4f8b58p-17)]; - tensor var_7169 = add(x = var_7167, y = var_7168)[name = tensor("op_7169")]; - tensor denom_211_epsilon_0 = const()[name = tensor("denom_211_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_211 = rsqrt(epsilon = denom_211_epsilon_0, x = var_7169)[name = tensor("denom_211")]; - tensor out_211 = mul(x = zero_mean_211, y = denom_211)[name = tensor("out_211")]; - tensor var_7173 = const()[name = tensor("op_7173"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268834176)))]; - tensor var_7174 = add(x = out_211, y = var_7173)[name = tensor("op_7174")]; - tensor var_7176 = const()[name = tensor("op_7176"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268839360)))]; - tensor hidden_states_293 = mul(x = var_7174, y = var_7176)[name = tensor("hidden_states_293")]; - tensor var_7183 = const()[name = tensor("op_7183"), val = tensor([1, 1])]; - tensor var_7185 = const()[name = tensor("op_7185"), val = tensor([1, 1])]; + tensor attn_139_cast = matmul(transpose_x = attn_139_transpose_x_0, transpose_y = attn_139_transpose_y_0, x = var_6995_cast, y = var_6999_cast)[name = tensor("attn_139_cast")]; + tensor var_7003 = const()[name = tensor("op_7003"), val = tensor([2, 1280, 1, -1])]; + tensor input_441_cast = reshape(shape = var_7003, x = attn_139_cast)[name = tensor("input_441_cast")]; + tensor var_7008 = const()[name = tensor("op_7008"), val = tensor([1, 1])]; + tensor var_7010 = const()[name = tensor("op_7010"), val = tensor([1, 1])]; + tensor var_7012_pad_type_0 = const()[name = tensor("op_7012_pad_type_0"), val = tensor("custom")]; + tensor var_7012_pad_0 = const()[name = tensor("op_7012_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2615304704)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2618581568)))]; + tensor var_7012_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_7010, groups = var_31, pad = var_7012_pad_0, pad_type = var_7012_pad_type_0, strides = var_7008, weight = unet_up_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_441_cast)[name = tensor("op_7012_cast")]; + tensor inputs_209_cast = add(x = var_7012_cast, y = inputs_207_cast)[name = tensor("inputs_209_cast")]; + tensor var_7016 = const()[name = tensor("op_7016"), val = tensor([1])]; + tensor channels_mean_209_cast = reduce_mean(axes = var_7016, keep_dims = var_23, x = inputs_209_cast)[name = tensor("channels_mean_209_cast")]; + tensor zero_mean_209_cast = sub(x = inputs_209_cast, y = channels_mean_209_cast)[name = tensor("zero_mean_209_cast")]; + tensor zero_mean_sq_209_cast = mul(x = zero_mean_209_cast, y = zero_mean_209_cast)[name = tensor("zero_mean_sq_209_cast")]; + tensor var_7020 = const()[name = tensor("op_7020"), val = tensor([1])]; + tensor var_7021_cast = reduce_mean(axes = var_7020, keep_dims = var_23, x = zero_mean_sq_209_cast)[name = tensor("op_7021_cast")]; + tensor var_7022_to_fp16 = const()[name = tensor("op_7022_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7023_cast = add(x = var_7021_cast, y = var_7022_to_fp16)[name = tensor("op_7023_cast")]; + tensor denom_209_epsilon_0_to_fp16 = const()[name = tensor("denom_209_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_209_cast = rsqrt(epsilon = denom_209_epsilon_0_to_fp16, x = var_7023_cast)[name = tensor("denom_209_cast")]; + tensor out_209_cast = mul(x = zero_mean_209_cast, y = denom_209_cast)[name = tensor("out_209_cast")]; + tensor var_7027_to_fp16 = const()[name = tensor("op_7027_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2618584192)))]; + tensor var_7028_cast = add(x = out_209_cast, y = var_7027_to_fp16)[name = tensor("op_7028_cast")]; + tensor var_7030_to_fp16 = const()[name = tensor("op_7030_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2618586816)))]; + tensor input_443_cast = mul(x = var_7028_cast, y = var_7030_to_fp16)[name = tensor("input_443_cast")]; + tensor var_7038 = const()[name = tensor("op_7038"), val = tensor([1, 1])]; + tensor var_7040 = const()[name = tensor("op_7040"), val = tensor([1, 1])]; + tensor var_7042_pad_type_0 = const()[name = tensor("op_7042_pad_type_0"), val = tensor("custom")]; + tensor var_7042_pad_0 = const()[name = tensor("op_7042_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2618589440)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2644803904)))]; + tensor var_7042_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_7040, groups = var_31, pad = var_7042_pad_0, pad_type = var_7042_pad_type_0, strides = var_7038, weight = unet_up_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_443_cast)[name = tensor("op_7042_cast")]; + tensor var_7043_split_sizes_0 = const()[name = tensor("op_7043_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7043_axis_0 = const()[name = tensor("op_7043_axis_0"), val = tensor(1)]; + tensor var_7043_cast_0, tensor var_7043_cast_1 = split(axis = var_7043_axis_0, split_sizes = var_7043_split_sizes_0, x = var_7042_cast)[name = tensor("op_7043_cast")]; + tensor var_7045_mode_0 = const()[name = tensor("op_7045_mode_0"), val = tensor("EXACT")]; + tensor var_7045_cast = gelu(mode = var_7045_mode_0, x = var_7043_cast_1)[name = tensor("op_7045_cast")]; + tensor input_445_cast = mul(x = var_7043_cast_0, y = var_7045_cast)[name = tensor("input_445_cast")]; + tensor var_7049 = const()[name = tensor("op_7049"), val = tensor([1, 1])]; + tensor var_7051 = const()[name = tensor("op_7051"), val = tensor([1, 1])]; + tensor var_7053_pad_type_0 = const()[name = tensor("op_7053_pad_type_0"), val = tensor("custom")]; + tensor var_7053_pad_0 = const()[name = tensor("op_7053_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2644824448)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2657931712)))]; + tensor var_7053_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_7051, groups = var_31, pad = var_7053_pad_0, pad_type = var_7053_pad_type_0, strides = var_7049, weight = unet_up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_445_cast)[name = tensor("op_7053_cast")]; + tensor inputs_211_cast = add(x = var_7053_cast, y = inputs_209_cast)[name = tensor("inputs_211_cast")]; + tensor var_7063 = const()[name = tensor("op_7063"), val = tensor([1])]; + tensor channels_mean_211_cast = reduce_mean(axes = var_7063, keep_dims = var_23, x = inputs_211_cast)[name = tensor("channels_mean_211_cast")]; + tensor zero_mean_211_cast = sub(x = inputs_211_cast, y = channels_mean_211_cast)[name = tensor("zero_mean_211_cast")]; + tensor zero_mean_sq_211_cast = mul(x = zero_mean_211_cast, y = zero_mean_211_cast)[name = tensor("zero_mean_sq_211_cast")]; + tensor var_7067 = const()[name = tensor("op_7067"), val = tensor([1])]; + tensor var_7068_cast = reduce_mean(axes = var_7067, keep_dims = var_23, x = zero_mean_sq_211_cast)[name = tensor("op_7068_cast")]; + tensor var_7069_to_fp16 = const()[name = tensor("op_7069_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7070_cast = add(x = var_7068_cast, y = var_7069_to_fp16)[name = tensor("op_7070_cast")]; + tensor denom_211_epsilon_0_to_fp16 = const()[name = tensor("denom_211_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_211_cast = rsqrt(epsilon = denom_211_epsilon_0_to_fp16, x = var_7070_cast)[name = tensor("denom_211_cast")]; + tensor out_211_cast = mul(x = zero_mean_211_cast, y = denom_211_cast)[name = tensor("out_211_cast")]; + tensor var_7074_to_fp16 = const()[name = tensor("op_7074_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2657934336)))]; + tensor var_7075_cast = add(x = out_211_cast, y = var_7074_to_fp16)[name = tensor("op_7075_cast")]; + tensor var_7077_to_fp16 = const()[name = tensor("op_7077_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2657936960)))]; + tensor hidden_states_293_cast = mul(x = var_7075_cast, y = var_7077_to_fp16)[name = tensor("hidden_states_293_cast")]; + tensor var_7084 = const()[name = tensor("op_7084"), val = tensor([1, 1])]; + tensor var_7086 = const()[name = tensor("op_7086"), val = tensor([1, 1])]; tensor q_141_pad_type_0 = const()[name = tensor("q_141_pad_type_0"), val = tensor("custom")]; tensor q_141_pad_0 = const()[name = tensor("q_141_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_141 = conv(dilations = var_7185, groups = var_6872, pad = q_141_pad_0, pad_type = q_141_pad_type_0, strides = var_7183, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight, x = hidden_states_293)[name = tensor("q_141")]; - tensor var_7189 = const()[name = tensor("op_7189"), val = tensor([1, 1])]; - tensor var_7191 = const()[name = tensor("op_7191"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2657939584)))]; + tensor q_141_cast = conv(dilations = var_7086, groups = var_31, pad = q_141_pad_0, pad_type = q_141_pad_type_0, strides = var_7084, weight = unet_up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_293_cast)[name = tensor("q_141_cast")]; + tensor var_7090 = const()[name = tensor("op_7090"), val = tensor([1, 1])]; + tensor var_7092 = const()[name = tensor("op_7092"), val = tensor([1, 1])]; tensor k_141_pad_type_0 = const()[name = tensor("k_141_pad_type_0"), val = tensor("custom")]; tensor k_141_pad_0 = const()[name = tensor("k_141_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_141 = conv(dilations = var_7191, groups = var_6872, pad = k_141_pad_0, pad_type = k_141_pad_type_0, strides = var_7189, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight, x = hidden_states_293)[name = tensor("k_141")]; - tensor var_7195 = const()[name = tensor("op_7195"), val = tensor([1, 1])]; - tensor var_7197 = const()[name = tensor("op_7197"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2661216448)))]; + tensor k_141_cast = conv(dilations = var_7092, groups = var_31, pad = k_141_pad_0, pad_type = k_141_pad_type_0, strides = var_7090, weight = unet_up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_293_cast)[name = tensor("k_141_cast")]; + tensor var_7096 = const()[name = tensor("op_7096"), val = tensor([1, 1])]; + tensor var_7098 = const()[name = tensor("op_7098"), val = tensor([1, 1])]; tensor v_141_pad_type_0 = const()[name = tensor("v_141_pad_type_0"), val = tensor("custom")]; tensor v_141_pad_0 = const()[name = tensor("v_141_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_141 = conv(dilations = var_7197, groups = var_6872, pad = v_141_pad_0, pad_type = v_141_pad_type_0, strides = var_7195, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight, x = hidden_states_293)[name = tensor("v_141")]; - tensor var_7201 = const()[name = tensor("op_7201"), val = tensor([2, 20, 64, -1])]; - tensor var_7202 = reshape(shape = var_7201, x = q_141)[name = tensor("op_7202")]; - tensor var_7203 = const()[name = tensor("op_7203"), val = tensor([2, 20, 64, -1])]; - tensor var_7204 = reshape(shape = var_7203, x = k_141)[name = tensor("op_7204")]; - tensor var_7205 = const()[name = tensor("op_7205"), val = tensor([2, 20, 64, -1])]; - tensor var_7206 = reshape(shape = var_7205, x = v_141)[name = tensor("op_7206")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2664493312)))]; + tensor v_141_cast = conv(dilations = var_7098, groups = var_31, pad = v_141_pad_0, pad_type = v_141_pad_type_0, strides = var_7096, weight = unet_up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_293_cast)[name = tensor("v_141_cast")]; + tensor var_7102 = const()[name = tensor("op_7102"), val = tensor([2, 20, 64, -1])]; + tensor var_7103_cast = reshape(shape = var_7102, x = q_141_cast)[name = tensor("op_7103_cast")]; + tensor var_7104 = const()[name = tensor("op_7104"), val = tensor([2, 20, 64, -1])]; + tensor var_7105_cast = reshape(shape = var_7104, x = k_141_cast)[name = tensor("op_7105_cast")]; + tensor var_7106 = const()[name = tensor("op_7106"), val = tensor([2, 20, 64, -1])]; + tensor var_7107_cast = reshape(shape = var_7106, x = v_141_cast)[name = tensor("op_7107_cast")]; tensor attn_weights_281_transpose_x_0 = const()[name = tensor("attn_weights_281_transpose_x_0"), val = tensor(true)]; tensor attn_weights_281_transpose_y_0 = const()[name = tensor("attn_weights_281_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_281 = matmul(transpose_x = attn_weights_281_transpose_x_0, transpose_y = attn_weights_281_transpose_y_0, x = var_7202, y = var_7204)[name = tensor("attn_weights_281")]; - tensor attn_weights_283 = mul(x = attn_weights_281, y = var_6863)[name = tensor("attn_weights_283")]; - tensor var_7210 = softmax(axis = var_6856, x = attn_weights_283)[name = tensor("op_7210")]; + tensor attn_weights_281_cast = matmul(transpose_x = attn_weights_281_transpose_x_0, transpose_y = attn_weights_281_transpose_y_0, x = var_7103_cast, y = var_7105_cast)[name = tensor("attn_weights_281_cast")]; + tensor attn_weights_283_cast = mul(x = attn_weights_281_cast, y = var_12_to_fp16)[name = tensor("attn_weights_283_cast")]; + tensor var_7111_cast = softmax(axis = var_18, x = attn_weights_283_cast)[name = tensor("op_7111_cast")]; tensor attn_141_transpose_x_0 = const()[name = tensor("attn_141_transpose_x_0"), val = tensor(false)]; tensor attn_141_transpose_y_0 = const()[name = tensor("attn_141_transpose_y_0"), val = tensor(true)]; - tensor attn_141 = matmul(transpose_x = attn_141_transpose_x_0, transpose_y = attn_141_transpose_y_0, x = var_7206, y = var_7210)[name = tensor("attn_141")]; - tensor var_7214 = const()[name = tensor("op_7214"), val = tensor([2, 1280, 1, -1])]; - tensor input_447 = reshape(shape = var_7214, x = attn_141)[name = tensor("input_447")]; - tensor var_7219 = const()[name = tensor("op_7219"), val = tensor([1, 1])]; - tensor var_7221 = const()[name = tensor("op_7221"), val = tensor([1, 1])]; - tensor var_7223_pad_type_0 = const()[name = tensor("op_7223_pad_type_0"), val = tensor("custom")]; - tensor var_7223_pad_0 = const()[name = tensor("op_7223_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7223 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias, dilations = var_7221, groups = var_6872, pad = var_7223_pad_0, pad_type = var_7223_pad_type_0, strides = var_7219, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight, x = input_447)[name = tensor("op_7223")]; - tensor inputs_213 = add(x = var_7223, y = inputs_211)[name = tensor("inputs_213")]; - tensor var_7227 = const()[name = tensor("op_7227"), val = tensor([1])]; - tensor channels_mean_213 = reduce_mean(axes = var_7227, keep_dims = var_6867, x = inputs_213)[name = tensor("channels_mean_213")]; - tensor zero_mean_213 = sub(x = inputs_213, y = channels_mean_213)[name = tensor("zero_mean_213")]; - tensor zero_mean_sq_213 = mul(x = zero_mean_213, y = zero_mean_213)[name = tensor("zero_mean_sq_213")]; - tensor var_7231 = const()[name = tensor("op_7231"), val = tensor([1])]; - tensor var_7232 = reduce_mean(axes = var_7231, keep_dims = var_6867, x = zero_mean_sq_213)[name = tensor("op_7232")]; - tensor var_7233 = const()[name = tensor("op_7233"), val = tensor(0x1.4f8b58p-17)]; - tensor var_7234 = add(x = var_7232, y = var_7233)[name = tensor("op_7234")]; - tensor denom_213_epsilon_0 = const()[name = tensor("denom_213_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_213 = rsqrt(epsilon = denom_213_epsilon_0, x = var_7234)[name = tensor("denom_213")]; - tensor out_213 = mul(x = zero_mean_213, y = denom_213)[name = tensor("out_213")]; - tensor var_7238 = const()[name = tensor("op_7238"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268844544)))]; - tensor var_7239 = add(x = out_213, y = var_7238)[name = tensor("op_7239")]; - tensor var_7241 = const()[name = tensor("op_7241"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268849728)))]; - tensor hidden_states_295 = mul(x = var_7239, y = var_7241)[name = tensor("hidden_states_295")]; - tensor var_7248 = const()[name = tensor("op_7248"), val = tensor([1, 1])]; - tensor var_7250 = const()[name = tensor("op_7250"), val = tensor([1, 1])]; + tensor attn_141_cast = matmul(transpose_x = attn_141_transpose_x_0, transpose_y = attn_141_transpose_y_0, x = var_7107_cast, y = var_7111_cast)[name = tensor("attn_141_cast")]; + tensor var_7115 = const()[name = tensor("op_7115"), val = tensor([2, 1280, 1, -1])]; + tensor input_447_cast = reshape(shape = var_7115, x = attn_141_cast)[name = tensor("input_447_cast")]; + tensor var_7120 = const()[name = tensor("op_7120"), val = tensor([1, 1])]; + tensor var_7122 = const()[name = tensor("op_7122"), val = tensor([1, 1])]; + tensor var_7124_pad_type_0 = const()[name = tensor("op_7124_pad_type_0"), val = tensor("custom")]; + tensor var_7124_pad_0 = const()[name = tensor("op_7124_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2667770176)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2671047040)))]; + tensor var_7124_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_7122, groups = var_31, pad = var_7124_pad_0, pad_type = var_7124_pad_type_0, strides = var_7120, weight = unet_up_blocks_0_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_447_cast)[name = tensor("op_7124_cast")]; + tensor inputs_213_cast = add(x = var_7124_cast, y = inputs_211_cast)[name = tensor("inputs_213_cast")]; + tensor var_7128 = const()[name = tensor("op_7128"), val = tensor([1])]; + tensor channels_mean_213_cast = reduce_mean(axes = var_7128, keep_dims = var_23, x = inputs_213_cast)[name = tensor("channels_mean_213_cast")]; + tensor zero_mean_213_cast = sub(x = inputs_213_cast, y = channels_mean_213_cast)[name = tensor("zero_mean_213_cast")]; + tensor zero_mean_sq_213_cast = mul(x = zero_mean_213_cast, y = zero_mean_213_cast)[name = tensor("zero_mean_sq_213_cast")]; + tensor var_7132 = const()[name = tensor("op_7132"), val = tensor([1])]; + tensor var_7133_cast = reduce_mean(axes = var_7132, keep_dims = var_23, x = zero_mean_sq_213_cast)[name = tensor("op_7133_cast")]; + tensor var_7134_to_fp16 = const()[name = tensor("op_7134_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7135_cast = add(x = var_7133_cast, y = var_7134_to_fp16)[name = tensor("op_7135_cast")]; + tensor denom_213_epsilon_0_to_fp16 = const()[name = tensor("denom_213_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_213_cast = rsqrt(epsilon = denom_213_epsilon_0_to_fp16, x = var_7135_cast)[name = tensor("denom_213_cast")]; + tensor out_213_cast = mul(x = zero_mean_213_cast, y = denom_213_cast)[name = tensor("out_213_cast")]; + tensor var_7139_to_fp16 = const()[name = tensor("op_7139_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2671049664)))]; + tensor var_7140_cast = add(x = out_213_cast, y = var_7139_to_fp16)[name = tensor("op_7140_cast")]; + tensor var_7142_to_fp16 = const()[name = tensor("op_7142_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2671052288)))]; + tensor hidden_states_295_cast = mul(x = var_7140_cast, y = var_7142_to_fp16)[name = tensor("hidden_states_295_cast")]; + tensor var_7149 = const()[name = tensor("op_7149"), val = tensor([1, 1])]; + tensor var_7151 = const()[name = tensor("op_7151"), val = tensor([1, 1])]; tensor q_143_pad_type_0 = const()[name = tensor("q_143_pad_type_0"), val = tensor("custom")]; tensor q_143_pad_0 = const()[name = tensor("q_143_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_143 = conv(dilations = var_7250, groups = var_6872, pad = q_143_pad_0, pad_type = q_143_pad_type_0, strides = var_7248, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight, x = hidden_states_295)[name = tensor("q_143")]; - tensor var_7254 = const()[name = tensor("op_7254"), val = tensor([1, 1])]; - tensor var_7256 = const()[name = tensor("op_7256"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2671054912)))]; + tensor q_143_cast = conv(dilations = var_7151, groups = var_31, pad = q_143_pad_0, pad_type = q_143_pad_type_0, strides = var_7149, weight = unet_up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_295_cast)[name = tensor("q_143_cast")]; + tensor var_7155 = const()[name = tensor("op_7155"), val = tensor([1, 1])]; + tensor var_7157 = const()[name = tensor("op_7157"), val = tensor([1, 1])]; tensor k_143_pad_type_0 = const()[name = tensor("k_143_pad_type_0"), val = tensor("custom")]; tensor k_143_pad_0 = const()[name = tensor("k_143_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_143 = conv(dilations = var_7256, groups = var_6872, pad = k_143_pad_0, pad_type = k_143_pad_type_0, strides = var_7254, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_143")]; - tensor var_7260 = const()[name = tensor("op_7260"), val = tensor([1, 1])]; - tensor var_7262 = const()[name = tensor("op_7262"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2674331776)))]; + tensor k_143_cast = conv(dilations = var_7157, groups = var_31, pad = k_143_pad_0, pad_type = k_143_pad_type_0, strides = var_7155, weight = unet_up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_143_cast")]; + tensor var_7161 = const()[name = tensor("op_7161"), val = tensor([1, 1])]; + tensor var_7163 = const()[name = tensor("op_7163"), val = tensor([1, 1])]; tensor v_143_pad_type_0 = const()[name = tensor("v_143_pad_type_0"), val = tensor("custom")]; tensor v_143_pad_0 = const()[name = tensor("v_143_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_143 = conv(dilations = var_7262, groups = var_6872, pad = v_143_pad_0, pad_type = v_143_pad_type_0, strides = var_7260, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_143")]; - tensor var_7266 = const()[name = tensor("op_7266"), val = tensor([2, 20, 64, -1])]; - tensor var_7267 = reshape(shape = var_7266, x = q_143)[name = tensor("op_7267")]; - tensor var_7268 = const()[name = tensor("op_7268"), val = tensor([2, 20, 64, -1])]; - tensor var_7269 = reshape(shape = var_7268, x = k_143)[name = tensor("op_7269")]; - tensor var_7270 = const()[name = tensor("op_7270"), val = tensor([2, 20, 64, -1])]; - tensor var_7271 = reshape(shape = var_7270, x = v_143)[name = tensor("op_7271")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2679574720)))]; + tensor v_143_cast = conv(dilations = var_7163, groups = var_31, pad = v_143_pad_0, pad_type = v_143_pad_type_0, strides = var_7161, weight = unet_up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_143_cast")]; + tensor var_7167 = const()[name = tensor("op_7167"), val = tensor([2, 20, 64, -1])]; + tensor var_7168_cast = reshape(shape = var_7167, x = q_143_cast)[name = tensor("op_7168_cast")]; + tensor var_7169 = const()[name = tensor("op_7169"), val = tensor([2, 20, 64, -1])]; + tensor var_7170_cast = reshape(shape = var_7169, x = k_143_cast)[name = tensor("op_7170_cast")]; + tensor var_7171 = const()[name = tensor("op_7171"), val = tensor([2, 20, 64, -1])]; + tensor var_7172_cast = reshape(shape = var_7171, x = v_143_cast)[name = tensor("op_7172_cast")]; tensor attn_weights_285_transpose_x_0 = const()[name = tensor("attn_weights_285_transpose_x_0"), val = tensor(true)]; tensor attn_weights_285_transpose_y_0 = const()[name = tensor("attn_weights_285_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_285 = matmul(transpose_x = attn_weights_285_transpose_x_0, transpose_y = attn_weights_285_transpose_y_0, x = var_7267, y = var_7269)[name = tensor("attn_weights_285")]; - tensor attn_weights_287 = mul(x = attn_weights_285, y = var_6863)[name = tensor("attn_weights_287")]; - tensor var_7275 = softmax(axis = var_6856, x = attn_weights_287)[name = tensor("op_7275")]; + tensor attn_weights_285_cast = matmul(transpose_x = attn_weights_285_transpose_x_0, transpose_y = attn_weights_285_transpose_y_0, x = var_7168_cast, y = var_7170_cast)[name = tensor("attn_weights_285_cast")]; + tensor attn_weights_287_cast = mul(x = attn_weights_285_cast, y = var_12_to_fp16)[name = tensor("attn_weights_287_cast")]; + tensor var_7176_cast = softmax(axis = var_18, x = attn_weights_287_cast)[name = tensor("op_7176_cast")]; tensor attn_143_transpose_x_0 = const()[name = tensor("attn_143_transpose_x_0"), val = tensor(false)]; tensor attn_143_transpose_y_0 = const()[name = tensor("attn_143_transpose_y_0"), val = tensor(true)]; - tensor attn_143 = matmul(transpose_x = attn_143_transpose_x_0, transpose_y = attn_143_transpose_y_0, x = var_7271, y = var_7275)[name = tensor("attn_143")]; - tensor var_7279 = const()[name = tensor("op_7279"), val = tensor([2, 1280, 1, -1])]; - tensor input_449 = reshape(shape = var_7279, x = attn_143)[name = tensor("input_449")]; - tensor var_7284 = const()[name = tensor("op_7284"), val = tensor([1, 1])]; - tensor var_7286 = const()[name = tensor("op_7286"), val = tensor([1, 1])]; - tensor var_7288_pad_type_0 = const()[name = tensor("op_7288_pad_type_0"), val = tensor("custom")]; - tensor var_7288_pad_0 = const()[name = tensor("op_7288_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7288 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias, dilations = var_7286, groups = var_6872, pad = var_7288_pad_0, pad_type = var_7288_pad_type_0, strides = var_7284, weight = up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight, x = input_449)[name = tensor("op_7288")]; - tensor inputs_215 = add(x = var_7288, y = inputs_213)[name = tensor("inputs_215")]; - tensor var_7292 = const()[name = tensor("op_7292"), val = tensor([1])]; - tensor channels_mean_215 = reduce_mean(axes = var_7292, keep_dims = var_6867, x = inputs_215)[name = tensor("channels_mean_215")]; - tensor zero_mean_215 = sub(x = inputs_215, y = channels_mean_215)[name = tensor("zero_mean_215")]; - tensor zero_mean_sq_215 = mul(x = zero_mean_215, y = zero_mean_215)[name = tensor("zero_mean_sq_215")]; - tensor var_7296 = const()[name = tensor("op_7296"), val = tensor([1])]; - tensor var_7297 = reduce_mean(axes = var_7296, keep_dims = var_6867, x = zero_mean_sq_215)[name = tensor("op_7297")]; - tensor var_7298 = const()[name = tensor("op_7298"), val = tensor(0x1.4f8b58p-17)]; - tensor var_7299 = add(x = var_7297, y = var_7298)[name = tensor("op_7299")]; - tensor denom_215_epsilon_0 = const()[name = tensor("denom_215_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_215 = rsqrt(epsilon = denom_215_epsilon_0, x = var_7299)[name = tensor("denom_215")]; - tensor out_215 = mul(x = zero_mean_215, y = denom_215)[name = tensor("out_215")]; - tensor var_7303 = const()[name = tensor("op_7303"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268854912)))]; - tensor var_7304 = add(x = out_215, y = var_7303)[name = tensor("op_7304")]; - tensor var_7306 = const()[name = tensor("op_7306"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268860096)))]; - tensor input_451 = mul(x = var_7304, y = var_7306)[name = tensor("input_451")]; - tensor var_7314 = const()[name = tensor("op_7314"), val = tensor([1, 1])]; - tensor var_7316 = const()[name = tensor("op_7316"), val = tensor([1, 1])]; - tensor var_7318_pad_type_0 = const()[name = tensor("op_7318_pad_type_0"), val = tensor("custom")]; - tensor var_7318_pad_0 = const()[name = tensor("op_7318_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7318 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias, dilations = var_7316, groups = var_6872, pad = var_7318_pad_0, pad_type = var_7318_pad_type_0, strides = var_7314, weight = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight, x = input_451)[name = tensor("op_7318")]; - tensor var_7319_split_sizes_0 = const()[name = tensor("op_7319_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_7319_axis_0 = const()[name = tensor("op_7319_axis_0"), val = tensor(1)]; - tensor var_7319_0, tensor var_7319_1 = split(axis = var_7319_axis_0, split_sizes = var_7319_split_sizes_0, x = var_7318)[name = tensor("op_7319")]; - tensor var_7321_mode_0 = const()[name = tensor("op_7321_mode_0"), val = tensor("EXACT")]; - tensor var_7321 = gelu(mode = var_7321_mode_0, x = var_7319_1)[name = tensor("op_7321")]; - tensor input_453 = mul(x = var_7319_0, y = var_7321)[name = tensor("input_453")]; - tensor var_7325 = const()[name = tensor("op_7325"), val = tensor([1, 1])]; - tensor var_7327 = const()[name = tensor("op_7327"), val = tensor([1, 1])]; - tensor var_7329_pad_type_0 = const()[name = tensor("op_7329_pad_type_0"), val = tensor("custom")]; - tensor var_7329_pad_0 = const()[name = tensor("op_7329_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7329 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias, dilations = var_7327, groups = var_6872, pad = var_7329_pad_0, pad_type = var_7329_pad_type_0, strides = var_7325, weight = up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_weight, x = input_453)[name = tensor("op_7329")]; - tensor inputs_217 = add(x = var_7329, y = inputs_215)[name = tensor("inputs_217")]; - tensor var_7339 = const()[name = tensor("op_7339"), val = tensor([1])]; - tensor channels_mean_217 = reduce_mean(axes = var_7339, keep_dims = var_6867, x = inputs_217)[name = tensor("channels_mean_217")]; - tensor zero_mean_217 = sub(x = inputs_217, y = channels_mean_217)[name = tensor("zero_mean_217")]; - tensor zero_mean_sq_217 = mul(x = zero_mean_217, y = zero_mean_217)[name = tensor("zero_mean_sq_217")]; - tensor var_7343 = const()[name = tensor("op_7343"), val = tensor([1])]; - tensor var_7344 = reduce_mean(axes = var_7343, keep_dims = var_6867, x = zero_mean_sq_217)[name = tensor("op_7344")]; - tensor var_7345 = const()[name = tensor("op_7345"), val = tensor(0x1.4f8b58p-17)]; - tensor var_7346 = add(x = var_7344, y = var_7345)[name = tensor("op_7346")]; - tensor denom_217_epsilon_0 = const()[name = tensor("denom_217_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_217 = rsqrt(epsilon = denom_217_epsilon_0, x = var_7346)[name = tensor("denom_217")]; - tensor out_217 = mul(x = zero_mean_217, y = denom_217)[name = tensor("out_217")]; - tensor var_7350 = const()[name = tensor("op_7350"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268865280)))]; - tensor var_7351 = add(x = out_217, y = var_7350)[name = tensor("op_7351")]; - tensor var_7353 = const()[name = tensor("op_7353"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268870464)))]; - tensor hidden_states_299 = mul(x = var_7351, y = var_7353)[name = tensor("hidden_states_299")]; - tensor var_7360 = const()[name = tensor("op_7360"), val = tensor([1, 1])]; - tensor var_7362 = const()[name = tensor("op_7362"), val = tensor([1, 1])]; + tensor attn_143_cast = matmul(transpose_x = attn_143_transpose_x_0, transpose_y = attn_143_transpose_y_0, x = var_7172_cast, y = var_7176_cast)[name = tensor("attn_143_cast")]; + tensor var_7180 = const()[name = tensor("op_7180"), val = tensor([2, 1280, 1, -1])]; + tensor input_449_cast = reshape(shape = var_7180, x = attn_143_cast)[name = tensor("input_449_cast")]; + tensor var_7185 = const()[name = tensor("op_7185"), val = tensor([1, 1])]; + tensor var_7187 = const()[name = tensor("op_7187"), val = tensor([1, 1])]; + tensor var_7189_pad_type_0 = const()[name = tensor("op_7189_pad_type_0"), val = tensor("custom")]; + tensor var_7189_pad_0 = const()[name = tensor("op_7189_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2684817664)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2688094528)))]; + tensor var_7189_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_7187, groups = var_31, pad = var_7189_pad_0, pad_type = var_7189_pad_type_0, strides = var_7185, weight = unet_up_blocks_0_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_449_cast)[name = tensor("op_7189_cast")]; + tensor inputs_215_cast = add(x = var_7189_cast, y = inputs_213_cast)[name = tensor("inputs_215_cast")]; + tensor var_7193 = const()[name = tensor("op_7193"), val = tensor([1])]; + tensor channels_mean_215_cast = reduce_mean(axes = var_7193, keep_dims = var_23, x = inputs_215_cast)[name = tensor("channels_mean_215_cast")]; + tensor zero_mean_215_cast = sub(x = inputs_215_cast, y = channels_mean_215_cast)[name = tensor("zero_mean_215_cast")]; + tensor zero_mean_sq_215_cast = mul(x = zero_mean_215_cast, y = zero_mean_215_cast)[name = tensor("zero_mean_sq_215_cast")]; + tensor var_7197 = const()[name = tensor("op_7197"), val = tensor([1])]; + tensor var_7198_cast = reduce_mean(axes = var_7197, keep_dims = var_23, x = zero_mean_sq_215_cast)[name = tensor("op_7198_cast")]; + tensor var_7199_to_fp16 = const()[name = tensor("op_7199_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7200_cast = add(x = var_7198_cast, y = var_7199_to_fp16)[name = tensor("op_7200_cast")]; + tensor denom_215_epsilon_0_to_fp16 = const()[name = tensor("denom_215_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_215_cast = rsqrt(epsilon = denom_215_epsilon_0_to_fp16, x = var_7200_cast)[name = tensor("denom_215_cast")]; + tensor out_215_cast = mul(x = zero_mean_215_cast, y = denom_215_cast)[name = tensor("out_215_cast")]; + tensor var_7204_to_fp16 = const()[name = tensor("op_7204_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2688097152)))]; + tensor var_7205_cast = add(x = out_215_cast, y = var_7204_to_fp16)[name = tensor("op_7205_cast")]; + tensor var_7207_to_fp16 = const()[name = tensor("op_7207_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2688099776)))]; + tensor input_451_cast = mul(x = var_7205_cast, y = var_7207_to_fp16)[name = tensor("input_451_cast")]; + tensor var_7215 = const()[name = tensor("op_7215"), val = tensor([1, 1])]; + tensor var_7217 = const()[name = tensor("op_7217"), val = tensor([1, 1])]; + tensor var_7219_pad_type_0 = const()[name = tensor("op_7219_pad_type_0"), val = tensor("custom")]; + tensor var_7219_pad_0 = const()[name = tensor("op_7219_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2688102400)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2714316864)))]; + tensor var_7219_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_7217, groups = var_31, pad = var_7219_pad_0, pad_type = var_7219_pad_type_0, strides = var_7215, weight = unet_up_blocks_0_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_451_cast)[name = tensor("op_7219_cast")]; + tensor var_7220_split_sizes_0 = const()[name = tensor("op_7220_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7220_axis_0 = const()[name = tensor("op_7220_axis_0"), val = tensor(1)]; + tensor var_7220_cast_0, tensor var_7220_cast_1 = split(axis = var_7220_axis_0, split_sizes = var_7220_split_sizes_0, x = var_7219_cast)[name = tensor("op_7220_cast")]; + tensor var_7222_mode_0 = const()[name = tensor("op_7222_mode_0"), val = tensor("EXACT")]; + tensor var_7222_cast = gelu(mode = var_7222_mode_0, x = var_7220_cast_1)[name = tensor("op_7222_cast")]; + tensor input_453_cast = mul(x = var_7220_cast_0, y = var_7222_cast)[name = tensor("input_453_cast")]; + tensor var_7226 = const()[name = tensor("op_7226"), val = tensor([1, 1])]; + tensor var_7228 = const()[name = tensor("op_7228"), val = tensor([1, 1])]; + tensor var_7230_pad_type_0 = const()[name = tensor("op_7230_pad_type_0"), val = tensor("custom")]; + tensor var_7230_pad_0 = const()[name = tensor("op_7230_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2714337408)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2727444672)))]; + tensor var_7230_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_7228, groups = var_31, pad = var_7230_pad_0, pad_type = var_7230_pad_type_0, strides = var_7226, weight = unet_up_blocks_0_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_453_cast)[name = tensor("op_7230_cast")]; + tensor inputs_217_cast = add(x = var_7230_cast, y = inputs_215_cast)[name = tensor("inputs_217_cast")]; + tensor var_7240 = const()[name = tensor("op_7240"), val = tensor([1])]; + tensor channels_mean_217_cast = reduce_mean(axes = var_7240, keep_dims = var_23, x = inputs_217_cast)[name = tensor("channels_mean_217_cast")]; + tensor zero_mean_217_cast = sub(x = inputs_217_cast, y = channels_mean_217_cast)[name = tensor("zero_mean_217_cast")]; + tensor zero_mean_sq_217_cast = mul(x = zero_mean_217_cast, y = zero_mean_217_cast)[name = tensor("zero_mean_sq_217_cast")]; + tensor var_7244 = const()[name = tensor("op_7244"), val = tensor([1])]; + tensor var_7245_cast = reduce_mean(axes = var_7244, keep_dims = var_23, x = zero_mean_sq_217_cast)[name = tensor("op_7245_cast")]; + tensor var_7246_to_fp16 = const()[name = tensor("op_7246_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7247_cast = add(x = var_7245_cast, y = var_7246_to_fp16)[name = tensor("op_7247_cast")]; + tensor denom_217_epsilon_0_to_fp16 = const()[name = tensor("denom_217_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_217_cast = rsqrt(epsilon = denom_217_epsilon_0_to_fp16, x = var_7247_cast)[name = tensor("denom_217_cast")]; + tensor out_217_cast = mul(x = zero_mean_217_cast, y = denom_217_cast)[name = tensor("out_217_cast")]; + tensor var_7251_to_fp16 = const()[name = tensor("op_7251_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2727447296)))]; + tensor var_7252_cast = add(x = out_217_cast, y = var_7251_to_fp16)[name = tensor("op_7252_cast")]; + tensor var_7254_to_fp16 = const()[name = tensor("op_7254_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2727449920)))]; + tensor hidden_states_299_cast = mul(x = var_7252_cast, y = var_7254_to_fp16)[name = tensor("hidden_states_299_cast")]; + tensor var_7261 = const()[name = tensor("op_7261"), val = tensor([1, 1])]; + tensor var_7263 = const()[name = tensor("op_7263"), val = tensor([1, 1])]; tensor q_145_pad_type_0 = const()[name = tensor("q_145_pad_type_0"), val = tensor("custom")]; tensor q_145_pad_0 = const()[name = tensor("q_145_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_145 = conv(dilations = var_7362, groups = var_6872, pad = q_145_pad_0, pad_type = q_145_pad_type_0, strides = var_7360, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_q_weight, x = hidden_states_299)[name = tensor("q_145")]; - tensor var_7366 = const()[name = tensor("op_7366"), val = tensor([1, 1])]; - tensor var_7368 = const()[name = tensor("op_7368"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2727452544)))]; + tensor q_145_cast = conv(dilations = var_7263, groups = var_31, pad = q_145_pad_0, pad_type = q_145_pad_type_0, strides = var_7261, weight = unet_up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_299_cast)[name = tensor("q_145_cast")]; + tensor var_7267 = const()[name = tensor("op_7267"), val = tensor([1, 1])]; + tensor var_7269 = const()[name = tensor("op_7269"), val = tensor([1, 1])]; tensor k_145_pad_type_0 = const()[name = tensor("k_145_pad_type_0"), val = tensor("custom")]; tensor k_145_pad_0 = const()[name = tensor("k_145_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_145 = conv(dilations = var_7368, groups = var_6872, pad = k_145_pad_0, pad_type = k_145_pad_type_0, strides = var_7366, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_k_weight, x = hidden_states_299)[name = tensor("k_145")]; - tensor var_7372 = const()[name = tensor("op_7372"), val = tensor([1, 1])]; - tensor var_7374 = const()[name = tensor("op_7374"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2730729408)))]; + tensor k_145_cast = conv(dilations = var_7269, groups = var_31, pad = k_145_pad_0, pad_type = k_145_pad_type_0, strides = var_7267, weight = unet_up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_299_cast)[name = tensor("k_145_cast")]; + tensor var_7273 = const()[name = tensor("op_7273"), val = tensor([1, 1])]; + tensor var_7275 = const()[name = tensor("op_7275"), val = tensor([1, 1])]; tensor v_145_pad_type_0 = const()[name = tensor("v_145_pad_type_0"), val = tensor("custom")]; tensor v_145_pad_0 = const()[name = tensor("v_145_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_145 = conv(dilations = var_7374, groups = var_6872, pad = v_145_pad_0, pad_type = v_145_pad_type_0, strides = var_7372, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_v_weight, x = hidden_states_299)[name = tensor("v_145")]; - tensor var_7378 = const()[name = tensor("op_7378"), val = tensor([2, 20, 64, -1])]; - tensor var_7379 = reshape(shape = var_7378, x = q_145)[name = tensor("op_7379")]; - tensor var_7380 = const()[name = tensor("op_7380"), val = tensor([2, 20, 64, -1])]; - tensor var_7381 = reshape(shape = var_7380, x = k_145)[name = tensor("op_7381")]; - tensor var_7382 = const()[name = tensor("op_7382"), val = tensor([2, 20, 64, -1])]; - tensor var_7383 = reshape(shape = var_7382, x = v_145)[name = tensor("op_7383")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2734006272)))]; + tensor v_145_cast = conv(dilations = var_7275, groups = var_31, pad = v_145_pad_0, pad_type = v_145_pad_type_0, strides = var_7273, weight = unet_up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_299_cast)[name = tensor("v_145_cast")]; + tensor var_7279 = const()[name = tensor("op_7279"), val = tensor([2, 20, 64, -1])]; + tensor var_7280_cast = reshape(shape = var_7279, x = q_145_cast)[name = tensor("op_7280_cast")]; + tensor var_7281 = const()[name = tensor("op_7281"), val = tensor([2, 20, 64, -1])]; + tensor var_7282_cast = reshape(shape = var_7281, x = k_145_cast)[name = tensor("op_7282_cast")]; + tensor var_7283 = const()[name = tensor("op_7283"), val = tensor([2, 20, 64, -1])]; + tensor var_7284_cast = reshape(shape = var_7283, x = v_145_cast)[name = tensor("op_7284_cast")]; tensor attn_weights_289_transpose_x_0 = const()[name = tensor("attn_weights_289_transpose_x_0"), val = tensor(true)]; tensor attn_weights_289_transpose_y_0 = const()[name = tensor("attn_weights_289_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_289 = matmul(transpose_x = attn_weights_289_transpose_x_0, transpose_y = attn_weights_289_transpose_y_0, x = var_7379, y = var_7381)[name = tensor("attn_weights_289")]; - tensor attn_weights_291 = mul(x = attn_weights_289, y = var_6863)[name = tensor("attn_weights_291")]; - tensor var_7387 = softmax(axis = var_6856, x = attn_weights_291)[name = tensor("op_7387")]; + tensor attn_weights_289_cast = matmul(transpose_x = attn_weights_289_transpose_x_0, transpose_y = attn_weights_289_transpose_y_0, x = var_7280_cast, y = var_7282_cast)[name = tensor("attn_weights_289_cast")]; + tensor attn_weights_291_cast = mul(x = attn_weights_289_cast, y = var_12_to_fp16)[name = tensor("attn_weights_291_cast")]; + tensor var_7288_cast = softmax(axis = var_18, x = attn_weights_291_cast)[name = tensor("op_7288_cast")]; tensor attn_145_transpose_x_0 = const()[name = tensor("attn_145_transpose_x_0"), val = tensor(false)]; tensor attn_145_transpose_y_0 = const()[name = tensor("attn_145_transpose_y_0"), val = tensor(true)]; - tensor attn_145 = matmul(transpose_x = attn_145_transpose_x_0, transpose_y = attn_145_transpose_y_0, x = var_7383, y = var_7387)[name = tensor("attn_145")]; - tensor var_7391 = const()[name = tensor("op_7391"), val = tensor([2, 1280, 1, -1])]; - tensor input_455 = reshape(shape = var_7391, x = attn_145)[name = tensor("input_455")]; - tensor var_7396 = const()[name = tensor("op_7396"), val = tensor([1, 1])]; - tensor var_7398 = const()[name = tensor("op_7398"), val = tensor([1, 1])]; - tensor var_7400_pad_type_0 = const()[name = tensor("op_7400_pad_type_0"), val = tensor("custom")]; - tensor var_7400_pad_0 = const()[name = tensor("op_7400_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7400 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_bias, dilations = var_7398, groups = var_6872, pad = var_7400_pad_0, pad_type = var_7400_pad_type_0, strides = var_7396, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_weight, x = input_455)[name = tensor("op_7400")]; - tensor inputs_219 = add(x = var_7400, y = inputs_217)[name = tensor("inputs_219")]; - tensor var_7404 = const()[name = tensor("op_7404"), val = tensor([1])]; - tensor channels_mean_219 = reduce_mean(axes = var_7404, keep_dims = var_6867, x = inputs_219)[name = tensor("channels_mean_219")]; - tensor zero_mean_219 = sub(x = inputs_219, y = channels_mean_219)[name = tensor("zero_mean_219")]; - tensor zero_mean_sq_219 = mul(x = zero_mean_219, y = zero_mean_219)[name = tensor("zero_mean_sq_219")]; - tensor var_7408 = const()[name = tensor("op_7408"), val = tensor([1])]; - tensor var_7409 = reduce_mean(axes = var_7408, keep_dims = var_6867, x = zero_mean_sq_219)[name = tensor("op_7409")]; - tensor var_7410 = const()[name = tensor("op_7410"), val = tensor(0x1.4f8b58p-17)]; - tensor var_7411 = add(x = var_7409, y = var_7410)[name = tensor("op_7411")]; - tensor denom_219_epsilon_0 = const()[name = tensor("denom_219_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_219 = rsqrt(epsilon = denom_219_epsilon_0, x = var_7411)[name = tensor("denom_219")]; - tensor out_219 = mul(x = zero_mean_219, y = denom_219)[name = tensor("out_219")]; - tensor var_7415 = const()[name = tensor("op_7415"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268875648)))]; - tensor var_7416 = add(x = out_219, y = var_7415)[name = tensor("op_7416")]; - tensor var_7418 = const()[name = tensor("op_7418"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268880832)))]; - tensor hidden_states_301 = mul(x = var_7416, y = var_7418)[name = tensor("hidden_states_301")]; - tensor var_7425 = const()[name = tensor("op_7425"), val = tensor([1, 1])]; - tensor var_7427 = const()[name = tensor("op_7427"), val = tensor([1, 1])]; + tensor attn_145_cast = matmul(transpose_x = attn_145_transpose_x_0, transpose_y = attn_145_transpose_y_0, x = var_7284_cast, y = var_7288_cast)[name = tensor("attn_145_cast")]; + tensor var_7292 = const()[name = tensor("op_7292"), val = tensor([2, 1280, 1, -1])]; + tensor input_455_cast = reshape(shape = var_7292, x = attn_145_cast)[name = tensor("input_455_cast")]; + tensor var_7297 = const()[name = tensor("op_7297"), val = tensor([1, 1])]; + tensor var_7299 = const()[name = tensor("op_7299"), val = tensor([1, 1])]; + tensor var_7301_pad_type_0 = const()[name = tensor("op_7301_pad_type_0"), val = tensor("custom")]; + tensor var_7301_pad_0 = const()[name = tensor("op_7301_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2737283136)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2740560000)))]; + tensor var_7301_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_7299, groups = var_31, pad = var_7301_pad_0, pad_type = var_7301_pad_type_0, strides = var_7297, weight = unet_up_blocks_0_attentions_0_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_455_cast)[name = tensor("op_7301_cast")]; + tensor inputs_219_cast = add(x = var_7301_cast, y = inputs_217_cast)[name = tensor("inputs_219_cast")]; + tensor var_7305 = const()[name = tensor("op_7305"), val = tensor([1])]; + tensor channels_mean_219_cast = reduce_mean(axes = var_7305, keep_dims = var_23, x = inputs_219_cast)[name = tensor("channels_mean_219_cast")]; + tensor zero_mean_219_cast = sub(x = inputs_219_cast, y = channels_mean_219_cast)[name = tensor("zero_mean_219_cast")]; + tensor zero_mean_sq_219_cast = mul(x = zero_mean_219_cast, y = zero_mean_219_cast)[name = tensor("zero_mean_sq_219_cast")]; + tensor var_7309 = const()[name = tensor("op_7309"), val = tensor([1])]; + tensor var_7310_cast = reduce_mean(axes = var_7309, keep_dims = var_23, x = zero_mean_sq_219_cast)[name = tensor("op_7310_cast")]; + tensor var_7311_to_fp16 = const()[name = tensor("op_7311_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7312_cast = add(x = var_7310_cast, y = var_7311_to_fp16)[name = tensor("op_7312_cast")]; + tensor denom_219_epsilon_0_to_fp16 = const()[name = tensor("denom_219_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_219_cast = rsqrt(epsilon = denom_219_epsilon_0_to_fp16, x = var_7312_cast)[name = tensor("denom_219_cast")]; + tensor out_219_cast = mul(x = zero_mean_219_cast, y = denom_219_cast)[name = tensor("out_219_cast")]; + tensor var_7316_to_fp16 = const()[name = tensor("op_7316_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2740562624)))]; + tensor var_7317_cast = add(x = out_219_cast, y = var_7316_to_fp16)[name = tensor("op_7317_cast")]; + tensor var_7319_to_fp16 = const()[name = tensor("op_7319_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2740565248)))]; + tensor hidden_states_301_cast = mul(x = var_7317_cast, y = var_7319_to_fp16)[name = tensor("hidden_states_301_cast")]; + tensor var_7326 = const()[name = tensor("op_7326"), val = tensor([1, 1])]; + tensor var_7328 = const()[name = tensor("op_7328"), val = tensor([1, 1])]; tensor q_147_pad_type_0 = const()[name = tensor("q_147_pad_type_0"), val = tensor("custom")]; tensor q_147_pad_0 = const()[name = tensor("q_147_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_147 = conv(dilations = var_7427, groups = var_6872, pad = q_147_pad_0, pad_type = q_147_pad_type_0, strides = var_7425, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_q_weight, x = hidden_states_301)[name = tensor("q_147")]; - tensor var_7431 = const()[name = tensor("op_7431"), val = tensor([1, 1])]; - tensor var_7433 = const()[name = tensor("op_7433"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2740567872)))]; + tensor q_147_cast = conv(dilations = var_7328, groups = var_31, pad = q_147_pad_0, pad_type = q_147_pad_type_0, strides = var_7326, weight = unet_up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_301_cast)[name = tensor("q_147_cast")]; + tensor var_7332 = const()[name = tensor("op_7332"), val = tensor([1, 1])]; + tensor var_7334 = const()[name = tensor("op_7334"), val = tensor([1, 1])]; tensor k_147_pad_type_0 = const()[name = tensor("k_147_pad_type_0"), val = tensor("custom")]; tensor k_147_pad_0 = const()[name = tensor("k_147_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_147 = conv(dilations = var_7433, groups = var_6872, pad = k_147_pad_0, pad_type = k_147_pad_type_0, strides = var_7431, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_147")]; - tensor var_7437 = const()[name = tensor("op_7437"), val = tensor([1, 1])]; - tensor var_7439 = const()[name = tensor("op_7439"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2743844736)))]; + tensor k_147_cast = conv(dilations = var_7334, groups = var_31, pad = k_147_pad_0, pad_type = k_147_pad_type_0, strides = var_7332, weight = unet_up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_147_cast")]; + tensor var_7338 = const()[name = tensor("op_7338"), val = tensor([1, 1])]; + tensor var_7340 = const()[name = tensor("op_7340"), val = tensor([1, 1])]; tensor v_147_pad_type_0 = const()[name = tensor("v_147_pad_type_0"), val = tensor("custom")]; tensor v_147_pad_0 = const()[name = tensor("v_147_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_147 = conv(dilations = var_7439, groups = var_6872, pad = v_147_pad_0, pad_type = v_147_pad_type_0, strides = var_7437, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_147")]; - tensor var_7443 = const()[name = tensor("op_7443"), val = tensor([2, 20, 64, -1])]; - tensor var_7444 = reshape(shape = var_7443, x = q_147)[name = tensor("op_7444")]; - tensor var_7445 = const()[name = tensor("op_7445"), val = tensor([2, 20, 64, -1])]; - tensor var_7446 = reshape(shape = var_7445, x = k_147)[name = tensor("op_7446")]; - tensor var_7447 = const()[name = tensor("op_7447"), val = tensor([2, 20, 64, -1])]; - tensor var_7448 = reshape(shape = var_7447, x = v_147)[name = tensor("op_7448")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2749087680)))]; + tensor v_147_cast = conv(dilations = var_7340, groups = var_31, pad = v_147_pad_0, pad_type = v_147_pad_type_0, strides = var_7338, weight = unet_up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_147_cast")]; + tensor var_7344 = const()[name = tensor("op_7344"), val = tensor([2, 20, 64, -1])]; + tensor var_7345_cast = reshape(shape = var_7344, x = q_147_cast)[name = tensor("op_7345_cast")]; + tensor var_7346 = const()[name = tensor("op_7346"), val = tensor([2, 20, 64, -1])]; + tensor var_7347_cast = reshape(shape = var_7346, x = k_147_cast)[name = tensor("op_7347_cast")]; + tensor var_7348 = const()[name = tensor("op_7348"), val = tensor([2, 20, 64, -1])]; + tensor var_7349_cast = reshape(shape = var_7348, x = v_147_cast)[name = tensor("op_7349_cast")]; tensor attn_weights_293_transpose_x_0 = const()[name = tensor("attn_weights_293_transpose_x_0"), val = tensor(true)]; tensor attn_weights_293_transpose_y_0 = const()[name = tensor("attn_weights_293_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_293 = matmul(transpose_x = attn_weights_293_transpose_x_0, transpose_y = attn_weights_293_transpose_y_0, x = var_7444, y = var_7446)[name = tensor("attn_weights_293")]; - tensor attn_weights_295 = mul(x = attn_weights_293, y = var_6863)[name = tensor("attn_weights_295")]; - tensor var_7452 = softmax(axis = var_6856, x = attn_weights_295)[name = tensor("op_7452")]; + tensor attn_weights_293_cast = matmul(transpose_x = attn_weights_293_transpose_x_0, transpose_y = attn_weights_293_transpose_y_0, x = var_7345_cast, y = var_7347_cast)[name = tensor("attn_weights_293_cast")]; + tensor attn_weights_295_cast = mul(x = attn_weights_293_cast, y = var_12_to_fp16)[name = tensor("attn_weights_295_cast")]; + tensor var_7353_cast = softmax(axis = var_18, x = attn_weights_295_cast)[name = tensor("op_7353_cast")]; tensor attn_147_transpose_x_0 = const()[name = tensor("attn_147_transpose_x_0"), val = tensor(false)]; tensor attn_147_transpose_y_0 = const()[name = tensor("attn_147_transpose_y_0"), val = tensor(true)]; - tensor attn_147 = matmul(transpose_x = attn_147_transpose_x_0, transpose_y = attn_147_transpose_y_0, x = var_7448, y = var_7452)[name = tensor("attn_147")]; - tensor var_7456 = const()[name = tensor("op_7456"), val = tensor([2, 1280, 1, -1])]; - tensor input_457 = reshape(shape = var_7456, x = attn_147)[name = tensor("input_457")]; - tensor var_7461 = const()[name = tensor("op_7461"), val = tensor([1, 1])]; - tensor var_7463 = const()[name = tensor("op_7463"), val = tensor([1, 1])]; - tensor var_7465_pad_type_0 = const()[name = tensor("op_7465_pad_type_0"), val = tensor("custom")]; - tensor var_7465_pad_0 = const()[name = tensor("op_7465_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7465 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias, dilations = var_7463, groups = var_6872, pad = var_7465_pad_0, pad_type = var_7465_pad_type_0, strides = var_7461, weight = up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight, x = input_457)[name = tensor("op_7465")]; - tensor inputs_221 = add(x = var_7465, y = inputs_219)[name = tensor("inputs_221")]; - tensor var_7469 = const()[name = tensor("op_7469"), val = tensor([1])]; - tensor channels_mean_221 = reduce_mean(axes = var_7469, keep_dims = var_6867, x = inputs_221)[name = tensor("channels_mean_221")]; - tensor zero_mean_221 = sub(x = inputs_221, y = channels_mean_221)[name = tensor("zero_mean_221")]; - tensor zero_mean_sq_221 = mul(x = zero_mean_221, y = zero_mean_221)[name = tensor("zero_mean_sq_221")]; - tensor var_7473 = const()[name = tensor("op_7473"), val = tensor([1])]; - tensor var_7474 = reduce_mean(axes = var_7473, keep_dims = var_6867, x = zero_mean_sq_221)[name = tensor("op_7474")]; - tensor var_7475 = const()[name = tensor("op_7475"), val = tensor(0x1.4f8b58p-17)]; - tensor var_7476 = add(x = var_7474, y = var_7475)[name = tensor("op_7476")]; - tensor denom_221_epsilon_0 = const()[name = tensor("denom_221_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_221 = rsqrt(epsilon = denom_221_epsilon_0, x = var_7476)[name = tensor("denom_221")]; - tensor out_221 = mul(x = zero_mean_221, y = denom_221)[name = tensor("out_221")]; - tensor var_7480 = const()[name = tensor("op_7480"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268886016)))]; - tensor var_7481 = add(x = out_221, y = var_7480)[name = tensor("op_7481")]; - tensor var_7483 = const()[name = tensor("op_7483"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268891200)))]; - tensor input_459 = mul(x = var_7481, y = var_7483)[name = tensor("input_459")]; - tensor var_7491 = const()[name = tensor("op_7491"), val = tensor([1, 1])]; - tensor var_7493 = const()[name = tensor("op_7493"), val = tensor([1, 1])]; - tensor var_7495_pad_type_0 = const()[name = tensor("op_7495_pad_type_0"), val = tensor("custom")]; - tensor var_7495_pad_0 = const()[name = tensor("op_7495_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7495 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias, dilations = var_7493, groups = var_6872, pad = var_7495_pad_0, pad_type = var_7495_pad_type_0, strides = var_7491, weight = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight, x = input_459)[name = tensor("op_7495")]; - tensor var_7496_split_sizes_0 = const()[name = tensor("op_7496_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_7496_axis_0 = const()[name = tensor("op_7496_axis_0"), val = tensor(1)]; - tensor var_7496_0, tensor var_7496_1 = split(axis = var_7496_axis_0, split_sizes = var_7496_split_sizes_0, x = var_7495)[name = tensor("op_7496")]; - tensor var_7498_mode_0 = const()[name = tensor("op_7498_mode_0"), val = tensor("EXACT")]; - tensor var_7498 = gelu(mode = var_7498_mode_0, x = var_7496_1)[name = tensor("op_7498")]; - tensor input_461 = mul(x = var_7496_0, y = var_7498)[name = tensor("input_461")]; - tensor var_7502 = const()[name = tensor("op_7502"), val = tensor([1, 1])]; - tensor var_7504 = const()[name = tensor("op_7504"), val = tensor([1, 1])]; - tensor var_7506_pad_type_0 = const()[name = tensor("op_7506_pad_type_0"), val = tensor("custom")]; - tensor var_7506_pad_0 = const()[name = tensor("op_7506_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7506 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias, dilations = var_7504, groups = var_6872, pad = var_7506_pad_0, pad_type = var_7506_pad_type_0, strides = var_7502, weight = up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight, x = input_461)[name = tensor("op_7506")]; - tensor inputs_223 = add(x = var_7506, y = inputs_221)[name = tensor("inputs_223")]; - tensor var_7516 = const()[name = tensor("op_7516"), val = tensor([1])]; - tensor channels_mean_223 = reduce_mean(axes = var_7516, keep_dims = var_6867, x = inputs_223)[name = tensor("channels_mean_223")]; - tensor zero_mean_223 = sub(x = inputs_223, y = channels_mean_223)[name = tensor("zero_mean_223")]; - tensor zero_mean_sq_223 = mul(x = zero_mean_223, y = zero_mean_223)[name = tensor("zero_mean_sq_223")]; - tensor var_7520 = const()[name = tensor("op_7520"), val = tensor([1])]; - tensor var_7521 = reduce_mean(axes = var_7520, keep_dims = var_6867, x = zero_mean_sq_223)[name = tensor("op_7521")]; - tensor var_7522 = const()[name = tensor("op_7522"), val = tensor(0x1.4f8b58p-17)]; - tensor var_7523 = add(x = var_7521, y = var_7522)[name = tensor("op_7523")]; - tensor denom_223_epsilon_0 = const()[name = tensor("denom_223_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_223 = rsqrt(epsilon = denom_223_epsilon_0, x = var_7523)[name = tensor("denom_223")]; - tensor out_223 = mul(x = zero_mean_223, y = denom_223)[name = tensor("out_223")]; - tensor var_7527 = const()[name = tensor("op_7527"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268896384)))]; - tensor var_7528 = add(x = out_223, y = var_7527)[name = tensor("op_7528")]; - tensor var_7530 = const()[name = tensor("op_7530"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268901568)))]; - tensor hidden_states_305 = mul(x = var_7528, y = var_7530)[name = tensor("hidden_states_305")]; - tensor var_7537 = const()[name = tensor("op_7537"), val = tensor([1, 1])]; - tensor var_7539 = const()[name = tensor("op_7539"), val = tensor([1, 1])]; + tensor attn_147_cast = matmul(transpose_x = attn_147_transpose_x_0, transpose_y = attn_147_transpose_y_0, x = var_7349_cast, y = var_7353_cast)[name = tensor("attn_147_cast")]; + tensor var_7357 = const()[name = tensor("op_7357"), val = tensor([2, 1280, 1, -1])]; + tensor input_457_cast = reshape(shape = var_7357, x = attn_147_cast)[name = tensor("input_457_cast")]; + tensor var_7362 = const()[name = tensor("op_7362"), val = tensor([1, 1])]; + tensor var_7364 = const()[name = tensor("op_7364"), val = tensor([1, 1])]; + tensor var_7366_pad_type_0 = const()[name = tensor("op_7366_pad_type_0"), val = tensor("custom")]; + tensor var_7366_pad_0 = const()[name = tensor("op_7366_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2754330624)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2757607488)))]; + tensor var_7366_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_7364, groups = var_31, pad = var_7366_pad_0, pad_type = var_7366_pad_type_0, strides = var_7362, weight = unet_up_blocks_0_attentions_0_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_457_cast)[name = tensor("op_7366_cast")]; + tensor inputs_221_cast = add(x = var_7366_cast, y = inputs_219_cast)[name = tensor("inputs_221_cast")]; + tensor var_7370 = const()[name = tensor("op_7370"), val = tensor([1])]; + tensor channels_mean_221_cast = reduce_mean(axes = var_7370, keep_dims = var_23, x = inputs_221_cast)[name = tensor("channels_mean_221_cast")]; + tensor zero_mean_221_cast = sub(x = inputs_221_cast, y = channels_mean_221_cast)[name = tensor("zero_mean_221_cast")]; + tensor zero_mean_sq_221_cast = mul(x = zero_mean_221_cast, y = zero_mean_221_cast)[name = tensor("zero_mean_sq_221_cast")]; + tensor var_7374 = const()[name = tensor("op_7374"), val = tensor([1])]; + tensor var_7375_cast = reduce_mean(axes = var_7374, keep_dims = var_23, x = zero_mean_sq_221_cast)[name = tensor("op_7375_cast")]; + tensor var_7376_to_fp16 = const()[name = tensor("op_7376_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7377_cast = add(x = var_7375_cast, y = var_7376_to_fp16)[name = tensor("op_7377_cast")]; + tensor denom_221_epsilon_0_to_fp16 = const()[name = tensor("denom_221_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_221_cast = rsqrt(epsilon = denom_221_epsilon_0_to_fp16, x = var_7377_cast)[name = tensor("denom_221_cast")]; + tensor out_221_cast = mul(x = zero_mean_221_cast, y = denom_221_cast)[name = tensor("out_221_cast")]; + tensor var_7381_to_fp16 = const()[name = tensor("op_7381_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2757610112)))]; + tensor var_7382_cast = add(x = out_221_cast, y = var_7381_to_fp16)[name = tensor("op_7382_cast")]; + tensor var_7384_to_fp16 = const()[name = tensor("op_7384_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2757612736)))]; + tensor input_459_cast = mul(x = var_7382_cast, y = var_7384_to_fp16)[name = tensor("input_459_cast")]; + tensor var_7392 = const()[name = tensor("op_7392"), val = tensor([1, 1])]; + tensor var_7394 = const()[name = tensor("op_7394"), val = tensor([1, 1])]; + tensor var_7396_pad_type_0 = const()[name = tensor("op_7396_pad_type_0"), val = tensor("custom")]; + tensor var_7396_pad_0 = const()[name = tensor("op_7396_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2757615360)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2783829824)))]; + tensor var_7396_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_7394, groups = var_31, pad = var_7396_pad_0, pad_type = var_7396_pad_type_0, strides = var_7392, weight = unet_up_blocks_0_attentions_0_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_459_cast)[name = tensor("op_7396_cast")]; + tensor var_7397_split_sizes_0 = const()[name = tensor("op_7397_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7397_axis_0 = const()[name = tensor("op_7397_axis_0"), val = tensor(1)]; + tensor var_7397_cast_0, tensor var_7397_cast_1 = split(axis = var_7397_axis_0, split_sizes = var_7397_split_sizes_0, x = var_7396_cast)[name = tensor("op_7397_cast")]; + tensor var_7399_mode_0 = const()[name = tensor("op_7399_mode_0"), val = tensor("EXACT")]; + tensor var_7399_cast = gelu(mode = var_7399_mode_0, x = var_7397_cast_1)[name = tensor("op_7399_cast")]; + tensor input_461_cast = mul(x = var_7397_cast_0, y = var_7399_cast)[name = tensor("input_461_cast")]; + tensor var_7403 = const()[name = tensor("op_7403"), val = tensor([1, 1])]; + tensor var_7405 = const()[name = tensor("op_7405"), val = tensor([1, 1])]; + tensor var_7407_pad_type_0 = const()[name = tensor("op_7407_pad_type_0"), val = tensor("custom")]; + tensor var_7407_pad_0 = const()[name = tensor("op_7407_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2783850368)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2796957632)))]; + tensor var_7407_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_7405, groups = var_31, pad = var_7407_pad_0, pad_type = var_7407_pad_type_0, strides = var_7403, weight = unet_up_blocks_0_attentions_0_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_461_cast)[name = tensor("op_7407_cast")]; + tensor inputs_223_cast = add(x = var_7407_cast, y = inputs_221_cast)[name = tensor("inputs_223_cast")]; + tensor var_7417 = const()[name = tensor("op_7417"), val = tensor([1])]; + tensor channels_mean_223_cast = reduce_mean(axes = var_7417, keep_dims = var_23, x = inputs_223_cast)[name = tensor("channels_mean_223_cast")]; + tensor zero_mean_223_cast = sub(x = inputs_223_cast, y = channels_mean_223_cast)[name = tensor("zero_mean_223_cast")]; + tensor zero_mean_sq_223_cast = mul(x = zero_mean_223_cast, y = zero_mean_223_cast)[name = tensor("zero_mean_sq_223_cast")]; + tensor var_7421 = const()[name = tensor("op_7421"), val = tensor([1])]; + tensor var_7422_cast = reduce_mean(axes = var_7421, keep_dims = var_23, x = zero_mean_sq_223_cast)[name = tensor("op_7422_cast")]; + tensor var_7423_to_fp16 = const()[name = tensor("op_7423_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7424_cast = add(x = var_7422_cast, y = var_7423_to_fp16)[name = tensor("op_7424_cast")]; + tensor denom_223_epsilon_0_to_fp16 = const()[name = tensor("denom_223_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_223_cast = rsqrt(epsilon = denom_223_epsilon_0_to_fp16, x = var_7424_cast)[name = tensor("denom_223_cast")]; + tensor out_223_cast = mul(x = zero_mean_223_cast, y = denom_223_cast)[name = tensor("out_223_cast")]; + tensor var_7428_to_fp16 = const()[name = tensor("op_7428_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2796960256)))]; + tensor var_7429_cast = add(x = out_223_cast, y = var_7428_to_fp16)[name = tensor("op_7429_cast")]; + tensor var_7431_to_fp16 = const()[name = tensor("op_7431_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2796962880)))]; + tensor hidden_states_305_cast = mul(x = var_7429_cast, y = var_7431_to_fp16)[name = tensor("hidden_states_305_cast")]; + tensor var_7438 = const()[name = tensor("op_7438"), val = tensor([1, 1])]; + tensor var_7440 = const()[name = tensor("op_7440"), val = tensor([1, 1])]; tensor q_149_pad_type_0 = const()[name = tensor("q_149_pad_type_0"), val = tensor("custom")]; tensor q_149_pad_0 = const()[name = tensor("q_149_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_149 = conv(dilations = var_7539, groups = var_6872, pad = q_149_pad_0, pad_type = q_149_pad_type_0, strides = var_7537, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight, x = hidden_states_305)[name = tensor("q_149")]; - tensor var_7543 = const()[name = tensor("op_7543"), val = tensor([1, 1])]; - tensor var_7545 = const()[name = tensor("op_7545"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2796965504)))]; + tensor q_149_cast = conv(dilations = var_7440, groups = var_31, pad = q_149_pad_0, pad_type = q_149_pad_type_0, strides = var_7438, weight = unet_up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_305_cast)[name = tensor("q_149_cast")]; + tensor var_7444 = const()[name = tensor("op_7444"), val = tensor([1, 1])]; + tensor var_7446 = const()[name = tensor("op_7446"), val = tensor([1, 1])]; tensor k_149_pad_type_0 = const()[name = tensor("k_149_pad_type_0"), val = tensor("custom")]; tensor k_149_pad_0 = const()[name = tensor("k_149_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_149 = conv(dilations = var_7545, groups = var_6872, pad = k_149_pad_0, pad_type = k_149_pad_type_0, strides = var_7543, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight, x = hidden_states_305)[name = tensor("k_149")]; - tensor var_7549 = const()[name = tensor("op_7549"), val = tensor([1, 1])]; - tensor var_7551 = const()[name = tensor("op_7551"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2800242368)))]; + tensor k_149_cast = conv(dilations = var_7446, groups = var_31, pad = k_149_pad_0, pad_type = k_149_pad_type_0, strides = var_7444, weight = unet_up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_305_cast)[name = tensor("k_149_cast")]; + tensor var_7450 = const()[name = tensor("op_7450"), val = tensor([1, 1])]; + tensor var_7452 = const()[name = tensor("op_7452"), val = tensor([1, 1])]; tensor v_149_pad_type_0 = const()[name = tensor("v_149_pad_type_0"), val = tensor("custom")]; tensor v_149_pad_0 = const()[name = tensor("v_149_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_149 = conv(dilations = var_7551, groups = var_6872, pad = v_149_pad_0, pad_type = v_149_pad_type_0, strides = var_7549, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight, x = hidden_states_305)[name = tensor("v_149")]; - tensor var_7555 = const()[name = tensor("op_7555"), val = tensor([2, 20, 64, -1])]; - tensor var_7556 = reshape(shape = var_7555, x = q_149)[name = tensor("op_7556")]; - tensor var_7557 = const()[name = tensor("op_7557"), val = tensor([2, 20, 64, -1])]; - tensor var_7558 = reshape(shape = var_7557, x = k_149)[name = tensor("op_7558")]; - tensor var_7559 = const()[name = tensor("op_7559"), val = tensor([2, 20, 64, -1])]; - tensor var_7560 = reshape(shape = var_7559, x = v_149)[name = tensor("op_7560")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2803519232)))]; + tensor v_149_cast = conv(dilations = var_7452, groups = var_31, pad = v_149_pad_0, pad_type = v_149_pad_type_0, strides = var_7450, weight = unet_up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_305_cast)[name = tensor("v_149_cast")]; + tensor var_7456 = const()[name = tensor("op_7456"), val = tensor([2, 20, 64, -1])]; + tensor var_7457_cast = reshape(shape = var_7456, x = q_149_cast)[name = tensor("op_7457_cast")]; + tensor var_7458 = const()[name = tensor("op_7458"), val = tensor([2, 20, 64, -1])]; + tensor var_7459_cast = reshape(shape = var_7458, x = k_149_cast)[name = tensor("op_7459_cast")]; + tensor var_7460 = const()[name = tensor("op_7460"), val = tensor([2, 20, 64, -1])]; + tensor var_7461_cast = reshape(shape = var_7460, x = v_149_cast)[name = tensor("op_7461_cast")]; tensor attn_weights_297_transpose_x_0 = const()[name = tensor("attn_weights_297_transpose_x_0"), val = tensor(true)]; tensor attn_weights_297_transpose_y_0 = const()[name = tensor("attn_weights_297_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_297 = matmul(transpose_x = attn_weights_297_transpose_x_0, transpose_y = attn_weights_297_transpose_y_0, x = var_7556, y = var_7558)[name = tensor("attn_weights_297")]; - tensor attn_weights_299 = mul(x = attn_weights_297, y = var_6863)[name = tensor("attn_weights_299")]; - tensor var_7564 = softmax(axis = var_6856, x = attn_weights_299)[name = tensor("op_7564")]; + tensor attn_weights_297_cast = matmul(transpose_x = attn_weights_297_transpose_x_0, transpose_y = attn_weights_297_transpose_y_0, x = var_7457_cast, y = var_7459_cast)[name = tensor("attn_weights_297_cast")]; + tensor attn_weights_299_cast = mul(x = attn_weights_297_cast, y = var_12_to_fp16)[name = tensor("attn_weights_299_cast")]; + tensor var_7465_cast = softmax(axis = var_18, x = attn_weights_299_cast)[name = tensor("op_7465_cast")]; tensor attn_149_transpose_x_0 = const()[name = tensor("attn_149_transpose_x_0"), val = tensor(false)]; tensor attn_149_transpose_y_0 = const()[name = tensor("attn_149_transpose_y_0"), val = tensor(true)]; - tensor attn_149 = matmul(transpose_x = attn_149_transpose_x_0, transpose_y = attn_149_transpose_y_0, x = var_7560, y = var_7564)[name = tensor("attn_149")]; - tensor var_7568 = const()[name = tensor("op_7568"), val = tensor([2, 1280, 1, -1])]; - tensor input_463 = reshape(shape = var_7568, x = attn_149)[name = tensor("input_463")]; - tensor var_7573 = const()[name = tensor("op_7573"), val = tensor([1, 1])]; - tensor var_7575 = const()[name = tensor("op_7575"), val = tensor([1, 1])]; - tensor var_7577_pad_type_0 = const()[name = tensor("op_7577_pad_type_0"), val = tensor("custom")]; - tensor var_7577_pad_0 = const()[name = tensor("op_7577_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7577 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias, dilations = var_7575, groups = var_6872, pad = var_7577_pad_0, pad_type = var_7577_pad_type_0, strides = var_7573, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight, x = input_463)[name = tensor("op_7577")]; - tensor inputs_225 = add(x = var_7577, y = inputs_223)[name = tensor("inputs_225")]; - tensor var_7581 = const()[name = tensor("op_7581"), val = tensor([1])]; - tensor channels_mean_225 = reduce_mean(axes = var_7581, keep_dims = var_6867, x = inputs_225)[name = tensor("channels_mean_225")]; - tensor zero_mean_225 = sub(x = inputs_225, y = channels_mean_225)[name = tensor("zero_mean_225")]; - tensor zero_mean_sq_225 = mul(x = zero_mean_225, y = zero_mean_225)[name = tensor("zero_mean_sq_225")]; - tensor var_7585 = const()[name = tensor("op_7585"), val = tensor([1])]; - tensor var_7586 = reduce_mean(axes = var_7585, keep_dims = var_6867, x = zero_mean_sq_225)[name = tensor("op_7586")]; - tensor var_7587 = const()[name = tensor("op_7587"), val = tensor(0x1.4f8b58p-17)]; - tensor var_7588 = add(x = var_7586, y = var_7587)[name = tensor("op_7588")]; - tensor denom_225_epsilon_0 = const()[name = tensor("denom_225_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_225 = rsqrt(epsilon = denom_225_epsilon_0, x = var_7588)[name = tensor("denom_225")]; - tensor out_225 = mul(x = zero_mean_225, y = denom_225)[name = tensor("out_225")]; - tensor var_7592 = const()[name = tensor("op_7592"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268906752)))]; - tensor var_7593 = add(x = out_225, y = var_7592)[name = tensor("op_7593")]; - tensor var_7595 = const()[name = tensor("op_7595"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268911936)))]; - tensor hidden_states_307 = mul(x = var_7593, y = var_7595)[name = tensor("hidden_states_307")]; - tensor var_7602 = const()[name = tensor("op_7602"), val = tensor([1, 1])]; - tensor var_7604 = const()[name = tensor("op_7604"), val = tensor([1, 1])]; + tensor attn_149_cast = matmul(transpose_x = attn_149_transpose_x_0, transpose_y = attn_149_transpose_y_0, x = var_7461_cast, y = var_7465_cast)[name = tensor("attn_149_cast")]; + tensor var_7469 = const()[name = tensor("op_7469"), val = tensor([2, 1280, 1, -1])]; + tensor input_463_cast = reshape(shape = var_7469, x = attn_149_cast)[name = tensor("input_463_cast")]; + tensor var_7474 = const()[name = tensor("op_7474"), val = tensor([1, 1])]; + tensor var_7476 = const()[name = tensor("op_7476"), val = tensor([1, 1])]; + tensor var_7478_pad_type_0 = const()[name = tensor("op_7478_pad_type_0"), val = tensor("custom")]; + tensor var_7478_pad_0 = const()[name = tensor("op_7478_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2806796096)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2810072960)))]; + tensor var_7478_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_7476, groups = var_31, pad = var_7478_pad_0, pad_type = var_7478_pad_type_0, strides = var_7474, weight = unet_up_blocks_0_attentions_0_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_463_cast)[name = tensor("op_7478_cast")]; + tensor inputs_225_cast = add(x = var_7478_cast, y = inputs_223_cast)[name = tensor("inputs_225_cast")]; + tensor var_7482 = const()[name = tensor("op_7482"), val = tensor([1])]; + tensor channels_mean_225_cast = reduce_mean(axes = var_7482, keep_dims = var_23, x = inputs_225_cast)[name = tensor("channels_mean_225_cast")]; + tensor zero_mean_225_cast = sub(x = inputs_225_cast, y = channels_mean_225_cast)[name = tensor("zero_mean_225_cast")]; + tensor zero_mean_sq_225_cast = mul(x = zero_mean_225_cast, y = zero_mean_225_cast)[name = tensor("zero_mean_sq_225_cast")]; + tensor var_7486 = const()[name = tensor("op_7486"), val = tensor([1])]; + tensor var_7487_cast = reduce_mean(axes = var_7486, keep_dims = var_23, x = zero_mean_sq_225_cast)[name = tensor("op_7487_cast")]; + tensor var_7488_to_fp16 = const()[name = tensor("op_7488_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7489_cast = add(x = var_7487_cast, y = var_7488_to_fp16)[name = tensor("op_7489_cast")]; + tensor denom_225_epsilon_0_to_fp16 = const()[name = tensor("denom_225_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_225_cast = rsqrt(epsilon = denom_225_epsilon_0_to_fp16, x = var_7489_cast)[name = tensor("denom_225_cast")]; + tensor out_225_cast = mul(x = zero_mean_225_cast, y = denom_225_cast)[name = tensor("out_225_cast")]; + tensor var_7493_to_fp16 = const()[name = tensor("op_7493_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2810075584)))]; + tensor var_7494_cast = add(x = out_225_cast, y = var_7493_to_fp16)[name = tensor("op_7494_cast")]; + tensor var_7496_to_fp16 = const()[name = tensor("op_7496_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2810078208)))]; + tensor hidden_states_307_cast = mul(x = var_7494_cast, y = var_7496_to_fp16)[name = tensor("hidden_states_307_cast")]; + tensor var_7503 = const()[name = tensor("op_7503"), val = tensor([1, 1])]; + tensor var_7505 = const()[name = tensor("op_7505"), val = tensor([1, 1])]; tensor q_151_pad_type_0 = const()[name = tensor("q_151_pad_type_0"), val = tensor("custom")]; tensor q_151_pad_0 = const()[name = tensor("q_151_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_151 = conv(dilations = var_7604, groups = var_6872, pad = q_151_pad_0, pad_type = q_151_pad_type_0, strides = var_7602, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight, x = hidden_states_307)[name = tensor("q_151")]; - tensor var_7608 = const()[name = tensor("op_7608"), val = tensor([1, 1])]; - tensor var_7610 = const()[name = tensor("op_7610"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2810080832)))]; + tensor q_151_cast = conv(dilations = var_7505, groups = var_31, pad = q_151_pad_0, pad_type = q_151_pad_type_0, strides = var_7503, weight = unet_up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_307_cast)[name = tensor("q_151_cast")]; + tensor var_7509 = const()[name = tensor("op_7509"), val = tensor([1, 1])]; + tensor var_7511 = const()[name = tensor("op_7511"), val = tensor([1, 1])]; tensor k_151_pad_type_0 = const()[name = tensor("k_151_pad_type_0"), val = tensor("custom")]; tensor k_151_pad_0 = const()[name = tensor("k_151_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_151 = conv(dilations = var_7610, groups = var_6872, pad = k_151_pad_0, pad_type = k_151_pad_type_0, strides = var_7608, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_151")]; - tensor var_7614 = const()[name = tensor("op_7614"), val = tensor([1, 1])]; - tensor var_7616 = const()[name = tensor("op_7616"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2813357696)))]; + tensor k_151_cast = conv(dilations = var_7511, groups = var_31, pad = k_151_pad_0, pad_type = k_151_pad_type_0, strides = var_7509, weight = unet_up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_151_cast")]; + tensor var_7515 = const()[name = tensor("op_7515"), val = tensor([1, 1])]; + tensor var_7517 = const()[name = tensor("op_7517"), val = tensor([1, 1])]; tensor v_151_pad_type_0 = const()[name = tensor("v_151_pad_type_0"), val = tensor("custom")]; tensor v_151_pad_0 = const()[name = tensor("v_151_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_151 = conv(dilations = var_7616, groups = var_6872, pad = v_151_pad_0, pad_type = v_151_pad_type_0, strides = var_7614, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_151")]; - tensor var_7620 = const()[name = tensor("op_7620"), val = tensor([2, 20, 64, -1])]; - tensor var_7621 = reshape(shape = var_7620, x = q_151)[name = tensor("op_7621")]; - tensor var_7622 = const()[name = tensor("op_7622"), val = tensor([2, 20, 64, -1])]; - tensor var_7623 = reshape(shape = var_7622, x = k_151)[name = tensor("op_7623")]; - tensor var_7624 = const()[name = tensor("op_7624"), val = tensor([2, 20, 64, -1])]; - tensor var_7625 = reshape(shape = var_7624, x = v_151)[name = tensor("op_7625")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2818600640)))]; + tensor v_151_cast = conv(dilations = var_7517, groups = var_31, pad = v_151_pad_0, pad_type = v_151_pad_type_0, strides = var_7515, weight = unet_up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_151_cast")]; + tensor var_7521 = const()[name = tensor("op_7521"), val = tensor([2, 20, 64, -1])]; + tensor var_7522_cast = reshape(shape = var_7521, x = q_151_cast)[name = tensor("op_7522_cast")]; + tensor var_7523 = const()[name = tensor("op_7523"), val = tensor([2, 20, 64, -1])]; + tensor var_7524_cast = reshape(shape = var_7523, x = k_151_cast)[name = tensor("op_7524_cast")]; + tensor var_7525 = const()[name = tensor("op_7525"), val = tensor([2, 20, 64, -1])]; + tensor var_7526_cast = reshape(shape = var_7525, x = v_151_cast)[name = tensor("op_7526_cast")]; tensor attn_weights_301_transpose_x_0 = const()[name = tensor("attn_weights_301_transpose_x_0"), val = tensor(true)]; tensor attn_weights_301_transpose_y_0 = const()[name = tensor("attn_weights_301_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_301 = matmul(transpose_x = attn_weights_301_transpose_x_0, transpose_y = attn_weights_301_transpose_y_0, x = var_7621, y = var_7623)[name = tensor("attn_weights_301")]; - tensor attn_weights_303 = mul(x = attn_weights_301, y = var_6863)[name = tensor("attn_weights_303")]; - tensor var_7629 = softmax(axis = var_6856, x = attn_weights_303)[name = tensor("op_7629")]; + tensor attn_weights_301_cast = matmul(transpose_x = attn_weights_301_transpose_x_0, transpose_y = attn_weights_301_transpose_y_0, x = var_7522_cast, y = var_7524_cast)[name = tensor("attn_weights_301_cast")]; + tensor attn_weights_303_cast = mul(x = attn_weights_301_cast, y = var_12_to_fp16)[name = tensor("attn_weights_303_cast")]; + tensor var_7530_cast = softmax(axis = var_18, x = attn_weights_303_cast)[name = tensor("op_7530_cast")]; tensor attn_151_transpose_x_0 = const()[name = tensor("attn_151_transpose_x_0"), val = tensor(false)]; tensor attn_151_transpose_y_0 = const()[name = tensor("attn_151_transpose_y_0"), val = tensor(true)]; - tensor attn_151 = matmul(transpose_x = attn_151_transpose_x_0, transpose_y = attn_151_transpose_y_0, x = var_7625, y = var_7629)[name = tensor("attn_151")]; - tensor var_7633 = const()[name = tensor("op_7633"), val = tensor([2, 1280, 1, -1])]; - tensor input_465 = reshape(shape = var_7633, x = attn_151)[name = tensor("input_465")]; - tensor var_7638 = const()[name = tensor("op_7638"), val = tensor([1, 1])]; - tensor var_7640 = const()[name = tensor("op_7640"), val = tensor([1, 1])]; - tensor var_7642_pad_type_0 = const()[name = tensor("op_7642_pad_type_0"), val = tensor("custom")]; - tensor var_7642_pad_0 = const()[name = tensor("op_7642_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7642 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias, dilations = var_7640, groups = var_6872, pad = var_7642_pad_0, pad_type = var_7642_pad_type_0, strides = var_7638, weight = up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight, x = input_465)[name = tensor("op_7642")]; - tensor inputs_227 = add(x = var_7642, y = inputs_225)[name = tensor("inputs_227")]; - tensor var_7646 = const()[name = tensor("op_7646"), val = tensor([1])]; - tensor channels_mean_227 = reduce_mean(axes = var_7646, keep_dims = var_6867, x = inputs_227)[name = tensor("channels_mean_227")]; - tensor zero_mean_227 = sub(x = inputs_227, y = channels_mean_227)[name = tensor("zero_mean_227")]; - tensor zero_mean_sq_227 = mul(x = zero_mean_227, y = zero_mean_227)[name = tensor("zero_mean_sq_227")]; - tensor var_7650 = const()[name = tensor("op_7650"), val = tensor([1])]; - tensor var_7651 = reduce_mean(axes = var_7650, keep_dims = var_6867, x = zero_mean_sq_227)[name = tensor("op_7651")]; - tensor var_7652 = const()[name = tensor("op_7652"), val = tensor(0x1.4f8b58p-17)]; - tensor var_7653 = add(x = var_7651, y = var_7652)[name = tensor("op_7653")]; - tensor denom_227_epsilon_0 = const()[name = tensor("denom_227_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_227 = rsqrt(epsilon = denom_227_epsilon_0, x = var_7653)[name = tensor("denom_227")]; - tensor out_227 = mul(x = zero_mean_227, y = denom_227)[name = tensor("out_227")]; - tensor var_7657 = const()[name = tensor("op_7657"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268917120)))]; - tensor var_7658 = add(x = out_227, y = var_7657)[name = tensor("op_7658")]; - tensor var_7660 = const()[name = tensor("op_7660"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268922304)))]; - tensor input_467 = mul(x = var_7658, y = var_7660)[name = tensor("input_467")]; - tensor var_7668 = const()[name = tensor("op_7668"), val = tensor([1, 1])]; - tensor var_7670 = const()[name = tensor("op_7670"), val = tensor([1, 1])]; - tensor var_7672_pad_type_0 = const()[name = tensor("op_7672_pad_type_0"), val = tensor("custom")]; - tensor var_7672_pad_0 = const()[name = tensor("op_7672_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7672 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias, dilations = var_7670, groups = var_6872, pad = var_7672_pad_0, pad_type = var_7672_pad_type_0, strides = var_7668, weight = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight, x = input_467)[name = tensor("op_7672")]; - tensor var_7673_split_sizes_0 = const()[name = tensor("op_7673_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_7673_axis_0 = const()[name = tensor("op_7673_axis_0"), val = tensor(1)]; - tensor var_7673_0, tensor var_7673_1 = split(axis = var_7673_axis_0, split_sizes = var_7673_split_sizes_0, x = var_7672)[name = tensor("op_7673")]; - tensor var_7675_mode_0 = const()[name = tensor("op_7675_mode_0"), val = tensor("EXACT")]; - tensor var_7675 = gelu(mode = var_7675_mode_0, x = var_7673_1)[name = tensor("op_7675")]; - tensor input_469 = mul(x = var_7673_0, y = var_7675)[name = tensor("input_469")]; - tensor var_7679 = const()[name = tensor("op_7679"), val = tensor([1, 1])]; - tensor var_7681 = const()[name = tensor("op_7681"), val = tensor([1, 1])]; - tensor var_7683_pad_type_0 = const()[name = tensor("op_7683_pad_type_0"), val = tensor("custom")]; - tensor var_7683_pad_0 = const()[name = tensor("op_7683_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7683 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias, dilations = var_7681, groups = var_6872, pad = var_7683_pad_0, pad_type = var_7683_pad_type_0, strides = var_7679, weight = up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight, x = input_469)[name = tensor("op_7683")]; - tensor inputs_229 = add(x = var_7683, y = inputs_227)[name = tensor("inputs_229")]; - tensor var_7693 = const()[name = tensor("op_7693"), val = tensor([1])]; - tensor channels_mean_229 = reduce_mean(axes = var_7693, keep_dims = var_6867, x = inputs_229)[name = tensor("channels_mean_229")]; - tensor zero_mean_229 = sub(x = inputs_229, y = channels_mean_229)[name = tensor("zero_mean_229")]; - tensor zero_mean_sq_229 = mul(x = zero_mean_229, y = zero_mean_229)[name = tensor("zero_mean_sq_229")]; - tensor var_7697 = const()[name = tensor("op_7697"), val = tensor([1])]; - tensor var_7698 = reduce_mean(axes = var_7697, keep_dims = var_6867, x = zero_mean_sq_229)[name = tensor("op_7698")]; - tensor var_7699 = const()[name = tensor("op_7699"), val = tensor(0x1.4f8b58p-17)]; - tensor var_7700 = add(x = var_7698, y = var_7699)[name = tensor("op_7700")]; - tensor denom_229_epsilon_0 = const()[name = tensor("denom_229_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_229 = rsqrt(epsilon = denom_229_epsilon_0, x = var_7700)[name = tensor("denom_229")]; - tensor out_229 = mul(x = zero_mean_229, y = denom_229)[name = tensor("out_229")]; - tensor var_7704 = const()[name = tensor("op_7704"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268927488)))]; - tensor var_7705 = add(x = out_229, y = var_7704)[name = tensor("op_7705")]; - tensor var_7707 = const()[name = tensor("op_7707"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268932672)))]; - tensor hidden_states_311 = mul(x = var_7705, y = var_7707)[name = tensor("hidden_states_311")]; - tensor var_7714 = const()[name = tensor("op_7714"), val = tensor([1, 1])]; - tensor var_7716 = const()[name = tensor("op_7716"), val = tensor([1, 1])]; + tensor attn_151_cast = matmul(transpose_x = attn_151_transpose_x_0, transpose_y = attn_151_transpose_y_0, x = var_7526_cast, y = var_7530_cast)[name = tensor("attn_151_cast")]; + tensor var_7534 = const()[name = tensor("op_7534"), val = tensor([2, 1280, 1, -1])]; + tensor input_465_cast = reshape(shape = var_7534, x = attn_151_cast)[name = tensor("input_465_cast")]; + tensor var_7539 = const()[name = tensor("op_7539"), val = tensor([1, 1])]; + tensor var_7541 = const()[name = tensor("op_7541"), val = tensor([1, 1])]; + tensor var_7543_pad_type_0 = const()[name = tensor("op_7543_pad_type_0"), val = tensor("custom")]; + tensor var_7543_pad_0 = const()[name = tensor("op_7543_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2823843584)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2827120448)))]; + tensor var_7543_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_7541, groups = var_31, pad = var_7543_pad_0, pad_type = var_7543_pad_type_0, strides = var_7539, weight = unet_up_blocks_0_attentions_0_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_465_cast)[name = tensor("op_7543_cast")]; + tensor inputs_227_cast = add(x = var_7543_cast, y = inputs_225_cast)[name = tensor("inputs_227_cast")]; + tensor var_7547 = const()[name = tensor("op_7547"), val = tensor([1])]; + tensor channels_mean_227_cast = reduce_mean(axes = var_7547, keep_dims = var_23, x = inputs_227_cast)[name = tensor("channels_mean_227_cast")]; + tensor zero_mean_227_cast = sub(x = inputs_227_cast, y = channels_mean_227_cast)[name = tensor("zero_mean_227_cast")]; + tensor zero_mean_sq_227_cast = mul(x = zero_mean_227_cast, y = zero_mean_227_cast)[name = tensor("zero_mean_sq_227_cast")]; + tensor var_7551 = const()[name = tensor("op_7551"), val = tensor([1])]; + tensor var_7552_cast = reduce_mean(axes = var_7551, keep_dims = var_23, x = zero_mean_sq_227_cast)[name = tensor("op_7552_cast")]; + tensor var_7553_to_fp16 = const()[name = tensor("op_7553_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7554_cast = add(x = var_7552_cast, y = var_7553_to_fp16)[name = tensor("op_7554_cast")]; + tensor denom_227_epsilon_0_to_fp16 = const()[name = tensor("denom_227_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_227_cast = rsqrt(epsilon = denom_227_epsilon_0_to_fp16, x = var_7554_cast)[name = tensor("denom_227_cast")]; + tensor out_227_cast = mul(x = zero_mean_227_cast, y = denom_227_cast)[name = tensor("out_227_cast")]; + tensor var_7558_to_fp16 = const()[name = tensor("op_7558_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2827123072)))]; + tensor var_7559_cast = add(x = out_227_cast, y = var_7558_to_fp16)[name = tensor("op_7559_cast")]; + tensor var_7561_to_fp16 = const()[name = tensor("op_7561_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2827125696)))]; + tensor input_467_cast = mul(x = var_7559_cast, y = var_7561_to_fp16)[name = tensor("input_467_cast")]; + tensor var_7569 = const()[name = tensor("op_7569"), val = tensor([1, 1])]; + tensor var_7571 = const()[name = tensor("op_7571"), val = tensor([1, 1])]; + tensor var_7573_pad_type_0 = const()[name = tensor("op_7573_pad_type_0"), val = tensor("custom")]; + tensor var_7573_pad_0 = const()[name = tensor("op_7573_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2827128320)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2853342784)))]; + tensor var_7573_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_7571, groups = var_31, pad = var_7573_pad_0, pad_type = var_7573_pad_type_0, strides = var_7569, weight = unet_up_blocks_0_attentions_0_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_467_cast)[name = tensor("op_7573_cast")]; + tensor var_7574_split_sizes_0 = const()[name = tensor("op_7574_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7574_axis_0 = const()[name = tensor("op_7574_axis_0"), val = tensor(1)]; + tensor var_7574_cast_0, tensor var_7574_cast_1 = split(axis = var_7574_axis_0, split_sizes = var_7574_split_sizes_0, x = var_7573_cast)[name = tensor("op_7574_cast")]; + tensor var_7576_mode_0 = const()[name = tensor("op_7576_mode_0"), val = tensor("EXACT")]; + tensor var_7576_cast = gelu(mode = var_7576_mode_0, x = var_7574_cast_1)[name = tensor("op_7576_cast")]; + tensor input_469_cast = mul(x = var_7574_cast_0, y = var_7576_cast)[name = tensor("input_469_cast")]; + tensor var_7580 = const()[name = tensor("op_7580"), val = tensor([1, 1])]; + tensor var_7582 = const()[name = tensor("op_7582"), val = tensor([1, 1])]; + tensor var_7584_pad_type_0 = const()[name = tensor("op_7584_pad_type_0"), val = tensor("custom")]; + tensor var_7584_pad_0 = const()[name = tensor("op_7584_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2853363328)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2866470592)))]; + tensor var_7584_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_7582, groups = var_31, pad = var_7584_pad_0, pad_type = var_7584_pad_type_0, strides = var_7580, weight = unet_up_blocks_0_attentions_0_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_469_cast)[name = tensor("op_7584_cast")]; + tensor inputs_229_cast = add(x = var_7584_cast, y = inputs_227_cast)[name = tensor("inputs_229_cast")]; + tensor var_7594 = const()[name = tensor("op_7594"), val = tensor([1])]; + tensor channels_mean_229_cast = reduce_mean(axes = var_7594, keep_dims = var_23, x = inputs_229_cast)[name = tensor("channels_mean_229_cast")]; + tensor zero_mean_229_cast = sub(x = inputs_229_cast, y = channels_mean_229_cast)[name = tensor("zero_mean_229_cast")]; + tensor zero_mean_sq_229_cast = mul(x = zero_mean_229_cast, y = zero_mean_229_cast)[name = tensor("zero_mean_sq_229_cast")]; + tensor var_7598 = const()[name = tensor("op_7598"), val = tensor([1])]; + tensor var_7599_cast = reduce_mean(axes = var_7598, keep_dims = var_23, x = zero_mean_sq_229_cast)[name = tensor("op_7599_cast")]; + tensor var_7600_to_fp16 = const()[name = tensor("op_7600_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7601_cast = add(x = var_7599_cast, y = var_7600_to_fp16)[name = tensor("op_7601_cast")]; + tensor denom_229_epsilon_0_to_fp16 = const()[name = tensor("denom_229_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_229_cast = rsqrt(epsilon = denom_229_epsilon_0_to_fp16, x = var_7601_cast)[name = tensor("denom_229_cast")]; + tensor out_229_cast = mul(x = zero_mean_229_cast, y = denom_229_cast)[name = tensor("out_229_cast")]; + tensor var_7605_to_fp16 = const()[name = tensor("op_7605_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2866473216)))]; + tensor var_7606_cast = add(x = out_229_cast, y = var_7605_to_fp16)[name = tensor("op_7606_cast")]; + tensor var_7608_to_fp16 = const()[name = tensor("op_7608_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2866475840)))]; + tensor hidden_states_311_cast = mul(x = var_7606_cast, y = var_7608_to_fp16)[name = tensor("hidden_states_311_cast")]; + tensor var_7615 = const()[name = tensor("op_7615"), val = tensor([1, 1])]; + tensor var_7617 = const()[name = tensor("op_7617"), val = tensor([1, 1])]; tensor q_153_pad_type_0 = const()[name = tensor("q_153_pad_type_0"), val = tensor("custom")]; tensor q_153_pad_0 = const()[name = tensor("q_153_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_153 = conv(dilations = var_7716, groups = var_6872, pad = q_153_pad_0, pad_type = q_153_pad_type_0, strides = var_7714, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight, x = hidden_states_311)[name = tensor("q_153")]; - tensor var_7720 = const()[name = tensor("op_7720"), val = tensor([1, 1])]; - tensor var_7722 = const()[name = tensor("op_7722"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2866478464)))]; + tensor q_153_cast = conv(dilations = var_7617, groups = var_31, pad = q_153_pad_0, pad_type = q_153_pad_type_0, strides = var_7615, weight = unet_up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_q_weight_to_fp16, x = hidden_states_311_cast)[name = tensor("q_153_cast")]; + tensor var_7621 = const()[name = tensor("op_7621"), val = tensor([1, 1])]; + tensor var_7623 = const()[name = tensor("op_7623"), val = tensor([1, 1])]; tensor k_153_pad_type_0 = const()[name = tensor("k_153_pad_type_0"), val = tensor("custom")]; tensor k_153_pad_0 = const()[name = tensor("k_153_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_153 = conv(dilations = var_7722, groups = var_6872, pad = k_153_pad_0, pad_type = k_153_pad_type_0, strides = var_7720, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight, x = hidden_states_311)[name = tensor("k_153")]; - tensor var_7726 = const()[name = tensor("op_7726"), val = tensor([1, 1])]; - tensor var_7728 = const()[name = tensor("op_7728"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2869755328)))]; + tensor k_153_cast = conv(dilations = var_7623, groups = var_31, pad = k_153_pad_0, pad_type = k_153_pad_type_0, strides = var_7621, weight = unet_up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_k_weight_to_fp16, x = hidden_states_311_cast)[name = tensor("k_153_cast")]; + tensor var_7627 = const()[name = tensor("op_7627"), val = tensor([1, 1])]; + tensor var_7629 = const()[name = tensor("op_7629"), val = tensor([1, 1])]; tensor v_153_pad_type_0 = const()[name = tensor("v_153_pad_type_0"), val = tensor("custom")]; tensor v_153_pad_0 = const()[name = tensor("v_153_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_153 = conv(dilations = var_7728, groups = var_6872, pad = v_153_pad_0, pad_type = v_153_pad_type_0, strides = var_7726, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight, x = hidden_states_311)[name = tensor("v_153")]; - tensor var_7732 = const()[name = tensor("op_7732"), val = tensor([2, 20, 64, -1])]; - tensor var_7733 = reshape(shape = var_7732, x = q_153)[name = tensor("op_7733")]; - tensor var_7734 = const()[name = tensor("op_7734"), val = tensor([2, 20, 64, -1])]; - tensor var_7735 = reshape(shape = var_7734, x = k_153)[name = tensor("op_7735")]; - tensor var_7736 = const()[name = tensor("op_7736"), val = tensor([2, 20, 64, -1])]; - tensor var_7737 = reshape(shape = var_7736, x = v_153)[name = tensor("op_7737")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2873032192)))]; + tensor v_153_cast = conv(dilations = var_7629, groups = var_31, pad = v_153_pad_0, pad_type = v_153_pad_type_0, strides = var_7627, weight = unet_up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_v_weight_to_fp16, x = hidden_states_311_cast)[name = tensor("v_153_cast")]; + tensor var_7633 = const()[name = tensor("op_7633"), val = tensor([2, 20, 64, -1])]; + tensor var_7634_cast = reshape(shape = var_7633, x = q_153_cast)[name = tensor("op_7634_cast")]; + tensor var_7635 = const()[name = tensor("op_7635"), val = tensor([2, 20, 64, -1])]; + tensor var_7636_cast = reshape(shape = var_7635, x = k_153_cast)[name = tensor("op_7636_cast")]; + tensor var_7637 = const()[name = tensor("op_7637"), val = tensor([2, 20, 64, -1])]; + tensor var_7638_cast = reshape(shape = var_7637, x = v_153_cast)[name = tensor("op_7638_cast")]; tensor attn_weights_305_transpose_x_0 = const()[name = tensor("attn_weights_305_transpose_x_0"), val = tensor(true)]; tensor attn_weights_305_transpose_y_0 = const()[name = tensor("attn_weights_305_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_305 = matmul(transpose_x = attn_weights_305_transpose_x_0, transpose_y = attn_weights_305_transpose_y_0, x = var_7733, y = var_7735)[name = tensor("attn_weights_305")]; - tensor attn_weights_307 = mul(x = attn_weights_305, y = var_6863)[name = tensor("attn_weights_307")]; - tensor var_7741 = softmax(axis = var_6856, x = attn_weights_307)[name = tensor("op_7741")]; + tensor attn_weights_305_cast = matmul(transpose_x = attn_weights_305_transpose_x_0, transpose_y = attn_weights_305_transpose_y_0, x = var_7634_cast, y = var_7636_cast)[name = tensor("attn_weights_305_cast")]; + tensor attn_weights_307_cast = mul(x = attn_weights_305_cast, y = var_12_to_fp16)[name = tensor("attn_weights_307_cast")]; + tensor var_7642_cast = softmax(axis = var_18, x = attn_weights_307_cast)[name = tensor("op_7642_cast")]; tensor attn_153_transpose_x_0 = const()[name = tensor("attn_153_transpose_x_0"), val = tensor(false)]; tensor attn_153_transpose_y_0 = const()[name = tensor("attn_153_transpose_y_0"), val = tensor(true)]; - tensor attn_153 = matmul(transpose_x = attn_153_transpose_x_0, transpose_y = attn_153_transpose_y_0, x = var_7737, y = var_7741)[name = tensor("attn_153")]; - tensor var_7745 = const()[name = tensor("op_7745"), val = tensor([2, 1280, 1, -1])]; - tensor input_471 = reshape(shape = var_7745, x = attn_153)[name = tensor("input_471")]; - tensor var_7750 = const()[name = tensor("op_7750"), val = tensor([1, 1])]; - tensor var_7752 = const()[name = tensor("op_7752"), val = tensor([1, 1])]; - tensor var_7754_pad_type_0 = const()[name = tensor("op_7754_pad_type_0"), val = tensor("custom")]; - tensor var_7754_pad_0 = const()[name = tensor("op_7754_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7754 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias, dilations = var_7752, groups = var_6872, pad = var_7754_pad_0, pad_type = var_7754_pad_type_0, strides = var_7750, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight, x = input_471)[name = tensor("op_7754")]; - tensor inputs_231 = add(x = var_7754, y = inputs_229)[name = tensor("inputs_231")]; - tensor var_7758 = const()[name = tensor("op_7758"), val = tensor([1])]; - tensor channels_mean_231 = reduce_mean(axes = var_7758, keep_dims = var_6867, x = inputs_231)[name = tensor("channels_mean_231")]; - tensor zero_mean_231 = sub(x = inputs_231, y = channels_mean_231)[name = tensor("zero_mean_231")]; - tensor zero_mean_sq_231 = mul(x = zero_mean_231, y = zero_mean_231)[name = tensor("zero_mean_sq_231")]; - tensor var_7762 = const()[name = tensor("op_7762"), val = tensor([1])]; - tensor var_7763 = reduce_mean(axes = var_7762, keep_dims = var_6867, x = zero_mean_sq_231)[name = tensor("op_7763")]; - tensor var_7764 = const()[name = tensor("op_7764"), val = tensor(0x1.4f8b58p-17)]; - tensor var_7765 = add(x = var_7763, y = var_7764)[name = tensor("op_7765")]; - tensor denom_231_epsilon_0 = const()[name = tensor("denom_231_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_231 = rsqrt(epsilon = denom_231_epsilon_0, x = var_7765)[name = tensor("denom_231")]; - tensor out_231 = mul(x = zero_mean_231, y = denom_231)[name = tensor("out_231")]; - tensor var_7769 = const()[name = tensor("op_7769"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268937856)))]; - tensor var_7770 = add(x = out_231, y = var_7769)[name = tensor("op_7770")]; - tensor var_7772 = const()[name = tensor("op_7772"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268943040)))]; - tensor hidden_states_313 = mul(x = var_7770, y = var_7772)[name = tensor("hidden_states_313")]; - tensor var_7779 = const()[name = tensor("op_7779"), val = tensor([1, 1])]; - tensor var_7781 = const()[name = tensor("op_7781"), val = tensor([1, 1])]; + tensor attn_153_cast = matmul(transpose_x = attn_153_transpose_x_0, transpose_y = attn_153_transpose_y_0, x = var_7638_cast, y = var_7642_cast)[name = tensor("attn_153_cast")]; + tensor var_7646 = const()[name = tensor("op_7646"), val = tensor([2, 1280, 1, -1])]; + tensor input_471_cast = reshape(shape = var_7646, x = attn_153_cast)[name = tensor("input_471_cast")]; + tensor var_7651 = const()[name = tensor("op_7651"), val = tensor([1, 1])]; + tensor var_7653 = const()[name = tensor("op_7653"), val = tensor([1, 1])]; + tensor var_7655_pad_type_0 = const()[name = tensor("op_7655_pad_type_0"), val = tensor("custom")]; + tensor var_7655_pad_0 = const()[name = tensor("op_7655_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2876309056)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2879585920)))]; + tensor var_7655_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_7653, groups = var_31, pad = var_7655_pad_0, pad_type = var_7655_pad_type_0, strides = var_7651, weight = unet_up_blocks_0_attentions_0_transformer_blocks_4_attn1_to_out_0_weight_to_fp16, x = input_471_cast)[name = tensor("op_7655_cast")]; + tensor inputs_231_cast = add(x = var_7655_cast, y = inputs_229_cast)[name = tensor("inputs_231_cast")]; + tensor var_7659 = const()[name = tensor("op_7659"), val = tensor([1])]; + tensor channels_mean_231_cast = reduce_mean(axes = var_7659, keep_dims = var_23, x = inputs_231_cast)[name = tensor("channels_mean_231_cast")]; + tensor zero_mean_231_cast = sub(x = inputs_231_cast, y = channels_mean_231_cast)[name = tensor("zero_mean_231_cast")]; + tensor zero_mean_sq_231_cast = mul(x = zero_mean_231_cast, y = zero_mean_231_cast)[name = tensor("zero_mean_sq_231_cast")]; + tensor var_7663 = const()[name = tensor("op_7663"), val = tensor([1])]; + tensor var_7664_cast = reduce_mean(axes = var_7663, keep_dims = var_23, x = zero_mean_sq_231_cast)[name = tensor("op_7664_cast")]; + tensor var_7665_to_fp16 = const()[name = tensor("op_7665_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7666_cast = add(x = var_7664_cast, y = var_7665_to_fp16)[name = tensor("op_7666_cast")]; + tensor denom_231_epsilon_0_to_fp16 = const()[name = tensor("denom_231_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_231_cast = rsqrt(epsilon = denom_231_epsilon_0_to_fp16, x = var_7666_cast)[name = tensor("denom_231_cast")]; + tensor out_231_cast = mul(x = zero_mean_231_cast, y = denom_231_cast)[name = tensor("out_231_cast")]; + tensor var_7670_to_fp16 = const()[name = tensor("op_7670_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2879588544)))]; + tensor var_7671_cast = add(x = out_231_cast, y = var_7670_to_fp16)[name = tensor("op_7671_cast")]; + tensor var_7673_to_fp16 = const()[name = tensor("op_7673_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2879591168)))]; + tensor hidden_states_313_cast = mul(x = var_7671_cast, y = var_7673_to_fp16)[name = tensor("hidden_states_313_cast")]; + tensor var_7680 = const()[name = tensor("op_7680"), val = tensor([1, 1])]; + tensor var_7682 = const()[name = tensor("op_7682"), val = tensor([1, 1])]; tensor q_155_pad_type_0 = const()[name = tensor("q_155_pad_type_0"), val = tensor("custom")]; tensor q_155_pad_0 = const()[name = tensor("q_155_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_155 = conv(dilations = var_7781, groups = var_6872, pad = q_155_pad_0, pad_type = q_155_pad_type_0, strides = var_7779, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight, x = hidden_states_313)[name = tensor("q_155")]; - tensor var_7785 = const()[name = tensor("op_7785"), val = tensor([1, 1])]; - tensor var_7787 = const()[name = tensor("op_7787"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2879593792)))]; + tensor q_155_cast = conv(dilations = var_7682, groups = var_31, pad = q_155_pad_0, pad_type = q_155_pad_type_0, strides = var_7680, weight = unet_up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_q_weight_to_fp16, x = hidden_states_313_cast)[name = tensor("q_155_cast")]; + tensor var_7686 = const()[name = tensor("op_7686"), val = tensor([1, 1])]; + tensor var_7688 = const()[name = tensor("op_7688"), val = tensor([1, 1])]; tensor k_155_pad_type_0 = const()[name = tensor("k_155_pad_type_0"), val = tensor("custom")]; tensor k_155_pad_0 = const()[name = tensor("k_155_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_155 = conv(dilations = var_7787, groups = var_6872, pad = k_155_pad_0, pad_type = k_155_pad_type_0, strides = var_7785, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_155")]; - tensor var_7791 = const()[name = tensor("op_7791"), val = tensor([1, 1])]; - tensor var_7793 = const()[name = tensor("op_7793"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2882870656)))]; + tensor k_155_cast = conv(dilations = var_7688, groups = var_31, pad = k_155_pad_0, pad_type = k_155_pad_type_0, strides = var_7686, weight = unet_up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_155_cast")]; + tensor var_7692 = const()[name = tensor("op_7692"), val = tensor([1, 1])]; + tensor var_7694 = const()[name = tensor("op_7694"), val = tensor([1, 1])]; tensor v_155_pad_type_0 = const()[name = tensor("v_155_pad_type_0"), val = tensor("custom")]; tensor v_155_pad_0 = const()[name = tensor("v_155_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_155 = conv(dilations = var_7793, groups = var_6872, pad = v_155_pad_0, pad_type = v_155_pad_type_0, strides = var_7791, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_155")]; - tensor var_7797 = const()[name = tensor("op_7797"), val = tensor([2, 20, 64, -1])]; - tensor var_7798 = reshape(shape = var_7797, x = q_155)[name = tensor("op_7798")]; - tensor var_7799 = const()[name = tensor("op_7799"), val = tensor([2, 20, 64, -1])]; - tensor var_7800 = reshape(shape = var_7799, x = k_155)[name = tensor("op_7800")]; - tensor var_7801 = const()[name = tensor("op_7801"), val = tensor([2, 20, 64, -1])]; - tensor var_7802 = reshape(shape = var_7801, x = v_155)[name = tensor("op_7802")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2888113600)))]; + tensor v_155_cast = conv(dilations = var_7694, groups = var_31, pad = v_155_pad_0, pad_type = v_155_pad_type_0, strides = var_7692, weight = unet_up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_155_cast")]; + tensor var_7698 = const()[name = tensor("op_7698"), val = tensor([2, 20, 64, -1])]; + tensor var_7699_cast = reshape(shape = var_7698, x = q_155_cast)[name = tensor("op_7699_cast")]; + tensor var_7700 = const()[name = tensor("op_7700"), val = tensor([2, 20, 64, -1])]; + tensor var_7701_cast = reshape(shape = var_7700, x = k_155_cast)[name = tensor("op_7701_cast")]; + tensor var_7702 = const()[name = tensor("op_7702"), val = tensor([2, 20, 64, -1])]; + tensor var_7703_cast = reshape(shape = var_7702, x = v_155_cast)[name = tensor("op_7703_cast")]; tensor attn_weights_309_transpose_x_0 = const()[name = tensor("attn_weights_309_transpose_x_0"), val = tensor(true)]; tensor attn_weights_309_transpose_y_0 = const()[name = tensor("attn_weights_309_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_309 = matmul(transpose_x = attn_weights_309_transpose_x_0, transpose_y = attn_weights_309_transpose_y_0, x = var_7798, y = var_7800)[name = tensor("attn_weights_309")]; - tensor attn_weights_311 = mul(x = attn_weights_309, y = var_6863)[name = tensor("attn_weights_311")]; - tensor var_7806 = softmax(axis = var_6856, x = attn_weights_311)[name = tensor("op_7806")]; + tensor attn_weights_309_cast = matmul(transpose_x = attn_weights_309_transpose_x_0, transpose_y = attn_weights_309_transpose_y_0, x = var_7699_cast, y = var_7701_cast)[name = tensor("attn_weights_309_cast")]; + tensor attn_weights_311_cast = mul(x = attn_weights_309_cast, y = var_12_to_fp16)[name = tensor("attn_weights_311_cast")]; + tensor var_7707_cast = softmax(axis = var_18, x = attn_weights_311_cast)[name = tensor("op_7707_cast")]; tensor attn_155_transpose_x_0 = const()[name = tensor("attn_155_transpose_x_0"), val = tensor(false)]; tensor attn_155_transpose_y_0 = const()[name = tensor("attn_155_transpose_y_0"), val = tensor(true)]; - tensor attn_155 = matmul(transpose_x = attn_155_transpose_x_0, transpose_y = attn_155_transpose_y_0, x = var_7802, y = var_7806)[name = tensor("attn_155")]; - tensor var_7810 = const()[name = tensor("op_7810"), val = tensor([2, 1280, 1, -1])]; - tensor input_473 = reshape(shape = var_7810, x = attn_155)[name = tensor("input_473")]; - tensor var_7815 = const()[name = tensor("op_7815"), val = tensor([1, 1])]; - tensor var_7817 = const()[name = tensor("op_7817"), val = tensor([1, 1])]; - tensor var_7819_pad_type_0 = const()[name = tensor("op_7819_pad_type_0"), val = tensor("custom")]; - tensor var_7819_pad_0 = const()[name = tensor("op_7819_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7819 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias, dilations = var_7817, groups = var_6872, pad = var_7819_pad_0, pad_type = var_7819_pad_type_0, strides = var_7815, weight = up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight, x = input_473)[name = tensor("op_7819")]; - tensor inputs_233 = add(x = var_7819, y = inputs_231)[name = tensor("inputs_233")]; - tensor var_7823 = const()[name = tensor("op_7823"), val = tensor([1])]; - tensor channels_mean_233 = reduce_mean(axes = var_7823, keep_dims = var_6867, x = inputs_233)[name = tensor("channels_mean_233")]; - tensor zero_mean_233 = sub(x = inputs_233, y = channels_mean_233)[name = tensor("zero_mean_233")]; - tensor zero_mean_sq_233 = mul(x = zero_mean_233, y = zero_mean_233)[name = tensor("zero_mean_sq_233")]; - tensor var_7827 = const()[name = tensor("op_7827"), val = tensor([1])]; - tensor var_7828 = reduce_mean(axes = var_7827, keep_dims = var_6867, x = zero_mean_sq_233)[name = tensor("op_7828")]; - tensor var_7829 = const()[name = tensor("op_7829"), val = tensor(0x1.4f8b58p-17)]; - tensor var_7830 = add(x = var_7828, y = var_7829)[name = tensor("op_7830")]; - tensor denom_233_epsilon_0 = const()[name = tensor("denom_233_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_233 = rsqrt(epsilon = denom_233_epsilon_0, x = var_7830)[name = tensor("denom_233")]; - tensor out_233 = mul(x = zero_mean_233, y = denom_233)[name = tensor("out_233")]; - tensor var_7834 = const()[name = tensor("op_7834"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268948224)))]; - tensor var_7835 = add(x = out_233, y = var_7834)[name = tensor("op_7835")]; - tensor var_7837 = const()[name = tensor("op_7837"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268953408)))]; - tensor input_475 = mul(x = var_7835, y = var_7837)[name = tensor("input_475")]; - tensor var_7845 = const()[name = tensor("op_7845"), val = tensor([1, 1])]; - tensor var_7847 = const()[name = tensor("op_7847"), val = tensor([1, 1])]; - tensor var_7849_pad_type_0 = const()[name = tensor("op_7849_pad_type_0"), val = tensor("custom")]; - tensor var_7849_pad_0 = const()[name = tensor("op_7849_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7849 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias, dilations = var_7847, groups = var_6872, pad = var_7849_pad_0, pad_type = var_7849_pad_type_0, strides = var_7845, weight = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight, x = input_475)[name = tensor("op_7849")]; - tensor var_7850_split_sizes_0 = const()[name = tensor("op_7850_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_7850_axis_0 = const()[name = tensor("op_7850_axis_0"), val = tensor(1)]; - tensor var_7850_0, tensor var_7850_1 = split(axis = var_7850_axis_0, split_sizes = var_7850_split_sizes_0, x = var_7849)[name = tensor("op_7850")]; - tensor var_7852_mode_0 = const()[name = tensor("op_7852_mode_0"), val = tensor("EXACT")]; - tensor var_7852 = gelu(mode = var_7852_mode_0, x = var_7850_1)[name = tensor("op_7852")]; - tensor input_477 = mul(x = var_7850_0, y = var_7852)[name = tensor("input_477")]; - tensor var_7856 = const()[name = tensor("op_7856"), val = tensor([1, 1])]; - tensor var_7858 = const()[name = tensor("op_7858"), val = tensor([1, 1])]; - tensor var_7860_pad_type_0 = const()[name = tensor("op_7860_pad_type_0"), val = tensor("custom")]; - tensor var_7860_pad_0 = const()[name = tensor("op_7860_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7860 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias, dilations = var_7858, groups = var_6872, pad = var_7860_pad_0, pad_type = var_7860_pad_type_0, strides = var_7856, weight = up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight, x = input_477)[name = tensor("op_7860")]; - tensor inputs_235 = add(x = var_7860, y = inputs_233)[name = tensor("inputs_235")]; - tensor var_7870 = const()[name = tensor("op_7870"), val = tensor([1])]; - tensor channels_mean_235 = reduce_mean(axes = var_7870, keep_dims = var_6867, x = inputs_235)[name = tensor("channels_mean_235")]; - tensor zero_mean_235 = sub(x = inputs_235, y = channels_mean_235)[name = tensor("zero_mean_235")]; - tensor zero_mean_sq_235 = mul(x = zero_mean_235, y = zero_mean_235)[name = tensor("zero_mean_sq_235")]; - tensor var_7874 = const()[name = tensor("op_7874"), val = tensor([1])]; - tensor var_7875 = reduce_mean(axes = var_7874, keep_dims = var_6867, x = zero_mean_sq_235)[name = tensor("op_7875")]; - tensor var_7876 = const()[name = tensor("op_7876"), val = tensor(0x1.4f8b58p-17)]; - tensor var_7877 = add(x = var_7875, y = var_7876)[name = tensor("op_7877")]; - tensor denom_235_epsilon_0 = const()[name = tensor("denom_235_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_235 = rsqrt(epsilon = denom_235_epsilon_0, x = var_7877)[name = tensor("denom_235")]; - tensor out_235 = mul(x = zero_mean_235, y = denom_235)[name = tensor("out_235")]; - tensor var_7881 = const()[name = tensor("op_7881"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268958592)))]; - tensor var_7882 = add(x = out_235, y = var_7881)[name = tensor("op_7882")]; - tensor var_7884 = const()[name = tensor("op_7884"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268963776)))]; - tensor hidden_states_317 = mul(x = var_7882, y = var_7884)[name = tensor("hidden_states_317")]; - tensor var_7891 = const()[name = tensor("op_7891"), val = tensor([1, 1])]; - tensor var_7893 = const()[name = tensor("op_7893"), val = tensor([1, 1])]; + tensor attn_155_cast = matmul(transpose_x = attn_155_transpose_x_0, transpose_y = attn_155_transpose_y_0, x = var_7703_cast, y = var_7707_cast)[name = tensor("attn_155_cast")]; + tensor var_7711 = const()[name = tensor("op_7711"), val = tensor([2, 1280, 1, -1])]; + tensor input_473_cast = reshape(shape = var_7711, x = attn_155_cast)[name = tensor("input_473_cast")]; + tensor var_7716 = const()[name = tensor("op_7716"), val = tensor([1, 1])]; + tensor var_7718 = const()[name = tensor("op_7718"), val = tensor([1, 1])]; + tensor var_7720_pad_type_0 = const()[name = tensor("op_7720_pad_type_0"), val = tensor("custom")]; + tensor var_7720_pad_0 = const()[name = tensor("op_7720_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2893356544)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2896633408)))]; + tensor var_7720_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_7718, groups = var_31, pad = var_7720_pad_0, pad_type = var_7720_pad_type_0, strides = var_7716, weight = unet_up_blocks_0_attentions_0_transformer_blocks_4_attn2_to_out_0_weight_to_fp16, x = input_473_cast)[name = tensor("op_7720_cast")]; + tensor inputs_233_cast = add(x = var_7720_cast, y = inputs_231_cast)[name = tensor("inputs_233_cast")]; + tensor var_7724 = const()[name = tensor("op_7724"), val = tensor([1])]; + tensor channels_mean_233_cast = reduce_mean(axes = var_7724, keep_dims = var_23, x = inputs_233_cast)[name = tensor("channels_mean_233_cast")]; + tensor zero_mean_233_cast = sub(x = inputs_233_cast, y = channels_mean_233_cast)[name = tensor("zero_mean_233_cast")]; + tensor zero_mean_sq_233_cast = mul(x = zero_mean_233_cast, y = zero_mean_233_cast)[name = tensor("zero_mean_sq_233_cast")]; + tensor var_7728 = const()[name = tensor("op_7728"), val = tensor([1])]; + tensor var_7729_cast = reduce_mean(axes = var_7728, keep_dims = var_23, x = zero_mean_sq_233_cast)[name = tensor("op_7729_cast")]; + tensor var_7730_to_fp16 = const()[name = tensor("op_7730_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7731_cast = add(x = var_7729_cast, y = var_7730_to_fp16)[name = tensor("op_7731_cast")]; + tensor denom_233_epsilon_0_to_fp16 = const()[name = tensor("denom_233_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_233_cast = rsqrt(epsilon = denom_233_epsilon_0_to_fp16, x = var_7731_cast)[name = tensor("denom_233_cast")]; + tensor out_233_cast = mul(x = zero_mean_233_cast, y = denom_233_cast)[name = tensor("out_233_cast")]; + tensor var_7735_to_fp16 = const()[name = tensor("op_7735_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2896636032)))]; + tensor var_7736_cast = add(x = out_233_cast, y = var_7735_to_fp16)[name = tensor("op_7736_cast")]; + tensor var_7738_to_fp16 = const()[name = tensor("op_7738_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2896638656)))]; + tensor input_475_cast = mul(x = var_7736_cast, y = var_7738_to_fp16)[name = tensor("input_475_cast")]; + tensor var_7746 = const()[name = tensor("op_7746"), val = tensor([1, 1])]; + tensor var_7748 = const()[name = tensor("op_7748"), val = tensor([1, 1])]; + tensor var_7750_pad_type_0 = const()[name = tensor("op_7750_pad_type_0"), val = tensor("custom")]; + tensor var_7750_pad_0 = const()[name = tensor("op_7750_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2896641280)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2922855744)))]; + tensor var_7750_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_7748, groups = var_31, pad = var_7750_pad_0, pad_type = var_7750_pad_type_0, strides = var_7746, weight = unet_up_blocks_0_attentions_0_transformer_blocks_4_ff_net_0_proj_weight_to_fp16, x = input_475_cast)[name = tensor("op_7750_cast")]; + tensor var_7751_split_sizes_0 = const()[name = tensor("op_7751_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7751_axis_0 = const()[name = tensor("op_7751_axis_0"), val = tensor(1)]; + tensor var_7751_cast_0, tensor var_7751_cast_1 = split(axis = var_7751_axis_0, split_sizes = var_7751_split_sizes_0, x = var_7750_cast)[name = tensor("op_7751_cast")]; + tensor var_7753_mode_0 = const()[name = tensor("op_7753_mode_0"), val = tensor("EXACT")]; + tensor var_7753_cast = gelu(mode = var_7753_mode_0, x = var_7751_cast_1)[name = tensor("op_7753_cast")]; + tensor input_477_cast = mul(x = var_7751_cast_0, y = var_7753_cast)[name = tensor("input_477_cast")]; + tensor var_7757 = const()[name = tensor("op_7757"), val = tensor([1, 1])]; + tensor var_7759 = const()[name = tensor("op_7759"), val = tensor([1, 1])]; + tensor var_7761_pad_type_0 = const()[name = tensor("op_7761_pad_type_0"), val = tensor("custom")]; + tensor var_7761_pad_0 = const()[name = tensor("op_7761_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2922876288)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2935983552)))]; + tensor var_7761_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_7759, groups = var_31, pad = var_7761_pad_0, pad_type = var_7761_pad_type_0, strides = var_7757, weight = unet_up_blocks_0_attentions_0_transformer_blocks_4_ff_net_2_weight_to_fp16, x = input_477_cast)[name = tensor("op_7761_cast")]; + tensor inputs_235_cast = add(x = var_7761_cast, y = inputs_233_cast)[name = tensor("inputs_235_cast")]; + tensor var_7771 = const()[name = tensor("op_7771"), val = tensor([1])]; + tensor channels_mean_235_cast = reduce_mean(axes = var_7771, keep_dims = var_23, x = inputs_235_cast)[name = tensor("channels_mean_235_cast")]; + tensor zero_mean_235_cast = sub(x = inputs_235_cast, y = channels_mean_235_cast)[name = tensor("zero_mean_235_cast")]; + tensor zero_mean_sq_235_cast = mul(x = zero_mean_235_cast, y = zero_mean_235_cast)[name = tensor("zero_mean_sq_235_cast")]; + tensor var_7775 = const()[name = tensor("op_7775"), val = tensor([1])]; + tensor var_7776_cast = reduce_mean(axes = var_7775, keep_dims = var_23, x = zero_mean_sq_235_cast)[name = tensor("op_7776_cast")]; + tensor var_7777_to_fp16 = const()[name = tensor("op_7777_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7778_cast = add(x = var_7776_cast, y = var_7777_to_fp16)[name = tensor("op_7778_cast")]; + tensor denom_235_epsilon_0_to_fp16 = const()[name = tensor("denom_235_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_235_cast = rsqrt(epsilon = denom_235_epsilon_0_to_fp16, x = var_7778_cast)[name = tensor("denom_235_cast")]; + tensor out_235_cast = mul(x = zero_mean_235_cast, y = denom_235_cast)[name = tensor("out_235_cast")]; + tensor var_7782_to_fp16 = const()[name = tensor("op_7782_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2935986176)))]; + tensor var_7783_cast = add(x = out_235_cast, y = var_7782_to_fp16)[name = tensor("op_7783_cast")]; + tensor var_7785_to_fp16 = const()[name = tensor("op_7785_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2935988800)))]; + tensor hidden_states_317_cast = mul(x = var_7783_cast, y = var_7785_to_fp16)[name = tensor("hidden_states_317_cast")]; + tensor var_7792 = const()[name = tensor("op_7792"), val = tensor([1, 1])]; + tensor var_7794 = const()[name = tensor("op_7794"), val = tensor([1, 1])]; tensor q_157_pad_type_0 = const()[name = tensor("q_157_pad_type_0"), val = tensor("custom")]; tensor q_157_pad_0 = const()[name = tensor("q_157_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_157 = conv(dilations = var_7893, groups = var_6872, pad = q_157_pad_0, pad_type = q_157_pad_type_0, strides = var_7891, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight, x = hidden_states_317)[name = tensor("q_157")]; - tensor var_7897 = const()[name = tensor("op_7897"), val = tensor([1, 1])]; - tensor var_7899 = const()[name = tensor("op_7899"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2935991424)))]; + tensor q_157_cast = conv(dilations = var_7794, groups = var_31, pad = q_157_pad_0, pad_type = q_157_pad_type_0, strides = var_7792, weight = unet_up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_q_weight_to_fp16, x = hidden_states_317_cast)[name = tensor("q_157_cast")]; + tensor var_7798 = const()[name = tensor("op_7798"), val = tensor([1, 1])]; + tensor var_7800 = const()[name = tensor("op_7800"), val = tensor([1, 1])]; tensor k_157_pad_type_0 = const()[name = tensor("k_157_pad_type_0"), val = tensor("custom")]; tensor k_157_pad_0 = const()[name = tensor("k_157_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_157 = conv(dilations = var_7899, groups = var_6872, pad = k_157_pad_0, pad_type = k_157_pad_type_0, strides = var_7897, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight, x = hidden_states_317)[name = tensor("k_157")]; - tensor var_7903 = const()[name = tensor("op_7903"), val = tensor([1, 1])]; - tensor var_7905 = const()[name = tensor("op_7905"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2939268288)))]; + tensor k_157_cast = conv(dilations = var_7800, groups = var_31, pad = k_157_pad_0, pad_type = k_157_pad_type_0, strides = var_7798, weight = unet_up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_k_weight_to_fp16, x = hidden_states_317_cast)[name = tensor("k_157_cast")]; + tensor var_7804 = const()[name = tensor("op_7804"), val = tensor([1, 1])]; + tensor var_7806 = const()[name = tensor("op_7806"), val = tensor([1, 1])]; tensor v_157_pad_type_0 = const()[name = tensor("v_157_pad_type_0"), val = tensor("custom")]; tensor v_157_pad_0 = const()[name = tensor("v_157_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_157 = conv(dilations = var_7905, groups = var_6872, pad = v_157_pad_0, pad_type = v_157_pad_type_0, strides = var_7903, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight, x = hidden_states_317)[name = tensor("v_157")]; - tensor var_7909 = const()[name = tensor("op_7909"), val = tensor([2, 20, 64, -1])]; - tensor var_7910 = reshape(shape = var_7909, x = q_157)[name = tensor("op_7910")]; - tensor var_7911 = const()[name = tensor("op_7911"), val = tensor([2, 20, 64, -1])]; - tensor var_7912 = reshape(shape = var_7911, x = k_157)[name = tensor("op_7912")]; - tensor var_7913 = const()[name = tensor("op_7913"), val = tensor([2, 20, 64, -1])]; - tensor var_7914 = reshape(shape = var_7913, x = v_157)[name = tensor("op_7914")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2942545152)))]; + tensor v_157_cast = conv(dilations = var_7806, groups = var_31, pad = v_157_pad_0, pad_type = v_157_pad_type_0, strides = var_7804, weight = unet_up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_v_weight_to_fp16, x = hidden_states_317_cast)[name = tensor("v_157_cast")]; + tensor var_7810 = const()[name = tensor("op_7810"), val = tensor([2, 20, 64, -1])]; + tensor var_7811_cast = reshape(shape = var_7810, x = q_157_cast)[name = tensor("op_7811_cast")]; + tensor var_7812 = const()[name = tensor("op_7812"), val = tensor([2, 20, 64, -1])]; + tensor var_7813_cast = reshape(shape = var_7812, x = k_157_cast)[name = tensor("op_7813_cast")]; + tensor var_7814 = const()[name = tensor("op_7814"), val = tensor([2, 20, 64, -1])]; + tensor var_7815_cast = reshape(shape = var_7814, x = v_157_cast)[name = tensor("op_7815_cast")]; tensor attn_weights_313_transpose_x_0 = const()[name = tensor("attn_weights_313_transpose_x_0"), val = tensor(true)]; tensor attn_weights_313_transpose_y_0 = const()[name = tensor("attn_weights_313_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_313 = matmul(transpose_x = attn_weights_313_transpose_x_0, transpose_y = attn_weights_313_transpose_y_0, x = var_7910, y = var_7912)[name = tensor("attn_weights_313")]; - tensor attn_weights_315 = mul(x = attn_weights_313, y = var_6863)[name = tensor("attn_weights_315")]; - tensor var_7918 = softmax(axis = var_6856, x = attn_weights_315)[name = tensor("op_7918")]; + tensor attn_weights_313_cast = matmul(transpose_x = attn_weights_313_transpose_x_0, transpose_y = attn_weights_313_transpose_y_0, x = var_7811_cast, y = var_7813_cast)[name = tensor("attn_weights_313_cast")]; + tensor attn_weights_315_cast = mul(x = attn_weights_313_cast, y = var_12_to_fp16)[name = tensor("attn_weights_315_cast")]; + tensor var_7819_cast = softmax(axis = var_18, x = attn_weights_315_cast)[name = tensor("op_7819_cast")]; tensor attn_157_transpose_x_0 = const()[name = tensor("attn_157_transpose_x_0"), val = tensor(false)]; tensor attn_157_transpose_y_0 = const()[name = tensor("attn_157_transpose_y_0"), val = tensor(true)]; - tensor attn_157 = matmul(transpose_x = attn_157_transpose_x_0, transpose_y = attn_157_transpose_y_0, x = var_7914, y = var_7918)[name = tensor("attn_157")]; - tensor var_7922 = const()[name = tensor("op_7922"), val = tensor([2, 1280, 1, -1])]; - tensor input_479 = reshape(shape = var_7922, x = attn_157)[name = tensor("input_479")]; - tensor var_7927 = const()[name = tensor("op_7927"), val = tensor([1, 1])]; - tensor var_7929 = const()[name = tensor("op_7929"), val = tensor([1, 1])]; - tensor var_7931_pad_type_0 = const()[name = tensor("op_7931_pad_type_0"), val = tensor("custom")]; - tensor var_7931_pad_0 = const()[name = tensor("op_7931_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7931 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias, dilations = var_7929, groups = var_6872, pad = var_7931_pad_0, pad_type = var_7931_pad_type_0, strides = var_7927, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight, x = input_479)[name = tensor("op_7931")]; - tensor inputs_237 = add(x = var_7931, y = inputs_235)[name = tensor("inputs_237")]; - tensor var_7935 = const()[name = tensor("op_7935"), val = tensor([1])]; - tensor channels_mean_237 = reduce_mean(axes = var_7935, keep_dims = var_6867, x = inputs_237)[name = tensor("channels_mean_237")]; - tensor zero_mean_237 = sub(x = inputs_237, y = channels_mean_237)[name = tensor("zero_mean_237")]; - tensor zero_mean_sq_237 = mul(x = zero_mean_237, y = zero_mean_237)[name = tensor("zero_mean_sq_237")]; - tensor var_7939 = const()[name = tensor("op_7939"), val = tensor([1])]; - tensor var_7940 = reduce_mean(axes = var_7939, keep_dims = var_6867, x = zero_mean_sq_237)[name = tensor("op_7940")]; - tensor var_7941 = const()[name = tensor("op_7941"), val = tensor(0x1.4f8b58p-17)]; - tensor var_7942 = add(x = var_7940, y = var_7941)[name = tensor("op_7942")]; - tensor denom_237_epsilon_0 = const()[name = tensor("denom_237_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_237 = rsqrt(epsilon = denom_237_epsilon_0, x = var_7942)[name = tensor("denom_237")]; - tensor out_237 = mul(x = zero_mean_237, y = denom_237)[name = tensor("out_237")]; - tensor var_7946 = const()[name = tensor("op_7946"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268968960)))]; - tensor var_7947 = add(x = out_237, y = var_7946)[name = tensor("op_7947")]; - tensor var_7949 = const()[name = tensor("op_7949"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268974144)))]; - tensor hidden_states_319 = mul(x = var_7947, y = var_7949)[name = tensor("hidden_states_319")]; - tensor var_7956 = const()[name = tensor("op_7956"), val = tensor([1, 1])]; - tensor var_7958 = const()[name = tensor("op_7958"), val = tensor([1, 1])]; + tensor attn_157_cast = matmul(transpose_x = attn_157_transpose_x_0, transpose_y = attn_157_transpose_y_0, x = var_7815_cast, y = var_7819_cast)[name = tensor("attn_157_cast")]; + tensor var_7823 = const()[name = tensor("op_7823"), val = tensor([2, 1280, 1, -1])]; + tensor input_479_cast = reshape(shape = var_7823, x = attn_157_cast)[name = tensor("input_479_cast")]; + tensor var_7828 = const()[name = tensor("op_7828"), val = tensor([1, 1])]; + tensor var_7830 = const()[name = tensor("op_7830"), val = tensor([1, 1])]; + tensor var_7832_pad_type_0 = const()[name = tensor("op_7832_pad_type_0"), val = tensor("custom")]; + tensor var_7832_pad_0 = const()[name = tensor("op_7832_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2945822016)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2949098880)))]; + tensor var_7832_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_7830, groups = var_31, pad = var_7832_pad_0, pad_type = var_7832_pad_type_0, strides = var_7828, weight = unet_up_blocks_0_attentions_0_transformer_blocks_5_attn1_to_out_0_weight_to_fp16, x = input_479_cast)[name = tensor("op_7832_cast")]; + tensor inputs_237_cast = add(x = var_7832_cast, y = inputs_235_cast)[name = tensor("inputs_237_cast")]; + tensor var_7836 = const()[name = tensor("op_7836"), val = tensor([1])]; + tensor channels_mean_237_cast = reduce_mean(axes = var_7836, keep_dims = var_23, x = inputs_237_cast)[name = tensor("channels_mean_237_cast")]; + tensor zero_mean_237_cast = sub(x = inputs_237_cast, y = channels_mean_237_cast)[name = tensor("zero_mean_237_cast")]; + tensor zero_mean_sq_237_cast = mul(x = zero_mean_237_cast, y = zero_mean_237_cast)[name = tensor("zero_mean_sq_237_cast")]; + tensor var_7840 = const()[name = tensor("op_7840"), val = tensor([1])]; + tensor var_7841_cast = reduce_mean(axes = var_7840, keep_dims = var_23, x = zero_mean_sq_237_cast)[name = tensor("op_7841_cast")]; + tensor var_7842_to_fp16 = const()[name = tensor("op_7842_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7843_cast = add(x = var_7841_cast, y = var_7842_to_fp16)[name = tensor("op_7843_cast")]; + tensor denom_237_epsilon_0_to_fp16 = const()[name = tensor("denom_237_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_237_cast = rsqrt(epsilon = denom_237_epsilon_0_to_fp16, x = var_7843_cast)[name = tensor("denom_237_cast")]; + tensor out_237_cast = mul(x = zero_mean_237_cast, y = denom_237_cast)[name = tensor("out_237_cast")]; + tensor var_7847_to_fp16 = const()[name = tensor("op_7847_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2949101504)))]; + tensor var_7848_cast = add(x = out_237_cast, y = var_7847_to_fp16)[name = tensor("op_7848_cast")]; + tensor var_7850_to_fp16 = const()[name = tensor("op_7850_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2949104128)))]; + tensor hidden_states_319_cast = mul(x = var_7848_cast, y = var_7850_to_fp16)[name = tensor("hidden_states_319_cast")]; + tensor var_7857 = const()[name = tensor("op_7857"), val = tensor([1, 1])]; + tensor var_7859 = const()[name = tensor("op_7859"), val = tensor([1, 1])]; tensor q_159_pad_type_0 = const()[name = tensor("q_159_pad_type_0"), val = tensor("custom")]; tensor q_159_pad_0 = const()[name = tensor("q_159_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_159 = conv(dilations = var_7958, groups = var_6872, pad = q_159_pad_0, pad_type = q_159_pad_type_0, strides = var_7956, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight, x = hidden_states_319)[name = tensor("q_159")]; - tensor var_7962 = const()[name = tensor("op_7962"), val = tensor([1, 1])]; - tensor var_7964 = const()[name = tensor("op_7964"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2949106752)))]; + tensor q_159_cast = conv(dilations = var_7859, groups = var_31, pad = q_159_pad_0, pad_type = q_159_pad_type_0, strides = var_7857, weight = unet_up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_q_weight_to_fp16, x = hidden_states_319_cast)[name = tensor("q_159_cast")]; + tensor var_7863 = const()[name = tensor("op_7863"), val = tensor([1, 1])]; + tensor var_7865 = const()[name = tensor("op_7865"), val = tensor([1, 1])]; tensor k_159_pad_type_0 = const()[name = tensor("k_159_pad_type_0"), val = tensor("custom")]; tensor k_159_pad_0 = const()[name = tensor("k_159_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_159 = conv(dilations = var_7964, groups = var_6872, pad = k_159_pad_0, pad_type = k_159_pad_type_0, strides = var_7962, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_159")]; - tensor var_7968 = const()[name = tensor("op_7968"), val = tensor([1, 1])]; - tensor var_7970 = const()[name = tensor("op_7970"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2952383616)))]; + tensor k_159_cast = conv(dilations = var_7865, groups = var_31, pad = k_159_pad_0, pad_type = k_159_pad_type_0, strides = var_7863, weight = unet_up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_159_cast")]; + tensor var_7869 = const()[name = tensor("op_7869"), val = tensor([1, 1])]; + tensor var_7871 = const()[name = tensor("op_7871"), val = tensor([1, 1])]; tensor v_159_pad_type_0 = const()[name = tensor("v_159_pad_type_0"), val = tensor("custom")]; tensor v_159_pad_0 = const()[name = tensor("v_159_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_159 = conv(dilations = var_7970, groups = var_6872, pad = v_159_pad_0, pad_type = v_159_pad_type_0, strides = var_7968, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_159")]; - tensor var_7974 = const()[name = tensor("op_7974"), val = tensor([2, 20, 64, -1])]; - tensor var_7975 = reshape(shape = var_7974, x = q_159)[name = tensor("op_7975")]; - tensor var_7976 = const()[name = tensor("op_7976"), val = tensor([2, 20, 64, -1])]; - tensor var_7977 = reshape(shape = var_7976, x = k_159)[name = tensor("op_7977")]; - tensor var_7978 = const()[name = tensor("op_7978"), val = tensor([2, 20, 64, -1])]; - tensor var_7979 = reshape(shape = var_7978, x = v_159)[name = tensor("op_7979")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2957626560)))]; + tensor v_159_cast = conv(dilations = var_7871, groups = var_31, pad = v_159_pad_0, pad_type = v_159_pad_type_0, strides = var_7869, weight = unet_up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_159_cast")]; + tensor var_7875 = const()[name = tensor("op_7875"), val = tensor([2, 20, 64, -1])]; + tensor var_7876_cast = reshape(shape = var_7875, x = q_159_cast)[name = tensor("op_7876_cast")]; + tensor var_7877 = const()[name = tensor("op_7877"), val = tensor([2, 20, 64, -1])]; + tensor var_7878_cast = reshape(shape = var_7877, x = k_159_cast)[name = tensor("op_7878_cast")]; + tensor var_7879 = const()[name = tensor("op_7879"), val = tensor([2, 20, 64, -1])]; + tensor var_7880_cast = reshape(shape = var_7879, x = v_159_cast)[name = tensor("op_7880_cast")]; tensor attn_weights_317_transpose_x_0 = const()[name = tensor("attn_weights_317_transpose_x_0"), val = tensor(true)]; tensor attn_weights_317_transpose_y_0 = const()[name = tensor("attn_weights_317_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_317 = matmul(transpose_x = attn_weights_317_transpose_x_0, transpose_y = attn_weights_317_transpose_y_0, x = var_7975, y = var_7977)[name = tensor("attn_weights_317")]; - tensor attn_weights_319 = mul(x = attn_weights_317, y = var_6863)[name = tensor("attn_weights_319")]; - tensor var_7983 = softmax(axis = var_6856, x = attn_weights_319)[name = tensor("op_7983")]; + tensor attn_weights_317_cast = matmul(transpose_x = attn_weights_317_transpose_x_0, transpose_y = attn_weights_317_transpose_y_0, x = var_7876_cast, y = var_7878_cast)[name = tensor("attn_weights_317_cast")]; + tensor attn_weights_319_cast = mul(x = attn_weights_317_cast, y = var_12_to_fp16)[name = tensor("attn_weights_319_cast")]; + tensor var_7884_cast = softmax(axis = var_18, x = attn_weights_319_cast)[name = tensor("op_7884_cast")]; tensor attn_159_transpose_x_0 = const()[name = tensor("attn_159_transpose_x_0"), val = tensor(false)]; tensor attn_159_transpose_y_0 = const()[name = tensor("attn_159_transpose_y_0"), val = tensor(true)]; - tensor attn_159 = matmul(transpose_x = attn_159_transpose_x_0, transpose_y = attn_159_transpose_y_0, x = var_7979, y = var_7983)[name = tensor("attn_159")]; - tensor var_7987 = const()[name = tensor("op_7987"), val = tensor([2, 1280, 1, -1])]; - tensor input_481 = reshape(shape = var_7987, x = attn_159)[name = tensor("input_481")]; - tensor var_7992 = const()[name = tensor("op_7992"), val = tensor([1, 1])]; - tensor var_7994 = const()[name = tensor("op_7994"), val = tensor([1, 1])]; - tensor var_7996_pad_type_0 = const()[name = tensor("op_7996_pad_type_0"), val = tensor("custom")]; - tensor var_7996_pad_0 = const()[name = tensor("op_7996_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_7996 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias, dilations = var_7994, groups = var_6872, pad = var_7996_pad_0, pad_type = var_7996_pad_type_0, strides = var_7992, weight = up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight, x = input_481)[name = tensor("op_7996")]; - tensor inputs_239 = add(x = var_7996, y = inputs_237)[name = tensor("inputs_239")]; - tensor var_8000 = const()[name = tensor("op_8000"), val = tensor([1])]; - tensor channels_mean_239 = reduce_mean(axes = var_8000, keep_dims = var_6867, x = inputs_239)[name = tensor("channels_mean_239")]; - tensor zero_mean_239 = sub(x = inputs_239, y = channels_mean_239)[name = tensor("zero_mean_239")]; - tensor zero_mean_sq_239 = mul(x = zero_mean_239, y = zero_mean_239)[name = tensor("zero_mean_sq_239")]; - tensor var_8004 = const()[name = tensor("op_8004"), val = tensor([1])]; - tensor var_8005 = reduce_mean(axes = var_8004, keep_dims = var_6867, x = zero_mean_sq_239)[name = tensor("op_8005")]; - tensor var_8006 = const()[name = tensor("op_8006"), val = tensor(0x1.4f8b58p-17)]; - tensor var_8007 = add(x = var_8005, y = var_8006)[name = tensor("op_8007")]; - tensor denom_239_epsilon_0 = const()[name = tensor("denom_239_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_239 = rsqrt(epsilon = denom_239_epsilon_0, x = var_8007)[name = tensor("denom_239")]; - tensor out_239 = mul(x = zero_mean_239, y = denom_239)[name = tensor("out_239")]; - tensor var_8011 = const()[name = tensor("op_8011"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268979328)))]; - tensor var_8012 = add(x = out_239, y = var_8011)[name = tensor("op_8012")]; - tensor var_8014 = const()[name = tensor("op_8014"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268984512)))]; - tensor input_483 = mul(x = var_8012, y = var_8014)[name = tensor("input_483")]; - tensor var_8022 = const()[name = tensor("op_8022"), val = tensor([1, 1])]; - tensor var_8024 = const()[name = tensor("op_8024"), val = tensor([1, 1])]; - tensor var_8026_pad_type_0 = const()[name = tensor("op_8026_pad_type_0"), val = tensor("custom")]; - tensor var_8026_pad_0 = const()[name = tensor("op_8026_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8026 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias, dilations = var_8024, groups = var_6872, pad = var_8026_pad_0, pad_type = var_8026_pad_type_0, strides = var_8022, weight = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight, x = input_483)[name = tensor("op_8026")]; - tensor var_8027_split_sizes_0 = const()[name = tensor("op_8027_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_8027_axis_0 = const()[name = tensor("op_8027_axis_0"), val = tensor(1)]; - tensor var_8027_0, tensor var_8027_1 = split(axis = var_8027_axis_0, split_sizes = var_8027_split_sizes_0, x = var_8026)[name = tensor("op_8027")]; - tensor var_8029_mode_0 = const()[name = tensor("op_8029_mode_0"), val = tensor("EXACT")]; - tensor var_8029 = gelu(mode = var_8029_mode_0, x = var_8027_1)[name = tensor("op_8029")]; - tensor input_485 = mul(x = var_8027_0, y = var_8029)[name = tensor("input_485")]; - tensor var_8033 = const()[name = tensor("op_8033"), val = tensor([1, 1])]; - tensor var_8035 = const()[name = tensor("op_8035"), val = tensor([1, 1])]; - tensor var_8037_pad_type_0 = const()[name = tensor("op_8037_pad_type_0"), val = tensor("custom")]; - tensor var_8037_pad_0 = const()[name = tensor("op_8037_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8037 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias, dilations = var_8035, groups = var_6872, pad = var_8037_pad_0, pad_type = var_8037_pad_type_0, strides = var_8033, weight = up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight, x = input_485)[name = tensor("op_8037")]; - tensor inputs_241 = add(x = var_8037, y = inputs_239)[name = tensor("inputs_241")]; - tensor var_8047 = const()[name = tensor("op_8047"), val = tensor([1])]; - tensor channels_mean_241 = reduce_mean(axes = var_8047, keep_dims = var_6867, x = inputs_241)[name = tensor("channels_mean_241")]; - tensor zero_mean_241 = sub(x = inputs_241, y = channels_mean_241)[name = tensor("zero_mean_241")]; - tensor zero_mean_sq_241 = mul(x = zero_mean_241, y = zero_mean_241)[name = tensor("zero_mean_sq_241")]; - tensor var_8051 = const()[name = tensor("op_8051"), val = tensor([1])]; - tensor var_8052 = reduce_mean(axes = var_8051, keep_dims = var_6867, x = zero_mean_sq_241)[name = tensor("op_8052")]; - tensor var_8053 = const()[name = tensor("op_8053"), val = tensor(0x1.4f8b58p-17)]; - tensor var_8054 = add(x = var_8052, y = var_8053)[name = tensor("op_8054")]; - tensor denom_241_epsilon_0 = const()[name = tensor("denom_241_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_241 = rsqrt(epsilon = denom_241_epsilon_0, x = var_8054)[name = tensor("denom_241")]; - tensor out_241 = mul(x = zero_mean_241, y = denom_241)[name = tensor("out_241")]; - tensor var_8058 = const()[name = tensor("op_8058"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268989696)))]; - tensor var_8059 = add(x = out_241, y = var_8058)[name = tensor("op_8059")]; - tensor var_8061 = const()[name = tensor("op_8061"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10268994880)))]; - tensor hidden_states_323 = mul(x = var_8059, y = var_8061)[name = tensor("hidden_states_323")]; - tensor var_8068 = const()[name = tensor("op_8068"), val = tensor([1, 1])]; - tensor var_8070 = const()[name = tensor("op_8070"), val = tensor([1, 1])]; + tensor attn_159_cast = matmul(transpose_x = attn_159_transpose_x_0, transpose_y = attn_159_transpose_y_0, x = var_7880_cast, y = var_7884_cast)[name = tensor("attn_159_cast")]; + tensor var_7888 = const()[name = tensor("op_7888"), val = tensor([2, 1280, 1, -1])]; + tensor input_481_cast = reshape(shape = var_7888, x = attn_159_cast)[name = tensor("input_481_cast")]; + tensor var_7893 = const()[name = tensor("op_7893"), val = tensor([1, 1])]; + tensor var_7895 = const()[name = tensor("op_7895"), val = tensor([1, 1])]; + tensor var_7897_pad_type_0 = const()[name = tensor("op_7897_pad_type_0"), val = tensor("custom")]; + tensor var_7897_pad_0 = const()[name = tensor("op_7897_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2962869504)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2966146368)))]; + tensor var_7897_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_7895, groups = var_31, pad = var_7897_pad_0, pad_type = var_7897_pad_type_0, strides = var_7893, weight = unet_up_blocks_0_attentions_0_transformer_blocks_5_attn2_to_out_0_weight_to_fp16, x = input_481_cast)[name = tensor("op_7897_cast")]; + tensor inputs_239_cast = add(x = var_7897_cast, y = inputs_237_cast)[name = tensor("inputs_239_cast")]; + tensor var_7901 = const()[name = tensor("op_7901"), val = tensor([1])]; + tensor channels_mean_239_cast = reduce_mean(axes = var_7901, keep_dims = var_23, x = inputs_239_cast)[name = tensor("channels_mean_239_cast")]; + tensor zero_mean_239_cast = sub(x = inputs_239_cast, y = channels_mean_239_cast)[name = tensor("zero_mean_239_cast")]; + tensor zero_mean_sq_239_cast = mul(x = zero_mean_239_cast, y = zero_mean_239_cast)[name = tensor("zero_mean_sq_239_cast")]; + tensor var_7905 = const()[name = tensor("op_7905"), val = tensor([1])]; + tensor var_7906_cast = reduce_mean(axes = var_7905, keep_dims = var_23, x = zero_mean_sq_239_cast)[name = tensor("op_7906_cast")]; + tensor var_7907_to_fp16 = const()[name = tensor("op_7907_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7908_cast = add(x = var_7906_cast, y = var_7907_to_fp16)[name = tensor("op_7908_cast")]; + tensor denom_239_epsilon_0_to_fp16 = const()[name = tensor("denom_239_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_239_cast = rsqrt(epsilon = denom_239_epsilon_0_to_fp16, x = var_7908_cast)[name = tensor("denom_239_cast")]; + tensor out_239_cast = mul(x = zero_mean_239_cast, y = denom_239_cast)[name = tensor("out_239_cast")]; + tensor var_7912_to_fp16 = const()[name = tensor("op_7912_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2966148992)))]; + tensor var_7913_cast = add(x = out_239_cast, y = var_7912_to_fp16)[name = tensor("op_7913_cast")]; + tensor var_7915_to_fp16 = const()[name = tensor("op_7915_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2966151616)))]; + tensor input_483_cast = mul(x = var_7913_cast, y = var_7915_to_fp16)[name = tensor("input_483_cast")]; + tensor var_7923 = const()[name = tensor("op_7923"), val = tensor([1, 1])]; + tensor var_7925 = const()[name = tensor("op_7925"), val = tensor([1, 1])]; + tensor var_7927_pad_type_0 = const()[name = tensor("op_7927_pad_type_0"), val = tensor("custom")]; + tensor var_7927_pad_0 = const()[name = tensor("op_7927_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2966154240)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2992368704)))]; + tensor var_7927_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_7925, groups = var_31, pad = var_7927_pad_0, pad_type = var_7927_pad_type_0, strides = var_7923, weight = unet_up_blocks_0_attentions_0_transformer_blocks_5_ff_net_0_proj_weight_to_fp16, x = input_483_cast)[name = tensor("op_7927_cast")]; + tensor var_7928_split_sizes_0 = const()[name = tensor("op_7928_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_7928_axis_0 = const()[name = tensor("op_7928_axis_0"), val = tensor(1)]; + tensor var_7928_cast_0, tensor var_7928_cast_1 = split(axis = var_7928_axis_0, split_sizes = var_7928_split_sizes_0, x = var_7927_cast)[name = tensor("op_7928_cast")]; + tensor var_7930_mode_0 = const()[name = tensor("op_7930_mode_0"), val = tensor("EXACT")]; + tensor var_7930_cast = gelu(mode = var_7930_mode_0, x = var_7928_cast_1)[name = tensor("op_7930_cast")]; + tensor input_485_cast = mul(x = var_7928_cast_0, y = var_7930_cast)[name = tensor("input_485_cast")]; + tensor var_7934 = const()[name = tensor("op_7934"), val = tensor([1, 1])]; + tensor var_7936 = const()[name = tensor("op_7936"), val = tensor([1, 1])]; + tensor var_7938_pad_type_0 = const()[name = tensor("op_7938_pad_type_0"), val = tensor("custom")]; + tensor var_7938_pad_0 = const()[name = tensor("op_7938_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2992389248)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3005496512)))]; + tensor var_7938_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_7936, groups = var_31, pad = var_7938_pad_0, pad_type = var_7938_pad_type_0, strides = var_7934, weight = unet_up_blocks_0_attentions_0_transformer_blocks_5_ff_net_2_weight_to_fp16, x = input_485_cast)[name = tensor("op_7938_cast")]; + tensor inputs_241_cast = add(x = var_7938_cast, y = inputs_239_cast)[name = tensor("inputs_241_cast")]; + tensor var_7948 = const()[name = tensor("op_7948"), val = tensor([1])]; + tensor channels_mean_241_cast = reduce_mean(axes = var_7948, keep_dims = var_23, x = inputs_241_cast)[name = tensor("channels_mean_241_cast")]; + tensor zero_mean_241_cast = sub(x = inputs_241_cast, y = channels_mean_241_cast)[name = tensor("zero_mean_241_cast")]; + tensor zero_mean_sq_241_cast = mul(x = zero_mean_241_cast, y = zero_mean_241_cast)[name = tensor("zero_mean_sq_241_cast")]; + tensor var_7952 = const()[name = tensor("op_7952"), val = tensor([1])]; + tensor var_7953_cast = reduce_mean(axes = var_7952, keep_dims = var_23, x = zero_mean_sq_241_cast)[name = tensor("op_7953_cast")]; + tensor var_7954_to_fp16 = const()[name = tensor("op_7954_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_7955_cast = add(x = var_7953_cast, y = var_7954_to_fp16)[name = tensor("op_7955_cast")]; + tensor denom_241_epsilon_0_to_fp16 = const()[name = tensor("denom_241_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_241_cast = rsqrt(epsilon = denom_241_epsilon_0_to_fp16, x = var_7955_cast)[name = tensor("denom_241_cast")]; + tensor out_241_cast = mul(x = zero_mean_241_cast, y = denom_241_cast)[name = tensor("out_241_cast")]; + tensor var_7959_to_fp16 = const()[name = tensor("op_7959_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3005499136)))]; + tensor var_7960_cast = add(x = out_241_cast, y = var_7959_to_fp16)[name = tensor("op_7960_cast")]; + tensor var_7962_to_fp16 = const()[name = tensor("op_7962_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3005501760)))]; + tensor hidden_states_323_cast = mul(x = var_7960_cast, y = var_7962_to_fp16)[name = tensor("hidden_states_323_cast")]; + tensor var_7969 = const()[name = tensor("op_7969"), val = tensor([1, 1])]; + tensor var_7971 = const()[name = tensor("op_7971"), val = tensor([1, 1])]; tensor q_161_pad_type_0 = const()[name = tensor("q_161_pad_type_0"), val = tensor("custom")]; tensor q_161_pad_0 = const()[name = tensor("q_161_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_161 = conv(dilations = var_8070, groups = var_6872, pad = q_161_pad_0, pad_type = q_161_pad_type_0, strides = var_8068, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight, x = hidden_states_323)[name = tensor("q_161")]; - tensor var_8074 = const()[name = tensor("op_8074"), val = tensor([1, 1])]; - tensor var_8076 = const()[name = tensor("op_8076"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3005504384)))]; + tensor q_161_cast = conv(dilations = var_7971, groups = var_31, pad = q_161_pad_0, pad_type = q_161_pad_type_0, strides = var_7969, weight = unet_up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_q_weight_to_fp16, x = hidden_states_323_cast)[name = tensor("q_161_cast")]; + tensor var_7975 = const()[name = tensor("op_7975"), val = tensor([1, 1])]; + tensor var_7977 = const()[name = tensor("op_7977"), val = tensor([1, 1])]; tensor k_161_pad_type_0 = const()[name = tensor("k_161_pad_type_0"), val = tensor("custom")]; tensor k_161_pad_0 = const()[name = tensor("k_161_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_161 = conv(dilations = var_8076, groups = var_6872, pad = k_161_pad_0, pad_type = k_161_pad_type_0, strides = var_8074, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight, x = hidden_states_323)[name = tensor("k_161")]; - tensor var_8080 = const()[name = tensor("op_8080"), val = tensor([1, 1])]; - tensor var_8082 = const()[name = tensor("op_8082"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3008781248)))]; + tensor k_161_cast = conv(dilations = var_7977, groups = var_31, pad = k_161_pad_0, pad_type = k_161_pad_type_0, strides = var_7975, weight = unet_up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_k_weight_to_fp16, x = hidden_states_323_cast)[name = tensor("k_161_cast")]; + tensor var_7981 = const()[name = tensor("op_7981"), val = tensor([1, 1])]; + tensor var_7983 = const()[name = tensor("op_7983"), val = tensor([1, 1])]; tensor v_161_pad_type_0 = const()[name = tensor("v_161_pad_type_0"), val = tensor("custom")]; tensor v_161_pad_0 = const()[name = tensor("v_161_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_161 = conv(dilations = var_8082, groups = var_6872, pad = v_161_pad_0, pad_type = v_161_pad_type_0, strides = var_8080, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight, x = hidden_states_323)[name = tensor("v_161")]; - tensor var_8086 = const()[name = tensor("op_8086"), val = tensor([2, 20, 64, -1])]; - tensor var_8087 = reshape(shape = var_8086, x = q_161)[name = tensor("op_8087")]; - tensor var_8088 = const()[name = tensor("op_8088"), val = tensor([2, 20, 64, -1])]; - tensor var_8089 = reshape(shape = var_8088, x = k_161)[name = tensor("op_8089")]; - tensor var_8090 = const()[name = tensor("op_8090"), val = tensor([2, 20, 64, -1])]; - tensor var_8091 = reshape(shape = var_8090, x = v_161)[name = tensor("op_8091")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3012058112)))]; + tensor v_161_cast = conv(dilations = var_7983, groups = var_31, pad = v_161_pad_0, pad_type = v_161_pad_type_0, strides = var_7981, weight = unet_up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_v_weight_to_fp16, x = hidden_states_323_cast)[name = tensor("v_161_cast")]; + tensor var_7987 = const()[name = tensor("op_7987"), val = tensor([2, 20, 64, -1])]; + tensor var_7988_cast = reshape(shape = var_7987, x = q_161_cast)[name = tensor("op_7988_cast")]; + tensor var_7989 = const()[name = tensor("op_7989"), val = tensor([2, 20, 64, -1])]; + tensor var_7990_cast = reshape(shape = var_7989, x = k_161_cast)[name = tensor("op_7990_cast")]; + tensor var_7991 = const()[name = tensor("op_7991"), val = tensor([2, 20, 64, -1])]; + tensor var_7992_cast = reshape(shape = var_7991, x = v_161_cast)[name = tensor("op_7992_cast")]; tensor attn_weights_321_transpose_x_0 = const()[name = tensor("attn_weights_321_transpose_x_0"), val = tensor(true)]; tensor attn_weights_321_transpose_y_0 = const()[name = tensor("attn_weights_321_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_321 = matmul(transpose_x = attn_weights_321_transpose_x_0, transpose_y = attn_weights_321_transpose_y_0, x = var_8087, y = var_8089)[name = tensor("attn_weights_321")]; - tensor attn_weights_323 = mul(x = attn_weights_321, y = var_6863)[name = tensor("attn_weights_323")]; - tensor var_8095 = softmax(axis = var_6856, x = attn_weights_323)[name = tensor("op_8095")]; + tensor attn_weights_321_cast = matmul(transpose_x = attn_weights_321_transpose_x_0, transpose_y = attn_weights_321_transpose_y_0, x = var_7988_cast, y = var_7990_cast)[name = tensor("attn_weights_321_cast")]; + tensor attn_weights_323_cast = mul(x = attn_weights_321_cast, y = var_12_to_fp16)[name = tensor("attn_weights_323_cast")]; + tensor var_7996_cast = softmax(axis = var_18, x = attn_weights_323_cast)[name = tensor("op_7996_cast")]; tensor attn_161_transpose_x_0 = const()[name = tensor("attn_161_transpose_x_0"), val = tensor(false)]; tensor attn_161_transpose_y_0 = const()[name = tensor("attn_161_transpose_y_0"), val = tensor(true)]; - tensor attn_161 = matmul(transpose_x = attn_161_transpose_x_0, transpose_y = attn_161_transpose_y_0, x = var_8091, y = var_8095)[name = tensor("attn_161")]; - tensor var_8099 = const()[name = tensor("op_8099"), val = tensor([2, 1280, 1, -1])]; - tensor input_487 = reshape(shape = var_8099, x = attn_161)[name = tensor("input_487")]; - tensor var_8104 = const()[name = tensor("op_8104"), val = tensor([1, 1])]; - tensor var_8106 = const()[name = tensor("op_8106"), val = tensor([1, 1])]; - tensor var_8108_pad_type_0 = const()[name = tensor("op_8108_pad_type_0"), val = tensor("custom")]; - tensor var_8108_pad_0 = const()[name = tensor("op_8108_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8108 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias, dilations = var_8106, groups = var_6872, pad = var_8108_pad_0, pad_type = var_8108_pad_type_0, strides = var_8104, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight, x = input_487)[name = tensor("op_8108")]; - tensor inputs_243 = add(x = var_8108, y = inputs_241)[name = tensor("inputs_243")]; - tensor var_8112 = const()[name = tensor("op_8112"), val = tensor([1])]; - tensor channels_mean_243 = reduce_mean(axes = var_8112, keep_dims = var_6867, x = inputs_243)[name = tensor("channels_mean_243")]; - tensor zero_mean_243 = sub(x = inputs_243, y = channels_mean_243)[name = tensor("zero_mean_243")]; - tensor zero_mean_sq_243 = mul(x = zero_mean_243, y = zero_mean_243)[name = tensor("zero_mean_sq_243")]; - tensor var_8116 = const()[name = tensor("op_8116"), val = tensor([1])]; - tensor var_8117 = reduce_mean(axes = var_8116, keep_dims = var_6867, x = zero_mean_sq_243)[name = tensor("op_8117")]; - tensor var_8118 = const()[name = tensor("op_8118"), val = tensor(0x1.4f8b58p-17)]; - tensor var_8119 = add(x = var_8117, y = var_8118)[name = tensor("op_8119")]; - tensor denom_243_epsilon_0 = const()[name = tensor("denom_243_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_243 = rsqrt(epsilon = denom_243_epsilon_0, x = var_8119)[name = tensor("denom_243")]; - tensor out_243 = mul(x = zero_mean_243, y = denom_243)[name = tensor("out_243")]; - tensor var_8123 = const()[name = tensor("op_8123"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269000064)))]; - tensor var_8124 = add(x = out_243, y = var_8123)[name = tensor("op_8124")]; - tensor var_8126 = const()[name = tensor("op_8126"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269005248)))]; - tensor hidden_states_325 = mul(x = var_8124, y = var_8126)[name = tensor("hidden_states_325")]; - tensor var_8133 = const()[name = tensor("op_8133"), val = tensor([1, 1])]; - tensor var_8135 = const()[name = tensor("op_8135"), val = tensor([1, 1])]; + tensor attn_161_cast = matmul(transpose_x = attn_161_transpose_x_0, transpose_y = attn_161_transpose_y_0, x = var_7992_cast, y = var_7996_cast)[name = tensor("attn_161_cast")]; + tensor var_8000 = const()[name = tensor("op_8000"), val = tensor([2, 1280, 1, -1])]; + tensor input_487_cast = reshape(shape = var_8000, x = attn_161_cast)[name = tensor("input_487_cast")]; + tensor var_8005 = const()[name = tensor("op_8005"), val = tensor([1, 1])]; + tensor var_8007 = const()[name = tensor("op_8007"), val = tensor([1, 1])]; + tensor var_8009_pad_type_0 = const()[name = tensor("op_8009_pad_type_0"), val = tensor("custom")]; + tensor var_8009_pad_0 = const()[name = tensor("op_8009_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3015334976)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3018611840)))]; + tensor var_8009_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_8007, groups = var_31, pad = var_8009_pad_0, pad_type = var_8009_pad_type_0, strides = var_8005, weight = unet_up_blocks_0_attentions_0_transformer_blocks_6_attn1_to_out_0_weight_to_fp16, x = input_487_cast)[name = tensor("op_8009_cast")]; + tensor inputs_243_cast = add(x = var_8009_cast, y = inputs_241_cast)[name = tensor("inputs_243_cast")]; + tensor var_8013 = const()[name = tensor("op_8013"), val = tensor([1])]; + tensor channels_mean_243_cast = reduce_mean(axes = var_8013, keep_dims = var_23, x = inputs_243_cast)[name = tensor("channels_mean_243_cast")]; + tensor zero_mean_243_cast = sub(x = inputs_243_cast, y = channels_mean_243_cast)[name = tensor("zero_mean_243_cast")]; + tensor zero_mean_sq_243_cast = mul(x = zero_mean_243_cast, y = zero_mean_243_cast)[name = tensor("zero_mean_sq_243_cast")]; + tensor var_8017 = const()[name = tensor("op_8017"), val = tensor([1])]; + tensor var_8018_cast = reduce_mean(axes = var_8017, keep_dims = var_23, x = zero_mean_sq_243_cast)[name = tensor("op_8018_cast")]; + tensor var_8019_to_fp16 = const()[name = tensor("op_8019_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8020_cast = add(x = var_8018_cast, y = var_8019_to_fp16)[name = tensor("op_8020_cast")]; + tensor denom_243_epsilon_0_to_fp16 = const()[name = tensor("denom_243_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_243_cast = rsqrt(epsilon = denom_243_epsilon_0_to_fp16, x = var_8020_cast)[name = tensor("denom_243_cast")]; + tensor out_243_cast = mul(x = zero_mean_243_cast, y = denom_243_cast)[name = tensor("out_243_cast")]; + tensor var_8024_to_fp16 = const()[name = tensor("op_8024_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3018614464)))]; + tensor var_8025_cast = add(x = out_243_cast, y = var_8024_to_fp16)[name = tensor("op_8025_cast")]; + tensor var_8027_to_fp16 = const()[name = tensor("op_8027_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3018617088)))]; + tensor hidden_states_325_cast = mul(x = var_8025_cast, y = var_8027_to_fp16)[name = tensor("hidden_states_325_cast")]; + tensor var_8034 = const()[name = tensor("op_8034"), val = tensor([1, 1])]; + tensor var_8036 = const()[name = tensor("op_8036"), val = tensor([1, 1])]; tensor q_163_pad_type_0 = const()[name = tensor("q_163_pad_type_0"), val = tensor("custom")]; tensor q_163_pad_0 = const()[name = tensor("q_163_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_163 = conv(dilations = var_8135, groups = var_6872, pad = q_163_pad_0, pad_type = q_163_pad_type_0, strides = var_8133, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight, x = hidden_states_325)[name = tensor("q_163")]; - tensor var_8139 = const()[name = tensor("op_8139"), val = tensor([1, 1])]; - tensor var_8141 = const()[name = tensor("op_8141"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3018619712)))]; + tensor q_163_cast = conv(dilations = var_8036, groups = var_31, pad = q_163_pad_0, pad_type = q_163_pad_type_0, strides = var_8034, weight = unet_up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_q_weight_to_fp16, x = hidden_states_325_cast)[name = tensor("q_163_cast")]; + tensor var_8040 = const()[name = tensor("op_8040"), val = tensor([1, 1])]; + tensor var_8042 = const()[name = tensor("op_8042"), val = tensor([1, 1])]; tensor k_163_pad_type_0 = const()[name = tensor("k_163_pad_type_0"), val = tensor("custom")]; tensor k_163_pad_0 = const()[name = tensor("k_163_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_163 = conv(dilations = var_8141, groups = var_6872, pad = k_163_pad_0, pad_type = k_163_pad_type_0, strides = var_8139, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_163")]; - tensor var_8145 = const()[name = tensor("op_8145"), val = tensor([1, 1])]; - tensor var_8147 = const()[name = tensor("op_8147"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3021896576)))]; + tensor k_163_cast = conv(dilations = var_8042, groups = var_31, pad = k_163_pad_0, pad_type = k_163_pad_type_0, strides = var_8040, weight = unet_up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_163_cast")]; + tensor var_8046 = const()[name = tensor("op_8046"), val = tensor([1, 1])]; + tensor var_8048 = const()[name = tensor("op_8048"), val = tensor([1, 1])]; tensor v_163_pad_type_0 = const()[name = tensor("v_163_pad_type_0"), val = tensor("custom")]; tensor v_163_pad_0 = const()[name = tensor("v_163_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_163 = conv(dilations = var_8147, groups = var_6872, pad = v_163_pad_0, pad_type = v_163_pad_type_0, strides = var_8145, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_163")]; - tensor var_8151 = const()[name = tensor("op_8151"), val = tensor([2, 20, 64, -1])]; - tensor var_8152 = reshape(shape = var_8151, x = q_163)[name = tensor("op_8152")]; - tensor var_8153 = const()[name = tensor("op_8153"), val = tensor([2, 20, 64, -1])]; - tensor var_8154 = reshape(shape = var_8153, x = k_163)[name = tensor("op_8154")]; - tensor var_8155 = const()[name = tensor("op_8155"), val = tensor([2, 20, 64, -1])]; - tensor var_8156 = reshape(shape = var_8155, x = v_163)[name = tensor("op_8156")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3027139520)))]; + tensor v_163_cast = conv(dilations = var_8048, groups = var_31, pad = v_163_pad_0, pad_type = v_163_pad_type_0, strides = var_8046, weight = unet_up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_163_cast")]; + tensor var_8052 = const()[name = tensor("op_8052"), val = tensor([2, 20, 64, -1])]; + tensor var_8053_cast = reshape(shape = var_8052, x = q_163_cast)[name = tensor("op_8053_cast")]; + tensor var_8054 = const()[name = tensor("op_8054"), val = tensor([2, 20, 64, -1])]; + tensor var_8055_cast = reshape(shape = var_8054, x = k_163_cast)[name = tensor("op_8055_cast")]; + tensor var_8056 = const()[name = tensor("op_8056"), val = tensor([2, 20, 64, -1])]; + tensor var_8057_cast = reshape(shape = var_8056, x = v_163_cast)[name = tensor("op_8057_cast")]; tensor attn_weights_325_transpose_x_0 = const()[name = tensor("attn_weights_325_transpose_x_0"), val = tensor(true)]; tensor attn_weights_325_transpose_y_0 = const()[name = tensor("attn_weights_325_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_325 = matmul(transpose_x = attn_weights_325_transpose_x_0, transpose_y = attn_weights_325_transpose_y_0, x = var_8152, y = var_8154)[name = tensor("attn_weights_325")]; - tensor attn_weights_327 = mul(x = attn_weights_325, y = var_6863)[name = tensor("attn_weights_327")]; - tensor var_8160 = softmax(axis = var_6856, x = attn_weights_327)[name = tensor("op_8160")]; + tensor attn_weights_325_cast = matmul(transpose_x = attn_weights_325_transpose_x_0, transpose_y = attn_weights_325_transpose_y_0, x = var_8053_cast, y = var_8055_cast)[name = tensor("attn_weights_325_cast")]; + tensor attn_weights_327_cast = mul(x = attn_weights_325_cast, y = var_12_to_fp16)[name = tensor("attn_weights_327_cast")]; + tensor var_8061_cast = softmax(axis = var_18, x = attn_weights_327_cast)[name = tensor("op_8061_cast")]; tensor attn_163_transpose_x_0 = const()[name = tensor("attn_163_transpose_x_0"), val = tensor(false)]; tensor attn_163_transpose_y_0 = const()[name = tensor("attn_163_transpose_y_0"), val = tensor(true)]; - tensor attn_163 = matmul(transpose_x = attn_163_transpose_x_0, transpose_y = attn_163_transpose_y_0, x = var_8156, y = var_8160)[name = tensor("attn_163")]; - tensor var_8164 = const()[name = tensor("op_8164"), val = tensor([2, 1280, 1, -1])]; - tensor input_489 = reshape(shape = var_8164, x = attn_163)[name = tensor("input_489")]; - tensor var_8169 = const()[name = tensor("op_8169"), val = tensor([1, 1])]; - tensor var_8171 = const()[name = tensor("op_8171"), val = tensor([1, 1])]; - tensor var_8173_pad_type_0 = const()[name = tensor("op_8173_pad_type_0"), val = tensor("custom")]; - tensor var_8173_pad_0 = const()[name = tensor("op_8173_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8173 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias, dilations = var_8171, groups = var_6872, pad = var_8173_pad_0, pad_type = var_8173_pad_type_0, strides = var_8169, weight = up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight, x = input_489)[name = tensor("op_8173")]; - tensor inputs_245 = add(x = var_8173, y = inputs_243)[name = tensor("inputs_245")]; - tensor var_8177 = const()[name = tensor("op_8177"), val = tensor([1])]; - tensor channels_mean_245 = reduce_mean(axes = var_8177, keep_dims = var_6867, x = inputs_245)[name = tensor("channels_mean_245")]; - tensor zero_mean_245 = sub(x = inputs_245, y = channels_mean_245)[name = tensor("zero_mean_245")]; - tensor zero_mean_sq_245 = mul(x = zero_mean_245, y = zero_mean_245)[name = tensor("zero_mean_sq_245")]; - tensor var_8181 = const()[name = tensor("op_8181"), val = tensor([1])]; - tensor var_8182 = reduce_mean(axes = var_8181, keep_dims = var_6867, x = zero_mean_sq_245)[name = tensor("op_8182")]; - tensor var_8183 = const()[name = tensor("op_8183"), val = tensor(0x1.4f8b58p-17)]; - tensor var_8184 = add(x = var_8182, y = var_8183)[name = tensor("op_8184")]; - tensor denom_245_epsilon_0 = const()[name = tensor("denom_245_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_245 = rsqrt(epsilon = denom_245_epsilon_0, x = var_8184)[name = tensor("denom_245")]; - tensor out_245 = mul(x = zero_mean_245, y = denom_245)[name = tensor("out_245")]; - tensor var_8188 = const()[name = tensor("op_8188"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269010432)))]; - tensor var_8189 = add(x = out_245, y = var_8188)[name = tensor("op_8189")]; - tensor var_8191 = const()[name = tensor("op_8191"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269015616)))]; - tensor input_491 = mul(x = var_8189, y = var_8191)[name = tensor("input_491")]; - tensor var_8199 = const()[name = tensor("op_8199"), val = tensor([1, 1])]; - tensor var_8201 = const()[name = tensor("op_8201"), val = tensor([1, 1])]; - tensor var_8203_pad_type_0 = const()[name = tensor("op_8203_pad_type_0"), val = tensor("custom")]; - tensor var_8203_pad_0 = const()[name = tensor("op_8203_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8203 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias, dilations = var_8201, groups = var_6872, pad = var_8203_pad_0, pad_type = var_8203_pad_type_0, strides = var_8199, weight = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight, x = input_491)[name = tensor("op_8203")]; - tensor var_8204_split_sizes_0 = const()[name = tensor("op_8204_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_8204_axis_0 = const()[name = tensor("op_8204_axis_0"), val = tensor(1)]; - tensor var_8204_0, tensor var_8204_1 = split(axis = var_8204_axis_0, split_sizes = var_8204_split_sizes_0, x = var_8203)[name = tensor("op_8204")]; - tensor var_8206_mode_0 = const()[name = tensor("op_8206_mode_0"), val = tensor("EXACT")]; - tensor var_8206 = gelu(mode = var_8206_mode_0, x = var_8204_1)[name = tensor("op_8206")]; - tensor input_493 = mul(x = var_8204_0, y = var_8206)[name = tensor("input_493")]; - tensor var_8210 = const()[name = tensor("op_8210"), val = tensor([1, 1])]; - tensor var_8212 = const()[name = tensor("op_8212"), val = tensor([1, 1])]; - tensor var_8214_pad_type_0 = const()[name = tensor("op_8214_pad_type_0"), val = tensor("custom")]; - tensor var_8214_pad_0 = const()[name = tensor("op_8214_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8214 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias, dilations = var_8212, groups = var_6872, pad = var_8214_pad_0, pad_type = var_8214_pad_type_0, strides = var_8210, weight = up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight, x = input_493)[name = tensor("op_8214")]; - tensor inputs_247 = add(x = var_8214, y = inputs_245)[name = tensor("inputs_247")]; - tensor var_8224 = const()[name = tensor("op_8224"), val = tensor([1])]; - tensor channels_mean_247 = reduce_mean(axes = var_8224, keep_dims = var_6867, x = inputs_247)[name = tensor("channels_mean_247")]; - tensor zero_mean_247 = sub(x = inputs_247, y = channels_mean_247)[name = tensor("zero_mean_247")]; - tensor zero_mean_sq_247 = mul(x = zero_mean_247, y = zero_mean_247)[name = tensor("zero_mean_sq_247")]; - tensor var_8228 = const()[name = tensor("op_8228"), val = tensor([1])]; - tensor var_8229 = reduce_mean(axes = var_8228, keep_dims = var_6867, x = zero_mean_sq_247)[name = tensor("op_8229")]; - tensor var_8230 = const()[name = tensor("op_8230"), val = tensor(0x1.4f8b58p-17)]; - tensor var_8231 = add(x = var_8229, y = var_8230)[name = tensor("op_8231")]; - tensor denom_247_epsilon_0 = const()[name = tensor("denom_247_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_247 = rsqrt(epsilon = denom_247_epsilon_0, x = var_8231)[name = tensor("denom_247")]; - tensor out_247 = mul(x = zero_mean_247, y = denom_247)[name = tensor("out_247")]; - tensor var_8235 = const()[name = tensor("op_8235"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269020800)))]; - tensor var_8236 = add(x = out_247, y = var_8235)[name = tensor("op_8236")]; - tensor var_8238 = const()[name = tensor("op_8238"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269025984)))]; - tensor hidden_states_329 = mul(x = var_8236, y = var_8238)[name = tensor("hidden_states_329")]; - tensor var_8245 = const()[name = tensor("op_8245"), val = tensor([1, 1])]; - tensor var_8247 = const()[name = tensor("op_8247"), val = tensor([1, 1])]; + tensor attn_163_cast = matmul(transpose_x = attn_163_transpose_x_0, transpose_y = attn_163_transpose_y_0, x = var_8057_cast, y = var_8061_cast)[name = tensor("attn_163_cast")]; + tensor var_8065 = const()[name = tensor("op_8065"), val = tensor([2, 1280, 1, -1])]; + tensor input_489_cast = reshape(shape = var_8065, x = attn_163_cast)[name = tensor("input_489_cast")]; + tensor var_8070 = const()[name = tensor("op_8070"), val = tensor([1, 1])]; + tensor var_8072 = const()[name = tensor("op_8072"), val = tensor([1, 1])]; + tensor var_8074_pad_type_0 = const()[name = tensor("op_8074_pad_type_0"), val = tensor("custom")]; + tensor var_8074_pad_0 = const()[name = tensor("op_8074_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3032382464)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3035659328)))]; + tensor var_8074_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_8072, groups = var_31, pad = var_8074_pad_0, pad_type = var_8074_pad_type_0, strides = var_8070, weight = unet_up_blocks_0_attentions_0_transformer_blocks_6_attn2_to_out_0_weight_to_fp16, x = input_489_cast)[name = tensor("op_8074_cast")]; + tensor inputs_245_cast = add(x = var_8074_cast, y = inputs_243_cast)[name = tensor("inputs_245_cast")]; + tensor var_8078 = const()[name = tensor("op_8078"), val = tensor([1])]; + tensor channels_mean_245_cast = reduce_mean(axes = var_8078, keep_dims = var_23, x = inputs_245_cast)[name = tensor("channels_mean_245_cast")]; + tensor zero_mean_245_cast = sub(x = inputs_245_cast, y = channels_mean_245_cast)[name = tensor("zero_mean_245_cast")]; + tensor zero_mean_sq_245_cast = mul(x = zero_mean_245_cast, y = zero_mean_245_cast)[name = tensor("zero_mean_sq_245_cast")]; + tensor var_8082 = const()[name = tensor("op_8082"), val = tensor([1])]; + tensor var_8083_cast = reduce_mean(axes = var_8082, keep_dims = var_23, x = zero_mean_sq_245_cast)[name = tensor("op_8083_cast")]; + tensor var_8084_to_fp16 = const()[name = tensor("op_8084_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8085_cast = add(x = var_8083_cast, y = var_8084_to_fp16)[name = tensor("op_8085_cast")]; + tensor denom_245_epsilon_0_to_fp16 = const()[name = tensor("denom_245_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_245_cast = rsqrt(epsilon = denom_245_epsilon_0_to_fp16, x = var_8085_cast)[name = tensor("denom_245_cast")]; + tensor out_245_cast = mul(x = zero_mean_245_cast, y = denom_245_cast)[name = tensor("out_245_cast")]; + tensor var_8089_to_fp16 = const()[name = tensor("op_8089_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3035661952)))]; + tensor var_8090_cast = add(x = out_245_cast, y = var_8089_to_fp16)[name = tensor("op_8090_cast")]; + tensor var_8092_to_fp16 = const()[name = tensor("op_8092_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3035664576)))]; + tensor input_491_cast = mul(x = var_8090_cast, y = var_8092_to_fp16)[name = tensor("input_491_cast")]; + tensor var_8100 = const()[name = tensor("op_8100"), val = tensor([1, 1])]; + tensor var_8102 = const()[name = tensor("op_8102"), val = tensor([1, 1])]; + tensor var_8104_pad_type_0 = const()[name = tensor("op_8104_pad_type_0"), val = tensor("custom")]; + tensor var_8104_pad_0 = const()[name = tensor("op_8104_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3035667200)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3061881664)))]; + tensor var_8104_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_8102, groups = var_31, pad = var_8104_pad_0, pad_type = var_8104_pad_type_0, strides = var_8100, weight = unet_up_blocks_0_attentions_0_transformer_blocks_6_ff_net_0_proj_weight_to_fp16, x = input_491_cast)[name = tensor("op_8104_cast")]; + tensor var_8105_split_sizes_0 = const()[name = tensor("op_8105_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8105_axis_0 = const()[name = tensor("op_8105_axis_0"), val = tensor(1)]; + tensor var_8105_cast_0, tensor var_8105_cast_1 = split(axis = var_8105_axis_0, split_sizes = var_8105_split_sizes_0, x = var_8104_cast)[name = tensor("op_8105_cast")]; + tensor var_8107_mode_0 = const()[name = tensor("op_8107_mode_0"), val = tensor("EXACT")]; + tensor var_8107_cast = gelu(mode = var_8107_mode_0, x = var_8105_cast_1)[name = tensor("op_8107_cast")]; + tensor input_493_cast = mul(x = var_8105_cast_0, y = var_8107_cast)[name = tensor("input_493_cast")]; + tensor var_8111 = const()[name = tensor("op_8111"), val = tensor([1, 1])]; + tensor var_8113 = const()[name = tensor("op_8113"), val = tensor([1, 1])]; + tensor var_8115_pad_type_0 = const()[name = tensor("op_8115_pad_type_0"), val = tensor("custom")]; + tensor var_8115_pad_0 = const()[name = tensor("op_8115_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3061902208)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3075009472)))]; + tensor var_8115_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_8113, groups = var_31, pad = var_8115_pad_0, pad_type = var_8115_pad_type_0, strides = var_8111, weight = unet_up_blocks_0_attentions_0_transformer_blocks_6_ff_net_2_weight_to_fp16, x = input_493_cast)[name = tensor("op_8115_cast")]; + tensor inputs_247_cast = add(x = var_8115_cast, y = inputs_245_cast)[name = tensor("inputs_247_cast")]; + tensor var_8125 = const()[name = tensor("op_8125"), val = tensor([1])]; + tensor channels_mean_247_cast = reduce_mean(axes = var_8125, keep_dims = var_23, x = inputs_247_cast)[name = tensor("channels_mean_247_cast")]; + tensor zero_mean_247_cast = sub(x = inputs_247_cast, y = channels_mean_247_cast)[name = tensor("zero_mean_247_cast")]; + tensor zero_mean_sq_247_cast = mul(x = zero_mean_247_cast, y = zero_mean_247_cast)[name = tensor("zero_mean_sq_247_cast")]; + tensor var_8129 = const()[name = tensor("op_8129"), val = tensor([1])]; + tensor var_8130_cast = reduce_mean(axes = var_8129, keep_dims = var_23, x = zero_mean_sq_247_cast)[name = tensor("op_8130_cast")]; + tensor var_8131_to_fp16 = const()[name = tensor("op_8131_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8132_cast = add(x = var_8130_cast, y = var_8131_to_fp16)[name = tensor("op_8132_cast")]; + tensor denom_247_epsilon_0_to_fp16 = const()[name = tensor("denom_247_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_247_cast = rsqrt(epsilon = denom_247_epsilon_0_to_fp16, x = var_8132_cast)[name = tensor("denom_247_cast")]; + tensor out_247_cast = mul(x = zero_mean_247_cast, y = denom_247_cast)[name = tensor("out_247_cast")]; + tensor var_8136_to_fp16 = const()[name = tensor("op_8136_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3075012096)))]; + tensor var_8137_cast = add(x = out_247_cast, y = var_8136_to_fp16)[name = tensor("op_8137_cast")]; + tensor var_8139_to_fp16 = const()[name = tensor("op_8139_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3075014720)))]; + tensor hidden_states_329_cast = mul(x = var_8137_cast, y = var_8139_to_fp16)[name = tensor("hidden_states_329_cast")]; + tensor var_8146 = const()[name = tensor("op_8146"), val = tensor([1, 1])]; + tensor var_8148 = const()[name = tensor("op_8148"), val = tensor([1, 1])]; tensor q_165_pad_type_0 = const()[name = tensor("q_165_pad_type_0"), val = tensor("custom")]; tensor q_165_pad_0 = const()[name = tensor("q_165_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_165 = conv(dilations = var_8247, groups = var_6872, pad = q_165_pad_0, pad_type = q_165_pad_type_0, strides = var_8245, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight, x = hidden_states_329)[name = tensor("q_165")]; - tensor var_8251 = const()[name = tensor("op_8251"), val = tensor([1, 1])]; - tensor var_8253 = const()[name = tensor("op_8253"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3075017344)))]; + tensor q_165_cast = conv(dilations = var_8148, groups = var_31, pad = q_165_pad_0, pad_type = q_165_pad_type_0, strides = var_8146, weight = unet_up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_q_weight_to_fp16, x = hidden_states_329_cast)[name = tensor("q_165_cast")]; + tensor var_8152 = const()[name = tensor("op_8152"), val = tensor([1, 1])]; + tensor var_8154 = const()[name = tensor("op_8154"), val = tensor([1, 1])]; tensor k_165_pad_type_0 = const()[name = tensor("k_165_pad_type_0"), val = tensor("custom")]; tensor k_165_pad_0 = const()[name = tensor("k_165_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_165 = conv(dilations = var_8253, groups = var_6872, pad = k_165_pad_0, pad_type = k_165_pad_type_0, strides = var_8251, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight, x = hidden_states_329)[name = tensor("k_165")]; - tensor var_8257 = const()[name = tensor("op_8257"), val = tensor([1, 1])]; - tensor var_8259 = const()[name = tensor("op_8259"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3078294208)))]; + tensor k_165_cast = conv(dilations = var_8154, groups = var_31, pad = k_165_pad_0, pad_type = k_165_pad_type_0, strides = var_8152, weight = unet_up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_k_weight_to_fp16, x = hidden_states_329_cast)[name = tensor("k_165_cast")]; + tensor var_8158 = const()[name = tensor("op_8158"), val = tensor([1, 1])]; + tensor var_8160 = const()[name = tensor("op_8160"), val = tensor([1, 1])]; tensor v_165_pad_type_0 = const()[name = tensor("v_165_pad_type_0"), val = tensor("custom")]; tensor v_165_pad_0 = const()[name = tensor("v_165_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_165 = conv(dilations = var_8259, groups = var_6872, pad = v_165_pad_0, pad_type = v_165_pad_type_0, strides = var_8257, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight, x = hidden_states_329)[name = tensor("v_165")]; - tensor var_8263 = const()[name = tensor("op_8263"), val = tensor([2, 20, 64, -1])]; - tensor var_8264 = reshape(shape = var_8263, x = q_165)[name = tensor("op_8264")]; - tensor var_8265 = const()[name = tensor("op_8265"), val = tensor([2, 20, 64, -1])]; - tensor var_8266 = reshape(shape = var_8265, x = k_165)[name = tensor("op_8266")]; - tensor var_8267 = const()[name = tensor("op_8267"), val = tensor([2, 20, 64, -1])]; - tensor var_8268 = reshape(shape = var_8267, x = v_165)[name = tensor("op_8268")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3081571072)))]; + tensor v_165_cast = conv(dilations = var_8160, groups = var_31, pad = v_165_pad_0, pad_type = v_165_pad_type_0, strides = var_8158, weight = unet_up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_v_weight_to_fp16, x = hidden_states_329_cast)[name = tensor("v_165_cast")]; + tensor var_8164 = const()[name = tensor("op_8164"), val = tensor([2, 20, 64, -1])]; + tensor var_8165_cast = reshape(shape = var_8164, x = q_165_cast)[name = tensor("op_8165_cast")]; + tensor var_8166 = const()[name = tensor("op_8166"), val = tensor([2, 20, 64, -1])]; + tensor var_8167_cast = reshape(shape = var_8166, x = k_165_cast)[name = tensor("op_8167_cast")]; + tensor var_8168 = const()[name = tensor("op_8168"), val = tensor([2, 20, 64, -1])]; + tensor var_8169_cast = reshape(shape = var_8168, x = v_165_cast)[name = tensor("op_8169_cast")]; tensor attn_weights_329_transpose_x_0 = const()[name = tensor("attn_weights_329_transpose_x_0"), val = tensor(true)]; tensor attn_weights_329_transpose_y_0 = const()[name = tensor("attn_weights_329_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_329 = matmul(transpose_x = attn_weights_329_transpose_x_0, transpose_y = attn_weights_329_transpose_y_0, x = var_8264, y = var_8266)[name = tensor("attn_weights_329")]; - tensor attn_weights_331 = mul(x = attn_weights_329, y = var_6863)[name = tensor("attn_weights_331")]; - tensor var_8272 = softmax(axis = var_6856, x = attn_weights_331)[name = tensor("op_8272")]; + tensor attn_weights_329_cast = matmul(transpose_x = attn_weights_329_transpose_x_0, transpose_y = attn_weights_329_transpose_y_0, x = var_8165_cast, y = var_8167_cast)[name = tensor("attn_weights_329_cast")]; + tensor attn_weights_331_cast = mul(x = attn_weights_329_cast, y = var_12_to_fp16)[name = tensor("attn_weights_331_cast")]; + tensor var_8173_cast = softmax(axis = var_18, x = attn_weights_331_cast)[name = tensor("op_8173_cast")]; tensor attn_165_transpose_x_0 = const()[name = tensor("attn_165_transpose_x_0"), val = tensor(false)]; tensor attn_165_transpose_y_0 = const()[name = tensor("attn_165_transpose_y_0"), val = tensor(true)]; - tensor attn_165 = matmul(transpose_x = attn_165_transpose_x_0, transpose_y = attn_165_transpose_y_0, x = var_8268, y = var_8272)[name = tensor("attn_165")]; - tensor var_8276 = const()[name = tensor("op_8276"), val = tensor([2, 1280, 1, -1])]; - tensor input_495 = reshape(shape = var_8276, x = attn_165)[name = tensor("input_495")]; - tensor var_8281 = const()[name = tensor("op_8281"), val = tensor([1, 1])]; - tensor var_8283 = const()[name = tensor("op_8283"), val = tensor([1, 1])]; - tensor var_8285_pad_type_0 = const()[name = tensor("op_8285_pad_type_0"), val = tensor("custom")]; - tensor var_8285_pad_0 = const()[name = tensor("op_8285_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8285 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias, dilations = var_8283, groups = var_6872, pad = var_8285_pad_0, pad_type = var_8285_pad_type_0, strides = var_8281, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight, x = input_495)[name = tensor("op_8285")]; - tensor inputs_249 = add(x = var_8285, y = inputs_247)[name = tensor("inputs_249")]; - tensor var_8289 = const()[name = tensor("op_8289"), val = tensor([1])]; - tensor channels_mean_249 = reduce_mean(axes = var_8289, keep_dims = var_6867, x = inputs_249)[name = tensor("channels_mean_249")]; - tensor zero_mean_249 = sub(x = inputs_249, y = channels_mean_249)[name = tensor("zero_mean_249")]; - tensor zero_mean_sq_249 = mul(x = zero_mean_249, y = zero_mean_249)[name = tensor("zero_mean_sq_249")]; - tensor var_8293 = const()[name = tensor("op_8293"), val = tensor([1])]; - tensor var_8294 = reduce_mean(axes = var_8293, keep_dims = var_6867, x = zero_mean_sq_249)[name = tensor("op_8294")]; - tensor var_8295 = const()[name = tensor("op_8295"), val = tensor(0x1.4f8b58p-17)]; - tensor var_8296 = add(x = var_8294, y = var_8295)[name = tensor("op_8296")]; - tensor denom_249_epsilon_0 = const()[name = tensor("denom_249_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_249 = rsqrt(epsilon = denom_249_epsilon_0, x = var_8296)[name = tensor("denom_249")]; - tensor out_249 = mul(x = zero_mean_249, y = denom_249)[name = tensor("out_249")]; - tensor var_8300 = const()[name = tensor("op_8300"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269031168)))]; - tensor var_8301 = add(x = out_249, y = var_8300)[name = tensor("op_8301")]; - tensor var_8303 = const()[name = tensor("op_8303"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269036352)))]; - tensor hidden_states_331 = mul(x = var_8301, y = var_8303)[name = tensor("hidden_states_331")]; - tensor var_8310 = const()[name = tensor("op_8310"), val = tensor([1, 1])]; - tensor var_8312 = const()[name = tensor("op_8312"), val = tensor([1, 1])]; + tensor attn_165_cast = matmul(transpose_x = attn_165_transpose_x_0, transpose_y = attn_165_transpose_y_0, x = var_8169_cast, y = var_8173_cast)[name = tensor("attn_165_cast")]; + tensor var_8177 = const()[name = tensor("op_8177"), val = tensor([2, 1280, 1, -1])]; + tensor input_495_cast = reshape(shape = var_8177, x = attn_165_cast)[name = tensor("input_495_cast")]; + tensor var_8182 = const()[name = tensor("op_8182"), val = tensor([1, 1])]; + tensor var_8184 = const()[name = tensor("op_8184"), val = tensor([1, 1])]; + tensor var_8186_pad_type_0 = const()[name = tensor("op_8186_pad_type_0"), val = tensor("custom")]; + tensor var_8186_pad_0 = const()[name = tensor("op_8186_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3084847936)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3088124800)))]; + tensor var_8186_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_8184, groups = var_31, pad = var_8186_pad_0, pad_type = var_8186_pad_type_0, strides = var_8182, weight = unet_up_blocks_0_attentions_0_transformer_blocks_7_attn1_to_out_0_weight_to_fp16, x = input_495_cast)[name = tensor("op_8186_cast")]; + tensor inputs_249_cast = add(x = var_8186_cast, y = inputs_247_cast)[name = tensor("inputs_249_cast")]; + tensor var_8190 = const()[name = tensor("op_8190"), val = tensor([1])]; + tensor channels_mean_249_cast = reduce_mean(axes = var_8190, keep_dims = var_23, x = inputs_249_cast)[name = tensor("channels_mean_249_cast")]; + tensor zero_mean_249_cast = sub(x = inputs_249_cast, y = channels_mean_249_cast)[name = tensor("zero_mean_249_cast")]; + tensor zero_mean_sq_249_cast = mul(x = zero_mean_249_cast, y = zero_mean_249_cast)[name = tensor("zero_mean_sq_249_cast")]; + tensor var_8194 = const()[name = tensor("op_8194"), val = tensor([1])]; + tensor var_8195_cast = reduce_mean(axes = var_8194, keep_dims = var_23, x = zero_mean_sq_249_cast)[name = tensor("op_8195_cast")]; + tensor var_8196_to_fp16 = const()[name = tensor("op_8196_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8197_cast = add(x = var_8195_cast, y = var_8196_to_fp16)[name = tensor("op_8197_cast")]; + tensor denom_249_epsilon_0_to_fp16 = const()[name = tensor("denom_249_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_249_cast = rsqrt(epsilon = denom_249_epsilon_0_to_fp16, x = var_8197_cast)[name = tensor("denom_249_cast")]; + tensor out_249_cast = mul(x = zero_mean_249_cast, y = denom_249_cast)[name = tensor("out_249_cast")]; + tensor var_8201_to_fp16 = const()[name = tensor("op_8201_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3088127424)))]; + tensor var_8202_cast = add(x = out_249_cast, y = var_8201_to_fp16)[name = tensor("op_8202_cast")]; + tensor var_8204_to_fp16 = const()[name = tensor("op_8204_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3088130048)))]; + tensor hidden_states_331_cast = mul(x = var_8202_cast, y = var_8204_to_fp16)[name = tensor("hidden_states_331_cast")]; + tensor var_8211 = const()[name = tensor("op_8211"), val = tensor([1, 1])]; + tensor var_8213 = const()[name = tensor("op_8213"), val = tensor([1, 1])]; tensor q_167_pad_type_0 = const()[name = tensor("q_167_pad_type_0"), val = tensor("custom")]; tensor q_167_pad_0 = const()[name = tensor("q_167_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_167 = conv(dilations = var_8312, groups = var_6872, pad = q_167_pad_0, pad_type = q_167_pad_type_0, strides = var_8310, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight, x = hidden_states_331)[name = tensor("q_167")]; - tensor var_8316 = const()[name = tensor("op_8316"), val = tensor([1, 1])]; - tensor var_8318 = const()[name = tensor("op_8318"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3088132672)))]; + tensor q_167_cast = conv(dilations = var_8213, groups = var_31, pad = q_167_pad_0, pad_type = q_167_pad_type_0, strides = var_8211, weight = unet_up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_q_weight_to_fp16, x = hidden_states_331_cast)[name = tensor("q_167_cast")]; + tensor var_8217 = const()[name = tensor("op_8217"), val = tensor([1, 1])]; + tensor var_8219 = const()[name = tensor("op_8219"), val = tensor([1, 1])]; tensor k_167_pad_type_0 = const()[name = tensor("k_167_pad_type_0"), val = tensor("custom")]; tensor k_167_pad_0 = const()[name = tensor("k_167_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_167 = conv(dilations = var_8318, groups = var_6872, pad = k_167_pad_0, pad_type = k_167_pad_type_0, strides = var_8316, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_167")]; - tensor var_8322 = const()[name = tensor("op_8322"), val = tensor([1, 1])]; - tensor var_8324 = const()[name = tensor("op_8324"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3091409536)))]; + tensor k_167_cast = conv(dilations = var_8219, groups = var_31, pad = k_167_pad_0, pad_type = k_167_pad_type_0, strides = var_8217, weight = unet_up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_167_cast")]; + tensor var_8223 = const()[name = tensor("op_8223"), val = tensor([1, 1])]; + tensor var_8225 = const()[name = tensor("op_8225"), val = tensor([1, 1])]; tensor v_167_pad_type_0 = const()[name = tensor("v_167_pad_type_0"), val = tensor("custom")]; tensor v_167_pad_0 = const()[name = tensor("v_167_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_167 = conv(dilations = var_8324, groups = var_6872, pad = v_167_pad_0, pad_type = v_167_pad_type_0, strides = var_8322, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_167")]; - tensor var_8328 = const()[name = tensor("op_8328"), val = tensor([2, 20, 64, -1])]; - tensor var_8329 = reshape(shape = var_8328, x = q_167)[name = tensor("op_8329")]; - tensor var_8330 = const()[name = tensor("op_8330"), val = tensor([2, 20, 64, -1])]; - tensor var_8331 = reshape(shape = var_8330, x = k_167)[name = tensor("op_8331")]; - tensor var_8332 = const()[name = tensor("op_8332"), val = tensor([2, 20, 64, -1])]; - tensor var_8333 = reshape(shape = var_8332, x = v_167)[name = tensor("op_8333")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3096652480)))]; + tensor v_167_cast = conv(dilations = var_8225, groups = var_31, pad = v_167_pad_0, pad_type = v_167_pad_type_0, strides = var_8223, weight = unet_up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_167_cast")]; + tensor var_8229 = const()[name = tensor("op_8229"), val = tensor([2, 20, 64, -1])]; + tensor var_8230_cast = reshape(shape = var_8229, x = q_167_cast)[name = tensor("op_8230_cast")]; + tensor var_8231 = const()[name = tensor("op_8231"), val = tensor([2, 20, 64, -1])]; + tensor var_8232_cast = reshape(shape = var_8231, x = k_167_cast)[name = tensor("op_8232_cast")]; + tensor var_8233 = const()[name = tensor("op_8233"), val = tensor([2, 20, 64, -1])]; + tensor var_8234_cast = reshape(shape = var_8233, x = v_167_cast)[name = tensor("op_8234_cast")]; tensor attn_weights_333_transpose_x_0 = const()[name = tensor("attn_weights_333_transpose_x_0"), val = tensor(true)]; tensor attn_weights_333_transpose_y_0 = const()[name = tensor("attn_weights_333_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_333 = matmul(transpose_x = attn_weights_333_transpose_x_0, transpose_y = attn_weights_333_transpose_y_0, x = var_8329, y = var_8331)[name = tensor("attn_weights_333")]; - tensor attn_weights_335 = mul(x = attn_weights_333, y = var_6863)[name = tensor("attn_weights_335")]; - tensor var_8337 = softmax(axis = var_6856, x = attn_weights_335)[name = tensor("op_8337")]; + tensor attn_weights_333_cast = matmul(transpose_x = attn_weights_333_transpose_x_0, transpose_y = attn_weights_333_transpose_y_0, x = var_8230_cast, y = var_8232_cast)[name = tensor("attn_weights_333_cast")]; + tensor attn_weights_335_cast = mul(x = attn_weights_333_cast, y = var_12_to_fp16)[name = tensor("attn_weights_335_cast")]; + tensor var_8238_cast = softmax(axis = var_18, x = attn_weights_335_cast)[name = tensor("op_8238_cast")]; tensor attn_167_transpose_x_0 = const()[name = tensor("attn_167_transpose_x_0"), val = tensor(false)]; tensor attn_167_transpose_y_0 = const()[name = tensor("attn_167_transpose_y_0"), val = tensor(true)]; - tensor attn_167 = matmul(transpose_x = attn_167_transpose_x_0, transpose_y = attn_167_transpose_y_0, x = var_8333, y = var_8337)[name = tensor("attn_167")]; - tensor var_8341 = const()[name = tensor("op_8341"), val = tensor([2, 1280, 1, -1])]; - tensor input_497 = reshape(shape = var_8341, x = attn_167)[name = tensor("input_497")]; - tensor var_8346 = const()[name = tensor("op_8346"), val = tensor([1, 1])]; - tensor var_8348 = const()[name = tensor("op_8348"), val = tensor([1, 1])]; - tensor var_8350_pad_type_0 = const()[name = tensor("op_8350_pad_type_0"), val = tensor("custom")]; - tensor var_8350_pad_0 = const()[name = tensor("op_8350_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8350 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias, dilations = var_8348, groups = var_6872, pad = var_8350_pad_0, pad_type = var_8350_pad_type_0, strides = var_8346, weight = up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight, x = input_497)[name = tensor("op_8350")]; - tensor inputs_251 = add(x = var_8350, y = inputs_249)[name = tensor("inputs_251")]; - tensor var_8354 = const()[name = tensor("op_8354"), val = tensor([1])]; - tensor channels_mean_251 = reduce_mean(axes = var_8354, keep_dims = var_6867, x = inputs_251)[name = tensor("channels_mean_251")]; - tensor zero_mean_251 = sub(x = inputs_251, y = channels_mean_251)[name = tensor("zero_mean_251")]; - tensor zero_mean_sq_251 = mul(x = zero_mean_251, y = zero_mean_251)[name = tensor("zero_mean_sq_251")]; - tensor var_8358 = const()[name = tensor("op_8358"), val = tensor([1])]; - tensor var_8359 = reduce_mean(axes = var_8358, keep_dims = var_6867, x = zero_mean_sq_251)[name = tensor("op_8359")]; - tensor var_8360 = const()[name = tensor("op_8360"), val = tensor(0x1.4f8b58p-17)]; - tensor var_8361 = add(x = var_8359, y = var_8360)[name = tensor("op_8361")]; - tensor denom_251_epsilon_0 = const()[name = tensor("denom_251_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_251 = rsqrt(epsilon = denom_251_epsilon_0, x = var_8361)[name = tensor("denom_251")]; - tensor out_251 = mul(x = zero_mean_251, y = denom_251)[name = tensor("out_251")]; - tensor var_8365 = const()[name = tensor("op_8365"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269041536)))]; - tensor var_8366 = add(x = out_251, y = var_8365)[name = tensor("op_8366")]; - tensor var_8368 = const()[name = tensor("op_8368"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269046720)))]; - tensor input_499 = mul(x = var_8366, y = var_8368)[name = tensor("input_499")]; - tensor var_8376 = const()[name = tensor("op_8376"), val = tensor([1, 1])]; - tensor var_8378 = const()[name = tensor("op_8378"), val = tensor([1, 1])]; - tensor var_8380_pad_type_0 = const()[name = tensor("op_8380_pad_type_0"), val = tensor("custom")]; - tensor var_8380_pad_0 = const()[name = tensor("op_8380_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8380 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias, dilations = var_8378, groups = var_6872, pad = var_8380_pad_0, pad_type = var_8380_pad_type_0, strides = var_8376, weight = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight, x = input_499)[name = tensor("op_8380")]; - tensor var_8381_split_sizes_0 = const()[name = tensor("op_8381_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_8381_axis_0 = const()[name = tensor("op_8381_axis_0"), val = tensor(1)]; - tensor var_8381_0, tensor var_8381_1 = split(axis = var_8381_axis_0, split_sizes = var_8381_split_sizes_0, x = var_8380)[name = tensor("op_8381")]; - tensor var_8383_mode_0 = const()[name = tensor("op_8383_mode_0"), val = tensor("EXACT")]; - tensor var_8383 = gelu(mode = var_8383_mode_0, x = var_8381_1)[name = tensor("op_8383")]; - tensor input_501 = mul(x = var_8381_0, y = var_8383)[name = tensor("input_501")]; - tensor var_8387 = const()[name = tensor("op_8387"), val = tensor([1, 1])]; - tensor var_8389 = const()[name = tensor("op_8389"), val = tensor([1, 1])]; - tensor var_8391_pad_type_0 = const()[name = tensor("op_8391_pad_type_0"), val = tensor("custom")]; - tensor var_8391_pad_0 = const()[name = tensor("op_8391_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8391 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias, dilations = var_8389, groups = var_6872, pad = var_8391_pad_0, pad_type = var_8391_pad_type_0, strides = var_8387, weight = up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight, x = input_501)[name = tensor("op_8391")]; - tensor inputs_253 = add(x = var_8391, y = inputs_251)[name = tensor("inputs_253")]; - tensor var_8401 = const()[name = tensor("op_8401"), val = tensor([1])]; - tensor channels_mean_253 = reduce_mean(axes = var_8401, keep_dims = var_6867, x = inputs_253)[name = tensor("channels_mean_253")]; - tensor zero_mean_253 = sub(x = inputs_253, y = channels_mean_253)[name = tensor("zero_mean_253")]; - tensor zero_mean_sq_253 = mul(x = zero_mean_253, y = zero_mean_253)[name = tensor("zero_mean_sq_253")]; - tensor var_8405 = const()[name = tensor("op_8405"), val = tensor([1])]; - tensor var_8406 = reduce_mean(axes = var_8405, keep_dims = var_6867, x = zero_mean_sq_253)[name = tensor("op_8406")]; - tensor var_8407 = const()[name = tensor("op_8407"), val = tensor(0x1.4f8b58p-17)]; - tensor var_8408 = add(x = var_8406, y = var_8407)[name = tensor("op_8408")]; - tensor denom_253_epsilon_0 = const()[name = tensor("denom_253_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_253 = rsqrt(epsilon = denom_253_epsilon_0, x = var_8408)[name = tensor("denom_253")]; - tensor out_253 = mul(x = zero_mean_253, y = denom_253)[name = tensor("out_253")]; - tensor var_8412 = const()[name = tensor("op_8412"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269051904)))]; - tensor var_8413 = add(x = out_253, y = var_8412)[name = tensor("op_8413")]; - tensor var_8415 = const()[name = tensor("op_8415"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269057088)))]; - tensor hidden_states_335 = mul(x = var_8413, y = var_8415)[name = tensor("hidden_states_335")]; - tensor var_8422 = const()[name = tensor("op_8422"), val = tensor([1, 1])]; - tensor var_8424 = const()[name = tensor("op_8424"), val = tensor([1, 1])]; + tensor attn_167_cast = matmul(transpose_x = attn_167_transpose_x_0, transpose_y = attn_167_transpose_y_0, x = var_8234_cast, y = var_8238_cast)[name = tensor("attn_167_cast")]; + tensor var_8242 = const()[name = tensor("op_8242"), val = tensor([2, 1280, 1, -1])]; + tensor input_497_cast = reshape(shape = var_8242, x = attn_167_cast)[name = tensor("input_497_cast")]; + tensor var_8247 = const()[name = tensor("op_8247"), val = tensor([1, 1])]; + tensor var_8249 = const()[name = tensor("op_8249"), val = tensor([1, 1])]; + tensor var_8251_pad_type_0 = const()[name = tensor("op_8251_pad_type_0"), val = tensor("custom")]; + tensor var_8251_pad_0 = const()[name = tensor("op_8251_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3101895424)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3105172288)))]; + tensor var_8251_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_8249, groups = var_31, pad = var_8251_pad_0, pad_type = var_8251_pad_type_0, strides = var_8247, weight = unet_up_blocks_0_attentions_0_transformer_blocks_7_attn2_to_out_0_weight_to_fp16, x = input_497_cast)[name = tensor("op_8251_cast")]; + tensor inputs_251_cast = add(x = var_8251_cast, y = inputs_249_cast)[name = tensor("inputs_251_cast")]; + tensor var_8255 = const()[name = tensor("op_8255"), val = tensor([1])]; + tensor channels_mean_251_cast = reduce_mean(axes = var_8255, keep_dims = var_23, x = inputs_251_cast)[name = tensor("channels_mean_251_cast")]; + tensor zero_mean_251_cast = sub(x = inputs_251_cast, y = channels_mean_251_cast)[name = tensor("zero_mean_251_cast")]; + tensor zero_mean_sq_251_cast = mul(x = zero_mean_251_cast, y = zero_mean_251_cast)[name = tensor("zero_mean_sq_251_cast")]; + tensor var_8259 = const()[name = tensor("op_8259"), val = tensor([1])]; + tensor var_8260_cast = reduce_mean(axes = var_8259, keep_dims = var_23, x = zero_mean_sq_251_cast)[name = tensor("op_8260_cast")]; + tensor var_8261_to_fp16 = const()[name = tensor("op_8261_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8262_cast = add(x = var_8260_cast, y = var_8261_to_fp16)[name = tensor("op_8262_cast")]; + tensor denom_251_epsilon_0_to_fp16 = const()[name = tensor("denom_251_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_251_cast = rsqrt(epsilon = denom_251_epsilon_0_to_fp16, x = var_8262_cast)[name = tensor("denom_251_cast")]; + tensor out_251_cast = mul(x = zero_mean_251_cast, y = denom_251_cast)[name = tensor("out_251_cast")]; + tensor var_8266_to_fp16 = const()[name = tensor("op_8266_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3105174912)))]; + tensor var_8267_cast = add(x = out_251_cast, y = var_8266_to_fp16)[name = tensor("op_8267_cast")]; + tensor var_8269_to_fp16 = const()[name = tensor("op_8269_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3105177536)))]; + tensor input_499_cast = mul(x = var_8267_cast, y = var_8269_to_fp16)[name = tensor("input_499_cast")]; + tensor var_8277 = const()[name = tensor("op_8277"), val = tensor([1, 1])]; + tensor var_8279 = const()[name = tensor("op_8279"), val = tensor([1, 1])]; + tensor var_8281_pad_type_0 = const()[name = tensor("op_8281_pad_type_0"), val = tensor("custom")]; + tensor var_8281_pad_0 = const()[name = tensor("op_8281_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3105180160)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3131394624)))]; + tensor var_8281_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_8279, groups = var_31, pad = var_8281_pad_0, pad_type = var_8281_pad_type_0, strides = var_8277, weight = unet_up_blocks_0_attentions_0_transformer_blocks_7_ff_net_0_proj_weight_to_fp16, x = input_499_cast)[name = tensor("op_8281_cast")]; + tensor var_8282_split_sizes_0 = const()[name = tensor("op_8282_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8282_axis_0 = const()[name = tensor("op_8282_axis_0"), val = tensor(1)]; + tensor var_8282_cast_0, tensor var_8282_cast_1 = split(axis = var_8282_axis_0, split_sizes = var_8282_split_sizes_0, x = var_8281_cast)[name = tensor("op_8282_cast")]; + tensor var_8284_mode_0 = const()[name = tensor("op_8284_mode_0"), val = tensor("EXACT")]; + tensor var_8284_cast = gelu(mode = var_8284_mode_0, x = var_8282_cast_1)[name = tensor("op_8284_cast")]; + tensor input_501_cast = mul(x = var_8282_cast_0, y = var_8284_cast)[name = tensor("input_501_cast")]; + tensor var_8288 = const()[name = tensor("op_8288"), val = tensor([1, 1])]; + tensor var_8290 = const()[name = tensor("op_8290"), val = tensor([1, 1])]; + tensor var_8292_pad_type_0 = const()[name = tensor("op_8292_pad_type_0"), val = tensor("custom")]; + tensor var_8292_pad_0 = const()[name = tensor("op_8292_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3131415168)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3144522432)))]; + tensor var_8292_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_8290, groups = var_31, pad = var_8292_pad_0, pad_type = var_8292_pad_type_0, strides = var_8288, weight = unet_up_blocks_0_attentions_0_transformer_blocks_7_ff_net_2_weight_to_fp16, x = input_501_cast)[name = tensor("op_8292_cast")]; + tensor inputs_253_cast = add(x = var_8292_cast, y = inputs_251_cast)[name = tensor("inputs_253_cast")]; + tensor var_8302 = const()[name = tensor("op_8302"), val = tensor([1])]; + tensor channels_mean_253_cast = reduce_mean(axes = var_8302, keep_dims = var_23, x = inputs_253_cast)[name = tensor("channels_mean_253_cast")]; + tensor zero_mean_253_cast = sub(x = inputs_253_cast, y = channels_mean_253_cast)[name = tensor("zero_mean_253_cast")]; + tensor zero_mean_sq_253_cast = mul(x = zero_mean_253_cast, y = zero_mean_253_cast)[name = tensor("zero_mean_sq_253_cast")]; + tensor var_8306 = const()[name = tensor("op_8306"), val = tensor([1])]; + tensor var_8307_cast = reduce_mean(axes = var_8306, keep_dims = var_23, x = zero_mean_sq_253_cast)[name = tensor("op_8307_cast")]; + tensor var_8308_to_fp16 = const()[name = tensor("op_8308_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8309_cast = add(x = var_8307_cast, y = var_8308_to_fp16)[name = tensor("op_8309_cast")]; + tensor denom_253_epsilon_0_to_fp16 = const()[name = tensor("denom_253_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_253_cast = rsqrt(epsilon = denom_253_epsilon_0_to_fp16, x = var_8309_cast)[name = tensor("denom_253_cast")]; + tensor out_253_cast = mul(x = zero_mean_253_cast, y = denom_253_cast)[name = tensor("out_253_cast")]; + tensor var_8313_to_fp16 = const()[name = tensor("op_8313_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3144525056)))]; + tensor var_8314_cast = add(x = out_253_cast, y = var_8313_to_fp16)[name = tensor("op_8314_cast")]; + tensor var_8316_to_fp16 = const()[name = tensor("op_8316_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3144527680)))]; + tensor hidden_states_335_cast = mul(x = var_8314_cast, y = var_8316_to_fp16)[name = tensor("hidden_states_335_cast")]; + tensor var_8323 = const()[name = tensor("op_8323"), val = tensor([1, 1])]; + tensor var_8325 = const()[name = tensor("op_8325"), val = tensor([1, 1])]; tensor q_169_pad_type_0 = const()[name = tensor("q_169_pad_type_0"), val = tensor("custom")]; tensor q_169_pad_0 = const()[name = tensor("q_169_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_169 = conv(dilations = var_8424, groups = var_6872, pad = q_169_pad_0, pad_type = q_169_pad_type_0, strides = var_8422, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight, x = hidden_states_335)[name = tensor("q_169")]; - tensor var_8428 = const()[name = tensor("op_8428"), val = tensor([1, 1])]; - tensor var_8430 = const()[name = tensor("op_8430"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3144530304)))]; + tensor q_169_cast = conv(dilations = var_8325, groups = var_31, pad = q_169_pad_0, pad_type = q_169_pad_type_0, strides = var_8323, weight = unet_up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_q_weight_to_fp16, x = hidden_states_335_cast)[name = tensor("q_169_cast")]; + tensor var_8329 = const()[name = tensor("op_8329"), val = tensor([1, 1])]; + tensor var_8331 = const()[name = tensor("op_8331"), val = tensor([1, 1])]; tensor k_169_pad_type_0 = const()[name = tensor("k_169_pad_type_0"), val = tensor("custom")]; tensor k_169_pad_0 = const()[name = tensor("k_169_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_169 = conv(dilations = var_8430, groups = var_6872, pad = k_169_pad_0, pad_type = k_169_pad_type_0, strides = var_8428, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight, x = hidden_states_335)[name = tensor("k_169")]; - tensor var_8434 = const()[name = tensor("op_8434"), val = tensor([1, 1])]; - tensor var_8436 = const()[name = tensor("op_8436"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3147807168)))]; + tensor k_169_cast = conv(dilations = var_8331, groups = var_31, pad = k_169_pad_0, pad_type = k_169_pad_type_0, strides = var_8329, weight = unet_up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_k_weight_to_fp16, x = hidden_states_335_cast)[name = tensor("k_169_cast")]; + tensor var_8335 = const()[name = tensor("op_8335"), val = tensor([1, 1])]; + tensor var_8337 = const()[name = tensor("op_8337"), val = tensor([1, 1])]; tensor v_169_pad_type_0 = const()[name = tensor("v_169_pad_type_0"), val = tensor("custom")]; tensor v_169_pad_0 = const()[name = tensor("v_169_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_169 = conv(dilations = var_8436, groups = var_6872, pad = v_169_pad_0, pad_type = v_169_pad_type_0, strides = var_8434, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight, x = hidden_states_335)[name = tensor("v_169")]; - tensor var_8440 = const()[name = tensor("op_8440"), val = tensor([2, 20, 64, -1])]; - tensor var_8441 = reshape(shape = var_8440, x = q_169)[name = tensor("op_8441")]; - tensor var_8442 = const()[name = tensor("op_8442"), val = tensor([2, 20, 64, -1])]; - tensor var_8443 = reshape(shape = var_8442, x = k_169)[name = tensor("op_8443")]; - tensor var_8444 = const()[name = tensor("op_8444"), val = tensor([2, 20, 64, -1])]; - tensor var_8445 = reshape(shape = var_8444, x = v_169)[name = tensor("op_8445")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3151084032)))]; + tensor v_169_cast = conv(dilations = var_8337, groups = var_31, pad = v_169_pad_0, pad_type = v_169_pad_type_0, strides = var_8335, weight = unet_up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_v_weight_to_fp16, x = hidden_states_335_cast)[name = tensor("v_169_cast")]; + tensor var_8341 = const()[name = tensor("op_8341"), val = tensor([2, 20, 64, -1])]; + tensor var_8342_cast = reshape(shape = var_8341, x = q_169_cast)[name = tensor("op_8342_cast")]; + tensor var_8343 = const()[name = tensor("op_8343"), val = tensor([2, 20, 64, -1])]; + tensor var_8344_cast = reshape(shape = var_8343, x = k_169_cast)[name = tensor("op_8344_cast")]; + tensor var_8345 = const()[name = tensor("op_8345"), val = tensor([2, 20, 64, -1])]; + tensor var_8346_cast = reshape(shape = var_8345, x = v_169_cast)[name = tensor("op_8346_cast")]; tensor attn_weights_337_transpose_x_0 = const()[name = tensor("attn_weights_337_transpose_x_0"), val = tensor(true)]; tensor attn_weights_337_transpose_y_0 = const()[name = tensor("attn_weights_337_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_337 = matmul(transpose_x = attn_weights_337_transpose_x_0, transpose_y = attn_weights_337_transpose_y_0, x = var_8441, y = var_8443)[name = tensor("attn_weights_337")]; - tensor attn_weights_339 = mul(x = attn_weights_337, y = var_6863)[name = tensor("attn_weights_339")]; - tensor var_8449 = softmax(axis = var_6856, x = attn_weights_339)[name = tensor("op_8449")]; + tensor attn_weights_337_cast = matmul(transpose_x = attn_weights_337_transpose_x_0, transpose_y = attn_weights_337_transpose_y_0, x = var_8342_cast, y = var_8344_cast)[name = tensor("attn_weights_337_cast")]; + tensor attn_weights_339_cast = mul(x = attn_weights_337_cast, y = var_12_to_fp16)[name = tensor("attn_weights_339_cast")]; + tensor var_8350_cast = softmax(axis = var_18, x = attn_weights_339_cast)[name = tensor("op_8350_cast")]; tensor attn_169_transpose_x_0 = const()[name = tensor("attn_169_transpose_x_0"), val = tensor(false)]; tensor attn_169_transpose_y_0 = const()[name = tensor("attn_169_transpose_y_0"), val = tensor(true)]; - tensor attn_169 = matmul(transpose_x = attn_169_transpose_x_0, transpose_y = attn_169_transpose_y_0, x = var_8445, y = var_8449)[name = tensor("attn_169")]; - tensor var_8453 = const()[name = tensor("op_8453"), val = tensor([2, 1280, 1, -1])]; - tensor input_503 = reshape(shape = var_8453, x = attn_169)[name = tensor("input_503")]; - tensor var_8458 = const()[name = tensor("op_8458"), val = tensor([1, 1])]; - tensor var_8460 = const()[name = tensor("op_8460"), val = tensor([1, 1])]; - tensor var_8462_pad_type_0 = const()[name = tensor("op_8462_pad_type_0"), val = tensor("custom")]; - tensor var_8462_pad_0 = const()[name = tensor("op_8462_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8462 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias, dilations = var_8460, groups = var_6872, pad = var_8462_pad_0, pad_type = var_8462_pad_type_0, strides = var_8458, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight, x = input_503)[name = tensor("op_8462")]; - tensor inputs_255 = add(x = var_8462, y = inputs_253)[name = tensor("inputs_255")]; - tensor var_8466 = const()[name = tensor("op_8466"), val = tensor([1])]; - tensor channels_mean_255 = reduce_mean(axes = var_8466, keep_dims = var_6867, x = inputs_255)[name = tensor("channels_mean_255")]; - tensor zero_mean_255 = sub(x = inputs_255, y = channels_mean_255)[name = tensor("zero_mean_255")]; - tensor zero_mean_sq_255 = mul(x = zero_mean_255, y = zero_mean_255)[name = tensor("zero_mean_sq_255")]; - tensor var_8470 = const()[name = tensor("op_8470"), val = tensor([1])]; - tensor var_8471 = reduce_mean(axes = var_8470, keep_dims = var_6867, x = zero_mean_sq_255)[name = tensor("op_8471")]; - tensor var_8472 = const()[name = tensor("op_8472"), val = tensor(0x1.4f8b58p-17)]; - tensor var_8473 = add(x = var_8471, y = var_8472)[name = tensor("op_8473")]; - tensor denom_255_epsilon_0 = const()[name = tensor("denom_255_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_255 = rsqrt(epsilon = denom_255_epsilon_0, x = var_8473)[name = tensor("denom_255")]; - tensor out_255 = mul(x = zero_mean_255, y = denom_255)[name = tensor("out_255")]; - tensor var_8477 = const()[name = tensor("op_8477"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269062272)))]; - tensor var_8478 = add(x = out_255, y = var_8477)[name = tensor("op_8478")]; - tensor var_8480 = const()[name = tensor("op_8480"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269067456)))]; - tensor hidden_states_337 = mul(x = var_8478, y = var_8480)[name = tensor("hidden_states_337")]; - tensor var_8487 = const()[name = tensor("op_8487"), val = tensor([1, 1])]; - tensor var_8489 = const()[name = tensor("op_8489"), val = tensor([1, 1])]; + tensor attn_169_cast = matmul(transpose_x = attn_169_transpose_x_0, transpose_y = attn_169_transpose_y_0, x = var_8346_cast, y = var_8350_cast)[name = tensor("attn_169_cast")]; + tensor var_8354 = const()[name = tensor("op_8354"), val = tensor([2, 1280, 1, -1])]; + tensor input_503_cast = reshape(shape = var_8354, x = attn_169_cast)[name = tensor("input_503_cast")]; + tensor var_8359 = const()[name = tensor("op_8359"), val = tensor([1, 1])]; + tensor var_8361 = const()[name = tensor("op_8361"), val = tensor([1, 1])]; + tensor var_8363_pad_type_0 = const()[name = tensor("op_8363_pad_type_0"), val = tensor("custom")]; + tensor var_8363_pad_0 = const()[name = tensor("op_8363_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3154360896)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3157637760)))]; + tensor var_8363_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_8361, groups = var_31, pad = var_8363_pad_0, pad_type = var_8363_pad_type_0, strides = var_8359, weight = unet_up_blocks_0_attentions_0_transformer_blocks_8_attn1_to_out_0_weight_to_fp16, x = input_503_cast)[name = tensor("op_8363_cast")]; + tensor inputs_255_cast = add(x = var_8363_cast, y = inputs_253_cast)[name = tensor("inputs_255_cast")]; + tensor var_8367 = const()[name = tensor("op_8367"), val = tensor([1])]; + tensor channels_mean_255_cast = reduce_mean(axes = var_8367, keep_dims = var_23, x = inputs_255_cast)[name = tensor("channels_mean_255_cast")]; + tensor zero_mean_255_cast = sub(x = inputs_255_cast, y = channels_mean_255_cast)[name = tensor("zero_mean_255_cast")]; + tensor zero_mean_sq_255_cast = mul(x = zero_mean_255_cast, y = zero_mean_255_cast)[name = tensor("zero_mean_sq_255_cast")]; + tensor var_8371 = const()[name = tensor("op_8371"), val = tensor([1])]; + tensor var_8372_cast = reduce_mean(axes = var_8371, keep_dims = var_23, x = zero_mean_sq_255_cast)[name = tensor("op_8372_cast")]; + tensor var_8373_to_fp16 = const()[name = tensor("op_8373_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8374_cast = add(x = var_8372_cast, y = var_8373_to_fp16)[name = tensor("op_8374_cast")]; + tensor denom_255_epsilon_0_to_fp16 = const()[name = tensor("denom_255_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_255_cast = rsqrt(epsilon = denom_255_epsilon_0_to_fp16, x = var_8374_cast)[name = tensor("denom_255_cast")]; + tensor out_255_cast = mul(x = zero_mean_255_cast, y = denom_255_cast)[name = tensor("out_255_cast")]; + tensor var_8378_to_fp16 = const()[name = tensor("op_8378_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3157640384)))]; + tensor var_8379_cast = add(x = out_255_cast, y = var_8378_to_fp16)[name = tensor("op_8379_cast")]; + tensor var_8381_to_fp16 = const()[name = tensor("op_8381_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3157643008)))]; + tensor hidden_states_337_cast = mul(x = var_8379_cast, y = var_8381_to_fp16)[name = tensor("hidden_states_337_cast")]; + tensor var_8388 = const()[name = tensor("op_8388"), val = tensor([1, 1])]; + tensor var_8390 = const()[name = tensor("op_8390"), val = tensor([1, 1])]; tensor q_171_pad_type_0 = const()[name = tensor("q_171_pad_type_0"), val = tensor("custom")]; tensor q_171_pad_0 = const()[name = tensor("q_171_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_171 = conv(dilations = var_8489, groups = var_6872, pad = q_171_pad_0, pad_type = q_171_pad_type_0, strides = var_8487, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight, x = hidden_states_337)[name = tensor("q_171")]; - tensor var_8493 = const()[name = tensor("op_8493"), val = tensor([1, 1])]; - tensor var_8495 = const()[name = tensor("op_8495"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3157645632)))]; + tensor q_171_cast = conv(dilations = var_8390, groups = var_31, pad = q_171_pad_0, pad_type = q_171_pad_type_0, strides = var_8388, weight = unet_up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_q_weight_to_fp16, x = hidden_states_337_cast)[name = tensor("q_171_cast")]; + tensor var_8394 = const()[name = tensor("op_8394"), val = tensor([1, 1])]; + tensor var_8396 = const()[name = tensor("op_8396"), val = tensor([1, 1])]; tensor k_171_pad_type_0 = const()[name = tensor("k_171_pad_type_0"), val = tensor("custom")]; tensor k_171_pad_0 = const()[name = tensor("k_171_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_171 = conv(dilations = var_8495, groups = var_6872, pad = k_171_pad_0, pad_type = k_171_pad_type_0, strides = var_8493, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_171")]; - tensor var_8499 = const()[name = tensor("op_8499"), val = tensor([1, 1])]; - tensor var_8501 = const()[name = tensor("op_8501"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3160922496)))]; + tensor k_171_cast = conv(dilations = var_8396, groups = var_31, pad = k_171_pad_0, pad_type = k_171_pad_type_0, strides = var_8394, weight = unet_up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_171_cast")]; + tensor var_8400 = const()[name = tensor("op_8400"), val = tensor([1, 1])]; + tensor var_8402 = const()[name = tensor("op_8402"), val = tensor([1, 1])]; tensor v_171_pad_type_0 = const()[name = tensor("v_171_pad_type_0"), val = tensor("custom")]; tensor v_171_pad_0 = const()[name = tensor("v_171_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_171 = conv(dilations = var_8501, groups = var_6872, pad = v_171_pad_0, pad_type = v_171_pad_type_0, strides = var_8499, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_171")]; - tensor var_8505 = const()[name = tensor("op_8505"), val = tensor([2, 20, 64, -1])]; - tensor var_8506 = reshape(shape = var_8505, x = q_171)[name = tensor("op_8506")]; - tensor var_8507 = const()[name = tensor("op_8507"), val = tensor([2, 20, 64, -1])]; - tensor var_8508 = reshape(shape = var_8507, x = k_171)[name = tensor("op_8508")]; - tensor var_8509 = const()[name = tensor("op_8509"), val = tensor([2, 20, 64, -1])]; - tensor var_8510 = reshape(shape = var_8509, x = v_171)[name = tensor("op_8510")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3166165440)))]; + tensor v_171_cast = conv(dilations = var_8402, groups = var_31, pad = v_171_pad_0, pad_type = v_171_pad_type_0, strides = var_8400, weight = unet_up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_171_cast")]; + tensor var_8406 = const()[name = tensor("op_8406"), val = tensor([2, 20, 64, -1])]; + tensor var_8407_cast = reshape(shape = var_8406, x = q_171_cast)[name = tensor("op_8407_cast")]; + tensor var_8408 = const()[name = tensor("op_8408"), val = tensor([2, 20, 64, -1])]; + tensor var_8409_cast = reshape(shape = var_8408, x = k_171_cast)[name = tensor("op_8409_cast")]; + tensor var_8410 = const()[name = tensor("op_8410"), val = tensor([2, 20, 64, -1])]; + tensor var_8411_cast = reshape(shape = var_8410, x = v_171_cast)[name = tensor("op_8411_cast")]; tensor attn_weights_341_transpose_x_0 = const()[name = tensor("attn_weights_341_transpose_x_0"), val = tensor(true)]; tensor attn_weights_341_transpose_y_0 = const()[name = tensor("attn_weights_341_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_341 = matmul(transpose_x = attn_weights_341_transpose_x_0, transpose_y = attn_weights_341_transpose_y_0, x = var_8506, y = var_8508)[name = tensor("attn_weights_341")]; - tensor attn_weights_343 = mul(x = attn_weights_341, y = var_6863)[name = tensor("attn_weights_343")]; - tensor var_8514 = softmax(axis = var_6856, x = attn_weights_343)[name = tensor("op_8514")]; + tensor attn_weights_341_cast = matmul(transpose_x = attn_weights_341_transpose_x_0, transpose_y = attn_weights_341_transpose_y_0, x = var_8407_cast, y = var_8409_cast)[name = tensor("attn_weights_341_cast")]; + tensor attn_weights_343_cast = mul(x = attn_weights_341_cast, y = var_12_to_fp16)[name = tensor("attn_weights_343_cast")]; + tensor var_8415_cast = softmax(axis = var_18, x = attn_weights_343_cast)[name = tensor("op_8415_cast")]; tensor attn_171_transpose_x_0 = const()[name = tensor("attn_171_transpose_x_0"), val = tensor(false)]; tensor attn_171_transpose_y_0 = const()[name = tensor("attn_171_transpose_y_0"), val = tensor(true)]; - tensor attn_171 = matmul(transpose_x = attn_171_transpose_x_0, transpose_y = attn_171_transpose_y_0, x = var_8510, y = var_8514)[name = tensor("attn_171")]; - tensor var_8518 = const()[name = tensor("op_8518"), val = tensor([2, 1280, 1, -1])]; - tensor input_505 = reshape(shape = var_8518, x = attn_171)[name = tensor("input_505")]; - tensor var_8523 = const()[name = tensor("op_8523"), val = tensor([1, 1])]; - tensor var_8525 = const()[name = tensor("op_8525"), val = tensor([1, 1])]; - tensor var_8527_pad_type_0 = const()[name = tensor("op_8527_pad_type_0"), val = tensor("custom")]; - tensor var_8527_pad_0 = const()[name = tensor("op_8527_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8527 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias, dilations = var_8525, groups = var_6872, pad = var_8527_pad_0, pad_type = var_8527_pad_type_0, strides = var_8523, weight = up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight, x = input_505)[name = tensor("op_8527")]; - tensor inputs_257 = add(x = var_8527, y = inputs_255)[name = tensor("inputs_257")]; - tensor var_8531 = const()[name = tensor("op_8531"), val = tensor([1])]; - tensor channels_mean_257 = reduce_mean(axes = var_8531, keep_dims = var_6867, x = inputs_257)[name = tensor("channels_mean_257")]; - tensor zero_mean_257 = sub(x = inputs_257, y = channels_mean_257)[name = tensor("zero_mean_257")]; - tensor zero_mean_sq_257 = mul(x = zero_mean_257, y = zero_mean_257)[name = tensor("zero_mean_sq_257")]; - tensor var_8535 = const()[name = tensor("op_8535"), val = tensor([1])]; - tensor var_8536 = reduce_mean(axes = var_8535, keep_dims = var_6867, x = zero_mean_sq_257)[name = tensor("op_8536")]; - tensor var_8537 = const()[name = tensor("op_8537"), val = tensor(0x1.4f8b58p-17)]; - tensor var_8538 = add(x = var_8536, y = var_8537)[name = tensor("op_8538")]; - tensor denom_257_epsilon_0 = const()[name = tensor("denom_257_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_257 = rsqrt(epsilon = denom_257_epsilon_0, x = var_8538)[name = tensor("denom_257")]; - tensor out_257 = mul(x = zero_mean_257, y = denom_257)[name = tensor("out_257")]; - tensor var_8542 = const()[name = tensor("op_8542"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269072640)))]; - tensor var_8543 = add(x = out_257, y = var_8542)[name = tensor("op_8543")]; - tensor var_8545 = const()[name = tensor("op_8545"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269077824)))]; - tensor input_507 = mul(x = var_8543, y = var_8545)[name = tensor("input_507")]; - tensor var_8553 = const()[name = tensor("op_8553"), val = tensor([1, 1])]; - tensor var_8555 = const()[name = tensor("op_8555"), val = tensor([1, 1])]; - tensor var_8557_pad_type_0 = const()[name = tensor("op_8557_pad_type_0"), val = tensor("custom")]; - tensor var_8557_pad_0 = const()[name = tensor("op_8557_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8557 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias, dilations = var_8555, groups = var_6872, pad = var_8557_pad_0, pad_type = var_8557_pad_type_0, strides = var_8553, weight = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight, x = input_507)[name = tensor("op_8557")]; - tensor var_8558_split_sizes_0 = const()[name = tensor("op_8558_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_8558_axis_0 = const()[name = tensor("op_8558_axis_0"), val = tensor(1)]; - tensor var_8558_0, tensor var_8558_1 = split(axis = var_8558_axis_0, split_sizes = var_8558_split_sizes_0, x = var_8557)[name = tensor("op_8558")]; - tensor var_8560_mode_0 = const()[name = tensor("op_8560_mode_0"), val = tensor("EXACT")]; - tensor var_8560 = gelu(mode = var_8560_mode_0, x = var_8558_1)[name = tensor("op_8560")]; - tensor input_509 = mul(x = var_8558_0, y = var_8560)[name = tensor("input_509")]; - tensor var_8564 = const()[name = tensor("op_8564"), val = tensor([1, 1])]; - tensor var_8566 = const()[name = tensor("op_8566"), val = tensor([1, 1])]; - tensor var_8568_pad_type_0 = const()[name = tensor("op_8568_pad_type_0"), val = tensor("custom")]; - tensor var_8568_pad_0 = const()[name = tensor("op_8568_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8568 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias, dilations = var_8566, groups = var_6872, pad = var_8568_pad_0, pad_type = var_8568_pad_type_0, strides = var_8564, weight = up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight, x = input_509)[name = tensor("op_8568")]; - tensor inputs_259 = add(x = var_8568, y = inputs_257)[name = tensor("inputs_259")]; - tensor var_8578 = const()[name = tensor("op_8578"), val = tensor([1])]; - tensor channels_mean_259 = reduce_mean(axes = var_8578, keep_dims = var_6867, x = inputs_259)[name = tensor("channels_mean_259")]; - tensor zero_mean_259 = sub(x = inputs_259, y = channels_mean_259)[name = tensor("zero_mean_259")]; - tensor zero_mean_sq_259 = mul(x = zero_mean_259, y = zero_mean_259)[name = tensor("zero_mean_sq_259")]; - tensor var_8582 = const()[name = tensor("op_8582"), val = tensor([1])]; - tensor var_8583 = reduce_mean(axes = var_8582, keep_dims = var_6867, x = zero_mean_sq_259)[name = tensor("op_8583")]; - tensor var_8584 = const()[name = tensor("op_8584"), val = tensor(0x1.4f8b58p-17)]; - tensor var_8585 = add(x = var_8583, y = var_8584)[name = tensor("op_8585")]; - tensor denom_259_epsilon_0 = const()[name = tensor("denom_259_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_259 = rsqrt(epsilon = denom_259_epsilon_0, x = var_8585)[name = tensor("denom_259")]; - tensor out_259 = mul(x = zero_mean_259, y = denom_259)[name = tensor("out_259")]; - tensor var_8589 = const()[name = tensor("op_8589"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269083008)))]; - tensor var_8590 = add(x = out_259, y = var_8589)[name = tensor("op_8590")]; - tensor var_8592 = const()[name = tensor("op_8592"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269088192)))]; - tensor hidden_states_341 = mul(x = var_8590, y = var_8592)[name = tensor("hidden_states_341")]; - tensor var_8599 = const()[name = tensor("op_8599"), val = tensor([1, 1])]; - tensor var_8601 = const()[name = tensor("op_8601"), val = tensor([1, 1])]; + tensor attn_171_cast = matmul(transpose_x = attn_171_transpose_x_0, transpose_y = attn_171_transpose_y_0, x = var_8411_cast, y = var_8415_cast)[name = tensor("attn_171_cast")]; + tensor var_8419 = const()[name = tensor("op_8419"), val = tensor([2, 1280, 1, -1])]; + tensor input_505_cast = reshape(shape = var_8419, x = attn_171_cast)[name = tensor("input_505_cast")]; + tensor var_8424 = const()[name = tensor("op_8424"), val = tensor([1, 1])]; + tensor var_8426 = const()[name = tensor("op_8426"), val = tensor([1, 1])]; + tensor var_8428_pad_type_0 = const()[name = tensor("op_8428_pad_type_0"), val = tensor("custom")]; + tensor var_8428_pad_0 = const()[name = tensor("op_8428_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3171408384)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3174685248)))]; + tensor var_8428_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_8426, groups = var_31, pad = var_8428_pad_0, pad_type = var_8428_pad_type_0, strides = var_8424, weight = unet_up_blocks_0_attentions_0_transformer_blocks_8_attn2_to_out_0_weight_to_fp16, x = input_505_cast)[name = tensor("op_8428_cast")]; + tensor inputs_257_cast = add(x = var_8428_cast, y = inputs_255_cast)[name = tensor("inputs_257_cast")]; + tensor var_8432 = const()[name = tensor("op_8432"), val = tensor([1])]; + tensor channels_mean_257_cast = reduce_mean(axes = var_8432, keep_dims = var_23, x = inputs_257_cast)[name = tensor("channels_mean_257_cast")]; + tensor zero_mean_257_cast = sub(x = inputs_257_cast, y = channels_mean_257_cast)[name = tensor("zero_mean_257_cast")]; + tensor zero_mean_sq_257_cast = mul(x = zero_mean_257_cast, y = zero_mean_257_cast)[name = tensor("zero_mean_sq_257_cast")]; + tensor var_8436 = const()[name = tensor("op_8436"), val = tensor([1])]; + tensor var_8437_cast = reduce_mean(axes = var_8436, keep_dims = var_23, x = zero_mean_sq_257_cast)[name = tensor("op_8437_cast")]; + tensor var_8438_to_fp16 = const()[name = tensor("op_8438_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8439_cast = add(x = var_8437_cast, y = var_8438_to_fp16)[name = tensor("op_8439_cast")]; + tensor denom_257_epsilon_0_to_fp16 = const()[name = tensor("denom_257_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_257_cast = rsqrt(epsilon = denom_257_epsilon_0_to_fp16, x = var_8439_cast)[name = tensor("denom_257_cast")]; + tensor out_257_cast = mul(x = zero_mean_257_cast, y = denom_257_cast)[name = tensor("out_257_cast")]; + tensor var_8443_to_fp16 = const()[name = tensor("op_8443_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3174687872)))]; + tensor var_8444_cast = add(x = out_257_cast, y = var_8443_to_fp16)[name = tensor("op_8444_cast")]; + tensor var_8446_to_fp16 = const()[name = tensor("op_8446_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3174690496)))]; + tensor input_507_cast = mul(x = var_8444_cast, y = var_8446_to_fp16)[name = tensor("input_507_cast")]; + tensor var_8454 = const()[name = tensor("op_8454"), val = tensor([1, 1])]; + tensor var_8456 = const()[name = tensor("op_8456"), val = tensor([1, 1])]; + tensor var_8458_pad_type_0 = const()[name = tensor("op_8458_pad_type_0"), val = tensor("custom")]; + tensor var_8458_pad_0 = const()[name = tensor("op_8458_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3174693120)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3200907584)))]; + tensor var_8458_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_8456, groups = var_31, pad = var_8458_pad_0, pad_type = var_8458_pad_type_0, strides = var_8454, weight = unet_up_blocks_0_attentions_0_transformer_blocks_8_ff_net_0_proj_weight_to_fp16, x = input_507_cast)[name = tensor("op_8458_cast")]; + tensor var_8459_split_sizes_0 = const()[name = tensor("op_8459_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8459_axis_0 = const()[name = tensor("op_8459_axis_0"), val = tensor(1)]; + tensor var_8459_cast_0, tensor var_8459_cast_1 = split(axis = var_8459_axis_0, split_sizes = var_8459_split_sizes_0, x = var_8458_cast)[name = tensor("op_8459_cast")]; + tensor var_8461_mode_0 = const()[name = tensor("op_8461_mode_0"), val = tensor("EXACT")]; + tensor var_8461_cast = gelu(mode = var_8461_mode_0, x = var_8459_cast_1)[name = tensor("op_8461_cast")]; + tensor input_509_cast = mul(x = var_8459_cast_0, y = var_8461_cast)[name = tensor("input_509_cast")]; + tensor var_8465 = const()[name = tensor("op_8465"), val = tensor([1, 1])]; + tensor var_8467 = const()[name = tensor("op_8467"), val = tensor([1, 1])]; + tensor var_8469_pad_type_0 = const()[name = tensor("op_8469_pad_type_0"), val = tensor("custom")]; + tensor var_8469_pad_0 = const()[name = tensor("op_8469_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3200928128)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3214035392)))]; + tensor var_8469_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_8467, groups = var_31, pad = var_8469_pad_0, pad_type = var_8469_pad_type_0, strides = var_8465, weight = unet_up_blocks_0_attentions_0_transformer_blocks_8_ff_net_2_weight_to_fp16, x = input_509_cast)[name = tensor("op_8469_cast")]; + tensor inputs_259_cast = add(x = var_8469_cast, y = inputs_257_cast)[name = tensor("inputs_259_cast")]; + tensor var_8479 = const()[name = tensor("op_8479"), val = tensor([1])]; + tensor channels_mean_259_cast = reduce_mean(axes = var_8479, keep_dims = var_23, x = inputs_259_cast)[name = tensor("channels_mean_259_cast")]; + tensor zero_mean_259_cast = sub(x = inputs_259_cast, y = channels_mean_259_cast)[name = tensor("zero_mean_259_cast")]; + tensor zero_mean_sq_259_cast = mul(x = zero_mean_259_cast, y = zero_mean_259_cast)[name = tensor("zero_mean_sq_259_cast")]; + tensor var_8483 = const()[name = tensor("op_8483"), val = tensor([1])]; + tensor var_8484_cast = reduce_mean(axes = var_8483, keep_dims = var_23, x = zero_mean_sq_259_cast)[name = tensor("op_8484_cast")]; + tensor var_8485_to_fp16 = const()[name = tensor("op_8485_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8486_cast = add(x = var_8484_cast, y = var_8485_to_fp16)[name = tensor("op_8486_cast")]; + tensor denom_259_epsilon_0_to_fp16 = const()[name = tensor("denom_259_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_259_cast = rsqrt(epsilon = denom_259_epsilon_0_to_fp16, x = var_8486_cast)[name = tensor("denom_259_cast")]; + tensor out_259_cast = mul(x = zero_mean_259_cast, y = denom_259_cast)[name = tensor("out_259_cast")]; + tensor var_8490_to_fp16 = const()[name = tensor("op_8490_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3214038016)))]; + tensor var_8491_cast = add(x = out_259_cast, y = var_8490_to_fp16)[name = tensor("op_8491_cast")]; + tensor var_8493_to_fp16 = const()[name = tensor("op_8493_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3214040640)))]; + tensor hidden_states_341_cast = mul(x = var_8491_cast, y = var_8493_to_fp16)[name = tensor("hidden_states_341_cast")]; + tensor var_8500 = const()[name = tensor("op_8500"), val = tensor([1, 1])]; + tensor var_8502 = const()[name = tensor("op_8502"), val = tensor([1, 1])]; tensor q_173_pad_type_0 = const()[name = tensor("q_173_pad_type_0"), val = tensor("custom")]; tensor q_173_pad_0 = const()[name = tensor("q_173_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_173 = conv(dilations = var_8601, groups = var_6872, pad = q_173_pad_0, pad_type = q_173_pad_type_0, strides = var_8599, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight, x = hidden_states_341)[name = tensor("q_173")]; - tensor var_8605 = const()[name = tensor("op_8605"), val = tensor([1, 1])]; - tensor var_8607 = const()[name = tensor("op_8607"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3214043264)))]; + tensor q_173_cast = conv(dilations = var_8502, groups = var_31, pad = q_173_pad_0, pad_type = q_173_pad_type_0, strides = var_8500, weight = unet_up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_q_weight_to_fp16, x = hidden_states_341_cast)[name = tensor("q_173_cast")]; + tensor var_8506 = const()[name = tensor("op_8506"), val = tensor([1, 1])]; + tensor var_8508 = const()[name = tensor("op_8508"), val = tensor([1, 1])]; tensor k_173_pad_type_0 = const()[name = tensor("k_173_pad_type_0"), val = tensor("custom")]; tensor k_173_pad_0 = const()[name = tensor("k_173_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_173 = conv(dilations = var_8607, groups = var_6872, pad = k_173_pad_0, pad_type = k_173_pad_type_0, strides = var_8605, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight, x = hidden_states_341)[name = tensor("k_173")]; - tensor var_8611 = const()[name = tensor("op_8611"), val = tensor([1, 1])]; - tensor var_8613 = const()[name = tensor("op_8613"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3217320128)))]; + tensor k_173_cast = conv(dilations = var_8508, groups = var_31, pad = k_173_pad_0, pad_type = k_173_pad_type_0, strides = var_8506, weight = unet_up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_k_weight_to_fp16, x = hidden_states_341_cast)[name = tensor("k_173_cast")]; + tensor var_8512 = const()[name = tensor("op_8512"), val = tensor([1, 1])]; + tensor var_8514 = const()[name = tensor("op_8514"), val = tensor([1, 1])]; tensor v_173_pad_type_0 = const()[name = tensor("v_173_pad_type_0"), val = tensor("custom")]; tensor v_173_pad_0 = const()[name = tensor("v_173_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_173 = conv(dilations = var_8613, groups = var_6872, pad = v_173_pad_0, pad_type = v_173_pad_type_0, strides = var_8611, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight, x = hidden_states_341)[name = tensor("v_173")]; - tensor var_8617 = const()[name = tensor("op_8617"), val = tensor([2, 20, 64, -1])]; - tensor var_8618 = reshape(shape = var_8617, x = q_173)[name = tensor("op_8618")]; - tensor var_8619 = const()[name = tensor("op_8619"), val = tensor([2, 20, 64, -1])]; - tensor var_8620 = reshape(shape = var_8619, x = k_173)[name = tensor("op_8620")]; - tensor var_8621 = const()[name = tensor("op_8621"), val = tensor([2, 20, 64, -1])]; - tensor var_8622 = reshape(shape = var_8621, x = v_173)[name = tensor("op_8622")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3220596992)))]; + tensor v_173_cast = conv(dilations = var_8514, groups = var_31, pad = v_173_pad_0, pad_type = v_173_pad_type_0, strides = var_8512, weight = unet_up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_v_weight_to_fp16, x = hidden_states_341_cast)[name = tensor("v_173_cast")]; + tensor var_8518 = const()[name = tensor("op_8518"), val = tensor([2, 20, 64, -1])]; + tensor var_8519_cast = reshape(shape = var_8518, x = q_173_cast)[name = tensor("op_8519_cast")]; + tensor var_8520 = const()[name = tensor("op_8520"), val = tensor([2, 20, 64, -1])]; + tensor var_8521_cast = reshape(shape = var_8520, x = k_173_cast)[name = tensor("op_8521_cast")]; + tensor var_8522 = const()[name = tensor("op_8522"), val = tensor([2, 20, 64, -1])]; + tensor var_8523_cast = reshape(shape = var_8522, x = v_173_cast)[name = tensor("op_8523_cast")]; tensor attn_weights_345_transpose_x_0 = const()[name = tensor("attn_weights_345_transpose_x_0"), val = tensor(true)]; tensor attn_weights_345_transpose_y_0 = const()[name = tensor("attn_weights_345_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_345 = matmul(transpose_x = attn_weights_345_transpose_x_0, transpose_y = attn_weights_345_transpose_y_0, x = var_8618, y = var_8620)[name = tensor("attn_weights_345")]; - tensor attn_weights_347 = mul(x = attn_weights_345, y = var_6863)[name = tensor("attn_weights_347")]; - tensor var_8626 = softmax(axis = var_6856, x = attn_weights_347)[name = tensor("op_8626")]; + tensor attn_weights_345_cast = matmul(transpose_x = attn_weights_345_transpose_x_0, transpose_y = attn_weights_345_transpose_y_0, x = var_8519_cast, y = var_8521_cast)[name = tensor("attn_weights_345_cast")]; + tensor attn_weights_347_cast = mul(x = attn_weights_345_cast, y = var_12_to_fp16)[name = tensor("attn_weights_347_cast")]; + tensor var_8527_cast = softmax(axis = var_18, x = attn_weights_347_cast)[name = tensor("op_8527_cast")]; tensor attn_173_transpose_x_0 = const()[name = tensor("attn_173_transpose_x_0"), val = tensor(false)]; tensor attn_173_transpose_y_0 = const()[name = tensor("attn_173_transpose_y_0"), val = tensor(true)]; - tensor attn_173 = matmul(transpose_x = attn_173_transpose_x_0, transpose_y = attn_173_transpose_y_0, x = var_8622, y = var_8626)[name = tensor("attn_173")]; - tensor var_8630 = const()[name = tensor("op_8630"), val = tensor([2, 1280, 1, -1])]; - tensor input_511 = reshape(shape = var_8630, x = attn_173)[name = tensor("input_511")]; - tensor var_8635 = const()[name = tensor("op_8635"), val = tensor([1, 1])]; - tensor var_8637 = const()[name = tensor("op_8637"), val = tensor([1, 1])]; - tensor var_8639_pad_type_0 = const()[name = tensor("op_8639_pad_type_0"), val = tensor("custom")]; - tensor var_8639_pad_0 = const()[name = tensor("op_8639_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8639 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias, dilations = var_8637, groups = var_6872, pad = var_8639_pad_0, pad_type = var_8639_pad_type_0, strides = var_8635, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight, x = input_511)[name = tensor("op_8639")]; - tensor inputs_261 = add(x = var_8639, y = inputs_259)[name = tensor("inputs_261")]; - tensor var_8643 = const()[name = tensor("op_8643"), val = tensor([1])]; - tensor channels_mean_261 = reduce_mean(axes = var_8643, keep_dims = var_6867, x = inputs_261)[name = tensor("channels_mean_261")]; - tensor zero_mean_261 = sub(x = inputs_261, y = channels_mean_261)[name = tensor("zero_mean_261")]; - tensor zero_mean_sq_261 = mul(x = zero_mean_261, y = zero_mean_261)[name = tensor("zero_mean_sq_261")]; - tensor var_8647 = const()[name = tensor("op_8647"), val = tensor([1])]; - tensor var_8648 = reduce_mean(axes = var_8647, keep_dims = var_6867, x = zero_mean_sq_261)[name = tensor("op_8648")]; - tensor var_8649 = const()[name = tensor("op_8649"), val = tensor(0x1.4f8b58p-17)]; - tensor var_8650 = add(x = var_8648, y = var_8649)[name = tensor("op_8650")]; - tensor denom_261_epsilon_0 = const()[name = tensor("denom_261_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_261 = rsqrt(epsilon = denom_261_epsilon_0, x = var_8650)[name = tensor("denom_261")]; - tensor out_261 = mul(x = zero_mean_261, y = denom_261)[name = tensor("out_261")]; - tensor var_8654 = const()[name = tensor("op_8654"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269093376)))]; - tensor var_8655 = add(x = out_261, y = var_8654)[name = tensor("op_8655")]; - tensor var_8657 = const()[name = tensor("op_8657"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269098560)))]; - tensor hidden_states_343 = mul(x = var_8655, y = var_8657)[name = tensor("hidden_states_343")]; - tensor var_8664 = const()[name = tensor("op_8664"), val = tensor([1, 1])]; - tensor var_8666 = const()[name = tensor("op_8666"), val = tensor([1, 1])]; + tensor attn_173_cast = matmul(transpose_x = attn_173_transpose_x_0, transpose_y = attn_173_transpose_y_0, x = var_8523_cast, y = var_8527_cast)[name = tensor("attn_173_cast")]; + tensor var_8531 = const()[name = tensor("op_8531"), val = tensor([2, 1280, 1, -1])]; + tensor input_511_cast = reshape(shape = var_8531, x = attn_173_cast)[name = tensor("input_511_cast")]; + tensor var_8536 = const()[name = tensor("op_8536"), val = tensor([1, 1])]; + tensor var_8538 = const()[name = tensor("op_8538"), val = tensor([1, 1])]; + tensor var_8540_pad_type_0 = const()[name = tensor("op_8540_pad_type_0"), val = tensor("custom")]; + tensor var_8540_pad_0 = const()[name = tensor("op_8540_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3223873856)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3227150720)))]; + tensor var_8540_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_8538, groups = var_31, pad = var_8540_pad_0, pad_type = var_8540_pad_type_0, strides = var_8536, weight = unet_up_blocks_0_attentions_0_transformer_blocks_9_attn1_to_out_0_weight_to_fp16, x = input_511_cast)[name = tensor("op_8540_cast")]; + tensor inputs_261_cast = add(x = var_8540_cast, y = inputs_259_cast)[name = tensor("inputs_261_cast")]; + tensor var_8544 = const()[name = tensor("op_8544"), val = tensor([1])]; + tensor channels_mean_261_cast = reduce_mean(axes = var_8544, keep_dims = var_23, x = inputs_261_cast)[name = tensor("channels_mean_261_cast")]; + tensor zero_mean_261_cast = sub(x = inputs_261_cast, y = channels_mean_261_cast)[name = tensor("zero_mean_261_cast")]; + tensor zero_mean_sq_261_cast = mul(x = zero_mean_261_cast, y = zero_mean_261_cast)[name = tensor("zero_mean_sq_261_cast")]; + tensor var_8548 = const()[name = tensor("op_8548"), val = tensor([1])]; + tensor var_8549_cast = reduce_mean(axes = var_8548, keep_dims = var_23, x = zero_mean_sq_261_cast)[name = tensor("op_8549_cast")]; + tensor var_8550_to_fp16 = const()[name = tensor("op_8550_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8551_cast = add(x = var_8549_cast, y = var_8550_to_fp16)[name = tensor("op_8551_cast")]; + tensor denom_261_epsilon_0_to_fp16 = const()[name = tensor("denom_261_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_261_cast = rsqrt(epsilon = denom_261_epsilon_0_to_fp16, x = var_8551_cast)[name = tensor("denom_261_cast")]; + tensor out_261_cast = mul(x = zero_mean_261_cast, y = denom_261_cast)[name = tensor("out_261_cast")]; + tensor var_8555_to_fp16 = const()[name = tensor("op_8555_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3227153344)))]; + tensor var_8556_cast = add(x = out_261_cast, y = var_8555_to_fp16)[name = tensor("op_8556_cast")]; + tensor var_8558_to_fp16 = const()[name = tensor("op_8558_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3227155968)))]; + tensor hidden_states_343_cast = mul(x = var_8556_cast, y = var_8558_to_fp16)[name = tensor("hidden_states_343_cast")]; + tensor var_8565 = const()[name = tensor("op_8565"), val = tensor([1, 1])]; + tensor var_8567 = const()[name = tensor("op_8567"), val = tensor([1, 1])]; tensor q_175_pad_type_0 = const()[name = tensor("q_175_pad_type_0"), val = tensor("custom")]; tensor q_175_pad_0 = const()[name = tensor("q_175_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_175 = conv(dilations = var_8666, groups = var_6872, pad = q_175_pad_0, pad_type = q_175_pad_type_0, strides = var_8664, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight, x = hidden_states_343)[name = tensor("q_175")]; - tensor var_8670 = const()[name = tensor("op_8670"), val = tensor([1, 1])]; - tensor var_8672 = const()[name = tensor("op_8672"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3227158592)))]; + tensor q_175_cast = conv(dilations = var_8567, groups = var_31, pad = q_175_pad_0, pad_type = q_175_pad_type_0, strides = var_8565, weight = unet_up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_q_weight_to_fp16, x = hidden_states_343_cast)[name = tensor("q_175_cast")]; + tensor var_8571 = const()[name = tensor("op_8571"), val = tensor([1, 1])]; + tensor var_8573 = const()[name = tensor("op_8573"), val = tensor([1, 1])]; tensor k_175_pad_type_0 = const()[name = tensor("k_175_pad_type_0"), val = tensor("custom")]; tensor k_175_pad_0 = const()[name = tensor("k_175_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_175 = conv(dilations = var_8672, groups = var_6872, pad = k_175_pad_0, pad_type = k_175_pad_type_0, strides = var_8670, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_175")]; - tensor var_8676 = const()[name = tensor("op_8676"), val = tensor([1, 1])]; - tensor var_8678 = const()[name = tensor("op_8678"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3230435456)))]; + tensor k_175_cast = conv(dilations = var_8573, groups = var_31, pad = k_175_pad_0, pad_type = k_175_pad_type_0, strides = var_8571, weight = unet_up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_175_cast")]; + tensor var_8577 = const()[name = tensor("op_8577"), val = tensor([1, 1])]; + tensor var_8579 = const()[name = tensor("op_8579"), val = tensor([1, 1])]; tensor v_175_pad_type_0 = const()[name = tensor("v_175_pad_type_0"), val = tensor("custom")]; tensor v_175_pad_0 = const()[name = tensor("v_175_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_175 = conv(dilations = var_8678, groups = var_6872, pad = v_175_pad_0, pad_type = v_175_pad_type_0, strides = var_8676, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_175")]; - tensor var_8682 = const()[name = tensor("op_8682"), val = tensor([2, 20, 64, -1])]; - tensor var_8683 = reshape(shape = var_8682, x = q_175)[name = tensor("op_8683")]; - tensor var_8684 = const()[name = tensor("op_8684"), val = tensor([2, 20, 64, -1])]; - tensor var_8685 = reshape(shape = var_8684, x = k_175)[name = tensor("op_8685")]; - tensor var_8686 = const()[name = tensor("op_8686"), val = tensor([2, 20, 64, -1])]; - tensor var_8687 = reshape(shape = var_8686, x = v_175)[name = tensor("op_8687")]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3235678400)))]; + tensor v_175_cast = conv(dilations = var_8579, groups = var_31, pad = v_175_pad_0, pad_type = v_175_pad_type_0, strides = var_8577, weight = unet_up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_175_cast")]; + tensor var_8583 = const()[name = tensor("op_8583"), val = tensor([2, 20, 64, -1])]; + tensor var_8584_cast = reshape(shape = var_8583, x = q_175_cast)[name = tensor("op_8584_cast")]; + tensor var_8585 = const()[name = tensor("op_8585"), val = tensor([2, 20, 64, -1])]; + tensor var_8586_cast = reshape(shape = var_8585, x = k_175_cast)[name = tensor("op_8586_cast")]; + tensor var_8587 = const()[name = tensor("op_8587"), val = tensor([2, 20, 64, -1])]; + tensor var_8588_cast = reshape(shape = var_8587, x = v_175_cast)[name = tensor("op_8588_cast")]; tensor attn_weights_349_transpose_x_0 = const()[name = tensor("attn_weights_349_transpose_x_0"), val = tensor(true)]; tensor attn_weights_349_transpose_y_0 = const()[name = tensor("attn_weights_349_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_349 = matmul(transpose_x = attn_weights_349_transpose_x_0, transpose_y = attn_weights_349_transpose_y_0, x = var_8683, y = var_8685)[name = tensor("attn_weights_349")]; - tensor attn_weights_351 = mul(x = attn_weights_349, y = var_6863)[name = tensor("attn_weights_351")]; - tensor var_8691 = softmax(axis = var_6856, x = attn_weights_351)[name = tensor("op_8691")]; + tensor attn_weights_349_cast = matmul(transpose_x = attn_weights_349_transpose_x_0, transpose_y = attn_weights_349_transpose_y_0, x = var_8584_cast, y = var_8586_cast)[name = tensor("attn_weights_349_cast")]; + tensor attn_weights_351_cast = mul(x = attn_weights_349_cast, y = var_12_to_fp16)[name = tensor("attn_weights_351_cast")]; + tensor var_8592_cast = softmax(axis = var_18, x = attn_weights_351_cast)[name = tensor("op_8592_cast")]; tensor attn_175_transpose_x_0 = const()[name = tensor("attn_175_transpose_x_0"), val = tensor(false)]; tensor attn_175_transpose_y_0 = const()[name = tensor("attn_175_transpose_y_0"), val = tensor(true)]; - tensor attn_175 = matmul(transpose_x = attn_175_transpose_x_0, transpose_y = attn_175_transpose_y_0, x = var_8687, y = var_8691)[name = tensor("attn_175")]; - tensor var_8695 = const()[name = tensor("op_8695"), val = tensor([2, 1280, 1, -1])]; - tensor input_513 = reshape(shape = var_8695, x = attn_175)[name = tensor("input_513")]; - tensor var_8700 = const()[name = tensor("op_8700"), val = tensor([1, 1])]; - tensor var_8702 = const()[name = tensor("op_8702"), val = tensor([1, 1])]; - tensor var_8704_pad_type_0 = const()[name = tensor("op_8704_pad_type_0"), val = tensor("custom")]; - tensor var_8704_pad_0 = const()[name = tensor("op_8704_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8704 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias, dilations = var_8702, groups = var_6872, pad = var_8704_pad_0, pad_type = var_8704_pad_type_0, strides = var_8700, weight = up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight, x = input_513)[name = tensor("op_8704")]; - tensor inputs_263 = add(x = var_8704, y = inputs_261)[name = tensor("inputs_263")]; - tensor var_8708 = const()[name = tensor("op_8708"), val = tensor([1])]; - tensor channels_mean_263 = reduce_mean(axes = var_8708, keep_dims = var_6867, x = inputs_263)[name = tensor("channels_mean_263")]; - tensor zero_mean_263 = sub(x = inputs_263, y = channels_mean_263)[name = tensor("zero_mean_263")]; - tensor zero_mean_sq_263 = mul(x = zero_mean_263, y = zero_mean_263)[name = tensor("zero_mean_sq_263")]; - tensor var_8712 = const()[name = tensor("op_8712"), val = tensor([1])]; - tensor var_8713 = reduce_mean(axes = var_8712, keep_dims = var_6867, x = zero_mean_sq_263)[name = tensor("op_8713")]; - tensor var_8714 = const()[name = tensor("op_8714"), val = tensor(0x1.4f8b58p-17)]; - tensor var_8715 = add(x = var_8713, y = var_8714)[name = tensor("op_8715")]; - tensor denom_263_epsilon_0 = const()[name = tensor("denom_263_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_263 = rsqrt(epsilon = denom_263_epsilon_0, x = var_8715)[name = tensor("denom_263")]; - tensor out_263 = mul(x = zero_mean_263, y = denom_263)[name = tensor("out_263")]; - tensor var_8719 = const()[name = tensor("op_8719"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269103744)))]; - tensor var_8720 = add(x = out_263, y = var_8719)[name = tensor("op_8720")]; - tensor var_8722 = const()[name = tensor("op_8722"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269108928)))]; - tensor input_515 = mul(x = var_8720, y = var_8722)[name = tensor("input_515")]; - tensor var_8730 = const()[name = tensor("op_8730"), val = tensor([1, 1])]; - tensor var_8732 = const()[name = tensor("op_8732"), val = tensor([1, 1])]; - tensor var_8734_pad_type_0 = const()[name = tensor("op_8734_pad_type_0"), val = tensor("custom")]; - tensor var_8734_pad_0 = const()[name = tensor("op_8734_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8734 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias, dilations = var_8732, groups = var_6872, pad = var_8734_pad_0, pad_type = var_8734_pad_type_0, strides = var_8730, weight = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight, x = input_515)[name = tensor("op_8734")]; - tensor var_8735_split_sizes_0 = const()[name = tensor("op_8735_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_8735_axis_0 = const()[name = tensor("op_8735_axis_0"), val = tensor(1)]; - tensor var_8735_0, tensor var_8735_1 = split(axis = var_8735_axis_0, split_sizes = var_8735_split_sizes_0, x = var_8734)[name = tensor("op_8735")]; - tensor var_8737_mode_0 = const()[name = tensor("op_8737_mode_0"), val = tensor("EXACT")]; - tensor var_8737 = gelu(mode = var_8737_mode_0, x = var_8735_1)[name = tensor("op_8737")]; - tensor input_517 = mul(x = var_8735_0, y = var_8737)[name = tensor("input_517")]; - tensor var_8741 = const()[name = tensor("op_8741"), val = tensor([1, 1])]; - tensor var_8743 = const()[name = tensor("op_8743"), val = tensor([1, 1])]; - tensor var_8745_pad_type_0 = const()[name = tensor("op_8745_pad_type_0"), val = tensor("custom")]; - tensor var_8745_pad_0 = const()[name = tensor("op_8745_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8745 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias, dilations = var_8743, groups = var_6872, pad = var_8745_pad_0, pad_type = var_8745_pad_type_0, strides = var_8741, weight = up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight, x = input_517)[name = tensor("op_8745")]; - tensor hidden_states_347 = add(x = var_8745, y = inputs_263)[name = tensor("hidden_states_347")]; - tensor var_8747 = const()[name = tensor("op_8747"), val = tensor([2, 1280, 32, 32])]; - tensor input_519 = reshape(shape = var_8747, x = hidden_states_347)[name = tensor("input_519")]; - tensor var_8751 = const()[name = tensor("op_8751"), val = tensor([1, 1])]; - tensor var_8753 = const()[name = tensor("op_8753"), val = tensor([1, 1])]; + tensor attn_175_cast = matmul(transpose_x = attn_175_transpose_x_0, transpose_y = attn_175_transpose_y_0, x = var_8588_cast, y = var_8592_cast)[name = tensor("attn_175_cast")]; + tensor var_8596 = const()[name = tensor("op_8596"), val = tensor([2, 1280, 1, -1])]; + tensor input_513_cast = reshape(shape = var_8596, x = attn_175_cast)[name = tensor("input_513_cast")]; + tensor var_8601 = const()[name = tensor("op_8601"), val = tensor([1, 1])]; + tensor var_8603 = const()[name = tensor("op_8603"), val = tensor([1, 1])]; + tensor var_8605_pad_type_0 = const()[name = tensor("op_8605_pad_type_0"), val = tensor("custom")]; + tensor var_8605_pad_0 = const()[name = tensor("op_8605_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3240921344)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3244198208)))]; + tensor var_8605_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_8603, groups = var_31, pad = var_8605_pad_0, pad_type = var_8605_pad_type_0, strides = var_8601, weight = unet_up_blocks_0_attentions_0_transformer_blocks_9_attn2_to_out_0_weight_to_fp16, x = input_513_cast)[name = tensor("op_8605_cast")]; + tensor inputs_263_cast = add(x = var_8605_cast, y = inputs_261_cast)[name = tensor("inputs_263_cast")]; + tensor var_8609 = const()[name = tensor("op_8609"), val = tensor([1])]; + tensor channels_mean_263_cast = reduce_mean(axes = var_8609, keep_dims = var_23, x = inputs_263_cast)[name = tensor("channels_mean_263_cast")]; + tensor zero_mean_263_cast = sub(x = inputs_263_cast, y = channels_mean_263_cast)[name = tensor("zero_mean_263_cast")]; + tensor zero_mean_sq_263_cast = mul(x = zero_mean_263_cast, y = zero_mean_263_cast)[name = tensor("zero_mean_sq_263_cast")]; + tensor var_8613 = const()[name = tensor("op_8613"), val = tensor([1])]; + tensor var_8614_cast = reduce_mean(axes = var_8613, keep_dims = var_23, x = zero_mean_sq_263_cast)[name = tensor("op_8614_cast")]; + tensor var_8615_to_fp16 = const()[name = tensor("op_8615_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8616_cast = add(x = var_8614_cast, y = var_8615_to_fp16)[name = tensor("op_8616_cast")]; + tensor denom_263_epsilon_0_to_fp16 = const()[name = tensor("denom_263_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_263_cast = rsqrt(epsilon = denom_263_epsilon_0_to_fp16, x = var_8616_cast)[name = tensor("denom_263_cast")]; + tensor out_263_cast = mul(x = zero_mean_263_cast, y = denom_263_cast)[name = tensor("out_263_cast")]; + tensor var_8620_to_fp16 = const()[name = tensor("op_8620_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3244200832)))]; + tensor var_8621_cast = add(x = out_263_cast, y = var_8620_to_fp16)[name = tensor("op_8621_cast")]; + tensor var_8623_to_fp16 = const()[name = tensor("op_8623_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3244203456)))]; + tensor input_515_cast = mul(x = var_8621_cast, y = var_8623_to_fp16)[name = tensor("input_515_cast")]; + tensor var_8631 = const()[name = tensor("op_8631"), val = tensor([1, 1])]; + tensor var_8633 = const()[name = tensor("op_8633"), val = tensor([1, 1])]; + tensor var_8635_pad_type_0 = const()[name = tensor("op_8635_pad_type_0"), val = tensor("custom")]; + tensor var_8635_pad_0 = const()[name = tensor("op_8635_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3244206080)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3270420544)))]; + tensor var_8635_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_8633, groups = var_31, pad = var_8635_pad_0, pad_type = var_8635_pad_type_0, strides = var_8631, weight = unet_up_blocks_0_attentions_0_transformer_blocks_9_ff_net_0_proj_weight_to_fp16, x = input_515_cast)[name = tensor("op_8635_cast")]; + tensor var_8636_split_sizes_0 = const()[name = tensor("op_8636_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8636_axis_0 = const()[name = tensor("op_8636_axis_0"), val = tensor(1)]; + tensor var_8636_cast_0, tensor var_8636_cast_1 = split(axis = var_8636_axis_0, split_sizes = var_8636_split_sizes_0, x = var_8635_cast)[name = tensor("op_8636_cast")]; + tensor var_8638_mode_0 = const()[name = tensor("op_8638_mode_0"), val = tensor("EXACT")]; + tensor var_8638_cast = gelu(mode = var_8638_mode_0, x = var_8636_cast_1)[name = tensor("op_8638_cast")]; + tensor input_517_cast = mul(x = var_8636_cast_0, y = var_8638_cast)[name = tensor("input_517_cast")]; + tensor var_8642 = const()[name = tensor("op_8642"), val = tensor([1, 1])]; + tensor var_8644 = const()[name = tensor("op_8644"), val = tensor([1, 1])]; + tensor var_8646_pad_type_0 = const()[name = tensor("op_8646_pad_type_0"), val = tensor("custom")]; + tensor var_8646_pad_0 = const()[name = tensor("op_8646_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3270441088)))]; + tensor unet_up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3283548352)))]; + tensor var_8646_cast = conv(bias = unet_up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_8644, groups = var_31, pad = var_8646_pad_0, pad_type = var_8646_pad_type_0, strides = var_8642, weight = unet_up_blocks_0_attentions_0_transformer_blocks_9_ff_net_2_weight_to_fp16, x = input_517_cast)[name = tensor("op_8646_cast")]; + tensor hidden_states_347_cast = add(x = var_8646_cast, y = inputs_263_cast)[name = tensor("hidden_states_347_cast")]; + tensor var_8648 = const()[name = tensor("op_8648"), val = tensor([2, 1280, 32, 32])]; + tensor input_519_cast = reshape(shape = var_8648, x = hidden_states_347_cast)[name = tensor("input_519_cast")]; + tensor var_8652 = const()[name = tensor("op_8652"), val = tensor([1, 1])]; + tensor var_8654 = const()[name = tensor("op_8654"), val = tensor([1, 1])]; tensor hidden_states_349_pad_type_0 = const()[name = tensor("hidden_states_349_pad_type_0"), val = tensor("custom")]; tensor hidden_states_349_pad_0 = const()[name = tensor("hidden_states_349_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_349 = conv(bias = up_blocks_0_attentions_0_proj_out_bias, dilations = var_8753, groups = var_6872, pad = hidden_states_349_pad_0, pad_type = hidden_states_349_pad_type_0, strides = var_8751, weight = up_blocks_0_attentions_0_proj_out_weight, x = input_519)[name = tensor("hidden_states_349")]; - tensor hidden_states_351 = add(x = hidden_states_349, y = hidden_states_283)[name = tensor("hidden_states_351")]; + tensor unet_up_blocks_0_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3283550976)))]; + tensor unet_up_blocks_0_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3286827840)))]; + tensor hidden_states_349_cast = conv(bias = unet_up_blocks_0_attentions_0_proj_out_bias_to_fp16, dilations = var_8654, groups = var_31, pad = hidden_states_349_pad_0, pad_type = hidden_states_349_pad_type_0, strides = var_8652, weight = unet_up_blocks_0_attentions_0_proj_out_weight_to_fp16, x = input_519_cast)[name = tensor("hidden_states_349_cast")]; + tensor hidden_states_351_cast = add(x = hidden_states_349_cast, y = hidden_states_283_cast)[name = tensor("hidden_states_351_cast")]; tensor input_521_interleave_0 = const()[name = tensor("input_521_interleave_0"), val = tensor(false)]; - tensor input_521 = concat(axis = var_6872, interleave = input_521_interleave_0, values = (hidden_states_351, input_213))[name = tensor("input_521")]; + tensor input_521_cast = concat(axis = var_31, interleave = input_521_interleave_0, values = (hidden_states_351_cast, input_213_cast))[name = tensor("input_521_cast")]; tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([2, 32, 80, 32, 32])]; - tensor reshape_96 = reshape(shape = reshape_96_shape_0, x = input_521)[name = tensor("reshape_96")]; + tensor reshape_96_cast = reshape(shape = reshape_96_shape_0, x = input_521_cast)[name = tensor("reshape_96_cast")]; tensor reduce_mean_72_axes_0 = const()[name = tensor("reduce_mean_72_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_72_keep_dims_0 = const()[name = tensor("reduce_mean_72_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_72 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96)[name = tensor("reduce_mean_72")]; - tensor sub_48 = sub(x = reshape_96, y = reduce_mean_72)[name = tensor("sub_48")]; - tensor square_24 = square(x = sub_48)[name = tensor("square_24")]; + tensor reduce_mean_72_cast = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast)[name = tensor("reduce_mean_72_cast")]; + tensor sub_48_cast = sub(x = reshape_96_cast, y = reduce_mean_72_cast)[name = tensor("sub_48_cast")]; + tensor square_24_cast = square(x = sub_48_cast)[name = tensor("square_24_cast")]; tensor reduce_mean_74_axes_0 = const()[name = tensor("reduce_mean_74_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_74_keep_dims_0 = const()[name = tensor("reduce_mean_74_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_74 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24)[name = tensor("reduce_mean_74")]; - tensor add_48_y_0 = const()[name = tensor("add_48_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_48 = add(x = reduce_mean_74, y = add_48_y_0)[name = tensor("add_48")]; - tensor sqrt_24 = sqrt(x = add_48)[name = tensor("sqrt_24")]; - tensor real_div_24 = real_div(x = sub_48, y = sqrt_24)[name = tensor("real_div_24")]; + tensor reduce_mean_74_cast = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast)[name = tensor("reduce_mean_74_cast")]; + tensor add_48_y_0_to_fp16 = const()[name = tensor("add_48_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_48_cast = add(x = reduce_mean_74_cast, y = add_48_y_0_to_fp16)[name = tensor("add_48_cast")]; + tensor sqrt_24_cast = sqrt(x = add_48_cast)[name = tensor("sqrt_24_cast")]; + tensor real_div_24_cast = real_div(x = sub_48_cast, y = sqrt_24_cast)[name = tensor("real_div_24_cast")]; tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([2, 2560, 32, 32])]; - tensor reshape_97 = reshape(shape = reshape_97_shape_0, x = real_div_24)[name = tensor("reshape_97")]; - tensor add_49_gamma_0 = const()[name = tensor("add_49_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269114112)))]; - tensor add_49_beta_0 = const()[name = tensor("add_49_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269124416)))]; - tensor add_49_epsilon_0 = const()[name = tensor("add_49_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_49 = batch_norm(beta = add_49_beta_0, epsilon = add_49_epsilon_0, gamma = add_49_gamma_0, mean = add_43_mean_0, variance = add_43_variance_0, x = reshape_97)[name = tensor("add_49")]; - tensor input_525 = silu(x = add_49)[name = tensor("input_525")]; - tensor var_8771 = const()[name = tensor("op_8771"), val = tensor([1, 1])]; - tensor var_8773 = const()[name = tensor("op_8773"), val = tensor([1, 1])]; + tensor reshape_97_cast = reshape(shape = reshape_97_shape_0, x = real_div_24_cast)[name = tensor("reshape_97_cast")]; + tensor add_49_gamma_0_to_fp16 = const()[name = tensor("add_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3286830464)))]; + tensor add_49_beta_0_to_fp16 = const()[name = tensor("add_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3286835648)))]; + tensor add_49_epsilon_0_to_fp16 = const()[name = tensor("add_49_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_49_cast = batch_norm(beta = add_49_beta_0_to_fp16, epsilon = add_49_epsilon_0_to_fp16, gamma = add_49_gamma_0_to_fp16, mean = add_43_mean_0_to_fp16, variance = add_43_variance_0_to_fp16, x = reshape_97_cast)[name = tensor("add_49_cast")]; + tensor input_525_cast = silu(x = add_49_cast)[name = tensor("input_525_cast")]; + tensor var_8672 = const()[name = tensor("op_8672"), val = tensor([1, 1])]; + tensor var_8674 = const()[name = tensor("op_8674"), val = tensor([1, 1])]; tensor hidden_states_353_pad_type_0 = const()[name = tensor("hidden_states_353_pad_type_0"), val = tensor("custom")]; tensor hidden_states_353_pad_0 = const()[name = tensor("hidden_states_353_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_353 = conv(bias = up_blocks_0_resnets_1_conv1_bias, dilations = var_8773, groups = var_6872, pad = hidden_states_353_pad_0, pad_type = hidden_states_353_pad_type_0, strides = var_8771, weight = up_blocks_0_resnets_1_conv1_weight, x = input_525)[name = tensor("hidden_states_353")]; - tensor var_8779 = const()[name = tensor("op_8779"), val = tensor([1, 1])]; - tensor var_8781 = const()[name = tensor("op_8781"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3286840832)))]; + tensor unet_up_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3345823296)))]; + tensor hidden_states_353_cast = conv(bias = unet_up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_8674, groups = var_31, pad = hidden_states_353_pad_0, pad_type = hidden_states_353_pad_type_0, strides = var_8672, weight = unet_up_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_525_cast)[name = tensor("hidden_states_353_cast")]; + tensor var_8680 = const()[name = tensor("op_8680"), val = tensor([1, 1])]; + tensor var_8682 = const()[name = tensor("op_8682"), val = tensor([1, 1])]; tensor temb_19_pad_type_0 = const()[name = tensor("temb_19_pad_type_0"), val = tensor("custom")]; tensor temb_19_pad_0 = const()[name = tensor("temb_19_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb_19 = conv(bias = up_blocks_0_resnets_1_time_emb_proj_bias, dilations = var_8781, groups = var_6872, pad = temb_19_pad_0, pad_type = temb_19_pad_type_0, strides = var_8779, weight = up_blocks_0_resnets_1_time_emb_proj_weight, x = input_21)[name = tensor("temb_19")]; - tensor input_529 = add(x = hidden_states_353, y = temb_19)[name = tensor("input_529")]; + tensor unet_up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3345825920)))]; + tensor unet_up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3349102784)))]; + tensor temb_19_cast = conv(bias = unet_up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_8682, groups = var_31, pad = temb_19_pad_0, pad_type = temb_19_pad_type_0, strides = var_8680, weight = unet_up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_19_cast")]; + tensor input_529_cast = add(x = hidden_states_353_cast, y = temb_19_cast)[name = tensor("input_529_cast")]; tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([2, 32, 40, 32, 32])]; - tensor reshape_100 = reshape(shape = reshape_100_shape_0, x = input_529)[name = tensor("reshape_100")]; + tensor reshape_100_cast = reshape(shape = reshape_100_shape_0, x = input_529_cast)[name = tensor("reshape_100_cast")]; tensor reduce_mean_75_axes_0 = const()[name = tensor("reduce_mean_75_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_75_keep_dims_0 = const()[name = tensor("reduce_mean_75_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_75 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100)[name = tensor("reduce_mean_75")]; - tensor sub_50 = sub(x = reshape_100, y = reduce_mean_75)[name = tensor("sub_50")]; - tensor square_25 = square(x = sub_50)[name = tensor("square_25")]; + tensor reduce_mean_75_cast = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast)[name = tensor("reduce_mean_75_cast")]; + tensor sub_50_cast = sub(x = reshape_100_cast, y = reduce_mean_75_cast)[name = tensor("sub_50_cast")]; + tensor square_25_cast = square(x = sub_50_cast)[name = tensor("square_25_cast")]; tensor reduce_mean_77_axes_0 = const()[name = tensor("reduce_mean_77_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_77_keep_dims_0 = const()[name = tensor("reduce_mean_77_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_77 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25)[name = tensor("reduce_mean_77")]; - tensor add_50_y_0 = const()[name = tensor("add_50_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_50 = add(x = reduce_mean_77, y = add_50_y_0)[name = tensor("add_50")]; - tensor sqrt_25 = sqrt(x = add_50)[name = tensor("sqrt_25")]; - tensor real_div_25 = real_div(x = sub_50, y = sqrt_25)[name = tensor("real_div_25")]; + tensor reduce_mean_77_cast = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast)[name = tensor("reduce_mean_77_cast")]; + tensor add_50_y_0_to_fp16 = const()[name = tensor("add_50_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_50_cast = add(x = reduce_mean_77_cast, y = add_50_y_0_to_fp16)[name = tensor("add_50_cast")]; + tensor sqrt_25_cast = sqrt(x = add_50_cast)[name = tensor("sqrt_25_cast")]; + tensor real_div_25_cast = real_div(x = sub_50_cast, y = sqrt_25_cast)[name = tensor("real_div_25_cast")]; tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([2, 1280, 32, 32])]; - tensor reshape_101 = reshape(shape = reshape_101_shape_0, x = real_div_25)[name = tensor("reshape_101")]; - tensor add_51_gamma_0 = const()[name = tensor("add_51_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269134720)))]; - tensor add_51_beta_0 = const()[name = tensor("add_51_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269139904)))]; - tensor add_51_epsilon_0 = const()[name = tensor("add_51_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_51 = batch_norm(beta = add_51_beta_0, epsilon = add_51_epsilon_0, gamma = add_51_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_101)[name = tensor("add_51")]; - tensor input_533 = silu(x = add_51)[name = tensor("input_533")]; - tensor var_8791 = const()[name = tensor("op_8791"), val = tensor([1, 1])]; - tensor var_8793 = const()[name = tensor("op_8793"), val = tensor([1, 1])]; + tensor reshape_101_cast = reshape(shape = reshape_101_shape_0, x = real_div_25_cast)[name = tensor("reshape_101_cast")]; + tensor add_51_gamma_0_to_fp16 = const()[name = tensor("add_51_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3349105408)))]; + tensor add_51_beta_0_to_fp16 = const()[name = tensor("add_51_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3349108032)))]; + tensor add_51_epsilon_0_to_fp16 = const()[name = tensor("add_51_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_51_cast = batch_norm(beta = add_51_beta_0_to_fp16, epsilon = add_51_epsilon_0_to_fp16, gamma = add_51_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_101_cast)[name = tensor("add_51_cast")]; + tensor input_533_cast = silu(x = add_51_cast)[name = tensor("input_533_cast")]; + tensor var_8692 = const()[name = tensor("op_8692"), val = tensor([1, 1])]; + tensor var_8694 = const()[name = tensor("op_8694"), val = tensor([1, 1])]; tensor hidden_states_355_pad_type_0 = const()[name = tensor("hidden_states_355_pad_type_0"), val = tensor("custom")]; tensor hidden_states_355_pad_0 = const()[name = tensor("hidden_states_355_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_355 = conv(bias = up_blocks_0_resnets_1_conv2_bias, dilations = var_8793, groups = var_6872, pad = hidden_states_355_pad_0, pad_type = hidden_states_355_pad_type_0, strides = var_8791, weight = up_blocks_0_resnets_1_conv2_weight, x = input_533)[name = tensor("hidden_states_355")]; - tensor var_8798 = const()[name = tensor("op_8798"), val = tensor([1, 1])]; - tensor var_8800 = const()[name = tensor("op_8800"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3349110656)))]; + tensor unet_up_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3378601920)))]; + tensor hidden_states_355_cast = conv(bias = unet_up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_8694, groups = var_31, pad = hidden_states_355_pad_0, pad_type = hidden_states_355_pad_type_0, strides = var_8692, weight = unet_up_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_533_cast)[name = tensor("hidden_states_355_cast")]; + tensor var_8699 = const()[name = tensor("op_8699"), val = tensor([1, 1])]; + tensor var_8701 = const()[name = tensor("op_8701"), val = tensor([1, 1])]; tensor x_7_pad_type_0 = const()[name = tensor("x_7_pad_type_0"), val = tensor("custom")]; tensor x_7_pad_0 = const()[name = tensor("x_7_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor x_7 = conv(bias = up_blocks_0_resnets_1_conv_shortcut_bias, dilations = var_8800, groups = var_6872, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = var_8798, weight = up_blocks_0_resnets_1_conv_shortcut_weight, x = input_521)[name = tensor("x_7")]; - tensor hidden_states_357 = add(x = x_7, y = hidden_states_355)[name = tensor("hidden_states_357")]; + tensor unet_up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3378604544)))]; + tensor unet_up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3385158208)))]; + tensor x_7_cast = conv(bias = unet_up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_8701, groups = var_31, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = var_8699, weight = unet_up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16, x = input_521_cast)[name = tensor("x_7_cast")]; + tensor hidden_states_357_cast = add(x = x_7_cast, y = hidden_states_355_cast)[name = tensor("hidden_states_357_cast")]; tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([2, 32, 40, 32, 32])]; - tensor reshape_104 = reshape(shape = reshape_104_shape_0, x = hidden_states_357)[name = tensor("reshape_104")]; + tensor reshape_104_cast = reshape(shape = reshape_104_shape_0, x = hidden_states_357_cast)[name = tensor("reshape_104_cast")]; tensor reduce_mean_78_axes_0 = const()[name = tensor("reduce_mean_78_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_78_keep_dims_0 = const()[name = tensor("reduce_mean_78_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_78 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104)[name = tensor("reduce_mean_78")]; - tensor sub_52 = sub(x = reshape_104, y = reduce_mean_78)[name = tensor("sub_52")]; - tensor square_26 = square(x = sub_52)[name = tensor("square_26")]; + tensor reduce_mean_78_cast = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast)[name = tensor("reduce_mean_78_cast")]; + tensor sub_52_cast = sub(x = reshape_104_cast, y = reduce_mean_78_cast)[name = tensor("sub_52_cast")]; + tensor square_26_cast = square(x = sub_52_cast)[name = tensor("square_26_cast")]; tensor reduce_mean_80_axes_0 = const()[name = tensor("reduce_mean_80_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_80_keep_dims_0 = const()[name = tensor("reduce_mean_80_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_80 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26)[name = tensor("reduce_mean_80")]; - tensor add_52_y_0 = const()[name = tensor("add_52_y_0"), val = tensor(0x1.0c6f7ap-20)]; - tensor add_52 = add(x = reduce_mean_80, y = add_52_y_0)[name = tensor("add_52")]; - tensor sqrt_26 = sqrt(x = add_52)[name = tensor("sqrt_26")]; - tensor real_div_26 = real_div(x = sub_52, y = sqrt_26)[name = tensor("real_div_26")]; + tensor reduce_mean_80_cast = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast)[name = tensor("reduce_mean_80_cast")]; + tensor add_52_y_0_to_fp16 = const()[name = tensor("add_52_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_52_cast = add(x = reduce_mean_80_cast, y = add_52_y_0_to_fp16)[name = tensor("add_52_cast")]; + tensor sqrt_26_cast = sqrt(x = add_52_cast)[name = tensor("sqrt_26_cast")]; + tensor real_div_26_cast = real_div(x = sub_52_cast, y = sqrt_26_cast)[name = tensor("real_div_26_cast")]; tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([2, 1280, 32, 32])]; - tensor reshape_105 = reshape(shape = reshape_105_shape_0, x = real_div_26)[name = tensor("reshape_105")]; - tensor add_53_gamma_0 = const()[name = tensor("add_53_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269145088)))]; - tensor add_53_beta_0 = const()[name = tensor("add_53_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269150272)))]; - tensor add_53_epsilon_0 = const()[name = tensor("add_53_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_53 = batch_norm(beta = add_53_beta_0, epsilon = add_53_epsilon_0, gamma = add_53_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_105)[name = tensor("add_53")]; - tensor var_8838 = const()[name = tensor("op_8838"), val = tensor([1, 1])]; - tensor var_8840 = const()[name = tensor("op_8840"), val = tensor([1, 1])]; + tensor reshape_105_cast = reshape(shape = reshape_105_shape_0, x = real_div_26_cast)[name = tensor("reshape_105_cast")]; + tensor add_53_gamma_0_to_fp16 = const()[name = tensor("add_53_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3385160832)))]; + tensor add_53_beta_0_to_fp16 = const()[name = tensor("add_53_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3385163456)))]; + tensor add_53_epsilon_0_to_fp16 = const()[name = tensor("add_53_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_53_cast = batch_norm(beta = add_53_beta_0_to_fp16, epsilon = add_53_epsilon_0_to_fp16, gamma = add_53_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_105_cast)[name = tensor("add_53_cast")]; + tensor var_8739 = const()[name = tensor("op_8739"), val = tensor([1, 1])]; + tensor var_8741 = const()[name = tensor("op_8741"), val = tensor([1, 1])]; tensor hidden_states_359_pad_type_0 = const()[name = tensor("hidden_states_359_pad_type_0"), val = tensor("custom")]; tensor hidden_states_359_pad_0 = const()[name = tensor("hidden_states_359_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_359 = conv(bias = up_blocks_0_attentions_1_proj_in_bias, dilations = var_8840, groups = var_6872, pad = hidden_states_359_pad_0, pad_type = hidden_states_359_pad_type_0, strides = var_8838, weight = up_blocks_0_attentions_1_proj_in_weight, x = add_53)[name = tensor("hidden_states_359")]; - tensor var_8845 = const()[name = tensor("op_8845"), val = tensor([2, 1280, 1, 1024])]; - tensor inputs_265 = reshape(shape = var_8845, x = hidden_states_359)[name = tensor("inputs_265")]; - tensor var_8855 = const()[name = tensor("op_8855"), val = tensor([1])]; - tensor channels_mean_265 = reduce_mean(axes = var_8855, keep_dims = var_6867, x = inputs_265)[name = tensor("channels_mean_265")]; - tensor zero_mean_265 = sub(x = inputs_265, y = channels_mean_265)[name = tensor("zero_mean_265")]; - tensor zero_mean_sq_265 = mul(x = zero_mean_265, y = zero_mean_265)[name = tensor("zero_mean_sq_265")]; - tensor var_8859 = const()[name = tensor("op_8859"), val = tensor([1])]; - tensor var_8860 = reduce_mean(axes = var_8859, keep_dims = var_6867, x = zero_mean_sq_265)[name = tensor("op_8860")]; - tensor var_8861 = const()[name = tensor("op_8861"), val = tensor(0x1.4f8b58p-17)]; - tensor var_8862 = add(x = var_8860, y = var_8861)[name = tensor("op_8862")]; - tensor denom_265_epsilon_0 = const()[name = tensor("denom_265_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_265 = rsqrt(epsilon = denom_265_epsilon_0, x = var_8862)[name = tensor("denom_265")]; - tensor out_265 = mul(x = zero_mean_265, y = denom_265)[name = tensor("out_265")]; - tensor var_8866 = const()[name = tensor("op_8866"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269155456)))]; - tensor var_8867 = add(x = out_265, y = var_8866)[name = tensor("op_8867")]; - tensor var_8869 = const()[name = tensor("op_8869"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269160640)))]; - tensor hidden_states_361 = mul(x = var_8867, y = var_8869)[name = tensor("hidden_states_361")]; - tensor var_8876 = const()[name = tensor("op_8876"), val = tensor([1, 1])]; - tensor var_8878 = const()[name = tensor("op_8878"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3385166080)))]; + tensor unet_up_blocks_0_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3388442944)))]; + tensor hidden_states_359_cast = conv(bias = unet_up_blocks_0_attentions_1_proj_in_bias_to_fp16, dilations = var_8741, groups = var_31, pad = hidden_states_359_pad_0, pad_type = hidden_states_359_pad_type_0, strides = var_8739, weight = unet_up_blocks_0_attentions_1_proj_in_weight_to_fp16, x = add_53_cast)[name = tensor("hidden_states_359_cast")]; + tensor var_8746 = const()[name = tensor("op_8746"), val = tensor([2, 1280, 1, 1024])]; + tensor inputs_265_cast = reshape(shape = var_8746, x = hidden_states_359_cast)[name = tensor("inputs_265_cast")]; + tensor var_8756 = const()[name = tensor("op_8756"), val = tensor([1])]; + tensor channels_mean_265_cast = reduce_mean(axes = var_8756, keep_dims = var_23, x = inputs_265_cast)[name = tensor("channels_mean_265_cast")]; + tensor zero_mean_265_cast = sub(x = inputs_265_cast, y = channels_mean_265_cast)[name = tensor("zero_mean_265_cast")]; + tensor zero_mean_sq_265_cast = mul(x = zero_mean_265_cast, y = zero_mean_265_cast)[name = tensor("zero_mean_sq_265_cast")]; + tensor var_8760 = const()[name = tensor("op_8760"), val = tensor([1])]; + tensor var_8761_cast = reduce_mean(axes = var_8760, keep_dims = var_23, x = zero_mean_sq_265_cast)[name = tensor("op_8761_cast")]; + tensor var_8762_to_fp16 = const()[name = tensor("op_8762_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8763_cast = add(x = var_8761_cast, y = var_8762_to_fp16)[name = tensor("op_8763_cast")]; + tensor denom_265_epsilon_0_to_fp16 = const()[name = tensor("denom_265_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_265_cast = rsqrt(epsilon = denom_265_epsilon_0_to_fp16, x = var_8763_cast)[name = tensor("denom_265_cast")]; + tensor out_265_cast = mul(x = zero_mean_265_cast, y = denom_265_cast)[name = tensor("out_265_cast")]; + tensor var_8767_to_fp16 = const()[name = tensor("op_8767_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3388445568)))]; + tensor var_8768_cast = add(x = out_265_cast, y = var_8767_to_fp16)[name = tensor("op_8768_cast")]; + tensor var_8770_to_fp16 = const()[name = tensor("op_8770_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3388448192)))]; + tensor hidden_states_361_cast = mul(x = var_8768_cast, y = var_8770_to_fp16)[name = tensor("hidden_states_361_cast")]; + tensor var_8777 = const()[name = tensor("op_8777"), val = tensor([1, 1])]; + tensor var_8779 = const()[name = tensor("op_8779"), val = tensor([1, 1])]; tensor q_177_pad_type_0 = const()[name = tensor("q_177_pad_type_0"), val = tensor("custom")]; tensor q_177_pad_0 = const()[name = tensor("q_177_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_177 = conv(dilations = var_8878, groups = var_6872, pad = q_177_pad_0, pad_type = q_177_pad_type_0, strides = var_8876, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight, x = hidden_states_361)[name = tensor("q_177")]; - tensor var_8882 = const()[name = tensor("op_8882"), val = tensor([1, 1])]; - tensor var_8884 = const()[name = tensor("op_8884"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3388450816)))]; + tensor q_177_cast = conv(dilations = var_8779, groups = var_31, pad = q_177_pad_0, pad_type = q_177_pad_type_0, strides = var_8777, weight = unet_up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_361_cast)[name = tensor("q_177_cast")]; + tensor var_8783 = const()[name = tensor("op_8783"), val = tensor([1, 1])]; + tensor var_8785 = const()[name = tensor("op_8785"), val = tensor([1, 1])]; tensor k_177_pad_type_0 = const()[name = tensor("k_177_pad_type_0"), val = tensor("custom")]; tensor k_177_pad_0 = const()[name = tensor("k_177_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_177 = conv(dilations = var_8884, groups = var_6872, pad = k_177_pad_0, pad_type = k_177_pad_type_0, strides = var_8882, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight, x = hidden_states_361)[name = tensor("k_177")]; - tensor var_8888 = const()[name = tensor("op_8888"), val = tensor([1, 1])]; - tensor var_8890 = const()[name = tensor("op_8890"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3391727680)))]; + tensor k_177_cast = conv(dilations = var_8785, groups = var_31, pad = k_177_pad_0, pad_type = k_177_pad_type_0, strides = var_8783, weight = unet_up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_361_cast)[name = tensor("k_177_cast")]; + tensor var_8789 = const()[name = tensor("op_8789"), val = tensor([1, 1])]; + tensor var_8791 = const()[name = tensor("op_8791"), val = tensor([1, 1])]; tensor v_177_pad_type_0 = const()[name = tensor("v_177_pad_type_0"), val = tensor("custom")]; tensor v_177_pad_0 = const()[name = tensor("v_177_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_177 = conv(dilations = var_8890, groups = var_6872, pad = v_177_pad_0, pad_type = v_177_pad_type_0, strides = var_8888, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight, x = hidden_states_361)[name = tensor("v_177")]; - tensor var_8894 = const()[name = tensor("op_8894"), val = tensor([2, 20, 64, -1])]; - tensor var_8895 = reshape(shape = var_8894, x = q_177)[name = tensor("op_8895")]; - tensor var_8896 = const()[name = tensor("op_8896"), val = tensor([2, 20, 64, -1])]; - tensor var_8897 = reshape(shape = var_8896, x = k_177)[name = tensor("op_8897")]; - tensor var_8898 = const()[name = tensor("op_8898"), val = tensor([2, 20, 64, -1])]; - tensor var_8899 = reshape(shape = var_8898, x = v_177)[name = tensor("op_8899")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3395004544)))]; + tensor v_177_cast = conv(dilations = var_8791, groups = var_31, pad = v_177_pad_0, pad_type = v_177_pad_type_0, strides = var_8789, weight = unet_up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_361_cast)[name = tensor("v_177_cast")]; + tensor var_8795 = const()[name = tensor("op_8795"), val = tensor([2, 20, 64, -1])]; + tensor var_8796_cast = reshape(shape = var_8795, x = q_177_cast)[name = tensor("op_8796_cast")]; + tensor var_8797 = const()[name = tensor("op_8797"), val = tensor([2, 20, 64, -1])]; + tensor var_8798_cast = reshape(shape = var_8797, x = k_177_cast)[name = tensor("op_8798_cast")]; + tensor var_8799 = const()[name = tensor("op_8799"), val = tensor([2, 20, 64, -1])]; + tensor var_8800_cast = reshape(shape = var_8799, x = v_177_cast)[name = tensor("op_8800_cast")]; tensor attn_weights_353_transpose_x_0 = const()[name = tensor("attn_weights_353_transpose_x_0"), val = tensor(true)]; tensor attn_weights_353_transpose_y_0 = const()[name = tensor("attn_weights_353_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_353 = matmul(transpose_x = attn_weights_353_transpose_x_0, transpose_y = attn_weights_353_transpose_y_0, x = var_8895, y = var_8897)[name = tensor("attn_weights_353")]; - tensor attn_weights_355 = mul(x = attn_weights_353, y = var_6863)[name = tensor("attn_weights_355")]; - tensor var_8903 = softmax(axis = var_6856, x = attn_weights_355)[name = tensor("op_8903")]; + tensor attn_weights_353_cast = matmul(transpose_x = attn_weights_353_transpose_x_0, transpose_y = attn_weights_353_transpose_y_0, x = var_8796_cast, y = var_8798_cast)[name = tensor("attn_weights_353_cast")]; + tensor attn_weights_355_cast = mul(x = attn_weights_353_cast, y = var_12_to_fp16)[name = tensor("attn_weights_355_cast")]; + tensor var_8804_cast = softmax(axis = var_18, x = attn_weights_355_cast)[name = tensor("op_8804_cast")]; tensor attn_177_transpose_x_0 = const()[name = tensor("attn_177_transpose_x_0"), val = tensor(false)]; tensor attn_177_transpose_y_0 = const()[name = tensor("attn_177_transpose_y_0"), val = tensor(true)]; - tensor attn_177 = matmul(transpose_x = attn_177_transpose_x_0, transpose_y = attn_177_transpose_y_0, x = var_8899, y = var_8903)[name = tensor("attn_177")]; - tensor var_8907 = const()[name = tensor("op_8907"), val = tensor([2, 1280, 1, -1])]; - tensor input_537 = reshape(shape = var_8907, x = attn_177)[name = tensor("input_537")]; - tensor var_8912 = const()[name = tensor("op_8912"), val = tensor([1, 1])]; - tensor var_8914 = const()[name = tensor("op_8914"), val = tensor([1, 1])]; - tensor var_8916_pad_type_0 = const()[name = tensor("op_8916_pad_type_0"), val = tensor("custom")]; - tensor var_8916_pad_0 = const()[name = tensor("op_8916_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8916 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias, dilations = var_8914, groups = var_6872, pad = var_8916_pad_0, pad_type = var_8916_pad_type_0, strides = var_8912, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight, x = input_537)[name = tensor("op_8916")]; - tensor inputs_267 = add(x = var_8916, y = inputs_265)[name = tensor("inputs_267")]; - tensor var_8920 = const()[name = tensor("op_8920"), val = tensor([1])]; - tensor channels_mean_267 = reduce_mean(axes = var_8920, keep_dims = var_6867, x = inputs_267)[name = tensor("channels_mean_267")]; - tensor zero_mean_267 = sub(x = inputs_267, y = channels_mean_267)[name = tensor("zero_mean_267")]; - tensor zero_mean_sq_267 = mul(x = zero_mean_267, y = zero_mean_267)[name = tensor("zero_mean_sq_267")]; - tensor var_8924 = const()[name = tensor("op_8924"), val = tensor([1])]; - tensor var_8925 = reduce_mean(axes = var_8924, keep_dims = var_6867, x = zero_mean_sq_267)[name = tensor("op_8925")]; - tensor var_8926 = const()[name = tensor("op_8926"), val = tensor(0x1.4f8b58p-17)]; - tensor var_8927 = add(x = var_8925, y = var_8926)[name = tensor("op_8927")]; - tensor denom_267_epsilon_0 = const()[name = tensor("denom_267_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_267 = rsqrt(epsilon = denom_267_epsilon_0, x = var_8927)[name = tensor("denom_267")]; - tensor out_267 = mul(x = zero_mean_267, y = denom_267)[name = tensor("out_267")]; - tensor var_8931 = const()[name = tensor("op_8931"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269165824)))]; - tensor var_8932 = add(x = out_267, y = var_8931)[name = tensor("op_8932")]; - tensor var_8934 = const()[name = tensor("op_8934"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269171008)))]; - tensor hidden_states_363 = mul(x = var_8932, y = var_8934)[name = tensor("hidden_states_363")]; - tensor var_8941 = const()[name = tensor("op_8941"), val = tensor([1, 1])]; - tensor var_8943 = const()[name = tensor("op_8943"), val = tensor([1, 1])]; + tensor attn_177_cast = matmul(transpose_x = attn_177_transpose_x_0, transpose_y = attn_177_transpose_y_0, x = var_8800_cast, y = var_8804_cast)[name = tensor("attn_177_cast")]; + tensor var_8808 = const()[name = tensor("op_8808"), val = tensor([2, 1280, 1, -1])]; + tensor input_537_cast = reshape(shape = var_8808, x = attn_177_cast)[name = tensor("input_537_cast")]; + tensor var_8813 = const()[name = tensor("op_8813"), val = tensor([1, 1])]; + tensor var_8815 = const()[name = tensor("op_8815"), val = tensor([1, 1])]; + tensor var_8817_pad_type_0 = const()[name = tensor("op_8817_pad_type_0"), val = tensor("custom")]; + tensor var_8817_pad_0 = const()[name = tensor("op_8817_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3398281408)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3401558272)))]; + tensor var_8817_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_8815, groups = var_31, pad = var_8817_pad_0, pad_type = var_8817_pad_type_0, strides = var_8813, weight = unet_up_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_537_cast)[name = tensor("op_8817_cast")]; + tensor inputs_267_cast = add(x = var_8817_cast, y = inputs_265_cast)[name = tensor("inputs_267_cast")]; + tensor var_8821 = const()[name = tensor("op_8821"), val = tensor([1])]; + tensor channels_mean_267_cast = reduce_mean(axes = var_8821, keep_dims = var_23, x = inputs_267_cast)[name = tensor("channels_mean_267_cast")]; + tensor zero_mean_267_cast = sub(x = inputs_267_cast, y = channels_mean_267_cast)[name = tensor("zero_mean_267_cast")]; + tensor zero_mean_sq_267_cast = mul(x = zero_mean_267_cast, y = zero_mean_267_cast)[name = tensor("zero_mean_sq_267_cast")]; + tensor var_8825 = const()[name = tensor("op_8825"), val = tensor([1])]; + tensor var_8826_cast = reduce_mean(axes = var_8825, keep_dims = var_23, x = zero_mean_sq_267_cast)[name = tensor("op_8826_cast")]; + tensor var_8827_to_fp16 = const()[name = tensor("op_8827_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8828_cast = add(x = var_8826_cast, y = var_8827_to_fp16)[name = tensor("op_8828_cast")]; + tensor denom_267_epsilon_0_to_fp16 = const()[name = tensor("denom_267_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_267_cast = rsqrt(epsilon = denom_267_epsilon_0_to_fp16, x = var_8828_cast)[name = tensor("denom_267_cast")]; + tensor out_267_cast = mul(x = zero_mean_267_cast, y = denom_267_cast)[name = tensor("out_267_cast")]; + tensor var_8832_to_fp16 = const()[name = tensor("op_8832_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3401560896)))]; + tensor var_8833_cast = add(x = out_267_cast, y = var_8832_to_fp16)[name = tensor("op_8833_cast")]; + tensor var_8835_to_fp16 = const()[name = tensor("op_8835_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3401563520)))]; + tensor hidden_states_363_cast = mul(x = var_8833_cast, y = var_8835_to_fp16)[name = tensor("hidden_states_363_cast")]; + tensor var_8842 = const()[name = tensor("op_8842"), val = tensor([1, 1])]; + tensor var_8844 = const()[name = tensor("op_8844"), val = tensor([1, 1])]; tensor q_179_pad_type_0 = const()[name = tensor("q_179_pad_type_0"), val = tensor("custom")]; tensor q_179_pad_0 = const()[name = tensor("q_179_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_179 = conv(dilations = var_8943, groups = var_6872, pad = q_179_pad_0, pad_type = q_179_pad_type_0, strides = var_8941, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight, x = hidden_states_363)[name = tensor("q_179")]; - tensor var_8947 = const()[name = tensor("op_8947"), val = tensor([1, 1])]; - tensor var_8949 = const()[name = tensor("op_8949"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3401566144)))]; + tensor q_179_cast = conv(dilations = var_8844, groups = var_31, pad = q_179_pad_0, pad_type = q_179_pad_type_0, strides = var_8842, weight = unet_up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_363_cast)[name = tensor("q_179_cast")]; + tensor var_8848 = const()[name = tensor("op_8848"), val = tensor([1, 1])]; + tensor var_8850 = const()[name = tensor("op_8850"), val = tensor([1, 1])]; tensor k_179_pad_type_0 = const()[name = tensor("k_179_pad_type_0"), val = tensor("custom")]; tensor k_179_pad_0 = const()[name = tensor("k_179_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_179 = conv(dilations = var_8949, groups = var_6872, pad = k_179_pad_0, pad_type = k_179_pad_type_0, strides = var_8947, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_179")]; - tensor var_8953 = const()[name = tensor("op_8953"), val = tensor([1, 1])]; - tensor var_8955 = const()[name = tensor("op_8955"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3404843008)))]; + tensor k_179_cast = conv(dilations = var_8850, groups = var_31, pad = k_179_pad_0, pad_type = k_179_pad_type_0, strides = var_8848, weight = unet_up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_179_cast")]; + tensor var_8854 = const()[name = tensor("op_8854"), val = tensor([1, 1])]; + tensor var_8856 = const()[name = tensor("op_8856"), val = tensor([1, 1])]; tensor v_179_pad_type_0 = const()[name = tensor("v_179_pad_type_0"), val = tensor("custom")]; tensor v_179_pad_0 = const()[name = tensor("v_179_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_179 = conv(dilations = var_8955, groups = var_6872, pad = v_179_pad_0, pad_type = v_179_pad_type_0, strides = var_8953, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_179")]; - tensor var_8959 = const()[name = tensor("op_8959"), val = tensor([2, 20, 64, -1])]; - tensor var_8960 = reshape(shape = var_8959, x = q_179)[name = tensor("op_8960")]; - tensor var_8961 = const()[name = tensor("op_8961"), val = tensor([2, 20, 64, -1])]; - tensor var_8962 = reshape(shape = var_8961, x = k_179)[name = tensor("op_8962")]; - tensor var_8963 = const()[name = tensor("op_8963"), val = tensor([2, 20, 64, -1])]; - tensor var_8964 = reshape(shape = var_8963, x = v_179)[name = tensor("op_8964")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3410085952)))]; + tensor v_179_cast = conv(dilations = var_8856, groups = var_31, pad = v_179_pad_0, pad_type = v_179_pad_type_0, strides = var_8854, weight = unet_up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_179_cast")]; + tensor var_8860 = const()[name = tensor("op_8860"), val = tensor([2, 20, 64, -1])]; + tensor var_8861_cast = reshape(shape = var_8860, x = q_179_cast)[name = tensor("op_8861_cast")]; + tensor var_8862 = const()[name = tensor("op_8862"), val = tensor([2, 20, 64, -1])]; + tensor var_8863_cast = reshape(shape = var_8862, x = k_179_cast)[name = tensor("op_8863_cast")]; + tensor var_8864 = const()[name = tensor("op_8864"), val = tensor([2, 20, 64, -1])]; + tensor var_8865_cast = reshape(shape = var_8864, x = v_179_cast)[name = tensor("op_8865_cast")]; tensor attn_weights_357_transpose_x_0 = const()[name = tensor("attn_weights_357_transpose_x_0"), val = tensor(true)]; tensor attn_weights_357_transpose_y_0 = const()[name = tensor("attn_weights_357_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_357 = matmul(transpose_x = attn_weights_357_transpose_x_0, transpose_y = attn_weights_357_transpose_y_0, x = var_8960, y = var_8962)[name = tensor("attn_weights_357")]; - tensor attn_weights_359 = mul(x = attn_weights_357, y = var_6863)[name = tensor("attn_weights_359")]; - tensor var_8968 = softmax(axis = var_6856, x = attn_weights_359)[name = tensor("op_8968")]; + tensor attn_weights_357_cast = matmul(transpose_x = attn_weights_357_transpose_x_0, transpose_y = attn_weights_357_transpose_y_0, x = var_8861_cast, y = var_8863_cast)[name = tensor("attn_weights_357_cast")]; + tensor attn_weights_359_cast = mul(x = attn_weights_357_cast, y = var_12_to_fp16)[name = tensor("attn_weights_359_cast")]; + tensor var_8869_cast = softmax(axis = var_18, x = attn_weights_359_cast)[name = tensor("op_8869_cast")]; tensor attn_179_transpose_x_0 = const()[name = tensor("attn_179_transpose_x_0"), val = tensor(false)]; tensor attn_179_transpose_y_0 = const()[name = tensor("attn_179_transpose_y_0"), val = tensor(true)]; - tensor attn_179 = matmul(transpose_x = attn_179_transpose_x_0, transpose_y = attn_179_transpose_y_0, x = var_8964, y = var_8968)[name = tensor("attn_179")]; - tensor var_8972 = const()[name = tensor("op_8972"), val = tensor([2, 1280, 1, -1])]; - tensor input_539 = reshape(shape = var_8972, x = attn_179)[name = tensor("input_539")]; - tensor var_8977 = const()[name = tensor("op_8977"), val = tensor([1, 1])]; - tensor var_8979 = const()[name = tensor("op_8979"), val = tensor([1, 1])]; - tensor var_8981_pad_type_0 = const()[name = tensor("op_8981_pad_type_0"), val = tensor("custom")]; - tensor var_8981_pad_0 = const()[name = tensor("op_8981_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_8981 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias, dilations = var_8979, groups = var_6872, pad = var_8981_pad_0, pad_type = var_8981_pad_type_0, strides = var_8977, weight = up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight, x = input_539)[name = tensor("op_8981")]; - tensor inputs_269 = add(x = var_8981, y = inputs_267)[name = tensor("inputs_269")]; - tensor var_8985 = const()[name = tensor("op_8985"), val = tensor([1])]; - tensor channels_mean_269 = reduce_mean(axes = var_8985, keep_dims = var_6867, x = inputs_269)[name = tensor("channels_mean_269")]; - tensor zero_mean_269 = sub(x = inputs_269, y = channels_mean_269)[name = tensor("zero_mean_269")]; - tensor zero_mean_sq_269 = mul(x = zero_mean_269, y = zero_mean_269)[name = tensor("zero_mean_sq_269")]; - tensor var_8989 = const()[name = tensor("op_8989"), val = tensor([1])]; - tensor var_8990 = reduce_mean(axes = var_8989, keep_dims = var_6867, x = zero_mean_sq_269)[name = tensor("op_8990")]; - tensor var_8991 = const()[name = tensor("op_8991"), val = tensor(0x1.4f8b58p-17)]; - tensor var_8992 = add(x = var_8990, y = var_8991)[name = tensor("op_8992")]; - tensor denom_269_epsilon_0 = const()[name = tensor("denom_269_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_269 = rsqrt(epsilon = denom_269_epsilon_0, x = var_8992)[name = tensor("denom_269")]; - tensor out_269 = mul(x = zero_mean_269, y = denom_269)[name = tensor("out_269")]; - tensor var_8996 = const()[name = tensor("op_8996"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269176192)))]; - tensor var_8997 = add(x = out_269, y = var_8996)[name = tensor("op_8997")]; - tensor var_8999 = const()[name = tensor("op_8999"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269181376)))]; - tensor input_541 = mul(x = var_8997, y = var_8999)[name = tensor("input_541")]; - tensor var_9007 = const()[name = tensor("op_9007"), val = tensor([1, 1])]; - tensor var_9009 = const()[name = tensor("op_9009"), val = tensor([1, 1])]; - tensor var_9011_pad_type_0 = const()[name = tensor("op_9011_pad_type_0"), val = tensor("custom")]; - tensor var_9011_pad_0 = const()[name = tensor("op_9011_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9011 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias, dilations = var_9009, groups = var_6872, pad = var_9011_pad_0, pad_type = var_9011_pad_type_0, strides = var_9007, weight = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight, x = input_541)[name = tensor("op_9011")]; - tensor var_9012_split_sizes_0 = const()[name = tensor("op_9012_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_9012_axis_0 = const()[name = tensor("op_9012_axis_0"), val = tensor(1)]; - tensor var_9012_0, tensor var_9012_1 = split(axis = var_9012_axis_0, split_sizes = var_9012_split_sizes_0, x = var_9011)[name = tensor("op_9012")]; - tensor var_9014_mode_0 = const()[name = tensor("op_9014_mode_0"), val = tensor("EXACT")]; - tensor var_9014 = gelu(mode = var_9014_mode_0, x = var_9012_1)[name = tensor("op_9014")]; - tensor input_543 = mul(x = var_9012_0, y = var_9014)[name = tensor("input_543")]; - tensor var_9018 = const()[name = tensor("op_9018"), val = tensor([1, 1])]; - tensor var_9020 = const()[name = tensor("op_9020"), val = tensor([1, 1])]; - tensor var_9022_pad_type_0 = const()[name = tensor("op_9022_pad_type_0"), val = tensor("custom")]; - tensor var_9022_pad_0 = const()[name = tensor("op_9022_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9022 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias, dilations = var_9020, groups = var_6872, pad = var_9022_pad_0, pad_type = var_9022_pad_type_0, strides = var_9018, weight = up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight, x = input_543)[name = tensor("op_9022")]; - tensor inputs_271 = add(x = var_9022, y = inputs_269)[name = tensor("inputs_271")]; - tensor var_9032 = const()[name = tensor("op_9032"), val = tensor([1])]; - tensor channels_mean_271 = reduce_mean(axes = var_9032, keep_dims = var_6867, x = inputs_271)[name = tensor("channels_mean_271")]; - tensor zero_mean_271 = sub(x = inputs_271, y = channels_mean_271)[name = tensor("zero_mean_271")]; - tensor zero_mean_sq_271 = mul(x = zero_mean_271, y = zero_mean_271)[name = tensor("zero_mean_sq_271")]; - tensor var_9036 = const()[name = tensor("op_9036"), val = tensor([1])]; - tensor var_9037 = reduce_mean(axes = var_9036, keep_dims = var_6867, x = zero_mean_sq_271)[name = tensor("op_9037")]; - tensor var_9038 = const()[name = tensor("op_9038"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9039 = add(x = var_9037, y = var_9038)[name = tensor("op_9039")]; - tensor denom_271_epsilon_0 = const()[name = tensor("denom_271_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_271 = rsqrt(epsilon = denom_271_epsilon_0, x = var_9039)[name = tensor("denom_271")]; - tensor out_271 = mul(x = zero_mean_271, y = denom_271)[name = tensor("out_271")]; - tensor var_9043 = const()[name = tensor("op_9043"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269186560)))]; - tensor var_9044 = add(x = out_271, y = var_9043)[name = tensor("op_9044")]; - tensor var_9046 = const()[name = tensor("op_9046"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269191744)))]; - tensor hidden_states_367 = mul(x = var_9044, y = var_9046)[name = tensor("hidden_states_367")]; - tensor var_9053 = const()[name = tensor("op_9053"), val = tensor([1, 1])]; - tensor var_9055 = const()[name = tensor("op_9055"), val = tensor([1, 1])]; + tensor attn_179_cast = matmul(transpose_x = attn_179_transpose_x_0, transpose_y = attn_179_transpose_y_0, x = var_8865_cast, y = var_8869_cast)[name = tensor("attn_179_cast")]; + tensor var_8873 = const()[name = tensor("op_8873"), val = tensor([2, 1280, 1, -1])]; + tensor input_539_cast = reshape(shape = var_8873, x = attn_179_cast)[name = tensor("input_539_cast")]; + tensor var_8878 = const()[name = tensor("op_8878"), val = tensor([1, 1])]; + tensor var_8880 = const()[name = tensor("op_8880"), val = tensor([1, 1])]; + tensor var_8882_pad_type_0 = const()[name = tensor("op_8882_pad_type_0"), val = tensor("custom")]; + tensor var_8882_pad_0 = const()[name = tensor("op_8882_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3415328896)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3418605760)))]; + tensor var_8882_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_8880, groups = var_31, pad = var_8882_pad_0, pad_type = var_8882_pad_type_0, strides = var_8878, weight = unet_up_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_539_cast)[name = tensor("op_8882_cast")]; + tensor inputs_269_cast = add(x = var_8882_cast, y = inputs_267_cast)[name = tensor("inputs_269_cast")]; + tensor var_8886 = const()[name = tensor("op_8886"), val = tensor([1])]; + tensor channels_mean_269_cast = reduce_mean(axes = var_8886, keep_dims = var_23, x = inputs_269_cast)[name = tensor("channels_mean_269_cast")]; + tensor zero_mean_269_cast = sub(x = inputs_269_cast, y = channels_mean_269_cast)[name = tensor("zero_mean_269_cast")]; + tensor zero_mean_sq_269_cast = mul(x = zero_mean_269_cast, y = zero_mean_269_cast)[name = tensor("zero_mean_sq_269_cast")]; + tensor var_8890 = const()[name = tensor("op_8890"), val = tensor([1])]; + tensor var_8891_cast = reduce_mean(axes = var_8890, keep_dims = var_23, x = zero_mean_sq_269_cast)[name = tensor("op_8891_cast")]; + tensor var_8892_to_fp16 = const()[name = tensor("op_8892_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8893_cast = add(x = var_8891_cast, y = var_8892_to_fp16)[name = tensor("op_8893_cast")]; + tensor denom_269_epsilon_0_to_fp16 = const()[name = tensor("denom_269_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_269_cast = rsqrt(epsilon = denom_269_epsilon_0_to_fp16, x = var_8893_cast)[name = tensor("denom_269_cast")]; + tensor out_269_cast = mul(x = zero_mean_269_cast, y = denom_269_cast)[name = tensor("out_269_cast")]; + tensor var_8897_to_fp16 = const()[name = tensor("op_8897_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3418608384)))]; + tensor var_8898_cast = add(x = out_269_cast, y = var_8897_to_fp16)[name = tensor("op_8898_cast")]; + tensor var_8900_to_fp16 = const()[name = tensor("op_8900_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3418611008)))]; + tensor input_541_cast = mul(x = var_8898_cast, y = var_8900_to_fp16)[name = tensor("input_541_cast")]; + tensor var_8908 = const()[name = tensor("op_8908"), val = tensor([1, 1])]; + tensor var_8910 = const()[name = tensor("op_8910"), val = tensor([1, 1])]; + tensor var_8912_pad_type_0 = const()[name = tensor("op_8912_pad_type_0"), val = tensor("custom")]; + tensor var_8912_pad_0 = const()[name = tensor("op_8912_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3418613632)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3444828096)))]; + tensor var_8912_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_8910, groups = var_31, pad = var_8912_pad_0, pad_type = var_8912_pad_type_0, strides = var_8908, weight = unet_up_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_541_cast)[name = tensor("op_8912_cast")]; + tensor var_8913_split_sizes_0 = const()[name = tensor("op_8913_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_8913_axis_0 = const()[name = tensor("op_8913_axis_0"), val = tensor(1)]; + tensor var_8913_cast_0, tensor var_8913_cast_1 = split(axis = var_8913_axis_0, split_sizes = var_8913_split_sizes_0, x = var_8912_cast)[name = tensor("op_8913_cast")]; + tensor var_8915_mode_0 = const()[name = tensor("op_8915_mode_0"), val = tensor("EXACT")]; + tensor var_8915_cast = gelu(mode = var_8915_mode_0, x = var_8913_cast_1)[name = tensor("op_8915_cast")]; + tensor input_543_cast = mul(x = var_8913_cast_0, y = var_8915_cast)[name = tensor("input_543_cast")]; + tensor var_8919 = const()[name = tensor("op_8919"), val = tensor([1, 1])]; + tensor var_8921 = const()[name = tensor("op_8921"), val = tensor([1, 1])]; + tensor var_8923_pad_type_0 = const()[name = tensor("op_8923_pad_type_0"), val = tensor("custom")]; + tensor var_8923_pad_0 = const()[name = tensor("op_8923_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3444848640)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3457955904)))]; + tensor var_8923_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_8921, groups = var_31, pad = var_8923_pad_0, pad_type = var_8923_pad_type_0, strides = var_8919, weight = unet_up_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_543_cast)[name = tensor("op_8923_cast")]; + tensor inputs_271_cast = add(x = var_8923_cast, y = inputs_269_cast)[name = tensor("inputs_271_cast")]; + tensor var_8933 = const()[name = tensor("op_8933"), val = tensor([1])]; + tensor channels_mean_271_cast = reduce_mean(axes = var_8933, keep_dims = var_23, x = inputs_271_cast)[name = tensor("channels_mean_271_cast")]; + tensor zero_mean_271_cast = sub(x = inputs_271_cast, y = channels_mean_271_cast)[name = tensor("zero_mean_271_cast")]; + tensor zero_mean_sq_271_cast = mul(x = zero_mean_271_cast, y = zero_mean_271_cast)[name = tensor("zero_mean_sq_271_cast")]; + tensor var_8937 = const()[name = tensor("op_8937"), val = tensor([1])]; + tensor var_8938_cast = reduce_mean(axes = var_8937, keep_dims = var_23, x = zero_mean_sq_271_cast)[name = tensor("op_8938_cast")]; + tensor var_8939_to_fp16 = const()[name = tensor("op_8939_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_8940_cast = add(x = var_8938_cast, y = var_8939_to_fp16)[name = tensor("op_8940_cast")]; + tensor denom_271_epsilon_0_to_fp16 = const()[name = tensor("denom_271_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_271_cast = rsqrt(epsilon = denom_271_epsilon_0_to_fp16, x = var_8940_cast)[name = tensor("denom_271_cast")]; + tensor out_271_cast = mul(x = zero_mean_271_cast, y = denom_271_cast)[name = tensor("out_271_cast")]; + tensor var_8944_to_fp16 = const()[name = tensor("op_8944_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3457958528)))]; + tensor var_8945_cast = add(x = out_271_cast, y = var_8944_to_fp16)[name = tensor("op_8945_cast")]; + tensor var_8947_to_fp16 = const()[name = tensor("op_8947_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3457961152)))]; + tensor hidden_states_367_cast = mul(x = var_8945_cast, y = var_8947_to_fp16)[name = tensor("hidden_states_367_cast")]; + tensor var_8954 = const()[name = tensor("op_8954"), val = tensor([1, 1])]; + tensor var_8956 = const()[name = tensor("op_8956"), val = tensor([1, 1])]; tensor q_181_pad_type_0 = const()[name = tensor("q_181_pad_type_0"), val = tensor("custom")]; tensor q_181_pad_0 = const()[name = tensor("q_181_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_181 = conv(dilations = var_9055, groups = var_6872, pad = q_181_pad_0, pad_type = q_181_pad_type_0, strides = var_9053, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight, x = hidden_states_367)[name = tensor("q_181")]; - tensor var_9059 = const()[name = tensor("op_9059"), val = tensor([1, 1])]; - tensor var_9061 = const()[name = tensor("op_9061"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3457963776)))]; + tensor q_181_cast = conv(dilations = var_8956, groups = var_31, pad = q_181_pad_0, pad_type = q_181_pad_type_0, strides = var_8954, weight = unet_up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_367_cast)[name = tensor("q_181_cast")]; + tensor var_8960 = const()[name = tensor("op_8960"), val = tensor([1, 1])]; + tensor var_8962 = const()[name = tensor("op_8962"), val = tensor([1, 1])]; tensor k_181_pad_type_0 = const()[name = tensor("k_181_pad_type_0"), val = tensor("custom")]; tensor k_181_pad_0 = const()[name = tensor("k_181_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_181 = conv(dilations = var_9061, groups = var_6872, pad = k_181_pad_0, pad_type = k_181_pad_type_0, strides = var_9059, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight, x = hidden_states_367)[name = tensor("k_181")]; - tensor var_9065 = const()[name = tensor("op_9065"), val = tensor([1, 1])]; - tensor var_9067 = const()[name = tensor("op_9067"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3461240640)))]; + tensor k_181_cast = conv(dilations = var_8962, groups = var_31, pad = k_181_pad_0, pad_type = k_181_pad_type_0, strides = var_8960, weight = unet_up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_367_cast)[name = tensor("k_181_cast")]; + tensor var_8966 = const()[name = tensor("op_8966"), val = tensor([1, 1])]; + tensor var_8968 = const()[name = tensor("op_8968"), val = tensor([1, 1])]; tensor v_181_pad_type_0 = const()[name = tensor("v_181_pad_type_0"), val = tensor("custom")]; tensor v_181_pad_0 = const()[name = tensor("v_181_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_181 = conv(dilations = var_9067, groups = var_6872, pad = v_181_pad_0, pad_type = v_181_pad_type_0, strides = var_9065, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight, x = hidden_states_367)[name = tensor("v_181")]; - tensor var_9071 = const()[name = tensor("op_9071"), val = tensor([2, 20, 64, -1])]; - tensor var_9072 = reshape(shape = var_9071, x = q_181)[name = tensor("op_9072")]; - tensor var_9073 = const()[name = tensor("op_9073"), val = tensor([2, 20, 64, -1])]; - tensor var_9074 = reshape(shape = var_9073, x = k_181)[name = tensor("op_9074")]; - tensor var_9075 = const()[name = tensor("op_9075"), val = tensor([2, 20, 64, -1])]; - tensor var_9076 = reshape(shape = var_9075, x = v_181)[name = tensor("op_9076")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3464517504)))]; + tensor v_181_cast = conv(dilations = var_8968, groups = var_31, pad = v_181_pad_0, pad_type = v_181_pad_type_0, strides = var_8966, weight = unet_up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_367_cast)[name = tensor("v_181_cast")]; + tensor var_8972 = const()[name = tensor("op_8972"), val = tensor([2, 20, 64, -1])]; + tensor var_8973_cast = reshape(shape = var_8972, x = q_181_cast)[name = tensor("op_8973_cast")]; + tensor var_8974 = const()[name = tensor("op_8974"), val = tensor([2, 20, 64, -1])]; + tensor var_8975_cast = reshape(shape = var_8974, x = k_181_cast)[name = tensor("op_8975_cast")]; + tensor var_8976 = const()[name = tensor("op_8976"), val = tensor([2, 20, 64, -1])]; + tensor var_8977_cast = reshape(shape = var_8976, x = v_181_cast)[name = tensor("op_8977_cast")]; tensor attn_weights_361_transpose_x_0 = const()[name = tensor("attn_weights_361_transpose_x_0"), val = tensor(true)]; tensor attn_weights_361_transpose_y_0 = const()[name = tensor("attn_weights_361_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_361 = matmul(transpose_x = attn_weights_361_transpose_x_0, transpose_y = attn_weights_361_transpose_y_0, x = var_9072, y = var_9074)[name = tensor("attn_weights_361")]; - tensor attn_weights_363 = mul(x = attn_weights_361, y = var_6863)[name = tensor("attn_weights_363")]; - tensor var_9080 = softmax(axis = var_6856, x = attn_weights_363)[name = tensor("op_9080")]; + tensor attn_weights_361_cast = matmul(transpose_x = attn_weights_361_transpose_x_0, transpose_y = attn_weights_361_transpose_y_0, x = var_8973_cast, y = var_8975_cast)[name = tensor("attn_weights_361_cast")]; + tensor attn_weights_363_cast = mul(x = attn_weights_361_cast, y = var_12_to_fp16)[name = tensor("attn_weights_363_cast")]; + tensor var_8981_cast = softmax(axis = var_18, x = attn_weights_363_cast)[name = tensor("op_8981_cast")]; tensor attn_181_transpose_x_0 = const()[name = tensor("attn_181_transpose_x_0"), val = tensor(false)]; tensor attn_181_transpose_y_0 = const()[name = tensor("attn_181_transpose_y_0"), val = tensor(true)]; - tensor attn_181 = matmul(transpose_x = attn_181_transpose_x_0, transpose_y = attn_181_transpose_y_0, x = var_9076, y = var_9080)[name = tensor("attn_181")]; - tensor var_9084 = const()[name = tensor("op_9084"), val = tensor([2, 1280, 1, -1])]; - tensor input_545 = reshape(shape = var_9084, x = attn_181)[name = tensor("input_545")]; - tensor var_9089 = const()[name = tensor("op_9089"), val = tensor([1, 1])]; - tensor var_9091 = const()[name = tensor("op_9091"), val = tensor([1, 1])]; - tensor var_9093_pad_type_0 = const()[name = tensor("op_9093_pad_type_0"), val = tensor("custom")]; - tensor var_9093_pad_0 = const()[name = tensor("op_9093_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9093 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias, dilations = var_9091, groups = var_6872, pad = var_9093_pad_0, pad_type = var_9093_pad_type_0, strides = var_9089, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight, x = input_545)[name = tensor("op_9093")]; - tensor inputs_273 = add(x = var_9093, y = inputs_271)[name = tensor("inputs_273")]; - tensor var_9097 = const()[name = tensor("op_9097"), val = tensor([1])]; - tensor channels_mean_273 = reduce_mean(axes = var_9097, keep_dims = var_6867, x = inputs_273)[name = tensor("channels_mean_273")]; - tensor zero_mean_273 = sub(x = inputs_273, y = channels_mean_273)[name = tensor("zero_mean_273")]; - tensor zero_mean_sq_273 = mul(x = zero_mean_273, y = zero_mean_273)[name = tensor("zero_mean_sq_273")]; - tensor var_9101 = const()[name = tensor("op_9101"), val = tensor([1])]; - tensor var_9102 = reduce_mean(axes = var_9101, keep_dims = var_6867, x = zero_mean_sq_273)[name = tensor("op_9102")]; - tensor var_9103 = const()[name = tensor("op_9103"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9104 = add(x = var_9102, y = var_9103)[name = tensor("op_9104")]; - tensor denom_273_epsilon_0 = const()[name = tensor("denom_273_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_273 = rsqrt(epsilon = denom_273_epsilon_0, x = var_9104)[name = tensor("denom_273")]; - tensor out_273 = mul(x = zero_mean_273, y = denom_273)[name = tensor("out_273")]; - tensor var_9108 = const()[name = tensor("op_9108"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269196928)))]; - tensor var_9109 = add(x = out_273, y = var_9108)[name = tensor("op_9109")]; - tensor var_9111 = const()[name = tensor("op_9111"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269202112)))]; - tensor hidden_states_369 = mul(x = var_9109, y = var_9111)[name = tensor("hidden_states_369")]; - tensor var_9118 = const()[name = tensor("op_9118"), val = tensor([1, 1])]; - tensor var_9120 = const()[name = tensor("op_9120"), val = tensor([1, 1])]; + tensor attn_181_cast = matmul(transpose_x = attn_181_transpose_x_0, transpose_y = attn_181_transpose_y_0, x = var_8977_cast, y = var_8981_cast)[name = tensor("attn_181_cast")]; + tensor var_8985 = const()[name = tensor("op_8985"), val = tensor([2, 1280, 1, -1])]; + tensor input_545_cast = reshape(shape = var_8985, x = attn_181_cast)[name = tensor("input_545_cast")]; + tensor var_8990 = const()[name = tensor("op_8990"), val = tensor([1, 1])]; + tensor var_8992 = const()[name = tensor("op_8992"), val = tensor([1, 1])]; + tensor var_8994_pad_type_0 = const()[name = tensor("op_8994_pad_type_0"), val = tensor("custom")]; + tensor var_8994_pad_0 = const()[name = tensor("op_8994_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3467794368)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3471071232)))]; + tensor var_8994_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_8992, groups = var_31, pad = var_8994_pad_0, pad_type = var_8994_pad_type_0, strides = var_8990, weight = unet_up_blocks_0_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_545_cast)[name = tensor("op_8994_cast")]; + tensor inputs_273_cast = add(x = var_8994_cast, y = inputs_271_cast)[name = tensor("inputs_273_cast")]; + tensor var_8998 = const()[name = tensor("op_8998"), val = tensor([1])]; + tensor channels_mean_273_cast = reduce_mean(axes = var_8998, keep_dims = var_23, x = inputs_273_cast)[name = tensor("channels_mean_273_cast")]; + tensor zero_mean_273_cast = sub(x = inputs_273_cast, y = channels_mean_273_cast)[name = tensor("zero_mean_273_cast")]; + tensor zero_mean_sq_273_cast = mul(x = zero_mean_273_cast, y = zero_mean_273_cast)[name = tensor("zero_mean_sq_273_cast")]; + tensor var_9002 = const()[name = tensor("op_9002"), val = tensor([1])]; + tensor var_9003_cast = reduce_mean(axes = var_9002, keep_dims = var_23, x = zero_mean_sq_273_cast)[name = tensor("op_9003_cast")]; + tensor var_9004_to_fp16 = const()[name = tensor("op_9004_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9005_cast = add(x = var_9003_cast, y = var_9004_to_fp16)[name = tensor("op_9005_cast")]; + tensor denom_273_epsilon_0_to_fp16 = const()[name = tensor("denom_273_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_273_cast = rsqrt(epsilon = denom_273_epsilon_0_to_fp16, x = var_9005_cast)[name = tensor("denom_273_cast")]; + tensor out_273_cast = mul(x = zero_mean_273_cast, y = denom_273_cast)[name = tensor("out_273_cast")]; + tensor var_9009_to_fp16 = const()[name = tensor("op_9009_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3471073856)))]; + tensor var_9010_cast = add(x = out_273_cast, y = var_9009_to_fp16)[name = tensor("op_9010_cast")]; + tensor var_9012_to_fp16 = const()[name = tensor("op_9012_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3471076480)))]; + tensor hidden_states_369_cast = mul(x = var_9010_cast, y = var_9012_to_fp16)[name = tensor("hidden_states_369_cast")]; + tensor var_9019 = const()[name = tensor("op_9019"), val = tensor([1, 1])]; + tensor var_9021 = const()[name = tensor("op_9021"), val = tensor([1, 1])]; tensor q_183_pad_type_0 = const()[name = tensor("q_183_pad_type_0"), val = tensor("custom")]; tensor q_183_pad_0 = const()[name = tensor("q_183_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_183 = conv(dilations = var_9120, groups = var_6872, pad = q_183_pad_0, pad_type = q_183_pad_type_0, strides = var_9118, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight, x = hidden_states_369)[name = tensor("q_183")]; - tensor var_9124 = const()[name = tensor("op_9124"), val = tensor([1, 1])]; - tensor var_9126 = const()[name = tensor("op_9126"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3471079104)))]; + tensor q_183_cast = conv(dilations = var_9021, groups = var_31, pad = q_183_pad_0, pad_type = q_183_pad_type_0, strides = var_9019, weight = unet_up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_369_cast)[name = tensor("q_183_cast")]; + tensor var_9025 = const()[name = tensor("op_9025"), val = tensor([1, 1])]; + tensor var_9027 = const()[name = tensor("op_9027"), val = tensor([1, 1])]; tensor k_183_pad_type_0 = const()[name = tensor("k_183_pad_type_0"), val = tensor("custom")]; tensor k_183_pad_0 = const()[name = tensor("k_183_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_183 = conv(dilations = var_9126, groups = var_6872, pad = k_183_pad_0, pad_type = k_183_pad_type_0, strides = var_9124, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_183")]; - tensor var_9130 = const()[name = tensor("op_9130"), val = tensor([1, 1])]; - tensor var_9132 = const()[name = tensor("op_9132"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3474355968)))]; + tensor k_183_cast = conv(dilations = var_9027, groups = var_31, pad = k_183_pad_0, pad_type = k_183_pad_type_0, strides = var_9025, weight = unet_up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_183_cast")]; + tensor var_9031 = const()[name = tensor("op_9031"), val = tensor([1, 1])]; + tensor var_9033 = const()[name = tensor("op_9033"), val = tensor([1, 1])]; tensor v_183_pad_type_0 = const()[name = tensor("v_183_pad_type_0"), val = tensor("custom")]; tensor v_183_pad_0 = const()[name = tensor("v_183_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_183 = conv(dilations = var_9132, groups = var_6872, pad = v_183_pad_0, pad_type = v_183_pad_type_0, strides = var_9130, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_183")]; - tensor var_9136 = const()[name = tensor("op_9136"), val = tensor([2, 20, 64, -1])]; - tensor var_9137 = reshape(shape = var_9136, x = q_183)[name = tensor("op_9137")]; - tensor var_9138 = const()[name = tensor("op_9138"), val = tensor([2, 20, 64, -1])]; - tensor var_9139 = reshape(shape = var_9138, x = k_183)[name = tensor("op_9139")]; - tensor var_9140 = const()[name = tensor("op_9140"), val = tensor([2, 20, 64, -1])]; - tensor var_9141 = reshape(shape = var_9140, x = v_183)[name = tensor("op_9141")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3479598912)))]; + tensor v_183_cast = conv(dilations = var_9033, groups = var_31, pad = v_183_pad_0, pad_type = v_183_pad_type_0, strides = var_9031, weight = unet_up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_183_cast")]; + tensor var_9037 = const()[name = tensor("op_9037"), val = tensor([2, 20, 64, -1])]; + tensor var_9038_cast = reshape(shape = var_9037, x = q_183_cast)[name = tensor("op_9038_cast")]; + tensor var_9039 = const()[name = tensor("op_9039"), val = tensor([2, 20, 64, -1])]; + tensor var_9040_cast = reshape(shape = var_9039, x = k_183_cast)[name = tensor("op_9040_cast")]; + tensor var_9041 = const()[name = tensor("op_9041"), val = tensor([2, 20, 64, -1])]; + tensor var_9042_cast = reshape(shape = var_9041, x = v_183_cast)[name = tensor("op_9042_cast")]; tensor attn_weights_365_transpose_x_0 = const()[name = tensor("attn_weights_365_transpose_x_0"), val = tensor(true)]; tensor attn_weights_365_transpose_y_0 = const()[name = tensor("attn_weights_365_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_365 = matmul(transpose_x = attn_weights_365_transpose_x_0, transpose_y = attn_weights_365_transpose_y_0, x = var_9137, y = var_9139)[name = tensor("attn_weights_365")]; - tensor attn_weights_367 = mul(x = attn_weights_365, y = var_6863)[name = tensor("attn_weights_367")]; - tensor var_9145 = softmax(axis = var_6856, x = attn_weights_367)[name = tensor("op_9145")]; + tensor attn_weights_365_cast = matmul(transpose_x = attn_weights_365_transpose_x_0, transpose_y = attn_weights_365_transpose_y_0, x = var_9038_cast, y = var_9040_cast)[name = tensor("attn_weights_365_cast")]; + tensor attn_weights_367_cast = mul(x = attn_weights_365_cast, y = var_12_to_fp16)[name = tensor("attn_weights_367_cast")]; + tensor var_9046_cast = softmax(axis = var_18, x = attn_weights_367_cast)[name = tensor("op_9046_cast")]; tensor attn_183_transpose_x_0 = const()[name = tensor("attn_183_transpose_x_0"), val = tensor(false)]; tensor attn_183_transpose_y_0 = const()[name = tensor("attn_183_transpose_y_0"), val = tensor(true)]; - tensor attn_183 = matmul(transpose_x = attn_183_transpose_x_0, transpose_y = attn_183_transpose_y_0, x = var_9141, y = var_9145)[name = tensor("attn_183")]; - tensor var_9149 = const()[name = tensor("op_9149"), val = tensor([2, 1280, 1, -1])]; - tensor input_547 = reshape(shape = var_9149, x = attn_183)[name = tensor("input_547")]; - tensor var_9154 = const()[name = tensor("op_9154"), val = tensor([1, 1])]; - tensor var_9156 = const()[name = tensor("op_9156"), val = tensor([1, 1])]; - tensor var_9158_pad_type_0 = const()[name = tensor("op_9158_pad_type_0"), val = tensor("custom")]; - tensor var_9158_pad_0 = const()[name = tensor("op_9158_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9158 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias, dilations = var_9156, groups = var_6872, pad = var_9158_pad_0, pad_type = var_9158_pad_type_0, strides = var_9154, weight = up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight, x = input_547)[name = tensor("op_9158")]; - tensor inputs_275 = add(x = var_9158, y = inputs_273)[name = tensor("inputs_275")]; - tensor var_9162 = const()[name = tensor("op_9162"), val = tensor([1])]; - tensor channels_mean_275 = reduce_mean(axes = var_9162, keep_dims = var_6867, x = inputs_275)[name = tensor("channels_mean_275")]; - tensor zero_mean_275 = sub(x = inputs_275, y = channels_mean_275)[name = tensor("zero_mean_275")]; - tensor zero_mean_sq_275 = mul(x = zero_mean_275, y = zero_mean_275)[name = tensor("zero_mean_sq_275")]; - tensor var_9166 = const()[name = tensor("op_9166"), val = tensor([1])]; - tensor var_9167 = reduce_mean(axes = var_9166, keep_dims = var_6867, x = zero_mean_sq_275)[name = tensor("op_9167")]; - tensor var_9168 = const()[name = tensor("op_9168"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9169 = add(x = var_9167, y = var_9168)[name = tensor("op_9169")]; - tensor denom_275_epsilon_0 = const()[name = tensor("denom_275_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_275 = rsqrt(epsilon = denom_275_epsilon_0, x = var_9169)[name = tensor("denom_275")]; - tensor out_275 = mul(x = zero_mean_275, y = denom_275)[name = tensor("out_275")]; - tensor var_9173 = const()[name = tensor("op_9173"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269207296)))]; - tensor var_9174 = add(x = out_275, y = var_9173)[name = tensor("op_9174")]; - tensor var_9176 = const()[name = tensor("op_9176"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269212480)))]; - tensor input_549 = mul(x = var_9174, y = var_9176)[name = tensor("input_549")]; - tensor var_9184 = const()[name = tensor("op_9184"), val = tensor([1, 1])]; - tensor var_9186 = const()[name = tensor("op_9186"), val = tensor([1, 1])]; - tensor var_9188_pad_type_0 = const()[name = tensor("op_9188_pad_type_0"), val = tensor("custom")]; - tensor var_9188_pad_0 = const()[name = tensor("op_9188_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9188 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias, dilations = var_9186, groups = var_6872, pad = var_9188_pad_0, pad_type = var_9188_pad_type_0, strides = var_9184, weight = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight, x = input_549)[name = tensor("op_9188")]; - tensor var_9189_split_sizes_0 = const()[name = tensor("op_9189_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_9189_axis_0 = const()[name = tensor("op_9189_axis_0"), val = tensor(1)]; - tensor var_9189_0, tensor var_9189_1 = split(axis = var_9189_axis_0, split_sizes = var_9189_split_sizes_0, x = var_9188)[name = tensor("op_9189")]; - tensor var_9191_mode_0 = const()[name = tensor("op_9191_mode_0"), val = tensor("EXACT")]; - tensor var_9191 = gelu(mode = var_9191_mode_0, x = var_9189_1)[name = tensor("op_9191")]; - tensor input_551 = mul(x = var_9189_0, y = var_9191)[name = tensor("input_551")]; - tensor var_9195 = const()[name = tensor("op_9195"), val = tensor([1, 1])]; - tensor var_9197 = const()[name = tensor("op_9197"), val = tensor([1, 1])]; - tensor var_9199_pad_type_0 = const()[name = tensor("op_9199_pad_type_0"), val = tensor("custom")]; - tensor var_9199_pad_0 = const()[name = tensor("op_9199_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9199 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias, dilations = var_9197, groups = var_6872, pad = var_9199_pad_0, pad_type = var_9199_pad_type_0, strides = var_9195, weight = up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight, x = input_551)[name = tensor("op_9199")]; - tensor inputs_277 = add(x = var_9199, y = inputs_275)[name = tensor("inputs_277")]; - tensor var_9209 = const()[name = tensor("op_9209"), val = tensor([1])]; - tensor channels_mean_277 = reduce_mean(axes = var_9209, keep_dims = var_6867, x = inputs_277)[name = tensor("channels_mean_277")]; - tensor zero_mean_277 = sub(x = inputs_277, y = channels_mean_277)[name = tensor("zero_mean_277")]; - tensor zero_mean_sq_277 = mul(x = zero_mean_277, y = zero_mean_277)[name = tensor("zero_mean_sq_277")]; - tensor var_9213 = const()[name = tensor("op_9213"), val = tensor([1])]; - tensor var_9214 = reduce_mean(axes = var_9213, keep_dims = var_6867, x = zero_mean_sq_277)[name = tensor("op_9214")]; - tensor var_9215 = const()[name = tensor("op_9215"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9216 = add(x = var_9214, y = var_9215)[name = tensor("op_9216")]; - tensor denom_277_epsilon_0 = const()[name = tensor("denom_277_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_277 = rsqrt(epsilon = denom_277_epsilon_0, x = var_9216)[name = tensor("denom_277")]; - tensor out_277 = mul(x = zero_mean_277, y = denom_277)[name = tensor("out_277")]; - tensor var_9220 = const()[name = tensor("op_9220"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269217664)))]; - tensor var_9221 = add(x = out_277, y = var_9220)[name = tensor("op_9221")]; - tensor var_9223 = const()[name = tensor("op_9223"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269222848)))]; - tensor hidden_states_373 = mul(x = var_9221, y = var_9223)[name = tensor("hidden_states_373")]; - tensor var_9230 = const()[name = tensor("op_9230"), val = tensor([1, 1])]; - tensor var_9232 = const()[name = tensor("op_9232"), val = tensor([1, 1])]; + tensor attn_183_cast = matmul(transpose_x = attn_183_transpose_x_0, transpose_y = attn_183_transpose_y_0, x = var_9042_cast, y = var_9046_cast)[name = tensor("attn_183_cast")]; + tensor var_9050 = const()[name = tensor("op_9050"), val = tensor([2, 1280, 1, -1])]; + tensor input_547_cast = reshape(shape = var_9050, x = attn_183_cast)[name = tensor("input_547_cast")]; + tensor var_9055 = const()[name = tensor("op_9055"), val = tensor([1, 1])]; + tensor var_9057 = const()[name = tensor("op_9057"), val = tensor([1, 1])]; + tensor var_9059_pad_type_0 = const()[name = tensor("op_9059_pad_type_0"), val = tensor("custom")]; + tensor var_9059_pad_0 = const()[name = tensor("op_9059_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3484841856)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3488118720)))]; + tensor var_9059_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_9057, groups = var_31, pad = var_9059_pad_0, pad_type = var_9059_pad_type_0, strides = var_9055, weight = unet_up_blocks_0_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_547_cast)[name = tensor("op_9059_cast")]; + tensor inputs_275_cast = add(x = var_9059_cast, y = inputs_273_cast)[name = tensor("inputs_275_cast")]; + tensor var_9063 = const()[name = tensor("op_9063"), val = tensor([1])]; + tensor channels_mean_275_cast = reduce_mean(axes = var_9063, keep_dims = var_23, x = inputs_275_cast)[name = tensor("channels_mean_275_cast")]; + tensor zero_mean_275_cast = sub(x = inputs_275_cast, y = channels_mean_275_cast)[name = tensor("zero_mean_275_cast")]; + tensor zero_mean_sq_275_cast = mul(x = zero_mean_275_cast, y = zero_mean_275_cast)[name = tensor("zero_mean_sq_275_cast")]; + tensor var_9067 = const()[name = tensor("op_9067"), val = tensor([1])]; + tensor var_9068_cast = reduce_mean(axes = var_9067, keep_dims = var_23, x = zero_mean_sq_275_cast)[name = tensor("op_9068_cast")]; + tensor var_9069_to_fp16 = const()[name = tensor("op_9069_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9070_cast = add(x = var_9068_cast, y = var_9069_to_fp16)[name = tensor("op_9070_cast")]; + tensor denom_275_epsilon_0_to_fp16 = const()[name = tensor("denom_275_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_275_cast = rsqrt(epsilon = denom_275_epsilon_0_to_fp16, x = var_9070_cast)[name = tensor("denom_275_cast")]; + tensor out_275_cast = mul(x = zero_mean_275_cast, y = denom_275_cast)[name = tensor("out_275_cast")]; + tensor var_9074_to_fp16 = const()[name = tensor("op_9074_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3488121344)))]; + tensor var_9075_cast = add(x = out_275_cast, y = var_9074_to_fp16)[name = tensor("op_9075_cast")]; + tensor var_9077_to_fp16 = const()[name = tensor("op_9077_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3488123968)))]; + tensor input_549_cast = mul(x = var_9075_cast, y = var_9077_to_fp16)[name = tensor("input_549_cast")]; + tensor var_9085 = const()[name = tensor("op_9085"), val = tensor([1, 1])]; + tensor var_9087 = const()[name = tensor("op_9087"), val = tensor([1, 1])]; + tensor var_9089_pad_type_0 = const()[name = tensor("op_9089_pad_type_0"), val = tensor("custom")]; + tensor var_9089_pad_0 = const()[name = tensor("op_9089_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3488126592)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3514341056)))]; + tensor var_9089_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_9087, groups = var_31, pad = var_9089_pad_0, pad_type = var_9089_pad_type_0, strides = var_9085, weight = unet_up_blocks_0_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_549_cast)[name = tensor("op_9089_cast")]; + tensor var_9090_split_sizes_0 = const()[name = tensor("op_9090_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9090_axis_0 = const()[name = tensor("op_9090_axis_0"), val = tensor(1)]; + tensor var_9090_cast_0, tensor var_9090_cast_1 = split(axis = var_9090_axis_0, split_sizes = var_9090_split_sizes_0, x = var_9089_cast)[name = tensor("op_9090_cast")]; + tensor var_9092_mode_0 = const()[name = tensor("op_9092_mode_0"), val = tensor("EXACT")]; + tensor var_9092_cast = gelu(mode = var_9092_mode_0, x = var_9090_cast_1)[name = tensor("op_9092_cast")]; + tensor input_551_cast = mul(x = var_9090_cast_0, y = var_9092_cast)[name = tensor("input_551_cast")]; + tensor var_9096 = const()[name = tensor("op_9096"), val = tensor([1, 1])]; + tensor var_9098 = const()[name = tensor("op_9098"), val = tensor([1, 1])]; + tensor var_9100_pad_type_0 = const()[name = tensor("op_9100_pad_type_0"), val = tensor("custom")]; + tensor var_9100_pad_0 = const()[name = tensor("op_9100_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3514361600)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3527468864)))]; + tensor var_9100_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_9098, groups = var_31, pad = var_9100_pad_0, pad_type = var_9100_pad_type_0, strides = var_9096, weight = unet_up_blocks_0_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_551_cast)[name = tensor("op_9100_cast")]; + tensor inputs_277_cast = add(x = var_9100_cast, y = inputs_275_cast)[name = tensor("inputs_277_cast")]; + tensor var_9110 = const()[name = tensor("op_9110"), val = tensor([1])]; + tensor channels_mean_277_cast = reduce_mean(axes = var_9110, keep_dims = var_23, x = inputs_277_cast)[name = tensor("channels_mean_277_cast")]; + tensor zero_mean_277_cast = sub(x = inputs_277_cast, y = channels_mean_277_cast)[name = tensor("zero_mean_277_cast")]; + tensor zero_mean_sq_277_cast = mul(x = zero_mean_277_cast, y = zero_mean_277_cast)[name = tensor("zero_mean_sq_277_cast")]; + tensor var_9114 = const()[name = tensor("op_9114"), val = tensor([1])]; + tensor var_9115_cast = reduce_mean(axes = var_9114, keep_dims = var_23, x = zero_mean_sq_277_cast)[name = tensor("op_9115_cast")]; + tensor var_9116_to_fp16 = const()[name = tensor("op_9116_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9117_cast = add(x = var_9115_cast, y = var_9116_to_fp16)[name = tensor("op_9117_cast")]; + tensor denom_277_epsilon_0_to_fp16 = const()[name = tensor("denom_277_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_277_cast = rsqrt(epsilon = denom_277_epsilon_0_to_fp16, x = var_9117_cast)[name = tensor("denom_277_cast")]; + tensor out_277_cast = mul(x = zero_mean_277_cast, y = denom_277_cast)[name = tensor("out_277_cast")]; + tensor var_9121_to_fp16 = const()[name = tensor("op_9121_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3527471488)))]; + tensor var_9122_cast = add(x = out_277_cast, y = var_9121_to_fp16)[name = tensor("op_9122_cast")]; + tensor var_9124_to_fp16 = const()[name = tensor("op_9124_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3527474112)))]; + tensor hidden_states_373_cast = mul(x = var_9122_cast, y = var_9124_to_fp16)[name = tensor("hidden_states_373_cast")]; + tensor var_9131 = const()[name = tensor("op_9131"), val = tensor([1, 1])]; + tensor var_9133 = const()[name = tensor("op_9133"), val = tensor([1, 1])]; tensor q_185_pad_type_0 = const()[name = tensor("q_185_pad_type_0"), val = tensor("custom")]; tensor q_185_pad_0 = const()[name = tensor("q_185_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_185 = conv(dilations = var_9232, groups = var_6872, pad = q_185_pad_0, pad_type = q_185_pad_type_0, strides = var_9230, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight, x = hidden_states_373)[name = tensor("q_185")]; - tensor var_9236 = const()[name = tensor("op_9236"), val = tensor([1, 1])]; - tensor var_9238 = const()[name = tensor("op_9238"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3527476736)))]; + tensor q_185_cast = conv(dilations = var_9133, groups = var_31, pad = q_185_pad_0, pad_type = q_185_pad_type_0, strides = var_9131, weight = unet_up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_373_cast)[name = tensor("q_185_cast")]; + tensor var_9137 = const()[name = tensor("op_9137"), val = tensor([1, 1])]; + tensor var_9139 = const()[name = tensor("op_9139"), val = tensor([1, 1])]; tensor k_185_pad_type_0 = const()[name = tensor("k_185_pad_type_0"), val = tensor("custom")]; tensor k_185_pad_0 = const()[name = tensor("k_185_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_185 = conv(dilations = var_9238, groups = var_6872, pad = k_185_pad_0, pad_type = k_185_pad_type_0, strides = var_9236, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight, x = hidden_states_373)[name = tensor("k_185")]; - tensor var_9242 = const()[name = tensor("op_9242"), val = tensor([1, 1])]; - tensor var_9244 = const()[name = tensor("op_9244"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3530753600)))]; + tensor k_185_cast = conv(dilations = var_9139, groups = var_31, pad = k_185_pad_0, pad_type = k_185_pad_type_0, strides = var_9137, weight = unet_up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_373_cast)[name = tensor("k_185_cast")]; + tensor var_9143 = const()[name = tensor("op_9143"), val = tensor([1, 1])]; + tensor var_9145 = const()[name = tensor("op_9145"), val = tensor([1, 1])]; tensor v_185_pad_type_0 = const()[name = tensor("v_185_pad_type_0"), val = tensor("custom")]; tensor v_185_pad_0 = const()[name = tensor("v_185_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_185 = conv(dilations = var_9244, groups = var_6872, pad = v_185_pad_0, pad_type = v_185_pad_type_0, strides = var_9242, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight, x = hidden_states_373)[name = tensor("v_185")]; - tensor var_9248 = const()[name = tensor("op_9248"), val = tensor([2, 20, 64, -1])]; - tensor var_9249 = reshape(shape = var_9248, x = q_185)[name = tensor("op_9249")]; - tensor var_9250 = const()[name = tensor("op_9250"), val = tensor([2, 20, 64, -1])]; - tensor var_9251 = reshape(shape = var_9250, x = k_185)[name = tensor("op_9251")]; - tensor var_9252 = const()[name = tensor("op_9252"), val = tensor([2, 20, 64, -1])]; - tensor var_9253 = reshape(shape = var_9252, x = v_185)[name = tensor("op_9253")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3534030464)))]; + tensor v_185_cast = conv(dilations = var_9145, groups = var_31, pad = v_185_pad_0, pad_type = v_185_pad_type_0, strides = var_9143, weight = unet_up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_373_cast)[name = tensor("v_185_cast")]; + tensor var_9149 = const()[name = tensor("op_9149"), val = tensor([2, 20, 64, -1])]; + tensor var_9150_cast = reshape(shape = var_9149, x = q_185_cast)[name = tensor("op_9150_cast")]; + tensor var_9151 = const()[name = tensor("op_9151"), val = tensor([2, 20, 64, -1])]; + tensor var_9152_cast = reshape(shape = var_9151, x = k_185_cast)[name = tensor("op_9152_cast")]; + tensor var_9153 = const()[name = tensor("op_9153"), val = tensor([2, 20, 64, -1])]; + tensor var_9154_cast = reshape(shape = var_9153, x = v_185_cast)[name = tensor("op_9154_cast")]; tensor attn_weights_369_transpose_x_0 = const()[name = tensor("attn_weights_369_transpose_x_0"), val = tensor(true)]; tensor attn_weights_369_transpose_y_0 = const()[name = tensor("attn_weights_369_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_369 = matmul(transpose_x = attn_weights_369_transpose_x_0, transpose_y = attn_weights_369_transpose_y_0, x = var_9249, y = var_9251)[name = tensor("attn_weights_369")]; - tensor attn_weights_371 = mul(x = attn_weights_369, y = var_6863)[name = tensor("attn_weights_371")]; - tensor var_9257 = softmax(axis = var_6856, x = attn_weights_371)[name = tensor("op_9257")]; + tensor attn_weights_369_cast = matmul(transpose_x = attn_weights_369_transpose_x_0, transpose_y = attn_weights_369_transpose_y_0, x = var_9150_cast, y = var_9152_cast)[name = tensor("attn_weights_369_cast")]; + tensor attn_weights_371_cast = mul(x = attn_weights_369_cast, y = var_12_to_fp16)[name = tensor("attn_weights_371_cast")]; + tensor var_9158_cast = softmax(axis = var_18, x = attn_weights_371_cast)[name = tensor("op_9158_cast")]; tensor attn_185_transpose_x_0 = const()[name = tensor("attn_185_transpose_x_0"), val = tensor(false)]; tensor attn_185_transpose_y_0 = const()[name = tensor("attn_185_transpose_y_0"), val = tensor(true)]; - tensor attn_185 = matmul(transpose_x = attn_185_transpose_x_0, transpose_y = attn_185_transpose_y_0, x = var_9253, y = var_9257)[name = tensor("attn_185")]; - tensor var_9261 = const()[name = tensor("op_9261"), val = tensor([2, 1280, 1, -1])]; - tensor input_553 = reshape(shape = var_9261, x = attn_185)[name = tensor("input_553")]; - tensor var_9266 = const()[name = tensor("op_9266"), val = tensor([1, 1])]; - tensor var_9268 = const()[name = tensor("op_9268"), val = tensor([1, 1])]; - tensor var_9270_pad_type_0 = const()[name = tensor("op_9270_pad_type_0"), val = tensor("custom")]; - tensor var_9270_pad_0 = const()[name = tensor("op_9270_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9270 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias, dilations = var_9268, groups = var_6872, pad = var_9270_pad_0, pad_type = var_9270_pad_type_0, strides = var_9266, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight, x = input_553)[name = tensor("op_9270")]; - tensor inputs_279 = add(x = var_9270, y = inputs_277)[name = tensor("inputs_279")]; - tensor var_9274 = const()[name = tensor("op_9274"), val = tensor([1])]; - tensor channels_mean_279 = reduce_mean(axes = var_9274, keep_dims = var_6867, x = inputs_279)[name = tensor("channels_mean_279")]; - tensor zero_mean_279 = sub(x = inputs_279, y = channels_mean_279)[name = tensor("zero_mean_279")]; - tensor zero_mean_sq_279 = mul(x = zero_mean_279, y = zero_mean_279)[name = tensor("zero_mean_sq_279")]; - tensor var_9278 = const()[name = tensor("op_9278"), val = tensor([1])]; - tensor var_9279 = reduce_mean(axes = var_9278, keep_dims = var_6867, x = zero_mean_sq_279)[name = tensor("op_9279")]; - tensor var_9280 = const()[name = tensor("op_9280"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9281 = add(x = var_9279, y = var_9280)[name = tensor("op_9281")]; - tensor denom_279_epsilon_0 = const()[name = tensor("denom_279_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_279 = rsqrt(epsilon = denom_279_epsilon_0, x = var_9281)[name = tensor("denom_279")]; - tensor out_279 = mul(x = zero_mean_279, y = denom_279)[name = tensor("out_279")]; - tensor var_9285 = const()[name = tensor("op_9285"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269228032)))]; - tensor var_9286 = add(x = out_279, y = var_9285)[name = tensor("op_9286")]; - tensor var_9288 = const()[name = tensor("op_9288"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269233216)))]; - tensor hidden_states_375 = mul(x = var_9286, y = var_9288)[name = tensor("hidden_states_375")]; - tensor var_9295 = const()[name = tensor("op_9295"), val = tensor([1, 1])]; - tensor var_9297 = const()[name = tensor("op_9297"), val = tensor([1, 1])]; + tensor attn_185_cast = matmul(transpose_x = attn_185_transpose_x_0, transpose_y = attn_185_transpose_y_0, x = var_9154_cast, y = var_9158_cast)[name = tensor("attn_185_cast")]; + tensor var_9162 = const()[name = tensor("op_9162"), val = tensor([2, 1280, 1, -1])]; + tensor input_553_cast = reshape(shape = var_9162, x = attn_185_cast)[name = tensor("input_553_cast")]; + tensor var_9167 = const()[name = tensor("op_9167"), val = tensor([1, 1])]; + tensor var_9169 = const()[name = tensor("op_9169"), val = tensor([1, 1])]; + tensor var_9171_pad_type_0 = const()[name = tensor("op_9171_pad_type_0"), val = tensor("custom")]; + tensor var_9171_pad_0 = const()[name = tensor("op_9171_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3537307328)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3540584192)))]; + tensor var_9171_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_9169, groups = var_31, pad = var_9171_pad_0, pad_type = var_9171_pad_type_0, strides = var_9167, weight = unet_up_blocks_0_attentions_1_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_553_cast)[name = tensor("op_9171_cast")]; + tensor inputs_279_cast = add(x = var_9171_cast, y = inputs_277_cast)[name = tensor("inputs_279_cast")]; + tensor var_9175 = const()[name = tensor("op_9175"), val = tensor([1])]; + tensor channels_mean_279_cast = reduce_mean(axes = var_9175, keep_dims = var_23, x = inputs_279_cast)[name = tensor("channels_mean_279_cast")]; + tensor zero_mean_279_cast = sub(x = inputs_279_cast, y = channels_mean_279_cast)[name = tensor("zero_mean_279_cast")]; + tensor zero_mean_sq_279_cast = mul(x = zero_mean_279_cast, y = zero_mean_279_cast)[name = tensor("zero_mean_sq_279_cast")]; + tensor var_9179 = const()[name = tensor("op_9179"), val = tensor([1])]; + tensor var_9180_cast = reduce_mean(axes = var_9179, keep_dims = var_23, x = zero_mean_sq_279_cast)[name = tensor("op_9180_cast")]; + tensor var_9181_to_fp16 = const()[name = tensor("op_9181_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9182_cast = add(x = var_9180_cast, y = var_9181_to_fp16)[name = tensor("op_9182_cast")]; + tensor denom_279_epsilon_0_to_fp16 = const()[name = tensor("denom_279_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_279_cast = rsqrt(epsilon = denom_279_epsilon_0_to_fp16, x = var_9182_cast)[name = tensor("denom_279_cast")]; + tensor out_279_cast = mul(x = zero_mean_279_cast, y = denom_279_cast)[name = tensor("out_279_cast")]; + tensor var_9186_to_fp16 = const()[name = tensor("op_9186_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3540586816)))]; + tensor var_9187_cast = add(x = out_279_cast, y = var_9186_to_fp16)[name = tensor("op_9187_cast")]; + tensor var_9189_to_fp16 = const()[name = tensor("op_9189_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3540589440)))]; + tensor hidden_states_375_cast = mul(x = var_9187_cast, y = var_9189_to_fp16)[name = tensor("hidden_states_375_cast")]; + tensor var_9196 = const()[name = tensor("op_9196"), val = tensor([1, 1])]; + tensor var_9198 = const()[name = tensor("op_9198"), val = tensor([1, 1])]; tensor q_187_pad_type_0 = const()[name = tensor("q_187_pad_type_0"), val = tensor("custom")]; tensor q_187_pad_0 = const()[name = tensor("q_187_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_187 = conv(dilations = var_9297, groups = var_6872, pad = q_187_pad_0, pad_type = q_187_pad_type_0, strides = var_9295, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight, x = hidden_states_375)[name = tensor("q_187")]; - tensor var_9301 = const()[name = tensor("op_9301"), val = tensor([1, 1])]; - tensor var_9303 = const()[name = tensor("op_9303"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3540592064)))]; + tensor q_187_cast = conv(dilations = var_9198, groups = var_31, pad = q_187_pad_0, pad_type = q_187_pad_type_0, strides = var_9196, weight = unet_up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_375_cast)[name = tensor("q_187_cast")]; + tensor var_9202 = const()[name = tensor("op_9202"), val = tensor([1, 1])]; + tensor var_9204 = const()[name = tensor("op_9204"), val = tensor([1, 1])]; tensor k_187_pad_type_0 = const()[name = tensor("k_187_pad_type_0"), val = tensor("custom")]; tensor k_187_pad_0 = const()[name = tensor("k_187_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_187 = conv(dilations = var_9303, groups = var_6872, pad = k_187_pad_0, pad_type = k_187_pad_type_0, strides = var_9301, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_187")]; - tensor var_9307 = const()[name = tensor("op_9307"), val = tensor([1, 1])]; - tensor var_9309 = const()[name = tensor("op_9309"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3543868928)))]; + tensor k_187_cast = conv(dilations = var_9204, groups = var_31, pad = k_187_pad_0, pad_type = k_187_pad_type_0, strides = var_9202, weight = unet_up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_187_cast")]; + tensor var_9208 = const()[name = tensor("op_9208"), val = tensor([1, 1])]; + tensor var_9210 = const()[name = tensor("op_9210"), val = tensor([1, 1])]; tensor v_187_pad_type_0 = const()[name = tensor("v_187_pad_type_0"), val = tensor("custom")]; tensor v_187_pad_0 = const()[name = tensor("v_187_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_187 = conv(dilations = var_9309, groups = var_6872, pad = v_187_pad_0, pad_type = v_187_pad_type_0, strides = var_9307, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_187")]; - tensor var_9313 = const()[name = tensor("op_9313"), val = tensor([2, 20, 64, -1])]; - tensor var_9314 = reshape(shape = var_9313, x = q_187)[name = tensor("op_9314")]; - tensor var_9315 = const()[name = tensor("op_9315"), val = tensor([2, 20, 64, -1])]; - tensor var_9316 = reshape(shape = var_9315, x = k_187)[name = tensor("op_9316")]; - tensor var_9317 = const()[name = tensor("op_9317"), val = tensor([2, 20, 64, -1])]; - tensor var_9318 = reshape(shape = var_9317, x = v_187)[name = tensor("op_9318")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3549111872)))]; + tensor v_187_cast = conv(dilations = var_9210, groups = var_31, pad = v_187_pad_0, pad_type = v_187_pad_type_0, strides = var_9208, weight = unet_up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_187_cast")]; + tensor var_9214 = const()[name = tensor("op_9214"), val = tensor([2, 20, 64, -1])]; + tensor var_9215_cast = reshape(shape = var_9214, x = q_187_cast)[name = tensor("op_9215_cast")]; + tensor var_9216 = const()[name = tensor("op_9216"), val = tensor([2, 20, 64, -1])]; + tensor var_9217_cast = reshape(shape = var_9216, x = k_187_cast)[name = tensor("op_9217_cast")]; + tensor var_9218 = const()[name = tensor("op_9218"), val = tensor([2, 20, 64, -1])]; + tensor var_9219_cast = reshape(shape = var_9218, x = v_187_cast)[name = tensor("op_9219_cast")]; tensor attn_weights_373_transpose_x_0 = const()[name = tensor("attn_weights_373_transpose_x_0"), val = tensor(true)]; tensor attn_weights_373_transpose_y_0 = const()[name = tensor("attn_weights_373_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_373 = matmul(transpose_x = attn_weights_373_transpose_x_0, transpose_y = attn_weights_373_transpose_y_0, x = var_9314, y = var_9316)[name = tensor("attn_weights_373")]; - tensor attn_weights_375 = mul(x = attn_weights_373, y = var_6863)[name = tensor("attn_weights_375")]; - tensor var_9322 = softmax(axis = var_6856, x = attn_weights_375)[name = tensor("op_9322")]; + tensor attn_weights_373_cast = matmul(transpose_x = attn_weights_373_transpose_x_0, transpose_y = attn_weights_373_transpose_y_0, x = var_9215_cast, y = var_9217_cast)[name = tensor("attn_weights_373_cast")]; + tensor attn_weights_375_cast = mul(x = attn_weights_373_cast, y = var_12_to_fp16)[name = tensor("attn_weights_375_cast")]; + tensor var_9223_cast = softmax(axis = var_18, x = attn_weights_375_cast)[name = tensor("op_9223_cast")]; tensor attn_187_transpose_x_0 = const()[name = tensor("attn_187_transpose_x_0"), val = tensor(false)]; tensor attn_187_transpose_y_0 = const()[name = tensor("attn_187_transpose_y_0"), val = tensor(true)]; - tensor attn_187 = matmul(transpose_x = attn_187_transpose_x_0, transpose_y = attn_187_transpose_y_0, x = var_9318, y = var_9322)[name = tensor("attn_187")]; - tensor var_9326 = const()[name = tensor("op_9326"), val = tensor([2, 1280, 1, -1])]; - tensor input_555 = reshape(shape = var_9326, x = attn_187)[name = tensor("input_555")]; - tensor var_9331 = const()[name = tensor("op_9331"), val = tensor([1, 1])]; - tensor var_9333 = const()[name = tensor("op_9333"), val = tensor([1, 1])]; - tensor var_9335_pad_type_0 = const()[name = tensor("op_9335_pad_type_0"), val = tensor("custom")]; - tensor var_9335_pad_0 = const()[name = tensor("op_9335_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9335 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias, dilations = var_9333, groups = var_6872, pad = var_9335_pad_0, pad_type = var_9335_pad_type_0, strides = var_9331, weight = up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight, x = input_555)[name = tensor("op_9335")]; - tensor inputs_281 = add(x = var_9335, y = inputs_279)[name = tensor("inputs_281")]; - tensor var_9339 = const()[name = tensor("op_9339"), val = tensor([1])]; - tensor channels_mean_281 = reduce_mean(axes = var_9339, keep_dims = var_6867, x = inputs_281)[name = tensor("channels_mean_281")]; - tensor zero_mean_281 = sub(x = inputs_281, y = channels_mean_281)[name = tensor("zero_mean_281")]; - tensor zero_mean_sq_281 = mul(x = zero_mean_281, y = zero_mean_281)[name = tensor("zero_mean_sq_281")]; - tensor var_9343 = const()[name = tensor("op_9343"), val = tensor([1])]; - tensor var_9344 = reduce_mean(axes = var_9343, keep_dims = var_6867, x = zero_mean_sq_281)[name = tensor("op_9344")]; - tensor var_9345 = const()[name = tensor("op_9345"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9346 = add(x = var_9344, y = var_9345)[name = tensor("op_9346")]; - tensor denom_281_epsilon_0 = const()[name = tensor("denom_281_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_281 = rsqrt(epsilon = denom_281_epsilon_0, x = var_9346)[name = tensor("denom_281")]; - tensor out_281 = mul(x = zero_mean_281, y = denom_281)[name = tensor("out_281")]; - tensor var_9350 = const()[name = tensor("op_9350"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269238400)))]; - tensor var_9351 = add(x = out_281, y = var_9350)[name = tensor("op_9351")]; - tensor var_9353 = const()[name = tensor("op_9353"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269243584)))]; - tensor input_557 = mul(x = var_9351, y = var_9353)[name = tensor("input_557")]; - tensor var_9361 = const()[name = tensor("op_9361"), val = tensor([1, 1])]; - tensor var_9363 = const()[name = tensor("op_9363"), val = tensor([1, 1])]; - tensor var_9365_pad_type_0 = const()[name = tensor("op_9365_pad_type_0"), val = tensor("custom")]; - tensor var_9365_pad_0 = const()[name = tensor("op_9365_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9365 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias, dilations = var_9363, groups = var_6872, pad = var_9365_pad_0, pad_type = var_9365_pad_type_0, strides = var_9361, weight = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight, x = input_557)[name = tensor("op_9365")]; - tensor var_9366_split_sizes_0 = const()[name = tensor("op_9366_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_9366_axis_0 = const()[name = tensor("op_9366_axis_0"), val = tensor(1)]; - tensor var_9366_0, tensor var_9366_1 = split(axis = var_9366_axis_0, split_sizes = var_9366_split_sizes_0, x = var_9365)[name = tensor("op_9366")]; - tensor var_9368_mode_0 = const()[name = tensor("op_9368_mode_0"), val = tensor("EXACT")]; - tensor var_9368 = gelu(mode = var_9368_mode_0, x = var_9366_1)[name = tensor("op_9368")]; - tensor input_559 = mul(x = var_9366_0, y = var_9368)[name = tensor("input_559")]; - tensor var_9372 = const()[name = tensor("op_9372"), val = tensor([1, 1])]; - tensor var_9374 = const()[name = tensor("op_9374"), val = tensor([1, 1])]; - tensor var_9376_pad_type_0 = const()[name = tensor("op_9376_pad_type_0"), val = tensor("custom")]; - tensor var_9376_pad_0 = const()[name = tensor("op_9376_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9376 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias, dilations = var_9374, groups = var_6872, pad = var_9376_pad_0, pad_type = var_9376_pad_type_0, strides = var_9372, weight = up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight, x = input_559)[name = tensor("op_9376")]; - tensor inputs_283 = add(x = var_9376, y = inputs_281)[name = tensor("inputs_283")]; - tensor var_9386 = const()[name = tensor("op_9386"), val = tensor([1])]; - tensor channels_mean_283 = reduce_mean(axes = var_9386, keep_dims = var_6867, x = inputs_283)[name = tensor("channels_mean_283")]; - tensor zero_mean_283 = sub(x = inputs_283, y = channels_mean_283)[name = tensor("zero_mean_283")]; - tensor zero_mean_sq_283 = mul(x = zero_mean_283, y = zero_mean_283)[name = tensor("zero_mean_sq_283")]; - tensor var_9390 = const()[name = tensor("op_9390"), val = tensor([1])]; - tensor var_9391 = reduce_mean(axes = var_9390, keep_dims = var_6867, x = zero_mean_sq_283)[name = tensor("op_9391")]; - tensor var_9392 = const()[name = tensor("op_9392"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9393 = add(x = var_9391, y = var_9392)[name = tensor("op_9393")]; - tensor denom_283_epsilon_0 = const()[name = tensor("denom_283_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_283 = rsqrt(epsilon = denom_283_epsilon_0, x = var_9393)[name = tensor("denom_283")]; - tensor out_283 = mul(x = zero_mean_283, y = denom_283)[name = tensor("out_283")]; - tensor var_9397 = const()[name = tensor("op_9397"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269248768)))]; - tensor var_9398 = add(x = out_283, y = var_9397)[name = tensor("op_9398")]; - tensor var_9400 = const()[name = tensor("op_9400"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269253952)))]; - tensor hidden_states_379 = mul(x = var_9398, y = var_9400)[name = tensor("hidden_states_379")]; - tensor var_9407 = const()[name = tensor("op_9407"), val = tensor([1, 1])]; - tensor var_9409 = const()[name = tensor("op_9409"), val = tensor([1, 1])]; + tensor attn_187_cast = matmul(transpose_x = attn_187_transpose_x_0, transpose_y = attn_187_transpose_y_0, x = var_9219_cast, y = var_9223_cast)[name = tensor("attn_187_cast")]; + tensor var_9227 = const()[name = tensor("op_9227"), val = tensor([2, 1280, 1, -1])]; + tensor input_555_cast = reshape(shape = var_9227, x = attn_187_cast)[name = tensor("input_555_cast")]; + tensor var_9232 = const()[name = tensor("op_9232"), val = tensor([1, 1])]; + tensor var_9234 = const()[name = tensor("op_9234"), val = tensor([1, 1])]; + tensor var_9236_pad_type_0 = const()[name = tensor("op_9236_pad_type_0"), val = tensor("custom")]; + tensor var_9236_pad_0 = const()[name = tensor("op_9236_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3554354816)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3557631680)))]; + tensor var_9236_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_9234, groups = var_31, pad = var_9236_pad_0, pad_type = var_9236_pad_type_0, strides = var_9232, weight = unet_up_blocks_0_attentions_1_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_555_cast)[name = tensor("op_9236_cast")]; + tensor inputs_281_cast = add(x = var_9236_cast, y = inputs_279_cast)[name = tensor("inputs_281_cast")]; + tensor var_9240 = const()[name = tensor("op_9240"), val = tensor([1])]; + tensor channels_mean_281_cast = reduce_mean(axes = var_9240, keep_dims = var_23, x = inputs_281_cast)[name = tensor("channels_mean_281_cast")]; + tensor zero_mean_281_cast = sub(x = inputs_281_cast, y = channels_mean_281_cast)[name = tensor("zero_mean_281_cast")]; + tensor zero_mean_sq_281_cast = mul(x = zero_mean_281_cast, y = zero_mean_281_cast)[name = tensor("zero_mean_sq_281_cast")]; + tensor var_9244 = const()[name = tensor("op_9244"), val = tensor([1])]; + tensor var_9245_cast = reduce_mean(axes = var_9244, keep_dims = var_23, x = zero_mean_sq_281_cast)[name = tensor("op_9245_cast")]; + tensor var_9246_to_fp16 = const()[name = tensor("op_9246_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9247_cast = add(x = var_9245_cast, y = var_9246_to_fp16)[name = tensor("op_9247_cast")]; + tensor denom_281_epsilon_0_to_fp16 = const()[name = tensor("denom_281_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_281_cast = rsqrt(epsilon = denom_281_epsilon_0_to_fp16, x = var_9247_cast)[name = tensor("denom_281_cast")]; + tensor out_281_cast = mul(x = zero_mean_281_cast, y = denom_281_cast)[name = tensor("out_281_cast")]; + tensor var_9251_to_fp16 = const()[name = tensor("op_9251_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3557634304)))]; + tensor var_9252_cast = add(x = out_281_cast, y = var_9251_to_fp16)[name = tensor("op_9252_cast")]; + tensor var_9254_to_fp16 = const()[name = tensor("op_9254_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3557636928)))]; + tensor input_557_cast = mul(x = var_9252_cast, y = var_9254_to_fp16)[name = tensor("input_557_cast")]; + tensor var_9262 = const()[name = tensor("op_9262"), val = tensor([1, 1])]; + tensor var_9264 = const()[name = tensor("op_9264"), val = tensor([1, 1])]; + tensor var_9266_pad_type_0 = const()[name = tensor("op_9266_pad_type_0"), val = tensor("custom")]; + tensor var_9266_pad_0 = const()[name = tensor("op_9266_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3557639552)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3583854016)))]; + tensor var_9266_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_9264, groups = var_31, pad = var_9266_pad_0, pad_type = var_9266_pad_type_0, strides = var_9262, weight = unet_up_blocks_0_attentions_1_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_557_cast)[name = tensor("op_9266_cast")]; + tensor var_9267_split_sizes_0 = const()[name = tensor("op_9267_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9267_axis_0 = const()[name = tensor("op_9267_axis_0"), val = tensor(1)]; + tensor var_9267_cast_0, tensor var_9267_cast_1 = split(axis = var_9267_axis_0, split_sizes = var_9267_split_sizes_0, x = var_9266_cast)[name = tensor("op_9267_cast")]; + tensor var_9269_mode_0 = const()[name = tensor("op_9269_mode_0"), val = tensor("EXACT")]; + tensor var_9269_cast = gelu(mode = var_9269_mode_0, x = var_9267_cast_1)[name = tensor("op_9269_cast")]; + tensor input_559_cast = mul(x = var_9267_cast_0, y = var_9269_cast)[name = tensor("input_559_cast")]; + tensor var_9273 = const()[name = tensor("op_9273"), val = tensor([1, 1])]; + tensor var_9275 = const()[name = tensor("op_9275"), val = tensor([1, 1])]; + tensor var_9277_pad_type_0 = const()[name = tensor("op_9277_pad_type_0"), val = tensor("custom")]; + tensor var_9277_pad_0 = const()[name = tensor("op_9277_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3583874560)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3596981824)))]; + tensor var_9277_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_9275, groups = var_31, pad = var_9277_pad_0, pad_type = var_9277_pad_type_0, strides = var_9273, weight = unet_up_blocks_0_attentions_1_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_559_cast)[name = tensor("op_9277_cast")]; + tensor inputs_283_cast = add(x = var_9277_cast, y = inputs_281_cast)[name = tensor("inputs_283_cast")]; + tensor var_9287 = const()[name = tensor("op_9287"), val = tensor([1])]; + tensor channels_mean_283_cast = reduce_mean(axes = var_9287, keep_dims = var_23, x = inputs_283_cast)[name = tensor("channels_mean_283_cast")]; + tensor zero_mean_283_cast = sub(x = inputs_283_cast, y = channels_mean_283_cast)[name = tensor("zero_mean_283_cast")]; + tensor zero_mean_sq_283_cast = mul(x = zero_mean_283_cast, y = zero_mean_283_cast)[name = tensor("zero_mean_sq_283_cast")]; + tensor var_9291 = const()[name = tensor("op_9291"), val = tensor([1])]; + tensor var_9292_cast = reduce_mean(axes = var_9291, keep_dims = var_23, x = zero_mean_sq_283_cast)[name = tensor("op_9292_cast")]; + tensor var_9293_to_fp16 = const()[name = tensor("op_9293_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9294_cast = add(x = var_9292_cast, y = var_9293_to_fp16)[name = tensor("op_9294_cast")]; + tensor denom_283_epsilon_0_to_fp16 = const()[name = tensor("denom_283_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_283_cast = rsqrt(epsilon = denom_283_epsilon_0_to_fp16, x = var_9294_cast)[name = tensor("denom_283_cast")]; + tensor out_283_cast = mul(x = zero_mean_283_cast, y = denom_283_cast)[name = tensor("out_283_cast")]; + tensor var_9298_to_fp16 = const()[name = tensor("op_9298_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3596984448)))]; + tensor var_9299_cast = add(x = out_283_cast, y = var_9298_to_fp16)[name = tensor("op_9299_cast")]; + tensor var_9301_to_fp16 = const()[name = tensor("op_9301_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3596987072)))]; + tensor hidden_states_379_cast = mul(x = var_9299_cast, y = var_9301_to_fp16)[name = tensor("hidden_states_379_cast")]; + tensor var_9308 = const()[name = tensor("op_9308"), val = tensor([1, 1])]; + tensor var_9310 = const()[name = tensor("op_9310"), val = tensor([1, 1])]; tensor q_189_pad_type_0 = const()[name = tensor("q_189_pad_type_0"), val = tensor("custom")]; tensor q_189_pad_0 = const()[name = tensor("q_189_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_189 = conv(dilations = var_9409, groups = var_6872, pad = q_189_pad_0, pad_type = q_189_pad_type_0, strides = var_9407, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight, x = hidden_states_379)[name = tensor("q_189")]; - tensor var_9413 = const()[name = tensor("op_9413"), val = tensor([1, 1])]; - tensor var_9415 = const()[name = tensor("op_9415"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3596989696)))]; + tensor q_189_cast = conv(dilations = var_9310, groups = var_31, pad = q_189_pad_0, pad_type = q_189_pad_type_0, strides = var_9308, weight = unet_up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_379_cast)[name = tensor("q_189_cast")]; + tensor var_9314 = const()[name = tensor("op_9314"), val = tensor([1, 1])]; + tensor var_9316 = const()[name = tensor("op_9316"), val = tensor([1, 1])]; tensor k_189_pad_type_0 = const()[name = tensor("k_189_pad_type_0"), val = tensor("custom")]; tensor k_189_pad_0 = const()[name = tensor("k_189_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_189 = conv(dilations = var_9415, groups = var_6872, pad = k_189_pad_0, pad_type = k_189_pad_type_0, strides = var_9413, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight, x = hidden_states_379)[name = tensor("k_189")]; - tensor var_9419 = const()[name = tensor("op_9419"), val = tensor([1, 1])]; - tensor var_9421 = const()[name = tensor("op_9421"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3600266560)))]; + tensor k_189_cast = conv(dilations = var_9316, groups = var_31, pad = k_189_pad_0, pad_type = k_189_pad_type_0, strides = var_9314, weight = unet_up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_379_cast)[name = tensor("k_189_cast")]; + tensor var_9320 = const()[name = tensor("op_9320"), val = tensor([1, 1])]; + tensor var_9322 = const()[name = tensor("op_9322"), val = tensor([1, 1])]; tensor v_189_pad_type_0 = const()[name = tensor("v_189_pad_type_0"), val = tensor("custom")]; tensor v_189_pad_0 = const()[name = tensor("v_189_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_189 = conv(dilations = var_9421, groups = var_6872, pad = v_189_pad_0, pad_type = v_189_pad_type_0, strides = var_9419, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight, x = hidden_states_379)[name = tensor("v_189")]; - tensor var_9425 = const()[name = tensor("op_9425"), val = tensor([2, 20, 64, -1])]; - tensor var_9426 = reshape(shape = var_9425, x = q_189)[name = tensor("op_9426")]; - tensor var_9427 = const()[name = tensor("op_9427"), val = tensor([2, 20, 64, -1])]; - tensor var_9428 = reshape(shape = var_9427, x = k_189)[name = tensor("op_9428")]; - tensor var_9429 = const()[name = tensor("op_9429"), val = tensor([2, 20, 64, -1])]; - tensor var_9430 = reshape(shape = var_9429, x = v_189)[name = tensor("op_9430")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3603543424)))]; + tensor v_189_cast = conv(dilations = var_9322, groups = var_31, pad = v_189_pad_0, pad_type = v_189_pad_type_0, strides = var_9320, weight = unet_up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_379_cast)[name = tensor("v_189_cast")]; + tensor var_9326 = const()[name = tensor("op_9326"), val = tensor([2, 20, 64, -1])]; + tensor var_9327_cast = reshape(shape = var_9326, x = q_189_cast)[name = tensor("op_9327_cast")]; + tensor var_9328 = const()[name = tensor("op_9328"), val = tensor([2, 20, 64, -1])]; + tensor var_9329_cast = reshape(shape = var_9328, x = k_189_cast)[name = tensor("op_9329_cast")]; + tensor var_9330 = const()[name = tensor("op_9330"), val = tensor([2, 20, 64, -1])]; + tensor var_9331_cast = reshape(shape = var_9330, x = v_189_cast)[name = tensor("op_9331_cast")]; tensor attn_weights_377_transpose_x_0 = const()[name = tensor("attn_weights_377_transpose_x_0"), val = tensor(true)]; tensor attn_weights_377_transpose_y_0 = const()[name = tensor("attn_weights_377_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_377 = matmul(transpose_x = attn_weights_377_transpose_x_0, transpose_y = attn_weights_377_transpose_y_0, x = var_9426, y = var_9428)[name = tensor("attn_weights_377")]; - tensor attn_weights_379 = mul(x = attn_weights_377, y = var_6863)[name = tensor("attn_weights_379")]; - tensor var_9434 = softmax(axis = var_6856, x = attn_weights_379)[name = tensor("op_9434")]; + tensor attn_weights_377_cast = matmul(transpose_x = attn_weights_377_transpose_x_0, transpose_y = attn_weights_377_transpose_y_0, x = var_9327_cast, y = var_9329_cast)[name = tensor("attn_weights_377_cast")]; + tensor attn_weights_379_cast = mul(x = attn_weights_377_cast, y = var_12_to_fp16)[name = tensor("attn_weights_379_cast")]; + tensor var_9335_cast = softmax(axis = var_18, x = attn_weights_379_cast)[name = tensor("op_9335_cast")]; tensor attn_189_transpose_x_0 = const()[name = tensor("attn_189_transpose_x_0"), val = tensor(false)]; tensor attn_189_transpose_y_0 = const()[name = tensor("attn_189_transpose_y_0"), val = tensor(true)]; - tensor attn_189 = matmul(transpose_x = attn_189_transpose_x_0, transpose_y = attn_189_transpose_y_0, x = var_9430, y = var_9434)[name = tensor("attn_189")]; - tensor var_9438 = const()[name = tensor("op_9438"), val = tensor([2, 1280, 1, -1])]; - tensor input_561 = reshape(shape = var_9438, x = attn_189)[name = tensor("input_561")]; - tensor var_9443 = const()[name = tensor("op_9443"), val = tensor([1, 1])]; - tensor var_9445 = const()[name = tensor("op_9445"), val = tensor([1, 1])]; - tensor var_9447_pad_type_0 = const()[name = tensor("op_9447_pad_type_0"), val = tensor("custom")]; - tensor var_9447_pad_0 = const()[name = tensor("op_9447_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9447 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias, dilations = var_9445, groups = var_6872, pad = var_9447_pad_0, pad_type = var_9447_pad_type_0, strides = var_9443, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight, x = input_561)[name = tensor("op_9447")]; - tensor inputs_285 = add(x = var_9447, y = inputs_283)[name = tensor("inputs_285")]; - tensor var_9451 = const()[name = tensor("op_9451"), val = tensor([1])]; - tensor channels_mean_285 = reduce_mean(axes = var_9451, keep_dims = var_6867, x = inputs_285)[name = tensor("channels_mean_285")]; - tensor zero_mean_285 = sub(x = inputs_285, y = channels_mean_285)[name = tensor("zero_mean_285")]; - tensor zero_mean_sq_285 = mul(x = zero_mean_285, y = zero_mean_285)[name = tensor("zero_mean_sq_285")]; - tensor var_9455 = const()[name = tensor("op_9455"), val = tensor([1])]; - tensor var_9456 = reduce_mean(axes = var_9455, keep_dims = var_6867, x = zero_mean_sq_285)[name = tensor("op_9456")]; - tensor var_9457 = const()[name = tensor("op_9457"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9458 = add(x = var_9456, y = var_9457)[name = tensor("op_9458")]; - tensor denom_285_epsilon_0 = const()[name = tensor("denom_285_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_285 = rsqrt(epsilon = denom_285_epsilon_0, x = var_9458)[name = tensor("denom_285")]; - tensor out_285 = mul(x = zero_mean_285, y = denom_285)[name = tensor("out_285")]; - tensor var_9462 = const()[name = tensor("op_9462"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269259136)))]; - tensor var_9463 = add(x = out_285, y = var_9462)[name = tensor("op_9463")]; - tensor var_9465 = const()[name = tensor("op_9465"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269264320)))]; - tensor hidden_states_381 = mul(x = var_9463, y = var_9465)[name = tensor("hidden_states_381")]; - tensor var_9472 = const()[name = tensor("op_9472"), val = tensor([1, 1])]; - tensor var_9474 = const()[name = tensor("op_9474"), val = tensor([1, 1])]; + tensor attn_189_cast = matmul(transpose_x = attn_189_transpose_x_0, transpose_y = attn_189_transpose_y_0, x = var_9331_cast, y = var_9335_cast)[name = tensor("attn_189_cast")]; + tensor var_9339 = const()[name = tensor("op_9339"), val = tensor([2, 1280, 1, -1])]; + tensor input_561_cast = reshape(shape = var_9339, x = attn_189_cast)[name = tensor("input_561_cast")]; + tensor var_9344 = const()[name = tensor("op_9344"), val = tensor([1, 1])]; + tensor var_9346 = const()[name = tensor("op_9346"), val = tensor([1, 1])]; + tensor var_9348_pad_type_0 = const()[name = tensor("op_9348_pad_type_0"), val = tensor("custom")]; + tensor var_9348_pad_0 = const()[name = tensor("op_9348_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3606820288)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3610097152)))]; + tensor var_9348_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_9346, groups = var_31, pad = var_9348_pad_0, pad_type = var_9348_pad_type_0, strides = var_9344, weight = unet_up_blocks_0_attentions_1_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_561_cast)[name = tensor("op_9348_cast")]; + tensor inputs_285_cast = add(x = var_9348_cast, y = inputs_283_cast)[name = tensor("inputs_285_cast")]; + tensor var_9352 = const()[name = tensor("op_9352"), val = tensor([1])]; + tensor channels_mean_285_cast = reduce_mean(axes = var_9352, keep_dims = var_23, x = inputs_285_cast)[name = tensor("channels_mean_285_cast")]; + tensor zero_mean_285_cast = sub(x = inputs_285_cast, y = channels_mean_285_cast)[name = tensor("zero_mean_285_cast")]; + tensor zero_mean_sq_285_cast = mul(x = zero_mean_285_cast, y = zero_mean_285_cast)[name = tensor("zero_mean_sq_285_cast")]; + tensor var_9356 = const()[name = tensor("op_9356"), val = tensor([1])]; + tensor var_9357_cast = reduce_mean(axes = var_9356, keep_dims = var_23, x = zero_mean_sq_285_cast)[name = tensor("op_9357_cast")]; + tensor var_9358_to_fp16 = const()[name = tensor("op_9358_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9359_cast = add(x = var_9357_cast, y = var_9358_to_fp16)[name = tensor("op_9359_cast")]; + tensor denom_285_epsilon_0_to_fp16 = const()[name = tensor("denom_285_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_285_cast = rsqrt(epsilon = denom_285_epsilon_0_to_fp16, x = var_9359_cast)[name = tensor("denom_285_cast")]; + tensor out_285_cast = mul(x = zero_mean_285_cast, y = denom_285_cast)[name = tensor("out_285_cast")]; + tensor var_9363_to_fp16 = const()[name = tensor("op_9363_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3610099776)))]; + tensor var_9364_cast = add(x = out_285_cast, y = var_9363_to_fp16)[name = tensor("op_9364_cast")]; + tensor var_9366_to_fp16 = const()[name = tensor("op_9366_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3610102400)))]; + tensor hidden_states_381_cast = mul(x = var_9364_cast, y = var_9366_to_fp16)[name = tensor("hidden_states_381_cast")]; + tensor var_9373 = const()[name = tensor("op_9373"), val = tensor([1, 1])]; + tensor var_9375 = const()[name = tensor("op_9375"), val = tensor([1, 1])]; tensor q_191_pad_type_0 = const()[name = tensor("q_191_pad_type_0"), val = tensor("custom")]; tensor q_191_pad_0 = const()[name = tensor("q_191_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_191 = conv(dilations = var_9474, groups = var_6872, pad = q_191_pad_0, pad_type = q_191_pad_type_0, strides = var_9472, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight, x = hidden_states_381)[name = tensor("q_191")]; - tensor var_9478 = const()[name = tensor("op_9478"), val = tensor([1, 1])]; - tensor var_9480 = const()[name = tensor("op_9480"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3610105024)))]; + tensor q_191_cast = conv(dilations = var_9375, groups = var_31, pad = q_191_pad_0, pad_type = q_191_pad_type_0, strides = var_9373, weight = unet_up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_381_cast)[name = tensor("q_191_cast")]; + tensor var_9379 = const()[name = tensor("op_9379"), val = tensor([1, 1])]; + tensor var_9381 = const()[name = tensor("op_9381"), val = tensor([1, 1])]; tensor k_191_pad_type_0 = const()[name = tensor("k_191_pad_type_0"), val = tensor("custom")]; tensor k_191_pad_0 = const()[name = tensor("k_191_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_191 = conv(dilations = var_9480, groups = var_6872, pad = k_191_pad_0, pad_type = k_191_pad_type_0, strides = var_9478, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_191")]; - tensor var_9484 = const()[name = tensor("op_9484"), val = tensor([1, 1])]; - tensor var_9486 = const()[name = tensor("op_9486"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3613381888)))]; + tensor k_191_cast = conv(dilations = var_9381, groups = var_31, pad = k_191_pad_0, pad_type = k_191_pad_type_0, strides = var_9379, weight = unet_up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_191_cast")]; + tensor var_9385 = const()[name = tensor("op_9385"), val = tensor([1, 1])]; + tensor var_9387 = const()[name = tensor("op_9387"), val = tensor([1, 1])]; tensor v_191_pad_type_0 = const()[name = tensor("v_191_pad_type_0"), val = tensor("custom")]; tensor v_191_pad_0 = const()[name = tensor("v_191_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_191 = conv(dilations = var_9486, groups = var_6872, pad = v_191_pad_0, pad_type = v_191_pad_type_0, strides = var_9484, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_191")]; - tensor var_9490 = const()[name = tensor("op_9490"), val = tensor([2, 20, 64, -1])]; - tensor var_9491 = reshape(shape = var_9490, x = q_191)[name = tensor("op_9491")]; - tensor var_9492 = const()[name = tensor("op_9492"), val = tensor([2, 20, 64, -1])]; - tensor var_9493 = reshape(shape = var_9492, x = k_191)[name = tensor("op_9493")]; - tensor var_9494 = const()[name = tensor("op_9494"), val = tensor([2, 20, 64, -1])]; - tensor var_9495 = reshape(shape = var_9494, x = v_191)[name = tensor("op_9495")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3618624832)))]; + tensor v_191_cast = conv(dilations = var_9387, groups = var_31, pad = v_191_pad_0, pad_type = v_191_pad_type_0, strides = var_9385, weight = unet_up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_191_cast")]; + tensor var_9391 = const()[name = tensor("op_9391"), val = tensor([2, 20, 64, -1])]; + tensor var_9392_cast = reshape(shape = var_9391, x = q_191_cast)[name = tensor("op_9392_cast")]; + tensor var_9393 = const()[name = tensor("op_9393"), val = tensor([2, 20, 64, -1])]; + tensor var_9394_cast = reshape(shape = var_9393, x = k_191_cast)[name = tensor("op_9394_cast")]; + tensor var_9395 = const()[name = tensor("op_9395"), val = tensor([2, 20, 64, -1])]; + tensor var_9396_cast = reshape(shape = var_9395, x = v_191_cast)[name = tensor("op_9396_cast")]; tensor attn_weights_381_transpose_x_0 = const()[name = tensor("attn_weights_381_transpose_x_0"), val = tensor(true)]; tensor attn_weights_381_transpose_y_0 = const()[name = tensor("attn_weights_381_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_381 = matmul(transpose_x = attn_weights_381_transpose_x_0, transpose_y = attn_weights_381_transpose_y_0, x = var_9491, y = var_9493)[name = tensor("attn_weights_381")]; - tensor attn_weights_383 = mul(x = attn_weights_381, y = var_6863)[name = tensor("attn_weights_383")]; - tensor var_9499 = softmax(axis = var_6856, x = attn_weights_383)[name = tensor("op_9499")]; + tensor attn_weights_381_cast = matmul(transpose_x = attn_weights_381_transpose_x_0, transpose_y = attn_weights_381_transpose_y_0, x = var_9392_cast, y = var_9394_cast)[name = tensor("attn_weights_381_cast")]; + tensor attn_weights_383_cast = mul(x = attn_weights_381_cast, y = var_12_to_fp16)[name = tensor("attn_weights_383_cast")]; + tensor var_9400_cast = softmax(axis = var_18, x = attn_weights_383_cast)[name = tensor("op_9400_cast")]; tensor attn_191_transpose_x_0 = const()[name = tensor("attn_191_transpose_x_0"), val = tensor(false)]; tensor attn_191_transpose_y_0 = const()[name = tensor("attn_191_transpose_y_0"), val = tensor(true)]; - tensor attn_191 = matmul(transpose_x = attn_191_transpose_x_0, transpose_y = attn_191_transpose_y_0, x = var_9495, y = var_9499)[name = tensor("attn_191")]; - tensor var_9503 = const()[name = tensor("op_9503"), val = tensor([2, 1280, 1, -1])]; - tensor input_563 = reshape(shape = var_9503, x = attn_191)[name = tensor("input_563")]; - tensor var_9508 = const()[name = tensor("op_9508"), val = tensor([1, 1])]; - tensor var_9510 = const()[name = tensor("op_9510"), val = tensor([1, 1])]; - tensor var_9512_pad_type_0 = const()[name = tensor("op_9512_pad_type_0"), val = tensor("custom")]; - tensor var_9512_pad_0 = const()[name = tensor("op_9512_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9512 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias, dilations = var_9510, groups = var_6872, pad = var_9512_pad_0, pad_type = var_9512_pad_type_0, strides = var_9508, weight = up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight, x = input_563)[name = tensor("op_9512")]; - tensor inputs_287 = add(x = var_9512, y = inputs_285)[name = tensor("inputs_287")]; - tensor var_9516 = const()[name = tensor("op_9516"), val = tensor([1])]; - tensor channels_mean_287 = reduce_mean(axes = var_9516, keep_dims = var_6867, x = inputs_287)[name = tensor("channels_mean_287")]; - tensor zero_mean_287 = sub(x = inputs_287, y = channels_mean_287)[name = tensor("zero_mean_287")]; - tensor zero_mean_sq_287 = mul(x = zero_mean_287, y = zero_mean_287)[name = tensor("zero_mean_sq_287")]; - tensor var_9520 = const()[name = tensor("op_9520"), val = tensor([1])]; - tensor var_9521 = reduce_mean(axes = var_9520, keep_dims = var_6867, x = zero_mean_sq_287)[name = tensor("op_9521")]; - tensor var_9522 = const()[name = tensor("op_9522"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9523 = add(x = var_9521, y = var_9522)[name = tensor("op_9523")]; - tensor denom_287_epsilon_0 = const()[name = tensor("denom_287_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_287 = rsqrt(epsilon = denom_287_epsilon_0, x = var_9523)[name = tensor("denom_287")]; - tensor out_287 = mul(x = zero_mean_287, y = denom_287)[name = tensor("out_287")]; - tensor var_9527 = const()[name = tensor("op_9527"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269269504)))]; - tensor var_9528 = add(x = out_287, y = var_9527)[name = tensor("op_9528")]; - tensor var_9530 = const()[name = tensor("op_9530"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269274688)))]; - tensor input_565 = mul(x = var_9528, y = var_9530)[name = tensor("input_565")]; - tensor var_9538 = const()[name = tensor("op_9538"), val = tensor([1, 1])]; - tensor var_9540 = const()[name = tensor("op_9540"), val = tensor([1, 1])]; - tensor var_9542_pad_type_0 = const()[name = tensor("op_9542_pad_type_0"), val = tensor("custom")]; - tensor var_9542_pad_0 = const()[name = tensor("op_9542_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9542 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias, dilations = var_9540, groups = var_6872, pad = var_9542_pad_0, pad_type = var_9542_pad_type_0, strides = var_9538, weight = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight, x = input_565)[name = tensor("op_9542")]; - tensor var_9543_split_sizes_0 = const()[name = tensor("op_9543_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_9543_axis_0 = const()[name = tensor("op_9543_axis_0"), val = tensor(1)]; - tensor var_9543_0, tensor var_9543_1 = split(axis = var_9543_axis_0, split_sizes = var_9543_split_sizes_0, x = var_9542)[name = tensor("op_9543")]; - tensor var_9545_mode_0 = const()[name = tensor("op_9545_mode_0"), val = tensor("EXACT")]; - tensor var_9545 = gelu(mode = var_9545_mode_0, x = var_9543_1)[name = tensor("op_9545")]; - tensor input_567 = mul(x = var_9543_0, y = var_9545)[name = tensor("input_567")]; - tensor var_9549 = const()[name = tensor("op_9549"), val = tensor([1, 1])]; - tensor var_9551 = const()[name = tensor("op_9551"), val = tensor([1, 1])]; - tensor var_9553_pad_type_0 = const()[name = tensor("op_9553_pad_type_0"), val = tensor("custom")]; - tensor var_9553_pad_0 = const()[name = tensor("op_9553_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9553 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias, dilations = var_9551, groups = var_6872, pad = var_9553_pad_0, pad_type = var_9553_pad_type_0, strides = var_9549, weight = up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight, x = input_567)[name = tensor("op_9553")]; - tensor inputs_289 = add(x = var_9553, y = inputs_287)[name = tensor("inputs_289")]; - tensor var_9563 = const()[name = tensor("op_9563"), val = tensor([1])]; - tensor channels_mean_289 = reduce_mean(axes = var_9563, keep_dims = var_6867, x = inputs_289)[name = tensor("channels_mean_289")]; - tensor zero_mean_289 = sub(x = inputs_289, y = channels_mean_289)[name = tensor("zero_mean_289")]; - tensor zero_mean_sq_289 = mul(x = zero_mean_289, y = zero_mean_289)[name = tensor("zero_mean_sq_289")]; - tensor var_9567 = const()[name = tensor("op_9567"), val = tensor([1])]; - tensor var_9568 = reduce_mean(axes = var_9567, keep_dims = var_6867, x = zero_mean_sq_289)[name = tensor("op_9568")]; - tensor var_9569 = const()[name = tensor("op_9569"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9570 = add(x = var_9568, y = var_9569)[name = tensor("op_9570")]; - tensor denom_289_epsilon_0 = const()[name = tensor("denom_289_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_289 = rsqrt(epsilon = denom_289_epsilon_0, x = var_9570)[name = tensor("denom_289")]; - tensor out_289 = mul(x = zero_mean_289, y = denom_289)[name = tensor("out_289")]; - tensor var_9574 = const()[name = tensor("op_9574"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269279872)))]; - tensor var_9575 = add(x = out_289, y = var_9574)[name = tensor("op_9575")]; - tensor var_9577 = const()[name = tensor("op_9577"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269285056)))]; - tensor hidden_states_385 = mul(x = var_9575, y = var_9577)[name = tensor("hidden_states_385")]; - tensor var_9584 = const()[name = tensor("op_9584"), val = tensor([1, 1])]; - tensor var_9586 = const()[name = tensor("op_9586"), val = tensor([1, 1])]; + tensor attn_191_cast = matmul(transpose_x = attn_191_transpose_x_0, transpose_y = attn_191_transpose_y_0, x = var_9396_cast, y = var_9400_cast)[name = tensor("attn_191_cast")]; + tensor var_9404 = const()[name = tensor("op_9404"), val = tensor([2, 1280, 1, -1])]; + tensor input_563_cast = reshape(shape = var_9404, x = attn_191_cast)[name = tensor("input_563_cast")]; + tensor var_9409 = const()[name = tensor("op_9409"), val = tensor([1, 1])]; + tensor var_9411 = const()[name = tensor("op_9411"), val = tensor([1, 1])]; + tensor var_9413_pad_type_0 = const()[name = tensor("op_9413_pad_type_0"), val = tensor("custom")]; + tensor var_9413_pad_0 = const()[name = tensor("op_9413_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3623867776)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3627144640)))]; + tensor var_9413_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_9411, groups = var_31, pad = var_9413_pad_0, pad_type = var_9413_pad_type_0, strides = var_9409, weight = unet_up_blocks_0_attentions_1_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_563_cast)[name = tensor("op_9413_cast")]; + tensor inputs_287_cast = add(x = var_9413_cast, y = inputs_285_cast)[name = tensor("inputs_287_cast")]; + tensor var_9417 = const()[name = tensor("op_9417"), val = tensor([1])]; + tensor channels_mean_287_cast = reduce_mean(axes = var_9417, keep_dims = var_23, x = inputs_287_cast)[name = tensor("channels_mean_287_cast")]; + tensor zero_mean_287_cast = sub(x = inputs_287_cast, y = channels_mean_287_cast)[name = tensor("zero_mean_287_cast")]; + tensor zero_mean_sq_287_cast = mul(x = zero_mean_287_cast, y = zero_mean_287_cast)[name = tensor("zero_mean_sq_287_cast")]; + tensor var_9421 = const()[name = tensor("op_9421"), val = tensor([1])]; + tensor var_9422_cast = reduce_mean(axes = var_9421, keep_dims = var_23, x = zero_mean_sq_287_cast)[name = tensor("op_9422_cast")]; + tensor var_9423_to_fp16 = const()[name = tensor("op_9423_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9424_cast = add(x = var_9422_cast, y = var_9423_to_fp16)[name = tensor("op_9424_cast")]; + tensor denom_287_epsilon_0_to_fp16 = const()[name = tensor("denom_287_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_287_cast = rsqrt(epsilon = denom_287_epsilon_0_to_fp16, x = var_9424_cast)[name = tensor("denom_287_cast")]; + tensor out_287_cast = mul(x = zero_mean_287_cast, y = denom_287_cast)[name = tensor("out_287_cast")]; + tensor var_9428_to_fp16 = const()[name = tensor("op_9428_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3627147264)))]; + tensor var_9429_cast = add(x = out_287_cast, y = var_9428_to_fp16)[name = tensor("op_9429_cast")]; + tensor var_9431_to_fp16 = const()[name = tensor("op_9431_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3627149888)))]; + tensor input_565_cast = mul(x = var_9429_cast, y = var_9431_to_fp16)[name = tensor("input_565_cast")]; + tensor var_9439 = const()[name = tensor("op_9439"), val = tensor([1, 1])]; + tensor var_9441 = const()[name = tensor("op_9441"), val = tensor([1, 1])]; + tensor var_9443_pad_type_0 = const()[name = tensor("op_9443_pad_type_0"), val = tensor("custom")]; + tensor var_9443_pad_0 = const()[name = tensor("op_9443_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3627152512)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3653366976)))]; + tensor var_9443_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_9441, groups = var_31, pad = var_9443_pad_0, pad_type = var_9443_pad_type_0, strides = var_9439, weight = unet_up_blocks_0_attentions_1_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_565_cast)[name = tensor("op_9443_cast")]; + tensor var_9444_split_sizes_0 = const()[name = tensor("op_9444_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9444_axis_0 = const()[name = tensor("op_9444_axis_0"), val = tensor(1)]; + tensor var_9444_cast_0, tensor var_9444_cast_1 = split(axis = var_9444_axis_0, split_sizes = var_9444_split_sizes_0, x = var_9443_cast)[name = tensor("op_9444_cast")]; + tensor var_9446_mode_0 = const()[name = tensor("op_9446_mode_0"), val = tensor("EXACT")]; + tensor var_9446_cast = gelu(mode = var_9446_mode_0, x = var_9444_cast_1)[name = tensor("op_9446_cast")]; + tensor input_567_cast = mul(x = var_9444_cast_0, y = var_9446_cast)[name = tensor("input_567_cast")]; + tensor var_9450 = const()[name = tensor("op_9450"), val = tensor([1, 1])]; + tensor var_9452 = const()[name = tensor("op_9452"), val = tensor([1, 1])]; + tensor var_9454_pad_type_0 = const()[name = tensor("op_9454_pad_type_0"), val = tensor("custom")]; + tensor var_9454_pad_0 = const()[name = tensor("op_9454_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3653387520)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3666494784)))]; + tensor var_9454_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_9452, groups = var_31, pad = var_9454_pad_0, pad_type = var_9454_pad_type_0, strides = var_9450, weight = unet_up_blocks_0_attentions_1_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_567_cast)[name = tensor("op_9454_cast")]; + tensor inputs_289_cast = add(x = var_9454_cast, y = inputs_287_cast)[name = tensor("inputs_289_cast")]; + tensor var_9464 = const()[name = tensor("op_9464"), val = tensor([1])]; + tensor channels_mean_289_cast = reduce_mean(axes = var_9464, keep_dims = var_23, x = inputs_289_cast)[name = tensor("channels_mean_289_cast")]; + tensor zero_mean_289_cast = sub(x = inputs_289_cast, y = channels_mean_289_cast)[name = tensor("zero_mean_289_cast")]; + tensor zero_mean_sq_289_cast = mul(x = zero_mean_289_cast, y = zero_mean_289_cast)[name = tensor("zero_mean_sq_289_cast")]; + tensor var_9468 = const()[name = tensor("op_9468"), val = tensor([1])]; + tensor var_9469_cast = reduce_mean(axes = var_9468, keep_dims = var_23, x = zero_mean_sq_289_cast)[name = tensor("op_9469_cast")]; + tensor var_9470_to_fp16 = const()[name = tensor("op_9470_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9471_cast = add(x = var_9469_cast, y = var_9470_to_fp16)[name = tensor("op_9471_cast")]; + tensor denom_289_epsilon_0_to_fp16 = const()[name = tensor("denom_289_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_289_cast = rsqrt(epsilon = denom_289_epsilon_0_to_fp16, x = var_9471_cast)[name = tensor("denom_289_cast")]; + tensor out_289_cast = mul(x = zero_mean_289_cast, y = denom_289_cast)[name = tensor("out_289_cast")]; + tensor var_9475_to_fp16 = const()[name = tensor("op_9475_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3666497408)))]; + tensor var_9476_cast = add(x = out_289_cast, y = var_9475_to_fp16)[name = tensor("op_9476_cast")]; + tensor var_9478_to_fp16 = const()[name = tensor("op_9478_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3666500032)))]; + tensor hidden_states_385_cast = mul(x = var_9476_cast, y = var_9478_to_fp16)[name = tensor("hidden_states_385_cast")]; + tensor var_9485 = const()[name = tensor("op_9485"), val = tensor([1, 1])]; + tensor var_9487 = const()[name = tensor("op_9487"), val = tensor([1, 1])]; tensor q_193_pad_type_0 = const()[name = tensor("q_193_pad_type_0"), val = tensor("custom")]; tensor q_193_pad_0 = const()[name = tensor("q_193_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_193 = conv(dilations = var_9586, groups = var_6872, pad = q_193_pad_0, pad_type = q_193_pad_type_0, strides = var_9584, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight, x = hidden_states_385)[name = tensor("q_193")]; - tensor var_9590 = const()[name = tensor("op_9590"), val = tensor([1, 1])]; - tensor var_9592 = const()[name = tensor("op_9592"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3666502656)))]; + tensor q_193_cast = conv(dilations = var_9487, groups = var_31, pad = q_193_pad_0, pad_type = q_193_pad_type_0, strides = var_9485, weight = unet_up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_q_weight_to_fp16, x = hidden_states_385_cast)[name = tensor("q_193_cast")]; + tensor var_9491 = const()[name = tensor("op_9491"), val = tensor([1, 1])]; + tensor var_9493 = const()[name = tensor("op_9493"), val = tensor([1, 1])]; tensor k_193_pad_type_0 = const()[name = tensor("k_193_pad_type_0"), val = tensor("custom")]; tensor k_193_pad_0 = const()[name = tensor("k_193_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_193 = conv(dilations = var_9592, groups = var_6872, pad = k_193_pad_0, pad_type = k_193_pad_type_0, strides = var_9590, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight, x = hidden_states_385)[name = tensor("k_193")]; - tensor var_9596 = const()[name = tensor("op_9596"), val = tensor([1, 1])]; - tensor var_9598 = const()[name = tensor("op_9598"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3669779520)))]; + tensor k_193_cast = conv(dilations = var_9493, groups = var_31, pad = k_193_pad_0, pad_type = k_193_pad_type_0, strides = var_9491, weight = unet_up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_k_weight_to_fp16, x = hidden_states_385_cast)[name = tensor("k_193_cast")]; + tensor var_9497 = const()[name = tensor("op_9497"), val = tensor([1, 1])]; + tensor var_9499 = const()[name = tensor("op_9499"), val = tensor([1, 1])]; tensor v_193_pad_type_0 = const()[name = tensor("v_193_pad_type_0"), val = tensor("custom")]; tensor v_193_pad_0 = const()[name = tensor("v_193_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_193 = conv(dilations = var_9598, groups = var_6872, pad = v_193_pad_0, pad_type = v_193_pad_type_0, strides = var_9596, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight, x = hidden_states_385)[name = tensor("v_193")]; - tensor var_9602 = const()[name = tensor("op_9602"), val = tensor([2, 20, 64, -1])]; - tensor var_9603 = reshape(shape = var_9602, x = q_193)[name = tensor("op_9603")]; - tensor var_9604 = const()[name = tensor("op_9604"), val = tensor([2, 20, 64, -1])]; - tensor var_9605 = reshape(shape = var_9604, x = k_193)[name = tensor("op_9605")]; - tensor var_9606 = const()[name = tensor("op_9606"), val = tensor([2, 20, 64, -1])]; - tensor var_9607 = reshape(shape = var_9606, x = v_193)[name = tensor("op_9607")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3673056384)))]; + tensor v_193_cast = conv(dilations = var_9499, groups = var_31, pad = v_193_pad_0, pad_type = v_193_pad_type_0, strides = var_9497, weight = unet_up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_v_weight_to_fp16, x = hidden_states_385_cast)[name = tensor("v_193_cast")]; + tensor var_9503 = const()[name = tensor("op_9503"), val = tensor([2, 20, 64, -1])]; + tensor var_9504_cast = reshape(shape = var_9503, x = q_193_cast)[name = tensor("op_9504_cast")]; + tensor var_9505 = const()[name = tensor("op_9505"), val = tensor([2, 20, 64, -1])]; + tensor var_9506_cast = reshape(shape = var_9505, x = k_193_cast)[name = tensor("op_9506_cast")]; + tensor var_9507 = const()[name = tensor("op_9507"), val = tensor([2, 20, 64, -1])]; + tensor var_9508_cast = reshape(shape = var_9507, x = v_193_cast)[name = tensor("op_9508_cast")]; tensor attn_weights_385_transpose_x_0 = const()[name = tensor("attn_weights_385_transpose_x_0"), val = tensor(true)]; tensor attn_weights_385_transpose_y_0 = const()[name = tensor("attn_weights_385_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_385 = matmul(transpose_x = attn_weights_385_transpose_x_0, transpose_y = attn_weights_385_transpose_y_0, x = var_9603, y = var_9605)[name = tensor("attn_weights_385")]; - tensor attn_weights_387 = mul(x = attn_weights_385, y = var_6863)[name = tensor("attn_weights_387")]; - tensor var_9611 = softmax(axis = var_6856, x = attn_weights_387)[name = tensor("op_9611")]; + tensor attn_weights_385_cast = matmul(transpose_x = attn_weights_385_transpose_x_0, transpose_y = attn_weights_385_transpose_y_0, x = var_9504_cast, y = var_9506_cast)[name = tensor("attn_weights_385_cast")]; + tensor attn_weights_387_cast = mul(x = attn_weights_385_cast, y = var_12_to_fp16)[name = tensor("attn_weights_387_cast")]; + tensor var_9512_cast = softmax(axis = var_18, x = attn_weights_387_cast)[name = tensor("op_9512_cast")]; tensor attn_193_transpose_x_0 = const()[name = tensor("attn_193_transpose_x_0"), val = tensor(false)]; tensor attn_193_transpose_y_0 = const()[name = tensor("attn_193_transpose_y_0"), val = tensor(true)]; - tensor attn_193 = matmul(transpose_x = attn_193_transpose_x_0, transpose_y = attn_193_transpose_y_0, x = var_9607, y = var_9611)[name = tensor("attn_193")]; - tensor var_9615 = const()[name = tensor("op_9615"), val = tensor([2, 1280, 1, -1])]; - tensor input_569 = reshape(shape = var_9615, x = attn_193)[name = tensor("input_569")]; - tensor var_9620 = const()[name = tensor("op_9620"), val = tensor([1, 1])]; - tensor var_9622 = const()[name = tensor("op_9622"), val = tensor([1, 1])]; - tensor var_9624_pad_type_0 = const()[name = tensor("op_9624_pad_type_0"), val = tensor("custom")]; - tensor var_9624_pad_0 = const()[name = tensor("op_9624_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9624 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias, dilations = var_9622, groups = var_6872, pad = var_9624_pad_0, pad_type = var_9624_pad_type_0, strides = var_9620, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight, x = input_569)[name = tensor("op_9624")]; - tensor inputs_291 = add(x = var_9624, y = inputs_289)[name = tensor("inputs_291")]; - tensor var_9628 = const()[name = tensor("op_9628"), val = tensor([1])]; - tensor channels_mean_291 = reduce_mean(axes = var_9628, keep_dims = var_6867, x = inputs_291)[name = tensor("channels_mean_291")]; - tensor zero_mean_291 = sub(x = inputs_291, y = channels_mean_291)[name = tensor("zero_mean_291")]; - tensor zero_mean_sq_291 = mul(x = zero_mean_291, y = zero_mean_291)[name = tensor("zero_mean_sq_291")]; - tensor var_9632 = const()[name = tensor("op_9632"), val = tensor([1])]; - tensor var_9633 = reduce_mean(axes = var_9632, keep_dims = var_6867, x = zero_mean_sq_291)[name = tensor("op_9633")]; - tensor var_9634 = const()[name = tensor("op_9634"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9635 = add(x = var_9633, y = var_9634)[name = tensor("op_9635")]; - tensor denom_291_epsilon_0 = const()[name = tensor("denom_291_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_291 = rsqrt(epsilon = denom_291_epsilon_0, x = var_9635)[name = tensor("denom_291")]; - tensor out_291 = mul(x = zero_mean_291, y = denom_291)[name = tensor("out_291")]; - tensor var_9639 = const()[name = tensor("op_9639"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269290240)))]; - tensor var_9640 = add(x = out_291, y = var_9639)[name = tensor("op_9640")]; - tensor var_9642 = const()[name = tensor("op_9642"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269295424)))]; - tensor hidden_states_387 = mul(x = var_9640, y = var_9642)[name = tensor("hidden_states_387")]; - tensor var_9649 = const()[name = tensor("op_9649"), val = tensor([1, 1])]; - tensor var_9651 = const()[name = tensor("op_9651"), val = tensor([1, 1])]; + tensor attn_193_cast = matmul(transpose_x = attn_193_transpose_x_0, transpose_y = attn_193_transpose_y_0, x = var_9508_cast, y = var_9512_cast)[name = tensor("attn_193_cast")]; + tensor var_9516 = const()[name = tensor("op_9516"), val = tensor([2, 1280, 1, -1])]; + tensor input_569_cast = reshape(shape = var_9516, x = attn_193_cast)[name = tensor("input_569_cast")]; + tensor var_9521 = const()[name = tensor("op_9521"), val = tensor([1, 1])]; + tensor var_9523 = const()[name = tensor("op_9523"), val = tensor([1, 1])]; + tensor var_9525_pad_type_0 = const()[name = tensor("op_9525_pad_type_0"), val = tensor("custom")]; + tensor var_9525_pad_0 = const()[name = tensor("op_9525_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3676333248)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3679610112)))]; + tensor var_9525_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_9523, groups = var_31, pad = var_9525_pad_0, pad_type = var_9525_pad_type_0, strides = var_9521, weight = unet_up_blocks_0_attentions_1_transformer_blocks_4_attn1_to_out_0_weight_to_fp16, x = input_569_cast)[name = tensor("op_9525_cast")]; + tensor inputs_291_cast = add(x = var_9525_cast, y = inputs_289_cast)[name = tensor("inputs_291_cast")]; + tensor var_9529 = const()[name = tensor("op_9529"), val = tensor([1])]; + tensor channels_mean_291_cast = reduce_mean(axes = var_9529, keep_dims = var_23, x = inputs_291_cast)[name = tensor("channels_mean_291_cast")]; + tensor zero_mean_291_cast = sub(x = inputs_291_cast, y = channels_mean_291_cast)[name = tensor("zero_mean_291_cast")]; + tensor zero_mean_sq_291_cast = mul(x = zero_mean_291_cast, y = zero_mean_291_cast)[name = tensor("zero_mean_sq_291_cast")]; + tensor var_9533 = const()[name = tensor("op_9533"), val = tensor([1])]; + tensor var_9534_cast = reduce_mean(axes = var_9533, keep_dims = var_23, x = zero_mean_sq_291_cast)[name = tensor("op_9534_cast")]; + tensor var_9535_to_fp16 = const()[name = tensor("op_9535_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9536_cast = add(x = var_9534_cast, y = var_9535_to_fp16)[name = tensor("op_9536_cast")]; + tensor denom_291_epsilon_0_to_fp16 = const()[name = tensor("denom_291_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_291_cast = rsqrt(epsilon = denom_291_epsilon_0_to_fp16, x = var_9536_cast)[name = tensor("denom_291_cast")]; + tensor out_291_cast = mul(x = zero_mean_291_cast, y = denom_291_cast)[name = tensor("out_291_cast")]; + tensor var_9540_to_fp16 = const()[name = tensor("op_9540_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3679612736)))]; + tensor var_9541_cast = add(x = out_291_cast, y = var_9540_to_fp16)[name = tensor("op_9541_cast")]; + tensor var_9543_to_fp16 = const()[name = tensor("op_9543_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3679615360)))]; + tensor hidden_states_387_cast = mul(x = var_9541_cast, y = var_9543_to_fp16)[name = tensor("hidden_states_387_cast")]; + tensor var_9550 = const()[name = tensor("op_9550"), val = tensor([1, 1])]; + tensor var_9552 = const()[name = tensor("op_9552"), val = tensor([1, 1])]; tensor q_195_pad_type_0 = const()[name = tensor("q_195_pad_type_0"), val = tensor("custom")]; tensor q_195_pad_0 = const()[name = tensor("q_195_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_195 = conv(dilations = var_9651, groups = var_6872, pad = q_195_pad_0, pad_type = q_195_pad_type_0, strides = var_9649, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight, x = hidden_states_387)[name = tensor("q_195")]; - tensor var_9655 = const()[name = tensor("op_9655"), val = tensor([1, 1])]; - tensor var_9657 = const()[name = tensor("op_9657"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3679617984)))]; + tensor q_195_cast = conv(dilations = var_9552, groups = var_31, pad = q_195_pad_0, pad_type = q_195_pad_type_0, strides = var_9550, weight = unet_up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_q_weight_to_fp16, x = hidden_states_387_cast)[name = tensor("q_195_cast")]; + tensor var_9556 = const()[name = tensor("op_9556"), val = tensor([1, 1])]; + tensor var_9558 = const()[name = tensor("op_9558"), val = tensor([1, 1])]; tensor k_195_pad_type_0 = const()[name = tensor("k_195_pad_type_0"), val = tensor("custom")]; tensor k_195_pad_0 = const()[name = tensor("k_195_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_195 = conv(dilations = var_9657, groups = var_6872, pad = k_195_pad_0, pad_type = k_195_pad_type_0, strides = var_9655, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_195")]; - tensor var_9661 = const()[name = tensor("op_9661"), val = tensor([1, 1])]; - tensor var_9663 = const()[name = tensor("op_9663"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3682894848)))]; + tensor k_195_cast = conv(dilations = var_9558, groups = var_31, pad = k_195_pad_0, pad_type = k_195_pad_type_0, strides = var_9556, weight = unet_up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_195_cast")]; + tensor var_9562 = const()[name = tensor("op_9562"), val = tensor([1, 1])]; + tensor var_9564 = const()[name = tensor("op_9564"), val = tensor([1, 1])]; tensor v_195_pad_type_0 = const()[name = tensor("v_195_pad_type_0"), val = tensor("custom")]; tensor v_195_pad_0 = const()[name = tensor("v_195_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_195 = conv(dilations = var_9663, groups = var_6872, pad = v_195_pad_0, pad_type = v_195_pad_type_0, strides = var_9661, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_195")]; - tensor var_9667 = const()[name = tensor("op_9667"), val = tensor([2, 20, 64, -1])]; - tensor var_9668 = reshape(shape = var_9667, x = q_195)[name = tensor("op_9668")]; - tensor var_9669 = const()[name = tensor("op_9669"), val = tensor([2, 20, 64, -1])]; - tensor var_9670 = reshape(shape = var_9669, x = k_195)[name = tensor("op_9670")]; - tensor var_9671 = const()[name = tensor("op_9671"), val = tensor([2, 20, 64, -1])]; - tensor var_9672 = reshape(shape = var_9671, x = v_195)[name = tensor("op_9672")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3688137792)))]; + tensor v_195_cast = conv(dilations = var_9564, groups = var_31, pad = v_195_pad_0, pad_type = v_195_pad_type_0, strides = var_9562, weight = unet_up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_195_cast")]; + tensor var_9568 = const()[name = tensor("op_9568"), val = tensor([2, 20, 64, -1])]; + tensor var_9569_cast = reshape(shape = var_9568, x = q_195_cast)[name = tensor("op_9569_cast")]; + tensor var_9570 = const()[name = tensor("op_9570"), val = tensor([2, 20, 64, -1])]; + tensor var_9571_cast = reshape(shape = var_9570, x = k_195_cast)[name = tensor("op_9571_cast")]; + tensor var_9572 = const()[name = tensor("op_9572"), val = tensor([2, 20, 64, -1])]; + tensor var_9573_cast = reshape(shape = var_9572, x = v_195_cast)[name = tensor("op_9573_cast")]; tensor attn_weights_389_transpose_x_0 = const()[name = tensor("attn_weights_389_transpose_x_0"), val = tensor(true)]; tensor attn_weights_389_transpose_y_0 = const()[name = tensor("attn_weights_389_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_389 = matmul(transpose_x = attn_weights_389_transpose_x_0, transpose_y = attn_weights_389_transpose_y_0, x = var_9668, y = var_9670)[name = tensor("attn_weights_389")]; - tensor attn_weights_391 = mul(x = attn_weights_389, y = var_6863)[name = tensor("attn_weights_391")]; - tensor var_9676 = softmax(axis = var_6856, x = attn_weights_391)[name = tensor("op_9676")]; + tensor attn_weights_389_cast = matmul(transpose_x = attn_weights_389_transpose_x_0, transpose_y = attn_weights_389_transpose_y_0, x = var_9569_cast, y = var_9571_cast)[name = tensor("attn_weights_389_cast")]; + tensor attn_weights_391_cast = mul(x = attn_weights_389_cast, y = var_12_to_fp16)[name = tensor("attn_weights_391_cast")]; + tensor var_9577_cast = softmax(axis = var_18, x = attn_weights_391_cast)[name = tensor("op_9577_cast")]; tensor attn_195_transpose_x_0 = const()[name = tensor("attn_195_transpose_x_0"), val = tensor(false)]; tensor attn_195_transpose_y_0 = const()[name = tensor("attn_195_transpose_y_0"), val = tensor(true)]; - tensor attn_195 = matmul(transpose_x = attn_195_transpose_x_0, transpose_y = attn_195_transpose_y_0, x = var_9672, y = var_9676)[name = tensor("attn_195")]; - tensor var_9680 = const()[name = tensor("op_9680"), val = tensor([2, 1280, 1, -1])]; - tensor input_571 = reshape(shape = var_9680, x = attn_195)[name = tensor("input_571")]; - tensor var_9685 = const()[name = tensor("op_9685"), val = tensor([1, 1])]; - tensor var_9687 = const()[name = tensor("op_9687"), val = tensor([1, 1])]; - tensor var_9689_pad_type_0 = const()[name = tensor("op_9689_pad_type_0"), val = tensor("custom")]; - tensor var_9689_pad_0 = const()[name = tensor("op_9689_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9689 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias, dilations = var_9687, groups = var_6872, pad = var_9689_pad_0, pad_type = var_9689_pad_type_0, strides = var_9685, weight = up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight, x = input_571)[name = tensor("op_9689")]; - tensor inputs_293 = add(x = var_9689, y = inputs_291)[name = tensor("inputs_293")]; - tensor var_9693 = const()[name = tensor("op_9693"), val = tensor([1])]; - tensor channels_mean_293 = reduce_mean(axes = var_9693, keep_dims = var_6867, x = inputs_293)[name = tensor("channels_mean_293")]; - tensor zero_mean_293 = sub(x = inputs_293, y = channels_mean_293)[name = tensor("zero_mean_293")]; - tensor zero_mean_sq_293 = mul(x = zero_mean_293, y = zero_mean_293)[name = tensor("zero_mean_sq_293")]; - tensor var_9697 = const()[name = tensor("op_9697"), val = tensor([1])]; - tensor var_9698 = reduce_mean(axes = var_9697, keep_dims = var_6867, x = zero_mean_sq_293)[name = tensor("op_9698")]; - tensor var_9699 = const()[name = tensor("op_9699"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9700 = add(x = var_9698, y = var_9699)[name = tensor("op_9700")]; - tensor denom_293_epsilon_0 = const()[name = tensor("denom_293_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_293 = rsqrt(epsilon = denom_293_epsilon_0, x = var_9700)[name = tensor("denom_293")]; - tensor out_293 = mul(x = zero_mean_293, y = denom_293)[name = tensor("out_293")]; - tensor var_9704 = const()[name = tensor("op_9704"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269300608)))]; - tensor var_9705 = add(x = out_293, y = var_9704)[name = tensor("op_9705")]; - tensor var_9707 = const()[name = tensor("op_9707"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269305792)))]; - tensor input_573 = mul(x = var_9705, y = var_9707)[name = tensor("input_573")]; - tensor var_9715 = const()[name = tensor("op_9715"), val = tensor([1, 1])]; - tensor var_9717 = const()[name = tensor("op_9717"), val = tensor([1, 1])]; - tensor var_9719_pad_type_0 = const()[name = tensor("op_9719_pad_type_0"), val = tensor("custom")]; - tensor var_9719_pad_0 = const()[name = tensor("op_9719_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9719 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias, dilations = var_9717, groups = var_6872, pad = var_9719_pad_0, pad_type = var_9719_pad_type_0, strides = var_9715, weight = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight, x = input_573)[name = tensor("op_9719")]; - tensor var_9720_split_sizes_0 = const()[name = tensor("op_9720_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_9720_axis_0 = const()[name = tensor("op_9720_axis_0"), val = tensor(1)]; - tensor var_9720_0, tensor var_9720_1 = split(axis = var_9720_axis_0, split_sizes = var_9720_split_sizes_0, x = var_9719)[name = tensor("op_9720")]; - tensor var_9722_mode_0 = const()[name = tensor("op_9722_mode_0"), val = tensor("EXACT")]; - tensor var_9722 = gelu(mode = var_9722_mode_0, x = var_9720_1)[name = tensor("op_9722")]; - tensor input_575 = mul(x = var_9720_0, y = var_9722)[name = tensor("input_575")]; - tensor var_9726 = const()[name = tensor("op_9726"), val = tensor([1, 1])]; - tensor var_9728 = const()[name = tensor("op_9728"), val = tensor([1, 1])]; - tensor var_9730_pad_type_0 = const()[name = tensor("op_9730_pad_type_0"), val = tensor("custom")]; - tensor var_9730_pad_0 = const()[name = tensor("op_9730_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9730 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias, dilations = var_9728, groups = var_6872, pad = var_9730_pad_0, pad_type = var_9730_pad_type_0, strides = var_9726, weight = up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight, x = input_575)[name = tensor("op_9730")]; - tensor inputs_295 = add(x = var_9730, y = inputs_293)[name = tensor("inputs_295")]; - tensor var_9740 = const()[name = tensor("op_9740"), val = tensor([1])]; - tensor channels_mean_295 = reduce_mean(axes = var_9740, keep_dims = var_6867, x = inputs_295)[name = tensor("channels_mean_295")]; - tensor zero_mean_295 = sub(x = inputs_295, y = channels_mean_295)[name = tensor("zero_mean_295")]; - tensor zero_mean_sq_295 = mul(x = zero_mean_295, y = zero_mean_295)[name = tensor("zero_mean_sq_295")]; - tensor var_9744 = const()[name = tensor("op_9744"), val = tensor([1])]; - tensor var_9745 = reduce_mean(axes = var_9744, keep_dims = var_6867, x = zero_mean_sq_295)[name = tensor("op_9745")]; - tensor var_9746 = const()[name = tensor("op_9746"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9747 = add(x = var_9745, y = var_9746)[name = tensor("op_9747")]; - tensor denom_295_epsilon_0 = const()[name = tensor("denom_295_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_295 = rsqrt(epsilon = denom_295_epsilon_0, x = var_9747)[name = tensor("denom_295")]; - tensor out_295 = mul(x = zero_mean_295, y = denom_295)[name = tensor("out_295")]; - tensor var_9751 = const()[name = tensor("op_9751"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269310976)))]; - tensor var_9752 = add(x = out_295, y = var_9751)[name = tensor("op_9752")]; - tensor var_9754 = const()[name = tensor("op_9754"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269316160)))]; - tensor hidden_states_391 = mul(x = var_9752, y = var_9754)[name = tensor("hidden_states_391")]; - tensor var_9761 = const()[name = tensor("op_9761"), val = tensor([1, 1])]; - tensor var_9763 = const()[name = tensor("op_9763"), val = tensor([1, 1])]; + tensor attn_195_cast = matmul(transpose_x = attn_195_transpose_x_0, transpose_y = attn_195_transpose_y_0, x = var_9573_cast, y = var_9577_cast)[name = tensor("attn_195_cast")]; + tensor var_9581 = const()[name = tensor("op_9581"), val = tensor([2, 1280, 1, -1])]; + tensor input_571_cast = reshape(shape = var_9581, x = attn_195_cast)[name = tensor("input_571_cast")]; + tensor var_9586 = const()[name = tensor("op_9586"), val = tensor([1, 1])]; + tensor var_9588 = const()[name = tensor("op_9588"), val = tensor([1, 1])]; + tensor var_9590_pad_type_0 = const()[name = tensor("op_9590_pad_type_0"), val = tensor("custom")]; + tensor var_9590_pad_0 = const()[name = tensor("op_9590_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3693380736)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3696657600)))]; + tensor var_9590_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_9588, groups = var_31, pad = var_9590_pad_0, pad_type = var_9590_pad_type_0, strides = var_9586, weight = unet_up_blocks_0_attentions_1_transformer_blocks_4_attn2_to_out_0_weight_to_fp16, x = input_571_cast)[name = tensor("op_9590_cast")]; + tensor inputs_293_cast = add(x = var_9590_cast, y = inputs_291_cast)[name = tensor("inputs_293_cast")]; + tensor var_9594 = const()[name = tensor("op_9594"), val = tensor([1])]; + tensor channels_mean_293_cast = reduce_mean(axes = var_9594, keep_dims = var_23, x = inputs_293_cast)[name = tensor("channels_mean_293_cast")]; + tensor zero_mean_293_cast = sub(x = inputs_293_cast, y = channels_mean_293_cast)[name = tensor("zero_mean_293_cast")]; + tensor zero_mean_sq_293_cast = mul(x = zero_mean_293_cast, y = zero_mean_293_cast)[name = tensor("zero_mean_sq_293_cast")]; + tensor var_9598 = const()[name = tensor("op_9598"), val = tensor([1])]; + tensor var_9599_cast = reduce_mean(axes = var_9598, keep_dims = var_23, x = zero_mean_sq_293_cast)[name = tensor("op_9599_cast")]; + tensor var_9600_to_fp16 = const()[name = tensor("op_9600_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9601_cast = add(x = var_9599_cast, y = var_9600_to_fp16)[name = tensor("op_9601_cast")]; + tensor denom_293_epsilon_0_to_fp16 = const()[name = tensor("denom_293_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_293_cast = rsqrt(epsilon = denom_293_epsilon_0_to_fp16, x = var_9601_cast)[name = tensor("denom_293_cast")]; + tensor out_293_cast = mul(x = zero_mean_293_cast, y = denom_293_cast)[name = tensor("out_293_cast")]; + tensor var_9605_to_fp16 = const()[name = tensor("op_9605_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3696660224)))]; + tensor var_9606_cast = add(x = out_293_cast, y = var_9605_to_fp16)[name = tensor("op_9606_cast")]; + tensor var_9608_to_fp16 = const()[name = tensor("op_9608_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3696662848)))]; + tensor input_573_cast = mul(x = var_9606_cast, y = var_9608_to_fp16)[name = tensor("input_573_cast")]; + tensor var_9616 = const()[name = tensor("op_9616"), val = tensor([1, 1])]; + tensor var_9618 = const()[name = tensor("op_9618"), val = tensor([1, 1])]; + tensor var_9620_pad_type_0 = const()[name = tensor("op_9620_pad_type_0"), val = tensor("custom")]; + tensor var_9620_pad_0 = const()[name = tensor("op_9620_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3696665472)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3722879936)))]; + tensor var_9620_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_9618, groups = var_31, pad = var_9620_pad_0, pad_type = var_9620_pad_type_0, strides = var_9616, weight = unet_up_blocks_0_attentions_1_transformer_blocks_4_ff_net_0_proj_weight_to_fp16, x = input_573_cast)[name = tensor("op_9620_cast")]; + tensor var_9621_split_sizes_0 = const()[name = tensor("op_9621_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9621_axis_0 = const()[name = tensor("op_9621_axis_0"), val = tensor(1)]; + tensor var_9621_cast_0, tensor var_9621_cast_1 = split(axis = var_9621_axis_0, split_sizes = var_9621_split_sizes_0, x = var_9620_cast)[name = tensor("op_9621_cast")]; + tensor var_9623_mode_0 = const()[name = tensor("op_9623_mode_0"), val = tensor("EXACT")]; + tensor var_9623_cast = gelu(mode = var_9623_mode_0, x = var_9621_cast_1)[name = tensor("op_9623_cast")]; + tensor input_575_cast = mul(x = var_9621_cast_0, y = var_9623_cast)[name = tensor("input_575_cast")]; + tensor var_9627 = const()[name = tensor("op_9627"), val = tensor([1, 1])]; + tensor var_9629 = const()[name = tensor("op_9629"), val = tensor([1, 1])]; + tensor var_9631_pad_type_0 = const()[name = tensor("op_9631_pad_type_0"), val = tensor("custom")]; + tensor var_9631_pad_0 = const()[name = tensor("op_9631_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3722900480)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3736007744)))]; + tensor var_9631_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_9629, groups = var_31, pad = var_9631_pad_0, pad_type = var_9631_pad_type_0, strides = var_9627, weight = unet_up_blocks_0_attentions_1_transformer_blocks_4_ff_net_2_weight_to_fp16, x = input_575_cast)[name = tensor("op_9631_cast")]; + tensor inputs_295_cast = add(x = var_9631_cast, y = inputs_293_cast)[name = tensor("inputs_295_cast")]; + tensor var_9641 = const()[name = tensor("op_9641"), val = tensor([1])]; + tensor channels_mean_295_cast = reduce_mean(axes = var_9641, keep_dims = var_23, x = inputs_295_cast)[name = tensor("channels_mean_295_cast")]; + tensor zero_mean_295_cast = sub(x = inputs_295_cast, y = channels_mean_295_cast)[name = tensor("zero_mean_295_cast")]; + tensor zero_mean_sq_295_cast = mul(x = zero_mean_295_cast, y = zero_mean_295_cast)[name = tensor("zero_mean_sq_295_cast")]; + tensor var_9645 = const()[name = tensor("op_9645"), val = tensor([1])]; + tensor var_9646_cast = reduce_mean(axes = var_9645, keep_dims = var_23, x = zero_mean_sq_295_cast)[name = tensor("op_9646_cast")]; + tensor var_9647_to_fp16 = const()[name = tensor("op_9647_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9648_cast = add(x = var_9646_cast, y = var_9647_to_fp16)[name = tensor("op_9648_cast")]; + tensor denom_295_epsilon_0_to_fp16 = const()[name = tensor("denom_295_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_295_cast = rsqrt(epsilon = denom_295_epsilon_0_to_fp16, x = var_9648_cast)[name = tensor("denom_295_cast")]; + tensor out_295_cast = mul(x = zero_mean_295_cast, y = denom_295_cast)[name = tensor("out_295_cast")]; + tensor var_9652_to_fp16 = const()[name = tensor("op_9652_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3736010368)))]; + tensor var_9653_cast = add(x = out_295_cast, y = var_9652_to_fp16)[name = tensor("op_9653_cast")]; + tensor var_9655_to_fp16 = const()[name = tensor("op_9655_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3736012992)))]; + tensor hidden_states_391_cast = mul(x = var_9653_cast, y = var_9655_to_fp16)[name = tensor("hidden_states_391_cast")]; + tensor var_9662 = const()[name = tensor("op_9662"), val = tensor([1, 1])]; + tensor var_9664 = const()[name = tensor("op_9664"), val = tensor([1, 1])]; tensor q_197_pad_type_0 = const()[name = tensor("q_197_pad_type_0"), val = tensor("custom")]; tensor q_197_pad_0 = const()[name = tensor("q_197_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_197 = conv(dilations = var_9763, groups = var_6872, pad = q_197_pad_0, pad_type = q_197_pad_type_0, strides = var_9761, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight, x = hidden_states_391)[name = tensor("q_197")]; - tensor var_9767 = const()[name = tensor("op_9767"), val = tensor([1, 1])]; - tensor var_9769 = const()[name = tensor("op_9769"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3736015616)))]; + tensor q_197_cast = conv(dilations = var_9664, groups = var_31, pad = q_197_pad_0, pad_type = q_197_pad_type_0, strides = var_9662, weight = unet_up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_q_weight_to_fp16, x = hidden_states_391_cast)[name = tensor("q_197_cast")]; + tensor var_9668 = const()[name = tensor("op_9668"), val = tensor([1, 1])]; + tensor var_9670 = const()[name = tensor("op_9670"), val = tensor([1, 1])]; tensor k_197_pad_type_0 = const()[name = tensor("k_197_pad_type_0"), val = tensor("custom")]; tensor k_197_pad_0 = const()[name = tensor("k_197_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_197 = conv(dilations = var_9769, groups = var_6872, pad = k_197_pad_0, pad_type = k_197_pad_type_0, strides = var_9767, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight, x = hidden_states_391)[name = tensor("k_197")]; - tensor var_9773 = const()[name = tensor("op_9773"), val = tensor([1, 1])]; - tensor var_9775 = const()[name = tensor("op_9775"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3739292480)))]; + tensor k_197_cast = conv(dilations = var_9670, groups = var_31, pad = k_197_pad_0, pad_type = k_197_pad_type_0, strides = var_9668, weight = unet_up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_k_weight_to_fp16, x = hidden_states_391_cast)[name = tensor("k_197_cast")]; + tensor var_9674 = const()[name = tensor("op_9674"), val = tensor([1, 1])]; + tensor var_9676 = const()[name = tensor("op_9676"), val = tensor([1, 1])]; tensor v_197_pad_type_0 = const()[name = tensor("v_197_pad_type_0"), val = tensor("custom")]; tensor v_197_pad_0 = const()[name = tensor("v_197_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_197 = conv(dilations = var_9775, groups = var_6872, pad = v_197_pad_0, pad_type = v_197_pad_type_0, strides = var_9773, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight, x = hidden_states_391)[name = tensor("v_197")]; - tensor var_9779 = const()[name = tensor("op_9779"), val = tensor([2, 20, 64, -1])]; - tensor var_9780 = reshape(shape = var_9779, x = q_197)[name = tensor("op_9780")]; - tensor var_9781 = const()[name = tensor("op_9781"), val = tensor([2, 20, 64, -1])]; - tensor var_9782 = reshape(shape = var_9781, x = k_197)[name = tensor("op_9782")]; - tensor var_9783 = const()[name = tensor("op_9783"), val = tensor([2, 20, 64, -1])]; - tensor var_9784 = reshape(shape = var_9783, x = v_197)[name = tensor("op_9784")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3742569344)))]; + tensor v_197_cast = conv(dilations = var_9676, groups = var_31, pad = v_197_pad_0, pad_type = v_197_pad_type_0, strides = var_9674, weight = unet_up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_v_weight_to_fp16, x = hidden_states_391_cast)[name = tensor("v_197_cast")]; + tensor var_9680 = const()[name = tensor("op_9680"), val = tensor([2, 20, 64, -1])]; + tensor var_9681_cast = reshape(shape = var_9680, x = q_197_cast)[name = tensor("op_9681_cast")]; + tensor var_9682 = const()[name = tensor("op_9682"), val = tensor([2, 20, 64, -1])]; + tensor var_9683_cast = reshape(shape = var_9682, x = k_197_cast)[name = tensor("op_9683_cast")]; + tensor var_9684 = const()[name = tensor("op_9684"), val = tensor([2, 20, 64, -1])]; + tensor var_9685_cast = reshape(shape = var_9684, x = v_197_cast)[name = tensor("op_9685_cast")]; tensor attn_weights_393_transpose_x_0 = const()[name = tensor("attn_weights_393_transpose_x_0"), val = tensor(true)]; tensor attn_weights_393_transpose_y_0 = const()[name = tensor("attn_weights_393_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_393 = matmul(transpose_x = attn_weights_393_transpose_x_0, transpose_y = attn_weights_393_transpose_y_0, x = var_9780, y = var_9782)[name = tensor("attn_weights_393")]; - tensor attn_weights_395 = mul(x = attn_weights_393, y = var_6863)[name = tensor("attn_weights_395")]; - tensor var_9788 = softmax(axis = var_6856, x = attn_weights_395)[name = tensor("op_9788")]; + tensor attn_weights_393_cast = matmul(transpose_x = attn_weights_393_transpose_x_0, transpose_y = attn_weights_393_transpose_y_0, x = var_9681_cast, y = var_9683_cast)[name = tensor("attn_weights_393_cast")]; + tensor attn_weights_395_cast = mul(x = attn_weights_393_cast, y = var_12_to_fp16)[name = tensor("attn_weights_395_cast")]; + tensor var_9689_cast = softmax(axis = var_18, x = attn_weights_395_cast)[name = tensor("op_9689_cast")]; tensor attn_197_transpose_x_0 = const()[name = tensor("attn_197_transpose_x_0"), val = tensor(false)]; tensor attn_197_transpose_y_0 = const()[name = tensor("attn_197_transpose_y_0"), val = tensor(true)]; - tensor attn_197 = matmul(transpose_x = attn_197_transpose_x_0, transpose_y = attn_197_transpose_y_0, x = var_9784, y = var_9788)[name = tensor("attn_197")]; - tensor var_9792 = const()[name = tensor("op_9792"), val = tensor([2, 1280, 1, -1])]; - tensor input_577 = reshape(shape = var_9792, x = attn_197)[name = tensor("input_577")]; - tensor var_9797 = const()[name = tensor("op_9797"), val = tensor([1, 1])]; - tensor var_9799 = const()[name = tensor("op_9799"), val = tensor([1, 1])]; - tensor var_9801_pad_type_0 = const()[name = tensor("op_9801_pad_type_0"), val = tensor("custom")]; - tensor var_9801_pad_0 = const()[name = tensor("op_9801_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9801 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias, dilations = var_9799, groups = var_6872, pad = var_9801_pad_0, pad_type = var_9801_pad_type_0, strides = var_9797, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight, x = input_577)[name = tensor("op_9801")]; - tensor inputs_297 = add(x = var_9801, y = inputs_295)[name = tensor("inputs_297")]; - tensor var_9805 = const()[name = tensor("op_9805"), val = tensor([1])]; - tensor channels_mean_297 = reduce_mean(axes = var_9805, keep_dims = var_6867, x = inputs_297)[name = tensor("channels_mean_297")]; - tensor zero_mean_297 = sub(x = inputs_297, y = channels_mean_297)[name = tensor("zero_mean_297")]; - tensor zero_mean_sq_297 = mul(x = zero_mean_297, y = zero_mean_297)[name = tensor("zero_mean_sq_297")]; - tensor var_9809 = const()[name = tensor("op_9809"), val = tensor([1])]; - tensor var_9810 = reduce_mean(axes = var_9809, keep_dims = var_6867, x = zero_mean_sq_297)[name = tensor("op_9810")]; - tensor var_9811 = const()[name = tensor("op_9811"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9812 = add(x = var_9810, y = var_9811)[name = tensor("op_9812")]; - tensor denom_297_epsilon_0 = const()[name = tensor("denom_297_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_297 = rsqrt(epsilon = denom_297_epsilon_0, x = var_9812)[name = tensor("denom_297")]; - tensor out_297 = mul(x = zero_mean_297, y = denom_297)[name = tensor("out_297")]; - tensor var_9816 = const()[name = tensor("op_9816"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269321344)))]; - tensor var_9817 = add(x = out_297, y = var_9816)[name = tensor("op_9817")]; - tensor var_9819 = const()[name = tensor("op_9819"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269326528)))]; - tensor hidden_states_393 = mul(x = var_9817, y = var_9819)[name = tensor("hidden_states_393")]; - tensor var_9826 = const()[name = tensor("op_9826"), val = tensor([1, 1])]; - tensor var_9828 = const()[name = tensor("op_9828"), val = tensor([1, 1])]; + tensor attn_197_cast = matmul(transpose_x = attn_197_transpose_x_0, transpose_y = attn_197_transpose_y_0, x = var_9685_cast, y = var_9689_cast)[name = tensor("attn_197_cast")]; + tensor var_9693 = const()[name = tensor("op_9693"), val = tensor([2, 1280, 1, -1])]; + tensor input_577_cast = reshape(shape = var_9693, x = attn_197_cast)[name = tensor("input_577_cast")]; + tensor var_9698 = const()[name = tensor("op_9698"), val = tensor([1, 1])]; + tensor var_9700 = const()[name = tensor("op_9700"), val = tensor([1, 1])]; + tensor var_9702_pad_type_0 = const()[name = tensor("op_9702_pad_type_0"), val = tensor("custom")]; + tensor var_9702_pad_0 = const()[name = tensor("op_9702_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3745846208)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3749123072)))]; + tensor var_9702_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_9700, groups = var_31, pad = var_9702_pad_0, pad_type = var_9702_pad_type_0, strides = var_9698, weight = unet_up_blocks_0_attentions_1_transformer_blocks_5_attn1_to_out_0_weight_to_fp16, x = input_577_cast)[name = tensor("op_9702_cast")]; + tensor inputs_297_cast = add(x = var_9702_cast, y = inputs_295_cast)[name = tensor("inputs_297_cast")]; + tensor var_9706 = const()[name = tensor("op_9706"), val = tensor([1])]; + tensor channels_mean_297_cast = reduce_mean(axes = var_9706, keep_dims = var_23, x = inputs_297_cast)[name = tensor("channels_mean_297_cast")]; + tensor zero_mean_297_cast = sub(x = inputs_297_cast, y = channels_mean_297_cast)[name = tensor("zero_mean_297_cast")]; + tensor zero_mean_sq_297_cast = mul(x = zero_mean_297_cast, y = zero_mean_297_cast)[name = tensor("zero_mean_sq_297_cast")]; + tensor var_9710 = const()[name = tensor("op_9710"), val = tensor([1])]; + tensor var_9711_cast = reduce_mean(axes = var_9710, keep_dims = var_23, x = zero_mean_sq_297_cast)[name = tensor("op_9711_cast")]; + tensor var_9712_to_fp16 = const()[name = tensor("op_9712_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9713_cast = add(x = var_9711_cast, y = var_9712_to_fp16)[name = tensor("op_9713_cast")]; + tensor denom_297_epsilon_0_to_fp16 = const()[name = tensor("denom_297_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_297_cast = rsqrt(epsilon = denom_297_epsilon_0_to_fp16, x = var_9713_cast)[name = tensor("denom_297_cast")]; + tensor out_297_cast = mul(x = zero_mean_297_cast, y = denom_297_cast)[name = tensor("out_297_cast")]; + tensor var_9717_to_fp16 = const()[name = tensor("op_9717_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3749125696)))]; + tensor var_9718_cast = add(x = out_297_cast, y = var_9717_to_fp16)[name = tensor("op_9718_cast")]; + tensor var_9720_to_fp16 = const()[name = tensor("op_9720_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3749128320)))]; + tensor hidden_states_393_cast = mul(x = var_9718_cast, y = var_9720_to_fp16)[name = tensor("hidden_states_393_cast")]; + tensor var_9727 = const()[name = tensor("op_9727"), val = tensor([1, 1])]; + tensor var_9729 = const()[name = tensor("op_9729"), val = tensor([1, 1])]; tensor q_199_pad_type_0 = const()[name = tensor("q_199_pad_type_0"), val = tensor("custom")]; tensor q_199_pad_0 = const()[name = tensor("q_199_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_199 = conv(dilations = var_9828, groups = var_6872, pad = q_199_pad_0, pad_type = q_199_pad_type_0, strides = var_9826, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight, x = hidden_states_393)[name = tensor("q_199")]; - tensor var_9832 = const()[name = tensor("op_9832"), val = tensor([1, 1])]; - tensor var_9834 = const()[name = tensor("op_9834"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3749130944)))]; + tensor q_199_cast = conv(dilations = var_9729, groups = var_31, pad = q_199_pad_0, pad_type = q_199_pad_type_0, strides = var_9727, weight = unet_up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_q_weight_to_fp16, x = hidden_states_393_cast)[name = tensor("q_199_cast")]; + tensor var_9733 = const()[name = tensor("op_9733"), val = tensor([1, 1])]; + tensor var_9735 = const()[name = tensor("op_9735"), val = tensor([1, 1])]; tensor k_199_pad_type_0 = const()[name = tensor("k_199_pad_type_0"), val = tensor("custom")]; tensor k_199_pad_0 = const()[name = tensor("k_199_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_199 = conv(dilations = var_9834, groups = var_6872, pad = k_199_pad_0, pad_type = k_199_pad_type_0, strides = var_9832, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_199")]; - tensor var_9838 = const()[name = tensor("op_9838"), val = tensor([1, 1])]; - tensor var_9840 = const()[name = tensor("op_9840"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3752407808)))]; + tensor k_199_cast = conv(dilations = var_9735, groups = var_31, pad = k_199_pad_0, pad_type = k_199_pad_type_0, strides = var_9733, weight = unet_up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_199_cast")]; + tensor var_9739 = const()[name = tensor("op_9739"), val = tensor([1, 1])]; + tensor var_9741 = const()[name = tensor("op_9741"), val = tensor([1, 1])]; tensor v_199_pad_type_0 = const()[name = tensor("v_199_pad_type_0"), val = tensor("custom")]; tensor v_199_pad_0 = const()[name = tensor("v_199_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_199 = conv(dilations = var_9840, groups = var_6872, pad = v_199_pad_0, pad_type = v_199_pad_type_0, strides = var_9838, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_199")]; - tensor var_9844 = const()[name = tensor("op_9844"), val = tensor([2, 20, 64, -1])]; - tensor var_9845 = reshape(shape = var_9844, x = q_199)[name = tensor("op_9845")]; - tensor var_9846 = const()[name = tensor("op_9846"), val = tensor([2, 20, 64, -1])]; - tensor var_9847 = reshape(shape = var_9846, x = k_199)[name = tensor("op_9847")]; - tensor var_9848 = const()[name = tensor("op_9848"), val = tensor([2, 20, 64, -1])]; - tensor var_9849 = reshape(shape = var_9848, x = v_199)[name = tensor("op_9849")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3757650752)))]; + tensor v_199_cast = conv(dilations = var_9741, groups = var_31, pad = v_199_pad_0, pad_type = v_199_pad_type_0, strides = var_9739, weight = unet_up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_199_cast")]; + tensor var_9745 = const()[name = tensor("op_9745"), val = tensor([2, 20, 64, -1])]; + tensor var_9746_cast = reshape(shape = var_9745, x = q_199_cast)[name = tensor("op_9746_cast")]; + tensor var_9747 = const()[name = tensor("op_9747"), val = tensor([2, 20, 64, -1])]; + tensor var_9748_cast = reshape(shape = var_9747, x = k_199_cast)[name = tensor("op_9748_cast")]; + tensor var_9749 = const()[name = tensor("op_9749"), val = tensor([2, 20, 64, -1])]; + tensor var_9750_cast = reshape(shape = var_9749, x = v_199_cast)[name = tensor("op_9750_cast")]; tensor attn_weights_397_transpose_x_0 = const()[name = tensor("attn_weights_397_transpose_x_0"), val = tensor(true)]; tensor attn_weights_397_transpose_y_0 = const()[name = tensor("attn_weights_397_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_397 = matmul(transpose_x = attn_weights_397_transpose_x_0, transpose_y = attn_weights_397_transpose_y_0, x = var_9845, y = var_9847)[name = tensor("attn_weights_397")]; - tensor attn_weights_399 = mul(x = attn_weights_397, y = var_6863)[name = tensor("attn_weights_399")]; - tensor var_9853 = softmax(axis = var_6856, x = attn_weights_399)[name = tensor("op_9853")]; + tensor attn_weights_397_cast = matmul(transpose_x = attn_weights_397_transpose_x_0, transpose_y = attn_weights_397_transpose_y_0, x = var_9746_cast, y = var_9748_cast)[name = tensor("attn_weights_397_cast")]; + tensor attn_weights_399_cast = mul(x = attn_weights_397_cast, y = var_12_to_fp16)[name = tensor("attn_weights_399_cast")]; + tensor var_9754_cast = softmax(axis = var_18, x = attn_weights_399_cast)[name = tensor("op_9754_cast")]; tensor attn_199_transpose_x_0 = const()[name = tensor("attn_199_transpose_x_0"), val = tensor(false)]; tensor attn_199_transpose_y_0 = const()[name = tensor("attn_199_transpose_y_0"), val = tensor(true)]; - tensor attn_199 = matmul(transpose_x = attn_199_transpose_x_0, transpose_y = attn_199_transpose_y_0, x = var_9849, y = var_9853)[name = tensor("attn_199")]; - tensor var_9857 = const()[name = tensor("op_9857"), val = tensor([2, 1280, 1, -1])]; - tensor input_579 = reshape(shape = var_9857, x = attn_199)[name = tensor("input_579")]; - tensor var_9862 = const()[name = tensor("op_9862"), val = tensor([1, 1])]; - tensor var_9864 = const()[name = tensor("op_9864"), val = tensor([1, 1])]; - tensor var_9866_pad_type_0 = const()[name = tensor("op_9866_pad_type_0"), val = tensor("custom")]; - tensor var_9866_pad_0 = const()[name = tensor("op_9866_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9866 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias, dilations = var_9864, groups = var_6872, pad = var_9866_pad_0, pad_type = var_9866_pad_type_0, strides = var_9862, weight = up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight, x = input_579)[name = tensor("op_9866")]; - tensor inputs_299 = add(x = var_9866, y = inputs_297)[name = tensor("inputs_299")]; - tensor var_9870 = const()[name = tensor("op_9870"), val = tensor([1])]; - tensor channels_mean_299 = reduce_mean(axes = var_9870, keep_dims = var_6867, x = inputs_299)[name = tensor("channels_mean_299")]; - tensor zero_mean_299 = sub(x = inputs_299, y = channels_mean_299)[name = tensor("zero_mean_299")]; - tensor zero_mean_sq_299 = mul(x = zero_mean_299, y = zero_mean_299)[name = tensor("zero_mean_sq_299")]; - tensor var_9874 = const()[name = tensor("op_9874"), val = tensor([1])]; - tensor var_9875 = reduce_mean(axes = var_9874, keep_dims = var_6867, x = zero_mean_sq_299)[name = tensor("op_9875")]; - tensor var_9876 = const()[name = tensor("op_9876"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9877 = add(x = var_9875, y = var_9876)[name = tensor("op_9877")]; - tensor denom_299_epsilon_0 = const()[name = tensor("denom_299_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_299 = rsqrt(epsilon = denom_299_epsilon_0, x = var_9877)[name = tensor("denom_299")]; - tensor out_299 = mul(x = zero_mean_299, y = denom_299)[name = tensor("out_299")]; - tensor var_9881 = const()[name = tensor("op_9881"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269331712)))]; - tensor var_9882 = add(x = out_299, y = var_9881)[name = tensor("op_9882")]; - tensor var_9884 = const()[name = tensor("op_9884"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269336896)))]; - tensor input_581 = mul(x = var_9882, y = var_9884)[name = tensor("input_581")]; - tensor var_9892 = const()[name = tensor("op_9892"), val = tensor([1, 1])]; - tensor var_9894 = const()[name = tensor("op_9894"), val = tensor([1, 1])]; - tensor var_9896_pad_type_0 = const()[name = tensor("op_9896_pad_type_0"), val = tensor("custom")]; - tensor var_9896_pad_0 = const()[name = tensor("op_9896_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9896 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias, dilations = var_9894, groups = var_6872, pad = var_9896_pad_0, pad_type = var_9896_pad_type_0, strides = var_9892, weight = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight, x = input_581)[name = tensor("op_9896")]; - tensor var_9897_split_sizes_0 = const()[name = tensor("op_9897_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_9897_axis_0 = const()[name = tensor("op_9897_axis_0"), val = tensor(1)]; - tensor var_9897_0, tensor var_9897_1 = split(axis = var_9897_axis_0, split_sizes = var_9897_split_sizes_0, x = var_9896)[name = tensor("op_9897")]; - tensor var_9899_mode_0 = const()[name = tensor("op_9899_mode_0"), val = tensor("EXACT")]; - tensor var_9899 = gelu(mode = var_9899_mode_0, x = var_9897_1)[name = tensor("op_9899")]; - tensor input_583 = mul(x = var_9897_0, y = var_9899)[name = tensor("input_583")]; - tensor var_9903 = const()[name = tensor("op_9903"), val = tensor([1, 1])]; - tensor var_9905 = const()[name = tensor("op_9905"), val = tensor([1, 1])]; - tensor var_9907_pad_type_0 = const()[name = tensor("op_9907_pad_type_0"), val = tensor("custom")]; - tensor var_9907_pad_0 = const()[name = tensor("op_9907_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9907 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias, dilations = var_9905, groups = var_6872, pad = var_9907_pad_0, pad_type = var_9907_pad_type_0, strides = var_9903, weight = up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight, x = input_583)[name = tensor("op_9907")]; - tensor inputs_301 = add(x = var_9907, y = inputs_299)[name = tensor("inputs_301")]; - tensor var_9917 = const()[name = tensor("op_9917"), val = tensor([1])]; - tensor channels_mean_301 = reduce_mean(axes = var_9917, keep_dims = var_6867, x = inputs_301)[name = tensor("channels_mean_301")]; - tensor zero_mean_301 = sub(x = inputs_301, y = channels_mean_301)[name = tensor("zero_mean_301")]; - tensor zero_mean_sq_301 = mul(x = zero_mean_301, y = zero_mean_301)[name = tensor("zero_mean_sq_301")]; - tensor var_9921 = const()[name = tensor("op_9921"), val = tensor([1])]; - tensor var_9922 = reduce_mean(axes = var_9921, keep_dims = var_6867, x = zero_mean_sq_301)[name = tensor("op_9922")]; - tensor var_9923 = const()[name = tensor("op_9923"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9924 = add(x = var_9922, y = var_9923)[name = tensor("op_9924")]; - tensor denom_301_epsilon_0 = const()[name = tensor("denom_301_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_301 = rsqrt(epsilon = denom_301_epsilon_0, x = var_9924)[name = tensor("denom_301")]; - tensor out_301 = mul(x = zero_mean_301, y = denom_301)[name = tensor("out_301")]; - tensor var_9928 = const()[name = tensor("op_9928"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269342080)))]; - tensor var_9929 = add(x = out_301, y = var_9928)[name = tensor("op_9929")]; - tensor var_9931 = const()[name = tensor("op_9931"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269347264)))]; - tensor hidden_states_397 = mul(x = var_9929, y = var_9931)[name = tensor("hidden_states_397")]; - tensor var_9938 = const()[name = tensor("op_9938"), val = tensor([1, 1])]; - tensor var_9940 = const()[name = tensor("op_9940"), val = tensor([1, 1])]; + tensor attn_199_cast = matmul(transpose_x = attn_199_transpose_x_0, transpose_y = attn_199_transpose_y_0, x = var_9750_cast, y = var_9754_cast)[name = tensor("attn_199_cast")]; + tensor var_9758 = const()[name = tensor("op_9758"), val = tensor([2, 1280, 1, -1])]; + tensor input_579_cast = reshape(shape = var_9758, x = attn_199_cast)[name = tensor("input_579_cast")]; + tensor var_9763 = const()[name = tensor("op_9763"), val = tensor([1, 1])]; + tensor var_9765 = const()[name = tensor("op_9765"), val = tensor([1, 1])]; + tensor var_9767_pad_type_0 = const()[name = tensor("op_9767_pad_type_0"), val = tensor("custom")]; + tensor var_9767_pad_0 = const()[name = tensor("op_9767_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3762893696)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3766170560)))]; + tensor var_9767_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_9765, groups = var_31, pad = var_9767_pad_0, pad_type = var_9767_pad_type_0, strides = var_9763, weight = unet_up_blocks_0_attentions_1_transformer_blocks_5_attn2_to_out_0_weight_to_fp16, x = input_579_cast)[name = tensor("op_9767_cast")]; + tensor inputs_299_cast = add(x = var_9767_cast, y = inputs_297_cast)[name = tensor("inputs_299_cast")]; + tensor var_9771 = const()[name = tensor("op_9771"), val = tensor([1])]; + tensor channels_mean_299_cast = reduce_mean(axes = var_9771, keep_dims = var_23, x = inputs_299_cast)[name = tensor("channels_mean_299_cast")]; + tensor zero_mean_299_cast = sub(x = inputs_299_cast, y = channels_mean_299_cast)[name = tensor("zero_mean_299_cast")]; + tensor zero_mean_sq_299_cast = mul(x = zero_mean_299_cast, y = zero_mean_299_cast)[name = tensor("zero_mean_sq_299_cast")]; + tensor var_9775 = const()[name = tensor("op_9775"), val = tensor([1])]; + tensor var_9776_cast = reduce_mean(axes = var_9775, keep_dims = var_23, x = zero_mean_sq_299_cast)[name = tensor("op_9776_cast")]; + tensor var_9777_to_fp16 = const()[name = tensor("op_9777_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9778_cast = add(x = var_9776_cast, y = var_9777_to_fp16)[name = tensor("op_9778_cast")]; + tensor denom_299_epsilon_0_to_fp16 = const()[name = tensor("denom_299_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_299_cast = rsqrt(epsilon = denom_299_epsilon_0_to_fp16, x = var_9778_cast)[name = tensor("denom_299_cast")]; + tensor out_299_cast = mul(x = zero_mean_299_cast, y = denom_299_cast)[name = tensor("out_299_cast")]; + tensor var_9782_to_fp16 = const()[name = tensor("op_9782_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3766173184)))]; + tensor var_9783_cast = add(x = out_299_cast, y = var_9782_to_fp16)[name = tensor("op_9783_cast")]; + tensor var_9785_to_fp16 = const()[name = tensor("op_9785_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3766175808)))]; + tensor input_581_cast = mul(x = var_9783_cast, y = var_9785_to_fp16)[name = tensor("input_581_cast")]; + tensor var_9793 = const()[name = tensor("op_9793"), val = tensor([1, 1])]; + tensor var_9795 = const()[name = tensor("op_9795"), val = tensor([1, 1])]; + tensor var_9797_pad_type_0 = const()[name = tensor("op_9797_pad_type_0"), val = tensor("custom")]; + tensor var_9797_pad_0 = const()[name = tensor("op_9797_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3766178432)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3792392896)))]; + tensor var_9797_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_9795, groups = var_31, pad = var_9797_pad_0, pad_type = var_9797_pad_type_0, strides = var_9793, weight = unet_up_blocks_0_attentions_1_transformer_blocks_5_ff_net_0_proj_weight_to_fp16, x = input_581_cast)[name = tensor("op_9797_cast")]; + tensor var_9798_split_sizes_0 = const()[name = tensor("op_9798_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9798_axis_0 = const()[name = tensor("op_9798_axis_0"), val = tensor(1)]; + tensor var_9798_cast_0, tensor var_9798_cast_1 = split(axis = var_9798_axis_0, split_sizes = var_9798_split_sizes_0, x = var_9797_cast)[name = tensor("op_9798_cast")]; + tensor var_9800_mode_0 = const()[name = tensor("op_9800_mode_0"), val = tensor("EXACT")]; + tensor var_9800_cast = gelu(mode = var_9800_mode_0, x = var_9798_cast_1)[name = tensor("op_9800_cast")]; + tensor input_583_cast = mul(x = var_9798_cast_0, y = var_9800_cast)[name = tensor("input_583_cast")]; + tensor var_9804 = const()[name = tensor("op_9804"), val = tensor([1, 1])]; + tensor var_9806 = const()[name = tensor("op_9806"), val = tensor([1, 1])]; + tensor var_9808_pad_type_0 = const()[name = tensor("op_9808_pad_type_0"), val = tensor("custom")]; + tensor var_9808_pad_0 = const()[name = tensor("op_9808_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3792413440)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3805520704)))]; + tensor var_9808_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_9806, groups = var_31, pad = var_9808_pad_0, pad_type = var_9808_pad_type_0, strides = var_9804, weight = unet_up_blocks_0_attentions_1_transformer_blocks_5_ff_net_2_weight_to_fp16, x = input_583_cast)[name = tensor("op_9808_cast")]; + tensor inputs_301_cast = add(x = var_9808_cast, y = inputs_299_cast)[name = tensor("inputs_301_cast")]; + tensor var_9818 = const()[name = tensor("op_9818"), val = tensor([1])]; + tensor channels_mean_301_cast = reduce_mean(axes = var_9818, keep_dims = var_23, x = inputs_301_cast)[name = tensor("channels_mean_301_cast")]; + tensor zero_mean_301_cast = sub(x = inputs_301_cast, y = channels_mean_301_cast)[name = tensor("zero_mean_301_cast")]; + tensor zero_mean_sq_301_cast = mul(x = zero_mean_301_cast, y = zero_mean_301_cast)[name = tensor("zero_mean_sq_301_cast")]; + tensor var_9822 = const()[name = tensor("op_9822"), val = tensor([1])]; + tensor var_9823_cast = reduce_mean(axes = var_9822, keep_dims = var_23, x = zero_mean_sq_301_cast)[name = tensor("op_9823_cast")]; + tensor var_9824_to_fp16 = const()[name = tensor("op_9824_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9825_cast = add(x = var_9823_cast, y = var_9824_to_fp16)[name = tensor("op_9825_cast")]; + tensor denom_301_epsilon_0_to_fp16 = const()[name = tensor("denom_301_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_301_cast = rsqrt(epsilon = denom_301_epsilon_0_to_fp16, x = var_9825_cast)[name = tensor("denom_301_cast")]; + tensor out_301_cast = mul(x = zero_mean_301_cast, y = denom_301_cast)[name = tensor("out_301_cast")]; + tensor var_9829_to_fp16 = const()[name = tensor("op_9829_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3805523328)))]; + tensor var_9830_cast = add(x = out_301_cast, y = var_9829_to_fp16)[name = tensor("op_9830_cast")]; + tensor var_9832_to_fp16 = const()[name = tensor("op_9832_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3805525952)))]; + tensor hidden_states_397_cast = mul(x = var_9830_cast, y = var_9832_to_fp16)[name = tensor("hidden_states_397_cast")]; + tensor var_9839 = const()[name = tensor("op_9839"), val = tensor([1, 1])]; + tensor var_9841 = const()[name = tensor("op_9841"), val = tensor([1, 1])]; tensor q_201_pad_type_0 = const()[name = tensor("q_201_pad_type_0"), val = tensor("custom")]; tensor q_201_pad_0 = const()[name = tensor("q_201_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_201 = conv(dilations = var_9940, groups = var_6872, pad = q_201_pad_0, pad_type = q_201_pad_type_0, strides = var_9938, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight, x = hidden_states_397)[name = tensor("q_201")]; - tensor var_9944 = const()[name = tensor("op_9944"), val = tensor([1, 1])]; - tensor var_9946 = const()[name = tensor("op_9946"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3805528576)))]; + tensor q_201_cast = conv(dilations = var_9841, groups = var_31, pad = q_201_pad_0, pad_type = q_201_pad_type_0, strides = var_9839, weight = unet_up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_q_weight_to_fp16, x = hidden_states_397_cast)[name = tensor("q_201_cast")]; + tensor var_9845 = const()[name = tensor("op_9845"), val = tensor([1, 1])]; + tensor var_9847 = const()[name = tensor("op_9847"), val = tensor([1, 1])]; tensor k_201_pad_type_0 = const()[name = tensor("k_201_pad_type_0"), val = tensor("custom")]; tensor k_201_pad_0 = const()[name = tensor("k_201_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_201 = conv(dilations = var_9946, groups = var_6872, pad = k_201_pad_0, pad_type = k_201_pad_type_0, strides = var_9944, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight, x = hidden_states_397)[name = tensor("k_201")]; - tensor var_9950 = const()[name = tensor("op_9950"), val = tensor([1, 1])]; - tensor var_9952 = const()[name = tensor("op_9952"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3808805440)))]; + tensor k_201_cast = conv(dilations = var_9847, groups = var_31, pad = k_201_pad_0, pad_type = k_201_pad_type_0, strides = var_9845, weight = unet_up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_k_weight_to_fp16, x = hidden_states_397_cast)[name = tensor("k_201_cast")]; + tensor var_9851 = const()[name = tensor("op_9851"), val = tensor([1, 1])]; + tensor var_9853 = const()[name = tensor("op_9853"), val = tensor([1, 1])]; tensor v_201_pad_type_0 = const()[name = tensor("v_201_pad_type_0"), val = tensor("custom")]; tensor v_201_pad_0 = const()[name = tensor("v_201_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_201 = conv(dilations = var_9952, groups = var_6872, pad = v_201_pad_0, pad_type = v_201_pad_type_0, strides = var_9950, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight, x = hidden_states_397)[name = tensor("v_201")]; - tensor var_9956 = const()[name = tensor("op_9956"), val = tensor([2, 20, 64, -1])]; - tensor var_9957 = reshape(shape = var_9956, x = q_201)[name = tensor("op_9957")]; - tensor var_9958 = const()[name = tensor("op_9958"), val = tensor([2, 20, 64, -1])]; - tensor var_9959 = reshape(shape = var_9958, x = k_201)[name = tensor("op_9959")]; - tensor var_9960 = const()[name = tensor("op_9960"), val = tensor([2, 20, 64, -1])]; - tensor var_9961 = reshape(shape = var_9960, x = v_201)[name = tensor("op_9961")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3812082304)))]; + tensor v_201_cast = conv(dilations = var_9853, groups = var_31, pad = v_201_pad_0, pad_type = v_201_pad_type_0, strides = var_9851, weight = unet_up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_v_weight_to_fp16, x = hidden_states_397_cast)[name = tensor("v_201_cast")]; + tensor var_9857 = const()[name = tensor("op_9857"), val = tensor([2, 20, 64, -1])]; + tensor var_9858_cast = reshape(shape = var_9857, x = q_201_cast)[name = tensor("op_9858_cast")]; + tensor var_9859 = const()[name = tensor("op_9859"), val = tensor([2, 20, 64, -1])]; + tensor var_9860_cast = reshape(shape = var_9859, x = k_201_cast)[name = tensor("op_9860_cast")]; + tensor var_9861 = const()[name = tensor("op_9861"), val = tensor([2, 20, 64, -1])]; + tensor var_9862_cast = reshape(shape = var_9861, x = v_201_cast)[name = tensor("op_9862_cast")]; tensor attn_weights_401_transpose_x_0 = const()[name = tensor("attn_weights_401_transpose_x_0"), val = tensor(true)]; tensor attn_weights_401_transpose_y_0 = const()[name = tensor("attn_weights_401_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_401 = matmul(transpose_x = attn_weights_401_transpose_x_0, transpose_y = attn_weights_401_transpose_y_0, x = var_9957, y = var_9959)[name = tensor("attn_weights_401")]; - tensor attn_weights_403 = mul(x = attn_weights_401, y = var_6863)[name = tensor("attn_weights_403")]; - tensor var_9965 = softmax(axis = var_6856, x = attn_weights_403)[name = tensor("op_9965")]; + tensor attn_weights_401_cast = matmul(transpose_x = attn_weights_401_transpose_x_0, transpose_y = attn_weights_401_transpose_y_0, x = var_9858_cast, y = var_9860_cast)[name = tensor("attn_weights_401_cast")]; + tensor attn_weights_403_cast = mul(x = attn_weights_401_cast, y = var_12_to_fp16)[name = tensor("attn_weights_403_cast")]; + tensor var_9866_cast = softmax(axis = var_18, x = attn_weights_403_cast)[name = tensor("op_9866_cast")]; tensor attn_201_transpose_x_0 = const()[name = tensor("attn_201_transpose_x_0"), val = tensor(false)]; tensor attn_201_transpose_y_0 = const()[name = tensor("attn_201_transpose_y_0"), val = tensor(true)]; - tensor attn_201 = matmul(transpose_x = attn_201_transpose_x_0, transpose_y = attn_201_transpose_y_0, x = var_9961, y = var_9965)[name = tensor("attn_201")]; - tensor var_9969 = const()[name = tensor("op_9969"), val = tensor([2, 1280, 1, -1])]; - tensor input_585 = reshape(shape = var_9969, x = attn_201)[name = tensor("input_585")]; - tensor var_9974 = const()[name = tensor("op_9974"), val = tensor([1, 1])]; - tensor var_9976 = const()[name = tensor("op_9976"), val = tensor([1, 1])]; - tensor var_9978_pad_type_0 = const()[name = tensor("op_9978_pad_type_0"), val = tensor("custom")]; - tensor var_9978_pad_0 = const()[name = tensor("op_9978_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_9978 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias, dilations = var_9976, groups = var_6872, pad = var_9978_pad_0, pad_type = var_9978_pad_type_0, strides = var_9974, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight, x = input_585)[name = tensor("op_9978")]; - tensor inputs_303 = add(x = var_9978, y = inputs_301)[name = tensor("inputs_303")]; - tensor var_9982 = const()[name = tensor("op_9982"), val = tensor([1])]; - tensor channels_mean_303 = reduce_mean(axes = var_9982, keep_dims = var_6867, x = inputs_303)[name = tensor("channels_mean_303")]; - tensor zero_mean_303 = sub(x = inputs_303, y = channels_mean_303)[name = tensor("zero_mean_303")]; - tensor zero_mean_sq_303 = mul(x = zero_mean_303, y = zero_mean_303)[name = tensor("zero_mean_sq_303")]; - tensor var_9986 = const()[name = tensor("op_9986"), val = tensor([1])]; - tensor var_9987 = reduce_mean(axes = var_9986, keep_dims = var_6867, x = zero_mean_sq_303)[name = tensor("op_9987")]; - tensor var_9988 = const()[name = tensor("op_9988"), val = tensor(0x1.4f8b58p-17)]; - tensor var_9989 = add(x = var_9987, y = var_9988)[name = tensor("op_9989")]; - tensor denom_303_epsilon_0 = const()[name = tensor("denom_303_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_303 = rsqrt(epsilon = denom_303_epsilon_0, x = var_9989)[name = tensor("denom_303")]; - tensor out_303 = mul(x = zero_mean_303, y = denom_303)[name = tensor("out_303")]; - tensor var_9993 = const()[name = tensor("op_9993"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269352448)))]; - tensor var_9994 = add(x = out_303, y = var_9993)[name = tensor("op_9994")]; - tensor var_9996 = const()[name = tensor("op_9996"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269357632)))]; - tensor hidden_states_399 = mul(x = var_9994, y = var_9996)[name = tensor("hidden_states_399")]; - tensor var_10003 = const()[name = tensor("op_10003"), val = tensor([1, 1])]; - tensor var_10005 = const()[name = tensor("op_10005"), val = tensor([1, 1])]; + tensor attn_201_cast = matmul(transpose_x = attn_201_transpose_x_0, transpose_y = attn_201_transpose_y_0, x = var_9862_cast, y = var_9866_cast)[name = tensor("attn_201_cast")]; + tensor var_9870 = const()[name = tensor("op_9870"), val = tensor([2, 1280, 1, -1])]; + tensor input_585_cast = reshape(shape = var_9870, x = attn_201_cast)[name = tensor("input_585_cast")]; + tensor var_9875 = const()[name = tensor("op_9875"), val = tensor([1, 1])]; + tensor var_9877 = const()[name = tensor("op_9877"), val = tensor([1, 1])]; + tensor var_9879_pad_type_0 = const()[name = tensor("op_9879_pad_type_0"), val = tensor("custom")]; + tensor var_9879_pad_0 = const()[name = tensor("op_9879_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3815359168)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3818636032)))]; + tensor var_9879_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_9877, groups = var_31, pad = var_9879_pad_0, pad_type = var_9879_pad_type_0, strides = var_9875, weight = unet_up_blocks_0_attentions_1_transformer_blocks_6_attn1_to_out_0_weight_to_fp16, x = input_585_cast)[name = tensor("op_9879_cast")]; + tensor inputs_303_cast = add(x = var_9879_cast, y = inputs_301_cast)[name = tensor("inputs_303_cast")]; + tensor var_9883 = const()[name = tensor("op_9883"), val = tensor([1])]; + tensor channels_mean_303_cast = reduce_mean(axes = var_9883, keep_dims = var_23, x = inputs_303_cast)[name = tensor("channels_mean_303_cast")]; + tensor zero_mean_303_cast = sub(x = inputs_303_cast, y = channels_mean_303_cast)[name = tensor("zero_mean_303_cast")]; + tensor zero_mean_sq_303_cast = mul(x = zero_mean_303_cast, y = zero_mean_303_cast)[name = tensor("zero_mean_sq_303_cast")]; + tensor var_9887 = const()[name = tensor("op_9887"), val = tensor([1])]; + tensor var_9888_cast = reduce_mean(axes = var_9887, keep_dims = var_23, x = zero_mean_sq_303_cast)[name = tensor("op_9888_cast")]; + tensor var_9889_to_fp16 = const()[name = tensor("op_9889_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9890_cast = add(x = var_9888_cast, y = var_9889_to_fp16)[name = tensor("op_9890_cast")]; + tensor denom_303_epsilon_0_to_fp16 = const()[name = tensor("denom_303_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_303_cast = rsqrt(epsilon = denom_303_epsilon_0_to_fp16, x = var_9890_cast)[name = tensor("denom_303_cast")]; + tensor out_303_cast = mul(x = zero_mean_303_cast, y = denom_303_cast)[name = tensor("out_303_cast")]; + tensor var_9894_to_fp16 = const()[name = tensor("op_9894_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3818638656)))]; + tensor var_9895_cast = add(x = out_303_cast, y = var_9894_to_fp16)[name = tensor("op_9895_cast")]; + tensor var_9897_to_fp16 = const()[name = tensor("op_9897_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3818641280)))]; + tensor hidden_states_399_cast = mul(x = var_9895_cast, y = var_9897_to_fp16)[name = tensor("hidden_states_399_cast")]; + tensor var_9904 = const()[name = tensor("op_9904"), val = tensor([1, 1])]; + tensor var_9906 = const()[name = tensor("op_9906"), val = tensor([1, 1])]; tensor q_203_pad_type_0 = const()[name = tensor("q_203_pad_type_0"), val = tensor("custom")]; tensor q_203_pad_0 = const()[name = tensor("q_203_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_203 = conv(dilations = var_10005, groups = var_6872, pad = q_203_pad_0, pad_type = q_203_pad_type_0, strides = var_10003, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight, x = hidden_states_399)[name = tensor("q_203")]; - tensor var_10009 = const()[name = tensor("op_10009"), val = tensor([1, 1])]; - tensor var_10011 = const()[name = tensor("op_10011"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3818643904)))]; + tensor q_203_cast = conv(dilations = var_9906, groups = var_31, pad = q_203_pad_0, pad_type = q_203_pad_type_0, strides = var_9904, weight = unet_up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_q_weight_to_fp16, x = hidden_states_399_cast)[name = tensor("q_203_cast")]; + tensor var_9910 = const()[name = tensor("op_9910"), val = tensor([1, 1])]; + tensor var_9912 = const()[name = tensor("op_9912"), val = tensor([1, 1])]; tensor k_203_pad_type_0 = const()[name = tensor("k_203_pad_type_0"), val = tensor("custom")]; tensor k_203_pad_0 = const()[name = tensor("k_203_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_203 = conv(dilations = var_10011, groups = var_6872, pad = k_203_pad_0, pad_type = k_203_pad_type_0, strides = var_10009, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_203")]; - tensor var_10015 = const()[name = tensor("op_10015"), val = tensor([1, 1])]; - tensor var_10017 = const()[name = tensor("op_10017"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3821920768)))]; + tensor k_203_cast = conv(dilations = var_9912, groups = var_31, pad = k_203_pad_0, pad_type = k_203_pad_type_0, strides = var_9910, weight = unet_up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_203_cast")]; + tensor var_9916 = const()[name = tensor("op_9916"), val = tensor([1, 1])]; + tensor var_9918 = const()[name = tensor("op_9918"), val = tensor([1, 1])]; tensor v_203_pad_type_0 = const()[name = tensor("v_203_pad_type_0"), val = tensor("custom")]; tensor v_203_pad_0 = const()[name = tensor("v_203_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_203 = conv(dilations = var_10017, groups = var_6872, pad = v_203_pad_0, pad_type = v_203_pad_type_0, strides = var_10015, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_203")]; - tensor var_10021 = const()[name = tensor("op_10021"), val = tensor([2, 20, 64, -1])]; - tensor var_10022 = reshape(shape = var_10021, x = q_203)[name = tensor("op_10022")]; - tensor var_10023 = const()[name = tensor("op_10023"), val = tensor([2, 20, 64, -1])]; - tensor var_10024 = reshape(shape = var_10023, x = k_203)[name = tensor("op_10024")]; - tensor var_10025 = const()[name = tensor("op_10025"), val = tensor([2, 20, 64, -1])]; - tensor var_10026 = reshape(shape = var_10025, x = v_203)[name = tensor("op_10026")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3827163712)))]; + tensor v_203_cast = conv(dilations = var_9918, groups = var_31, pad = v_203_pad_0, pad_type = v_203_pad_type_0, strides = var_9916, weight = unet_up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_203_cast")]; + tensor var_9922 = const()[name = tensor("op_9922"), val = tensor([2, 20, 64, -1])]; + tensor var_9923_cast = reshape(shape = var_9922, x = q_203_cast)[name = tensor("op_9923_cast")]; + tensor var_9924 = const()[name = tensor("op_9924"), val = tensor([2, 20, 64, -1])]; + tensor var_9925_cast = reshape(shape = var_9924, x = k_203_cast)[name = tensor("op_9925_cast")]; + tensor var_9926 = const()[name = tensor("op_9926"), val = tensor([2, 20, 64, -1])]; + tensor var_9927_cast = reshape(shape = var_9926, x = v_203_cast)[name = tensor("op_9927_cast")]; tensor attn_weights_405_transpose_x_0 = const()[name = tensor("attn_weights_405_transpose_x_0"), val = tensor(true)]; tensor attn_weights_405_transpose_y_0 = const()[name = tensor("attn_weights_405_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_405 = matmul(transpose_x = attn_weights_405_transpose_x_0, transpose_y = attn_weights_405_transpose_y_0, x = var_10022, y = var_10024)[name = tensor("attn_weights_405")]; - tensor attn_weights_407 = mul(x = attn_weights_405, y = var_6863)[name = tensor("attn_weights_407")]; - tensor var_10030 = softmax(axis = var_6856, x = attn_weights_407)[name = tensor("op_10030")]; + tensor attn_weights_405_cast = matmul(transpose_x = attn_weights_405_transpose_x_0, transpose_y = attn_weights_405_transpose_y_0, x = var_9923_cast, y = var_9925_cast)[name = tensor("attn_weights_405_cast")]; + tensor attn_weights_407_cast = mul(x = attn_weights_405_cast, y = var_12_to_fp16)[name = tensor("attn_weights_407_cast")]; + tensor var_9931_cast = softmax(axis = var_18, x = attn_weights_407_cast)[name = tensor("op_9931_cast")]; tensor attn_203_transpose_x_0 = const()[name = tensor("attn_203_transpose_x_0"), val = tensor(false)]; tensor attn_203_transpose_y_0 = const()[name = tensor("attn_203_transpose_y_0"), val = tensor(true)]; - tensor attn_203 = matmul(transpose_x = attn_203_transpose_x_0, transpose_y = attn_203_transpose_y_0, x = var_10026, y = var_10030)[name = tensor("attn_203")]; - tensor var_10034 = const()[name = tensor("op_10034"), val = tensor([2, 1280, 1, -1])]; - tensor input_587 = reshape(shape = var_10034, x = attn_203)[name = tensor("input_587")]; - tensor var_10039 = const()[name = tensor("op_10039"), val = tensor([1, 1])]; - tensor var_10041 = const()[name = tensor("op_10041"), val = tensor([1, 1])]; - tensor var_10043_pad_type_0 = const()[name = tensor("op_10043_pad_type_0"), val = tensor("custom")]; - tensor var_10043_pad_0 = const()[name = tensor("op_10043_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10043 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias, dilations = var_10041, groups = var_6872, pad = var_10043_pad_0, pad_type = var_10043_pad_type_0, strides = var_10039, weight = up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight, x = input_587)[name = tensor("op_10043")]; - tensor inputs_305 = add(x = var_10043, y = inputs_303)[name = tensor("inputs_305")]; - tensor var_10047 = const()[name = tensor("op_10047"), val = tensor([1])]; - tensor channels_mean_305 = reduce_mean(axes = var_10047, keep_dims = var_6867, x = inputs_305)[name = tensor("channels_mean_305")]; - tensor zero_mean_305 = sub(x = inputs_305, y = channels_mean_305)[name = tensor("zero_mean_305")]; - tensor zero_mean_sq_305 = mul(x = zero_mean_305, y = zero_mean_305)[name = tensor("zero_mean_sq_305")]; - tensor var_10051 = const()[name = tensor("op_10051"), val = tensor([1])]; - tensor var_10052 = reduce_mean(axes = var_10051, keep_dims = var_6867, x = zero_mean_sq_305)[name = tensor("op_10052")]; - tensor var_10053 = const()[name = tensor("op_10053"), val = tensor(0x1.4f8b58p-17)]; - tensor var_10054 = add(x = var_10052, y = var_10053)[name = tensor("op_10054")]; - tensor denom_305_epsilon_0 = const()[name = tensor("denom_305_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_305 = rsqrt(epsilon = denom_305_epsilon_0, x = var_10054)[name = tensor("denom_305")]; - tensor out_305 = mul(x = zero_mean_305, y = denom_305)[name = tensor("out_305")]; - tensor var_10058 = const()[name = tensor("op_10058"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269362816)))]; - tensor var_10059 = add(x = out_305, y = var_10058)[name = tensor("op_10059")]; - tensor var_10061 = const()[name = tensor("op_10061"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269368000)))]; - tensor input_589 = mul(x = var_10059, y = var_10061)[name = tensor("input_589")]; - tensor var_10069 = const()[name = tensor("op_10069"), val = tensor([1, 1])]; - tensor var_10071 = const()[name = tensor("op_10071"), val = tensor([1, 1])]; - tensor var_10073_pad_type_0 = const()[name = tensor("op_10073_pad_type_0"), val = tensor("custom")]; - tensor var_10073_pad_0 = const()[name = tensor("op_10073_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10073 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias, dilations = var_10071, groups = var_6872, pad = var_10073_pad_0, pad_type = var_10073_pad_type_0, strides = var_10069, weight = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight, x = input_589)[name = tensor("op_10073")]; - tensor var_10074_split_sizes_0 = const()[name = tensor("op_10074_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_10074_axis_0 = const()[name = tensor("op_10074_axis_0"), val = tensor(1)]; - tensor var_10074_0, tensor var_10074_1 = split(axis = var_10074_axis_0, split_sizes = var_10074_split_sizes_0, x = var_10073)[name = tensor("op_10074")]; - tensor var_10076_mode_0 = const()[name = tensor("op_10076_mode_0"), val = tensor("EXACT")]; - tensor var_10076 = gelu(mode = var_10076_mode_0, x = var_10074_1)[name = tensor("op_10076")]; - tensor input_591 = mul(x = var_10074_0, y = var_10076)[name = tensor("input_591")]; - tensor var_10080 = const()[name = tensor("op_10080"), val = tensor([1, 1])]; - tensor var_10082 = const()[name = tensor("op_10082"), val = tensor([1, 1])]; - tensor var_10084_pad_type_0 = const()[name = tensor("op_10084_pad_type_0"), val = tensor("custom")]; - tensor var_10084_pad_0 = const()[name = tensor("op_10084_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10084 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias, dilations = var_10082, groups = var_6872, pad = var_10084_pad_0, pad_type = var_10084_pad_type_0, strides = var_10080, weight = up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight, x = input_591)[name = tensor("op_10084")]; - tensor inputs_307 = add(x = var_10084, y = inputs_305)[name = tensor("inputs_307")]; - tensor var_10094 = const()[name = tensor("op_10094"), val = tensor([1])]; - tensor channels_mean_307 = reduce_mean(axes = var_10094, keep_dims = var_6867, x = inputs_307)[name = tensor("channels_mean_307")]; - tensor zero_mean_307 = sub(x = inputs_307, y = channels_mean_307)[name = tensor("zero_mean_307")]; - tensor zero_mean_sq_307 = mul(x = zero_mean_307, y = zero_mean_307)[name = tensor("zero_mean_sq_307")]; - tensor var_10098 = const()[name = tensor("op_10098"), val = tensor([1])]; - tensor var_10099 = reduce_mean(axes = var_10098, keep_dims = var_6867, x = zero_mean_sq_307)[name = tensor("op_10099")]; - tensor var_10100 = const()[name = tensor("op_10100"), val = tensor(0x1.4f8b58p-17)]; - tensor var_10101 = add(x = var_10099, y = var_10100)[name = tensor("op_10101")]; - tensor denom_307_epsilon_0 = const()[name = tensor("denom_307_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_307 = rsqrt(epsilon = denom_307_epsilon_0, x = var_10101)[name = tensor("denom_307")]; - tensor out_307 = mul(x = zero_mean_307, y = denom_307)[name = tensor("out_307")]; - tensor var_10105 = const()[name = tensor("op_10105"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269373184)))]; - tensor var_10106 = add(x = out_307, y = var_10105)[name = tensor("op_10106")]; - tensor var_10108 = const()[name = tensor("op_10108"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269378368)))]; - tensor hidden_states_403 = mul(x = var_10106, y = var_10108)[name = tensor("hidden_states_403")]; - tensor var_10115 = const()[name = tensor("op_10115"), val = tensor([1, 1])]; - tensor var_10117 = const()[name = tensor("op_10117"), val = tensor([1, 1])]; + tensor attn_203_cast = matmul(transpose_x = attn_203_transpose_x_0, transpose_y = attn_203_transpose_y_0, x = var_9927_cast, y = var_9931_cast)[name = tensor("attn_203_cast")]; + tensor var_9935 = const()[name = tensor("op_9935"), val = tensor([2, 1280, 1, -1])]; + tensor input_587_cast = reshape(shape = var_9935, x = attn_203_cast)[name = tensor("input_587_cast")]; + tensor var_9940 = const()[name = tensor("op_9940"), val = tensor([1, 1])]; + tensor var_9942 = const()[name = tensor("op_9942"), val = tensor([1, 1])]; + tensor var_9944_pad_type_0 = const()[name = tensor("op_9944_pad_type_0"), val = tensor("custom")]; + tensor var_9944_pad_0 = const()[name = tensor("op_9944_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3832406656)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3835683520)))]; + tensor var_9944_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_9942, groups = var_31, pad = var_9944_pad_0, pad_type = var_9944_pad_type_0, strides = var_9940, weight = unet_up_blocks_0_attentions_1_transformer_blocks_6_attn2_to_out_0_weight_to_fp16, x = input_587_cast)[name = tensor("op_9944_cast")]; + tensor inputs_305_cast = add(x = var_9944_cast, y = inputs_303_cast)[name = tensor("inputs_305_cast")]; + tensor var_9948 = const()[name = tensor("op_9948"), val = tensor([1])]; + tensor channels_mean_305_cast = reduce_mean(axes = var_9948, keep_dims = var_23, x = inputs_305_cast)[name = tensor("channels_mean_305_cast")]; + tensor zero_mean_305_cast = sub(x = inputs_305_cast, y = channels_mean_305_cast)[name = tensor("zero_mean_305_cast")]; + tensor zero_mean_sq_305_cast = mul(x = zero_mean_305_cast, y = zero_mean_305_cast)[name = tensor("zero_mean_sq_305_cast")]; + tensor var_9952 = const()[name = tensor("op_9952"), val = tensor([1])]; + tensor var_9953_cast = reduce_mean(axes = var_9952, keep_dims = var_23, x = zero_mean_sq_305_cast)[name = tensor("op_9953_cast")]; + tensor var_9954_to_fp16 = const()[name = tensor("op_9954_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_9955_cast = add(x = var_9953_cast, y = var_9954_to_fp16)[name = tensor("op_9955_cast")]; + tensor denom_305_epsilon_0_to_fp16 = const()[name = tensor("denom_305_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_305_cast = rsqrt(epsilon = denom_305_epsilon_0_to_fp16, x = var_9955_cast)[name = tensor("denom_305_cast")]; + tensor out_305_cast = mul(x = zero_mean_305_cast, y = denom_305_cast)[name = tensor("out_305_cast")]; + tensor var_9959_to_fp16 = const()[name = tensor("op_9959_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3835686144)))]; + tensor var_9960_cast = add(x = out_305_cast, y = var_9959_to_fp16)[name = tensor("op_9960_cast")]; + tensor var_9962_to_fp16 = const()[name = tensor("op_9962_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3835688768)))]; + tensor input_589_cast = mul(x = var_9960_cast, y = var_9962_to_fp16)[name = tensor("input_589_cast")]; + tensor var_9970 = const()[name = tensor("op_9970"), val = tensor([1, 1])]; + tensor var_9972 = const()[name = tensor("op_9972"), val = tensor([1, 1])]; + tensor var_9974_pad_type_0 = const()[name = tensor("op_9974_pad_type_0"), val = tensor("custom")]; + tensor var_9974_pad_0 = const()[name = tensor("op_9974_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3835691392)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3861905856)))]; + tensor var_9974_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_9972, groups = var_31, pad = var_9974_pad_0, pad_type = var_9974_pad_type_0, strides = var_9970, weight = unet_up_blocks_0_attentions_1_transformer_blocks_6_ff_net_0_proj_weight_to_fp16, x = input_589_cast)[name = tensor("op_9974_cast")]; + tensor var_9975_split_sizes_0 = const()[name = tensor("op_9975_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_9975_axis_0 = const()[name = tensor("op_9975_axis_0"), val = tensor(1)]; + tensor var_9975_cast_0, tensor var_9975_cast_1 = split(axis = var_9975_axis_0, split_sizes = var_9975_split_sizes_0, x = var_9974_cast)[name = tensor("op_9975_cast")]; + tensor var_9977_mode_0 = const()[name = tensor("op_9977_mode_0"), val = tensor("EXACT")]; + tensor var_9977_cast = gelu(mode = var_9977_mode_0, x = var_9975_cast_1)[name = tensor("op_9977_cast")]; + tensor input_591_cast = mul(x = var_9975_cast_0, y = var_9977_cast)[name = tensor("input_591_cast")]; + tensor var_9981 = const()[name = tensor("op_9981"), val = tensor([1, 1])]; + tensor var_9983 = const()[name = tensor("op_9983"), val = tensor([1, 1])]; + tensor var_9985_pad_type_0 = const()[name = tensor("op_9985_pad_type_0"), val = tensor("custom")]; + tensor var_9985_pad_0 = const()[name = tensor("op_9985_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3861926400)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3875033664)))]; + tensor var_9985_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_9983, groups = var_31, pad = var_9985_pad_0, pad_type = var_9985_pad_type_0, strides = var_9981, weight = unet_up_blocks_0_attentions_1_transformer_blocks_6_ff_net_2_weight_to_fp16, x = input_591_cast)[name = tensor("op_9985_cast")]; + tensor inputs_307_cast = add(x = var_9985_cast, y = inputs_305_cast)[name = tensor("inputs_307_cast")]; + tensor var_9995 = const()[name = tensor("op_9995"), val = tensor([1])]; + tensor channels_mean_307_cast = reduce_mean(axes = var_9995, keep_dims = var_23, x = inputs_307_cast)[name = tensor("channels_mean_307_cast")]; + tensor zero_mean_307_cast = sub(x = inputs_307_cast, y = channels_mean_307_cast)[name = tensor("zero_mean_307_cast")]; + tensor zero_mean_sq_307_cast = mul(x = zero_mean_307_cast, y = zero_mean_307_cast)[name = tensor("zero_mean_sq_307_cast")]; + tensor var_9999 = const()[name = tensor("op_9999"), val = tensor([1])]; + tensor var_10000_cast = reduce_mean(axes = var_9999, keep_dims = var_23, x = zero_mean_sq_307_cast)[name = tensor("op_10000_cast")]; + tensor var_10001_to_fp16 = const()[name = tensor("op_10001_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10002_cast = add(x = var_10000_cast, y = var_10001_to_fp16)[name = tensor("op_10002_cast")]; + tensor denom_307_epsilon_0_to_fp16 = const()[name = tensor("denom_307_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_307_cast = rsqrt(epsilon = denom_307_epsilon_0_to_fp16, x = var_10002_cast)[name = tensor("denom_307_cast")]; + tensor out_307_cast = mul(x = zero_mean_307_cast, y = denom_307_cast)[name = tensor("out_307_cast")]; + tensor var_10006_to_fp16 = const()[name = tensor("op_10006_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3875036288)))]; + tensor var_10007_cast = add(x = out_307_cast, y = var_10006_to_fp16)[name = tensor("op_10007_cast")]; + tensor var_10009_to_fp16 = const()[name = tensor("op_10009_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3875038912)))]; + tensor hidden_states_403_cast = mul(x = var_10007_cast, y = var_10009_to_fp16)[name = tensor("hidden_states_403_cast")]; + tensor var_10016 = const()[name = tensor("op_10016"), val = tensor([1, 1])]; + tensor var_10018 = const()[name = tensor("op_10018"), val = tensor([1, 1])]; tensor q_205_pad_type_0 = const()[name = tensor("q_205_pad_type_0"), val = tensor("custom")]; tensor q_205_pad_0 = const()[name = tensor("q_205_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_205 = conv(dilations = var_10117, groups = var_6872, pad = q_205_pad_0, pad_type = q_205_pad_type_0, strides = var_10115, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight, x = hidden_states_403)[name = tensor("q_205")]; - tensor var_10121 = const()[name = tensor("op_10121"), val = tensor([1, 1])]; - tensor var_10123 = const()[name = tensor("op_10123"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3875041536)))]; + tensor q_205_cast = conv(dilations = var_10018, groups = var_31, pad = q_205_pad_0, pad_type = q_205_pad_type_0, strides = var_10016, weight = unet_up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_q_weight_to_fp16, x = hidden_states_403_cast)[name = tensor("q_205_cast")]; + tensor var_10022 = const()[name = tensor("op_10022"), val = tensor([1, 1])]; + tensor var_10024 = const()[name = tensor("op_10024"), val = tensor([1, 1])]; tensor k_205_pad_type_0 = const()[name = tensor("k_205_pad_type_0"), val = tensor("custom")]; tensor k_205_pad_0 = const()[name = tensor("k_205_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_205 = conv(dilations = var_10123, groups = var_6872, pad = k_205_pad_0, pad_type = k_205_pad_type_0, strides = var_10121, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight, x = hidden_states_403)[name = tensor("k_205")]; - tensor var_10127 = const()[name = tensor("op_10127"), val = tensor([1, 1])]; - tensor var_10129 = const()[name = tensor("op_10129"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3878318400)))]; + tensor k_205_cast = conv(dilations = var_10024, groups = var_31, pad = k_205_pad_0, pad_type = k_205_pad_type_0, strides = var_10022, weight = unet_up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_k_weight_to_fp16, x = hidden_states_403_cast)[name = tensor("k_205_cast")]; + tensor var_10028 = const()[name = tensor("op_10028"), val = tensor([1, 1])]; + tensor var_10030 = const()[name = tensor("op_10030"), val = tensor([1, 1])]; tensor v_205_pad_type_0 = const()[name = tensor("v_205_pad_type_0"), val = tensor("custom")]; tensor v_205_pad_0 = const()[name = tensor("v_205_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_205 = conv(dilations = var_10129, groups = var_6872, pad = v_205_pad_0, pad_type = v_205_pad_type_0, strides = var_10127, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight, x = hidden_states_403)[name = tensor("v_205")]; - tensor var_10133 = const()[name = tensor("op_10133"), val = tensor([2, 20, 64, -1])]; - tensor var_10134 = reshape(shape = var_10133, x = q_205)[name = tensor("op_10134")]; - tensor var_10135 = const()[name = tensor("op_10135"), val = tensor([2, 20, 64, -1])]; - tensor var_10136 = reshape(shape = var_10135, x = k_205)[name = tensor("op_10136")]; - tensor var_10137 = const()[name = tensor("op_10137"), val = tensor([2, 20, 64, -1])]; - tensor var_10138 = reshape(shape = var_10137, x = v_205)[name = tensor("op_10138")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3881595264)))]; + tensor v_205_cast = conv(dilations = var_10030, groups = var_31, pad = v_205_pad_0, pad_type = v_205_pad_type_0, strides = var_10028, weight = unet_up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_v_weight_to_fp16, x = hidden_states_403_cast)[name = tensor("v_205_cast")]; + tensor var_10034 = const()[name = tensor("op_10034"), val = tensor([2, 20, 64, -1])]; + tensor var_10035_cast = reshape(shape = var_10034, x = q_205_cast)[name = tensor("op_10035_cast")]; + tensor var_10036 = const()[name = tensor("op_10036"), val = tensor([2, 20, 64, -1])]; + tensor var_10037_cast = reshape(shape = var_10036, x = k_205_cast)[name = tensor("op_10037_cast")]; + tensor var_10038 = const()[name = tensor("op_10038"), val = tensor([2, 20, 64, -1])]; + tensor var_10039_cast = reshape(shape = var_10038, x = v_205_cast)[name = tensor("op_10039_cast")]; tensor attn_weights_409_transpose_x_0 = const()[name = tensor("attn_weights_409_transpose_x_0"), val = tensor(true)]; tensor attn_weights_409_transpose_y_0 = const()[name = tensor("attn_weights_409_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_409 = matmul(transpose_x = attn_weights_409_transpose_x_0, transpose_y = attn_weights_409_transpose_y_0, x = var_10134, y = var_10136)[name = tensor("attn_weights_409")]; - tensor attn_weights_411 = mul(x = attn_weights_409, y = var_6863)[name = tensor("attn_weights_411")]; - tensor var_10142 = softmax(axis = var_6856, x = attn_weights_411)[name = tensor("op_10142")]; + tensor attn_weights_409_cast = matmul(transpose_x = attn_weights_409_transpose_x_0, transpose_y = attn_weights_409_transpose_y_0, x = var_10035_cast, y = var_10037_cast)[name = tensor("attn_weights_409_cast")]; + tensor attn_weights_411_cast = mul(x = attn_weights_409_cast, y = var_12_to_fp16)[name = tensor("attn_weights_411_cast")]; + tensor var_10043_cast = softmax(axis = var_18, x = attn_weights_411_cast)[name = tensor("op_10043_cast")]; tensor attn_205_transpose_x_0 = const()[name = tensor("attn_205_transpose_x_0"), val = tensor(false)]; tensor attn_205_transpose_y_0 = const()[name = tensor("attn_205_transpose_y_0"), val = tensor(true)]; - tensor attn_205 = matmul(transpose_x = attn_205_transpose_x_0, transpose_y = attn_205_transpose_y_0, x = var_10138, y = var_10142)[name = tensor("attn_205")]; - tensor var_10146 = const()[name = tensor("op_10146"), val = tensor([2, 1280, 1, -1])]; - tensor input_593 = reshape(shape = var_10146, x = attn_205)[name = tensor("input_593")]; - tensor var_10151 = const()[name = tensor("op_10151"), val = tensor([1, 1])]; - tensor var_10153 = const()[name = tensor("op_10153"), val = tensor([1, 1])]; - tensor var_10155_pad_type_0 = const()[name = tensor("op_10155_pad_type_0"), val = tensor("custom")]; - tensor var_10155_pad_0 = const()[name = tensor("op_10155_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10155 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias, dilations = var_10153, groups = var_6872, pad = var_10155_pad_0, pad_type = var_10155_pad_type_0, strides = var_10151, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight, x = input_593)[name = tensor("op_10155")]; - tensor inputs_309 = add(x = var_10155, y = inputs_307)[name = tensor("inputs_309")]; - tensor var_10159 = const()[name = tensor("op_10159"), val = tensor([1])]; - tensor channels_mean_309 = reduce_mean(axes = var_10159, keep_dims = var_6867, x = inputs_309)[name = tensor("channels_mean_309")]; - tensor zero_mean_309 = sub(x = inputs_309, y = channels_mean_309)[name = tensor("zero_mean_309")]; - tensor zero_mean_sq_309 = mul(x = zero_mean_309, y = zero_mean_309)[name = tensor("zero_mean_sq_309")]; - tensor var_10163 = const()[name = tensor("op_10163"), val = tensor([1])]; - tensor var_10164 = reduce_mean(axes = var_10163, keep_dims = var_6867, x = zero_mean_sq_309)[name = tensor("op_10164")]; - tensor var_10165 = const()[name = tensor("op_10165"), val = tensor(0x1.4f8b58p-17)]; - tensor var_10166 = add(x = var_10164, y = var_10165)[name = tensor("op_10166")]; - tensor denom_309_epsilon_0 = const()[name = tensor("denom_309_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_309 = rsqrt(epsilon = denom_309_epsilon_0, x = var_10166)[name = tensor("denom_309")]; - tensor out_309 = mul(x = zero_mean_309, y = denom_309)[name = tensor("out_309")]; - tensor var_10170 = const()[name = tensor("op_10170"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269383552)))]; - tensor var_10171 = add(x = out_309, y = var_10170)[name = tensor("op_10171")]; - tensor var_10173 = const()[name = tensor("op_10173"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269388736)))]; - tensor hidden_states_405 = mul(x = var_10171, y = var_10173)[name = tensor("hidden_states_405")]; - tensor var_10180 = const()[name = tensor("op_10180"), val = tensor([1, 1])]; - tensor var_10182 = const()[name = tensor("op_10182"), val = tensor([1, 1])]; + tensor attn_205_cast = matmul(transpose_x = attn_205_transpose_x_0, transpose_y = attn_205_transpose_y_0, x = var_10039_cast, y = var_10043_cast)[name = tensor("attn_205_cast")]; + tensor var_10047 = const()[name = tensor("op_10047"), val = tensor([2, 1280, 1, -1])]; + tensor input_593_cast = reshape(shape = var_10047, x = attn_205_cast)[name = tensor("input_593_cast")]; + tensor var_10052 = const()[name = tensor("op_10052"), val = tensor([1, 1])]; + tensor var_10054 = const()[name = tensor("op_10054"), val = tensor([1, 1])]; + tensor var_10056_pad_type_0 = const()[name = tensor("op_10056_pad_type_0"), val = tensor("custom")]; + tensor var_10056_pad_0 = const()[name = tensor("op_10056_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3884872128)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3888148992)))]; + tensor var_10056_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_10054, groups = var_31, pad = var_10056_pad_0, pad_type = var_10056_pad_type_0, strides = var_10052, weight = unet_up_blocks_0_attentions_1_transformer_blocks_7_attn1_to_out_0_weight_to_fp16, x = input_593_cast)[name = tensor("op_10056_cast")]; + tensor inputs_309_cast = add(x = var_10056_cast, y = inputs_307_cast)[name = tensor("inputs_309_cast")]; + tensor var_10060 = const()[name = tensor("op_10060"), val = tensor([1])]; + tensor channels_mean_309_cast = reduce_mean(axes = var_10060, keep_dims = var_23, x = inputs_309_cast)[name = tensor("channels_mean_309_cast")]; + tensor zero_mean_309_cast = sub(x = inputs_309_cast, y = channels_mean_309_cast)[name = tensor("zero_mean_309_cast")]; + tensor zero_mean_sq_309_cast = mul(x = zero_mean_309_cast, y = zero_mean_309_cast)[name = tensor("zero_mean_sq_309_cast")]; + tensor var_10064 = const()[name = tensor("op_10064"), val = tensor([1])]; + tensor var_10065_cast = reduce_mean(axes = var_10064, keep_dims = var_23, x = zero_mean_sq_309_cast)[name = tensor("op_10065_cast")]; + tensor var_10066_to_fp16 = const()[name = tensor("op_10066_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10067_cast = add(x = var_10065_cast, y = var_10066_to_fp16)[name = tensor("op_10067_cast")]; + tensor denom_309_epsilon_0_to_fp16 = const()[name = tensor("denom_309_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_309_cast = rsqrt(epsilon = denom_309_epsilon_0_to_fp16, x = var_10067_cast)[name = tensor("denom_309_cast")]; + tensor out_309_cast = mul(x = zero_mean_309_cast, y = denom_309_cast)[name = tensor("out_309_cast")]; + tensor var_10071_to_fp16 = const()[name = tensor("op_10071_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3888151616)))]; + tensor var_10072_cast = add(x = out_309_cast, y = var_10071_to_fp16)[name = tensor("op_10072_cast")]; + tensor var_10074_to_fp16 = const()[name = tensor("op_10074_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3888154240)))]; + tensor hidden_states_405_cast = mul(x = var_10072_cast, y = var_10074_to_fp16)[name = tensor("hidden_states_405_cast")]; + tensor var_10081 = const()[name = tensor("op_10081"), val = tensor([1, 1])]; + tensor var_10083 = const()[name = tensor("op_10083"), val = tensor([1, 1])]; tensor q_207_pad_type_0 = const()[name = tensor("q_207_pad_type_0"), val = tensor("custom")]; tensor q_207_pad_0 = const()[name = tensor("q_207_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_207 = conv(dilations = var_10182, groups = var_6872, pad = q_207_pad_0, pad_type = q_207_pad_type_0, strides = var_10180, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight, x = hidden_states_405)[name = tensor("q_207")]; - tensor var_10186 = const()[name = tensor("op_10186"), val = tensor([1, 1])]; - tensor var_10188 = const()[name = tensor("op_10188"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3888156864)))]; + tensor q_207_cast = conv(dilations = var_10083, groups = var_31, pad = q_207_pad_0, pad_type = q_207_pad_type_0, strides = var_10081, weight = unet_up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_q_weight_to_fp16, x = hidden_states_405_cast)[name = tensor("q_207_cast")]; + tensor var_10087 = const()[name = tensor("op_10087"), val = tensor([1, 1])]; + tensor var_10089 = const()[name = tensor("op_10089"), val = tensor([1, 1])]; tensor k_207_pad_type_0 = const()[name = tensor("k_207_pad_type_0"), val = tensor("custom")]; tensor k_207_pad_0 = const()[name = tensor("k_207_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_207 = conv(dilations = var_10188, groups = var_6872, pad = k_207_pad_0, pad_type = k_207_pad_type_0, strides = var_10186, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_207")]; - tensor var_10192 = const()[name = tensor("op_10192"), val = tensor([1, 1])]; - tensor var_10194 = const()[name = tensor("op_10194"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3891433728)))]; + tensor k_207_cast = conv(dilations = var_10089, groups = var_31, pad = k_207_pad_0, pad_type = k_207_pad_type_0, strides = var_10087, weight = unet_up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_207_cast")]; + tensor var_10093 = const()[name = tensor("op_10093"), val = tensor([1, 1])]; + tensor var_10095 = const()[name = tensor("op_10095"), val = tensor([1, 1])]; tensor v_207_pad_type_0 = const()[name = tensor("v_207_pad_type_0"), val = tensor("custom")]; tensor v_207_pad_0 = const()[name = tensor("v_207_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_207 = conv(dilations = var_10194, groups = var_6872, pad = v_207_pad_0, pad_type = v_207_pad_type_0, strides = var_10192, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_207")]; - tensor var_10198 = const()[name = tensor("op_10198"), val = tensor([2, 20, 64, -1])]; - tensor var_10199 = reshape(shape = var_10198, x = q_207)[name = tensor("op_10199")]; - tensor var_10200 = const()[name = tensor("op_10200"), val = tensor([2, 20, 64, -1])]; - tensor var_10201 = reshape(shape = var_10200, x = k_207)[name = tensor("op_10201")]; - tensor var_10202 = const()[name = tensor("op_10202"), val = tensor([2, 20, 64, -1])]; - tensor var_10203 = reshape(shape = var_10202, x = v_207)[name = tensor("op_10203")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3896676672)))]; + tensor v_207_cast = conv(dilations = var_10095, groups = var_31, pad = v_207_pad_0, pad_type = v_207_pad_type_0, strides = var_10093, weight = unet_up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_207_cast")]; + tensor var_10099 = const()[name = tensor("op_10099"), val = tensor([2, 20, 64, -1])]; + tensor var_10100_cast = reshape(shape = var_10099, x = q_207_cast)[name = tensor("op_10100_cast")]; + tensor var_10101 = const()[name = tensor("op_10101"), val = tensor([2, 20, 64, -1])]; + tensor var_10102_cast = reshape(shape = var_10101, x = k_207_cast)[name = tensor("op_10102_cast")]; + tensor var_10103 = const()[name = tensor("op_10103"), val = tensor([2, 20, 64, -1])]; + tensor var_10104_cast = reshape(shape = var_10103, x = v_207_cast)[name = tensor("op_10104_cast")]; tensor attn_weights_413_transpose_x_0 = const()[name = tensor("attn_weights_413_transpose_x_0"), val = tensor(true)]; tensor attn_weights_413_transpose_y_0 = const()[name = tensor("attn_weights_413_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_413 = matmul(transpose_x = attn_weights_413_transpose_x_0, transpose_y = attn_weights_413_transpose_y_0, x = var_10199, y = var_10201)[name = tensor("attn_weights_413")]; - tensor attn_weights_415 = mul(x = attn_weights_413, y = var_6863)[name = tensor("attn_weights_415")]; - tensor var_10207 = softmax(axis = var_6856, x = attn_weights_415)[name = tensor("op_10207")]; + tensor attn_weights_413_cast = matmul(transpose_x = attn_weights_413_transpose_x_0, transpose_y = attn_weights_413_transpose_y_0, x = var_10100_cast, y = var_10102_cast)[name = tensor("attn_weights_413_cast")]; + tensor attn_weights_415_cast = mul(x = attn_weights_413_cast, y = var_12_to_fp16)[name = tensor("attn_weights_415_cast")]; + tensor var_10108_cast = softmax(axis = var_18, x = attn_weights_415_cast)[name = tensor("op_10108_cast")]; tensor attn_207_transpose_x_0 = const()[name = tensor("attn_207_transpose_x_0"), val = tensor(false)]; tensor attn_207_transpose_y_0 = const()[name = tensor("attn_207_transpose_y_0"), val = tensor(true)]; - tensor attn_207 = matmul(transpose_x = attn_207_transpose_x_0, transpose_y = attn_207_transpose_y_0, x = var_10203, y = var_10207)[name = tensor("attn_207")]; - tensor var_10211 = const()[name = tensor("op_10211"), val = tensor([2, 1280, 1, -1])]; - tensor input_595 = reshape(shape = var_10211, x = attn_207)[name = tensor("input_595")]; - tensor var_10216 = const()[name = tensor("op_10216"), val = tensor([1, 1])]; - tensor var_10218 = const()[name = tensor("op_10218"), val = tensor([1, 1])]; - tensor var_10220_pad_type_0 = const()[name = tensor("op_10220_pad_type_0"), val = tensor("custom")]; - tensor var_10220_pad_0 = const()[name = tensor("op_10220_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10220 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias, dilations = var_10218, groups = var_6872, pad = var_10220_pad_0, pad_type = var_10220_pad_type_0, strides = var_10216, weight = up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight, x = input_595)[name = tensor("op_10220")]; - tensor inputs_311 = add(x = var_10220, y = inputs_309)[name = tensor("inputs_311")]; - tensor var_10224 = const()[name = tensor("op_10224"), val = tensor([1])]; - tensor channels_mean_311 = reduce_mean(axes = var_10224, keep_dims = var_6867, x = inputs_311)[name = tensor("channels_mean_311")]; - tensor zero_mean_311 = sub(x = inputs_311, y = channels_mean_311)[name = tensor("zero_mean_311")]; - tensor zero_mean_sq_311 = mul(x = zero_mean_311, y = zero_mean_311)[name = tensor("zero_mean_sq_311")]; - tensor var_10228 = const()[name = tensor("op_10228"), val = tensor([1])]; - tensor var_10229 = reduce_mean(axes = var_10228, keep_dims = var_6867, x = zero_mean_sq_311)[name = tensor("op_10229")]; - tensor var_10230 = const()[name = tensor("op_10230"), val = tensor(0x1.4f8b58p-17)]; - tensor var_10231 = add(x = var_10229, y = var_10230)[name = tensor("op_10231")]; - tensor denom_311_epsilon_0 = const()[name = tensor("denom_311_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_311 = rsqrt(epsilon = denom_311_epsilon_0, x = var_10231)[name = tensor("denom_311")]; - tensor out_311 = mul(x = zero_mean_311, y = denom_311)[name = tensor("out_311")]; - tensor var_10235 = const()[name = tensor("op_10235"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269393920)))]; - tensor var_10236 = add(x = out_311, y = var_10235)[name = tensor("op_10236")]; - tensor var_10238 = const()[name = tensor("op_10238"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269399104)))]; - tensor input_597 = mul(x = var_10236, y = var_10238)[name = tensor("input_597")]; - tensor var_10246 = const()[name = tensor("op_10246"), val = tensor([1, 1])]; - tensor var_10248 = const()[name = tensor("op_10248"), val = tensor([1, 1])]; - tensor var_10250_pad_type_0 = const()[name = tensor("op_10250_pad_type_0"), val = tensor("custom")]; - tensor var_10250_pad_0 = const()[name = tensor("op_10250_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10250 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias, dilations = var_10248, groups = var_6872, pad = var_10250_pad_0, pad_type = var_10250_pad_type_0, strides = var_10246, weight = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight, x = input_597)[name = tensor("op_10250")]; - tensor var_10251_split_sizes_0 = const()[name = tensor("op_10251_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_10251_axis_0 = const()[name = tensor("op_10251_axis_0"), val = tensor(1)]; - tensor var_10251_0, tensor var_10251_1 = split(axis = var_10251_axis_0, split_sizes = var_10251_split_sizes_0, x = var_10250)[name = tensor("op_10251")]; - tensor var_10253_mode_0 = const()[name = tensor("op_10253_mode_0"), val = tensor("EXACT")]; - tensor var_10253 = gelu(mode = var_10253_mode_0, x = var_10251_1)[name = tensor("op_10253")]; - tensor input_599 = mul(x = var_10251_0, y = var_10253)[name = tensor("input_599")]; - tensor var_10257 = const()[name = tensor("op_10257"), val = tensor([1, 1])]; - tensor var_10259 = const()[name = tensor("op_10259"), val = tensor([1, 1])]; - tensor var_10261_pad_type_0 = const()[name = tensor("op_10261_pad_type_0"), val = tensor("custom")]; - tensor var_10261_pad_0 = const()[name = tensor("op_10261_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10261 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias, dilations = var_10259, groups = var_6872, pad = var_10261_pad_0, pad_type = var_10261_pad_type_0, strides = var_10257, weight = up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight, x = input_599)[name = tensor("op_10261")]; - tensor inputs_313 = add(x = var_10261, y = inputs_311)[name = tensor("inputs_313")]; - tensor var_10271 = const()[name = tensor("op_10271"), val = tensor([1])]; - tensor channels_mean_313 = reduce_mean(axes = var_10271, keep_dims = var_6867, x = inputs_313)[name = tensor("channels_mean_313")]; - tensor zero_mean_313 = sub(x = inputs_313, y = channels_mean_313)[name = tensor("zero_mean_313")]; - tensor zero_mean_sq_313 = mul(x = zero_mean_313, y = zero_mean_313)[name = tensor("zero_mean_sq_313")]; - tensor var_10275 = const()[name = tensor("op_10275"), val = tensor([1])]; - tensor var_10276 = reduce_mean(axes = var_10275, keep_dims = var_6867, x = zero_mean_sq_313)[name = tensor("op_10276")]; - tensor var_10277 = const()[name = tensor("op_10277"), val = tensor(0x1.4f8b58p-17)]; - tensor var_10278 = add(x = var_10276, y = var_10277)[name = tensor("op_10278")]; - tensor denom_313_epsilon_0 = const()[name = tensor("denom_313_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_313 = rsqrt(epsilon = denom_313_epsilon_0, x = var_10278)[name = tensor("denom_313")]; - tensor out_313 = mul(x = zero_mean_313, y = denom_313)[name = tensor("out_313")]; - tensor var_10282 = const()[name = tensor("op_10282"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269404288)))]; - tensor var_10283 = add(x = out_313, y = var_10282)[name = tensor("op_10283")]; - tensor var_10285 = const()[name = tensor("op_10285"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269409472)))]; - tensor hidden_states_409 = mul(x = var_10283, y = var_10285)[name = tensor("hidden_states_409")]; - tensor var_10292 = const()[name = tensor("op_10292"), val = tensor([1, 1])]; - tensor var_10294 = const()[name = tensor("op_10294"), val = tensor([1, 1])]; + tensor attn_207_cast = matmul(transpose_x = attn_207_transpose_x_0, transpose_y = attn_207_transpose_y_0, x = var_10104_cast, y = var_10108_cast)[name = tensor("attn_207_cast")]; + tensor var_10112 = const()[name = tensor("op_10112"), val = tensor([2, 1280, 1, -1])]; + tensor input_595_cast = reshape(shape = var_10112, x = attn_207_cast)[name = tensor("input_595_cast")]; + tensor var_10117 = const()[name = tensor("op_10117"), val = tensor([1, 1])]; + tensor var_10119 = const()[name = tensor("op_10119"), val = tensor([1, 1])]; + tensor var_10121_pad_type_0 = const()[name = tensor("op_10121_pad_type_0"), val = tensor("custom")]; + tensor var_10121_pad_0 = const()[name = tensor("op_10121_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3901919616)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3905196480)))]; + tensor var_10121_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_10119, groups = var_31, pad = var_10121_pad_0, pad_type = var_10121_pad_type_0, strides = var_10117, weight = unet_up_blocks_0_attentions_1_transformer_blocks_7_attn2_to_out_0_weight_to_fp16, x = input_595_cast)[name = tensor("op_10121_cast")]; + tensor inputs_311_cast = add(x = var_10121_cast, y = inputs_309_cast)[name = tensor("inputs_311_cast")]; + tensor var_10125 = const()[name = tensor("op_10125"), val = tensor([1])]; + tensor channels_mean_311_cast = reduce_mean(axes = var_10125, keep_dims = var_23, x = inputs_311_cast)[name = tensor("channels_mean_311_cast")]; + tensor zero_mean_311_cast = sub(x = inputs_311_cast, y = channels_mean_311_cast)[name = tensor("zero_mean_311_cast")]; + tensor zero_mean_sq_311_cast = mul(x = zero_mean_311_cast, y = zero_mean_311_cast)[name = tensor("zero_mean_sq_311_cast")]; + tensor var_10129 = const()[name = tensor("op_10129"), val = tensor([1])]; + tensor var_10130_cast = reduce_mean(axes = var_10129, keep_dims = var_23, x = zero_mean_sq_311_cast)[name = tensor("op_10130_cast")]; + tensor var_10131_to_fp16 = const()[name = tensor("op_10131_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10132_cast = add(x = var_10130_cast, y = var_10131_to_fp16)[name = tensor("op_10132_cast")]; + tensor denom_311_epsilon_0_to_fp16 = const()[name = tensor("denom_311_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_311_cast = rsqrt(epsilon = denom_311_epsilon_0_to_fp16, x = var_10132_cast)[name = tensor("denom_311_cast")]; + tensor out_311_cast = mul(x = zero_mean_311_cast, y = denom_311_cast)[name = tensor("out_311_cast")]; + tensor var_10136_to_fp16 = const()[name = tensor("op_10136_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3905199104)))]; + tensor var_10137_cast = add(x = out_311_cast, y = var_10136_to_fp16)[name = tensor("op_10137_cast")]; + tensor var_10139_to_fp16 = const()[name = tensor("op_10139_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3905201728)))]; + tensor input_597_cast = mul(x = var_10137_cast, y = var_10139_to_fp16)[name = tensor("input_597_cast")]; + tensor var_10147 = const()[name = tensor("op_10147"), val = tensor([1, 1])]; + tensor var_10149 = const()[name = tensor("op_10149"), val = tensor([1, 1])]; + tensor var_10151_pad_type_0 = const()[name = tensor("op_10151_pad_type_0"), val = tensor("custom")]; + tensor var_10151_pad_0 = const()[name = tensor("op_10151_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3905204352)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3931418816)))]; + tensor var_10151_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_10149, groups = var_31, pad = var_10151_pad_0, pad_type = var_10151_pad_type_0, strides = var_10147, weight = unet_up_blocks_0_attentions_1_transformer_blocks_7_ff_net_0_proj_weight_to_fp16, x = input_597_cast)[name = tensor("op_10151_cast")]; + tensor var_10152_split_sizes_0 = const()[name = tensor("op_10152_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10152_axis_0 = const()[name = tensor("op_10152_axis_0"), val = tensor(1)]; + tensor var_10152_cast_0, tensor var_10152_cast_1 = split(axis = var_10152_axis_0, split_sizes = var_10152_split_sizes_0, x = var_10151_cast)[name = tensor("op_10152_cast")]; + tensor var_10154_mode_0 = const()[name = tensor("op_10154_mode_0"), val = tensor("EXACT")]; + tensor var_10154_cast = gelu(mode = var_10154_mode_0, x = var_10152_cast_1)[name = tensor("op_10154_cast")]; + tensor input_599_cast = mul(x = var_10152_cast_0, y = var_10154_cast)[name = tensor("input_599_cast")]; + tensor var_10158 = const()[name = tensor("op_10158"), val = tensor([1, 1])]; + tensor var_10160 = const()[name = tensor("op_10160"), val = tensor([1, 1])]; + tensor var_10162_pad_type_0 = const()[name = tensor("op_10162_pad_type_0"), val = tensor("custom")]; + tensor var_10162_pad_0 = const()[name = tensor("op_10162_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3931439360)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3944546624)))]; + tensor var_10162_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_10160, groups = var_31, pad = var_10162_pad_0, pad_type = var_10162_pad_type_0, strides = var_10158, weight = unet_up_blocks_0_attentions_1_transformer_blocks_7_ff_net_2_weight_to_fp16, x = input_599_cast)[name = tensor("op_10162_cast")]; + tensor inputs_313_cast = add(x = var_10162_cast, y = inputs_311_cast)[name = tensor("inputs_313_cast")]; + tensor var_10172 = const()[name = tensor("op_10172"), val = tensor([1])]; + tensor channels_mean_313_cast = reduce_mean(axes = var_10172, keep_dims = var_23, x = inputs_313_cast)[name = tensor("channels_mean_313_cast")]; + tensor zero_mean_313_cast = sub(x = inputs_313_cast, y = channels_mean_313_cast)[name = tensor("zero_mean_313_cast")]; + tensor zero_mean_sq_313_cast = mul(x = zero_mean_313_cast, y = zero_mean_313_cast)[name = tensor("zero_mean_sq_313_cast")]; + tensor var_10176 = const()[name = tensor("op_10176"), val = tensor([1])]; + tensor var_10177_cast = reduce_mean(axes = var_10176, keep_dims = var_23, x = zero_mean_sq_313_cast)[name = tensor("op_10177_cast")]; + tensor var_10178_to_fp16 = const()[name = tensor("op_10178_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10179_cast = add(x = var_10177_cast, y = var_10178_to_fp16)[name = tensor("op_10179_cast")]; + tensor denom_313_epsilon_0_to_fp16 = const()[name = tensor("denom_313_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_313_cast = rsqrt(epsilon = denom_313_epsilon_0_to_fp16, x = var_10179_cast)[name = tensor("denom_313_cast")]; + tensor out_313_cast = mul(x = zero_mean_313_cast, y = denom_313_cast)[name = tensor("out_313_cast")]; + tensor var_10183_to_fp16 = const()[name = tensor("op_10183_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3944549248)))]; + tensor var_10184_cast = add(x = out_313_cast, y = var_10183_to_fp16)[name = tensor("op_10184_cast")]; + tensor var_10186_to_fp16 = const()[name = tensor("op_10186_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3944551872)))]; + tensor hidden_states_409_cast = mul(x = var_10184_cast, y = var_10186_to_fp16)[name = tensor("hidden_states_409_cast")]; + tensor var_10193 = const()[name = tensor("op_10193"), val = tensor([1, 1])]; + tensor var_10195 = const()[name = tensor("op_10195"), val = tensor([1, 1])]; tensor q_209_pad_type_0 = const()[name = tensor("q_209_pad_type_0"), val = tensor("custom")]; tensor q_209_pad_0 = const()[name = tensor("q_209_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_209 = conv(dilations = var_10294, groups = var_6872, pad = q_209_pad_0, pad_type = q_209_pad_type_0, strides = var_10292, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight, x = hidden_states_409)[name = tensor("q_209")]; - tensor var_10298 = const()[name = tensor("op_10298"), val = tensor([1, 1])]; - tensor var_10300 = const()[name = tensor("op_10300"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3944554496)))]; + tensor q_209_cast = conv(dilations = var_10195, groups = var_31, pad = q_209_pad_0, pad_type = q_209_pad_type_0, strides = var_10193, weight = unet_up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_q_weight_to_fp16, x = hidden_states_409_cast)[name = tensor("q_209_cast")]; + tensor var_10199 = const()[name = tensor("op_10199"), val = tensor([1, 1])]; + tensor var_10201 = const()[name = tensor("op_10201"), val = tensor([1, 1])]; tensor k_209_pad_type_0 = const()[name = tensor("k_209_pad_type_0"), val = tensor("custom")]; tensor k_209_pad_0 = const()[name = tensor("k_209_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_209 = conv(dilations = var_10300, groups = var_6872, pad = k_209_pad_0, pad_type = k_209_pad_type_0, strides = var_10298, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight, x = hidden_states_409)[name = tensor("k_209")]; - tensor var_10304 = const()[name = tensor("op_10304"), val = tensor([1, 1])]; - tensor var_10306 = const()[name = tensor("op_10306"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3947831360)))]; + tensor k_209_cast = conv(dilations = var_10201, groups = var_31, pad = k_209_pad_0, pad_type = k_209_pad_type_0, strides = var_10199, weight = unet_up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_k_weight_to_fp16, x = hidden_states_409_cast)[name = tensor("k_209_cast")]; + tensor var_10205 = const()[name = tensor("op_10205"), val = tensor([1, 1])]; + tensor var_10207 = const()[name = tensor("op_10207"), val = tensor([1, 1])]; tensor v_209_pad_type_0 = const()[name = tensor("v_209_pad_type_0"), val = tensor("custom")]; tensor v_209_pad_0 = const()[name = tensor("v_209_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_209 = conv(dilations = var_10306, groups = var_6872, pad = v_209_pad_0, pad_type = v_209_pad_type_0, strides = var_10304, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight, x = hidden_states_409)[name = tensor("v_209")]; - tensor var_10310 = const()[name = tensor("op_10310"), val = tensor([2, 20, 64, -1])]; - tensor var_10311 = reshape(shape = var_10310, x = q_209)[name = tensor("op_10311")]; - tensor var_10312 = const()[name = tensor("op_10312"), val = tensor([2, 20, 64, -1])]; - tensor var_10313 = reshape(shape = var_10312, x = k_209)[name = tensor("op_10313")]; - tensor var_10314 = const()[name = tensor("op_10314"), val = tensor([2, 20, 64, -1])]; - tensor var_10315 = reshape(shape = var_10314, x = v_209)[name = tensor("op_10315")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3951108224)))]; + tensor v_209_cast = conv(dilations = var_10207, groups = var_31, pad = v_209_pad_0, pad_type = v_209_pad_type_0, strides = var_10205, weight = unet_up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_v_weight_to_fp16, x = hidden_states_409_cast)[name = tensor("v_209_cast")]; + tensor var_10211 = const()[name = tensor("op_10211"), val = tensor([2, 20, 64, -1])]; + tensor var_10212_cast = reshape(shape = var_10211, x = q_209_cast)[name = tensor("op_10212_cast")]; + tensor var_10213 = const()[name = tensor("op_10213"), val = tensor([2, 20, 64, -1])]; + tensor var_10214_cast = reshape(shape = var_10213, x = k_209_cast)[name = tensor("op_10214_cast")]; + tensor var_10215 = const()[name = tensor("op_10215"), val = tensor([2, 20, 64, -1])]; + tensor var_10216_cast = reshape(shape = var_10215, x = v_209_cast)[name = tensor("op_10216_cast")]; tensor attn_weights_417_transpose_x_0 = const()[name = tensor("attn_weights_417_transpose_x_0"), val = tensor(true)]; tensor attn_weights_417_transpose_y_0 = const()[name = tensor("attn_weights_417_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_417 = matmul(transpose_x = attn_weights_417_transpose_x_0, transpose_y = attn_weights_417_transpose_y_0, x = var_10311, y = var_10313)[name = tensor("attn_weights_417")]; - tensor attn_weights_419 = mul(x = attn_weights_417, y = var_6863)[name = tensor("attn_weights_419")]; - tensor var_10319 = softmax(axis = var_6856, x = attn_weights_419)[name = tensor("op_10319")]; + tensor attn_weights_417_cast = matmul(transpose_x = attn_weights_417_transpose_x_0, transpose_y = attn_weights_417_transpose_y_0, x = var_10212_cast, y = var_10214_cast)[name = tensor("attn_weights_417_cast")]; + tensor attn_weights_419_cast = mul(x = attn_weights_417_cast, y = var_12_to_fp16)[name = tensor("attn_weights_419_cast")]; + tensor var_10220_cast = softmax(axis = var_18, x = attn_weights_419_cast)[name = tensor("op_10220_cast")]; tensor attn_209_transpose_x_0 = const()[name = tensor("attn_209_transpose_x_0"), val = tensor(false)]; tensor attn_209_transpose_y_0 = const()[name = tensor("attn_209_transpose_y_0"), val = tensor(true)]; - tensor attn_209 = matmul(transpose_x = attn_209_transpose_x_0, transpose_y = attn_209_transpose_y_0, x = var_10315, y = var_10319)[name = tensor("attn_209")]; - tensor var_10323 = const()[name = tensor("op_10323"), val = tensor([2, 1280, 1, -1])]; - tensor input_601 = reshape(shape = var_10323, x = attn_209)[name = tensor("input_601")]; - tensor var_10328 = const()[name = tensor("op_10328"), val = tensor([1, 1])]; - tensor var_10330 = const()[name = tensor("op_10330"), val = tensor([1, 1])]; - tensor var_10332_pad_type_0 = const()[name = tensor("op_10332_pad_type_0"), val = tensor("custom")]; - tensor var_10332_pad_0 = const()[name = tensor("op_10332_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10332 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias, dilations = var_10330, groups = var_6872, pad = var_10332_pad_0, pad_type = var_10332_pad_type_0, strides = var_10328, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight, x = input_601)[name = tensor("op_10332")]; - tensor inputs_315 = add(x = var_10332, y = inputs_313)[name = tensor("inputs_315")]; - tensor var_10336 = const()[name = tensor("op_10336"), val = tensor([1])]; - tensor channels_mean_315 = reduce_mean(axes = var_10336, keep_dims = var_6867, x = inputs_315)[name = tensor("channels_mean_315")]; - tensor zero_mean_315 = sub(x = inputs_315, y = channels_mean_315)[name = tensor("zero_mean_315")]; - tensor zero_mean_sq_315 = mul(x = zero_mean_315, y = zero_mean_315)[name = tensor("zero_mean_sq_315")]; - tensor var_10340 = const()[name = tensor("op_10340"), val = tensor([1])]; - tensor var_10341 = reduce_mean(axes = var_10340, keep_dims = var_6867, x = zero_mean_sq_315)[name = tensor("op_10341")]; - tensor var_10342 = const()[name = tensor("op_10342"), val = tensor(0x1.4f8b58p-17)]; - tensor var_10343 = add(x = var_10341, y = var_10342)[name = tensor("op_10343")]; - tensor denom_315_epsilon_0 = const()[name = tensor("denom_315_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_315 = rsqrt(epsilon = denom_315_epsilon_0, x = var_10343)[name = tensor("denom_315")]; - tensor out_315 = mul(x = zero_mean_315, y = denom_315)[name = tensor("out_315")]; - tensor var_10347 = const()[name = tensor("op_10347"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269414656)))]; - tensor var_10348 = add(x = out_315, y = var_10347)[name = tensor("op_10348")]; - tensor var_10350 = const()[name = tensor("op_10350"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269419840)))]; - tensor hidden_states_411 = mul(x = var_10348, y = var_10350)[name = tensor("hidden_states_411")]; - tensor var_10357 = const()[name = tensor("op_10357"), val = tensor([1, 1])]; - tensor var_10359 = const()[name = tensor("op_10359"), val = tensor([1, 1])]; + tensor attn_209_cast = matmul(transpose_x = attn_209_transpose_x_0, transpose_y = attn_209_transpose_y_0, x = var_10216_cast, y = var_10220_cast)[name = tensor("attn_209_cast")]; + tensor var_10224 = const()[name = tensor("op_10224"), val = tensor([2, 1280, 1, -1])]; + tensor input_601_cast = reshape(shape = var_10224, x = attn_209_cast)[name = tensor("input_601_cast")]; + tensor var_10229 = const()[name = tensor("op_10229"), val = tensor([1, 1])]; + tensor var_10231 = const()[name = tensor("op_10231"), val = tensor([1, 1])]; + tensor var_10233_pad_type_0 = const()[name = tensor("op_10233_pad_type_0"), val = tensor("custom")]; + tensor var_10233_pad_0 = const()[name = tensor("op_10233_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3954385088)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3957661952)))]; + tensor var_10233_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_10231, groups = var_31, pad = var_10233_pad_0, pad_type = var_10233_pad_type_0, strides = var_10229, weight = unet_up_blocks_0_attentions_1_transformer_blocks_8_attn1_to_out_0_weight_to_fp16, x = input_601_cast)[name = tensor("op_10233_cast")]; + tensor inputs_315_cast = add(x = var_10233_cast, y = inputs_313_cast)[name = tensor("inputs_315_cast")]; + tensor var_10237 = const()[name = tensor("op_10237"), val = tensor([1])]; + tensor channels_mean_315_cast = reduce_mean(axes = var_10237, keep_dims = var_23, x = inputs_315_cast)[name = tensor("channels_mean_315_cast")]; + tensor zero_mean_315_cast = sub(x = inputs_315_cast, y = channels_mean_315_cast)[name = tensor("zero_mean_315_cast")]; + tensor zero_mean_sq_315_cast = mul(x = zero_mean_315_cast, y = zero_mean_315_cast)[name = tensor("zero_mean_sq_315_cast")]; + tensor var_10241 = const()[name = tensor("op_10241"), val = tensor([1])]; + tensor var_10242_cast = reduce_mean(axes = var_10241, keep_dims = var_23, x = zero_mean_sq_315_cast)[name = tensor("op_10242_cast")]; + tensor var_10243_to_fp16 = const()[name = tensor("op_10243_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10244_cast = add(x = var_10242_cast, y = var_10243_to_fp16)[name = tensor("op_10244_cast")]; + tensor denom_315_epsilon_0_to_fp16 = const()[name = tensor("denom_315_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_315_cast = rsqrt(epsilon = denom_315_epsilon_0_to_fp16, x = var_10244_cast)[name = tensor("denom_315_cast")]; + tensor out_315_cast = mul(x = zero_mean_315_cast, y = denom_315_cast)[name = tensor("out_315_cast")]; + tensor var_10248_to_fp16 = const()[name = tensor("op_10248_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3957664576)))]; + tensor var_10249_cast = add(x = out_315_cast, y = var_10248_to_fp16)[name = tensor("op_10249_cast")]; + tensor var_10251_to_fp16 = const()[name = tensor("op_10251_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3957667200)))]; + tensor hidden_states_411_cast = mul(x = var_10249_cast, y = var_10251_to_fp16)[name = tensor("hidden_states_411_cast")]; + tensor var_10258 = const()[name = tensor("op_10258"), val = tensor([1, 1])]; + tensor var_10260 = const()[name = tensor("op_10260"), val = tensor([1, 1])]; tensor q_211_pad_type_0 = const()[name = tensor("q_211_pad_type_0"), val = tensor("custom")]; tensor q_211_pad_0 = const()[name = tensor("q_211_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_211 = conv(dilations = var_10359, groups = var_6872, pad = q_211_pad_0, pad_type = q_211_pad_type_0, strides = var_10357, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight, x = hidden_states_411)[name = tensor("q_211")]; - tensor var_10363 = const()[name = tensor("op_10363"), val = tensor([1, 1])]; - tensor var_10365 = const()[name = tensor("op_10365"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3957669824)))]; + tensor q_211_cast = conv(dilations = var_10260, groups = var_31, pad = q_211_pad_0, pad_type = q_211_pad_type_0, strides = var_10258, weight = unet_up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_q_weight_to_fp16, x = hidden_states_411_cast)[name = tensor("q_211_cast")]; + tensor var_10264 = const()[name = tensor("op_10264"), val = tensor([1, 1])]; + tensor var_10266 = const()[name = tensor("op_10266"), val = tensor([1, 1])]; tensor k_211_pad_type_0 = const()[name = tensor("k_211_pad_type_0"), val = tensor("custom")]; tensor k_211_pad_0 = const()[name = tensor("k_211_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_211 = conv(dilations = var_10365, groups = var_6872, pad = k_211_pad_0, pad_type = k_211_pad_type_0, strides = var_10363, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_211")]; - tensor var_10369 = const()[name = tensor("op_10369"), val = tensor([1, 1])]; - tensor var_10371 = const()[name = tensor("op_10371"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3960946688)))]; + tensor k_211_cast = conv(dilations = var_10266, groups = var_31, pad = k_211_pad_0, pad_type = k_211_pad_type_0, strides = var_10264, weight = unet_up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_211_cast")]; + tensor var_10270 = const()[name = tensor("op_10270"), val = tensor([1, 1])]; + tensor var_10272 = const()[name = tensor("op_10272"), val = tensor([1, 1])]; tensor v_211_pad_type_0 = const()[name = tensor("v_211_pad_type_0"), val = tensor("custom")]; tensor v_211_pad_0 = const()[name = tensor("v_211_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_211 = conv(dilations = var_10371, groups = var_6872, pad = v_211_pad_0, pad_type = v_211_pad_type_0, strides = var_10369, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_211")]; - tensor var_10375 = const()[name = tensor("op_10375"), val = tensor([2, 20, 64, -1])]; - tensor var_10376 = reshape(shape = var_10375, x = q_211)[name = tensor("op_10376")]; - tensor var_10377 = const()[name = tensor("op_10377"), val = tensor([2, 20, 64, -1])]; - tensor var_10378 = reshape(shape = var_10377, x = k_211)[name = tensor("op_10378")]; - tensor var_10379 = const()[name = tensor("op_10379"), val = tensor([2, 20, 64, -1])]; - tensor var_10380 = reshape(shape = var_10379, x = v_211)[name = tensor("op_10380")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3966189632)))]; + tensor v_211_cast = conv(dilations = var_10272, groups = var_31, pad = v_211_pad_0, pad_type = v_211_pad_type_0, strides = var_10270, weight = unet_up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_211_cast")]; + tensor var_10276 = const()[name = tensor("op_10276"), val = tensor([2, 20, 64, -1])]; + tensor var_10277_cast = reshape(shape = var_10276, x = q_211_cast)[name = tensor("op_10277_cast")]; + tensor var_10278 = const()[name = tensor("op_10278"), val = tensor([2, 20, 64, -1])]; + tensor var_10279_cast = reshape(shape = var_10278, x = k_211_cast)[name = tensor("op_10279_cast")]; + tensor var_10280 = const()[name = tensor("op_10280"), val = tensor([2, 20, 64, -1])]; + tensor var_10281_cast = reshape(shape = var_10280, x = v_211_cast)[name = tensor("op_10281_cast")]; tensor attn_weights_421_transpose_x_0 = const()[name = tensor("attn_weights_421_transpose_x_0"), val = tensor(true)]; tensor attn_weights_421_transpose_y_0 = const()[name = tensor("attn_weights_421_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_421 = matmul(transpose_x = attn_weights_421_transpose_x_0, transpose_y = attn_weights_421_transpose_y_0, x = var_10376, y = var_10378)[name = tensor("attn_weights_421")]; - tensor attn_weights_423 = mul(x = attn_weights_421, y = var_6863)[name = tensor("attn_weights_423")]; - tensor var_10384 = softmax(axis = var_6856, x = attn_weights_423)[name = tensor("op_10384")]; + tensor attn_weights_421_cast = matmul(transpose_x = attn_weights_421_transpose_x_0, transpose_y = attn_weights_421_transpose_y_0, x = var_10277_cast, y = var_10279_cast)[name = tensor("attn_weights_421_cast")]; + tensor attn_weights_423_cast = mul(x = attn_weights_421_cast, y = var_12_to_fp16)[name = tensor("attn_weights_423_cast")]; + tensor var_10285_cast = softmax(axis = var_18, x = attn_weights_423_cast)[name = tensor("op_10285_cast")]; tensor attn_211_transpose_x_0 = const()[name = tensor("attn_211_transpose_x_0"), val = tensor(false)]; tensor attn_211_transpose_y_0 = const()[name = tensor("attn_211_transpose_y_0"), val = tensor(true)]; - tensor attn_211 = matmul(transpose_x = attn_211_transpose_x_0, transpose_y = attn_211_transpose_y_0, x = var_10380, y = var_10384)[name = tensor("attn_211")]; - tensor var_10388 = const()[name = tensor("op_10388"), val = tensor([2, 1280, 1, -1])]; - tensor input_603 = reshape(shape = var_10388, x = attn_211)[name = tensor("input_603")]; - tensor var_10393 = const()[name = tensor("op_10393"), val = tensor([1, 1])]; - tensor var_10395 = const()[name = tensor("op_10395"), val = tensor([1, 1])]; - tensor var_10397_pad_type_0 = const()[name = tensor("op_10397_pad_type_0"), val = tensor("custom")]; - tensor var_10397_pad_0 = const()[name = tensor("op_10397_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10397 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias, dilations = var_10395, groups = var_6872, pad = var_10397_pad_0, pad_type = var_10397_pad_type_0, strides = var_10393, weight = up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight, x = input_603)[name = tensor("op_10397")]; - tensor inputs_317 = add(x = var_10397, y = inputs_315)[name = tensor("inputs_317")]; - tensor var_10401 = const()[name = tensor("op_10401"), val = tensor([1])]; - tensor channels_mean_317 = reduce_mean(axes = var_10401, keep_dims = var_6867, x = inputs_317)[name = tensor("channels_mean_317")]; - tensor zero_mean_317 = sub(x = inputs_317, y = channels_mean_317)[name = tensor("zero_mean_317")]; - tensor zero_mean_sq_317 = mul(x = zero_mean_317, y = zero_mean_317)[name = tensor("zero_mean_sq_317")]; - tensor var_10405 = const()[name = tensor("op_10405"), val = tensor([1])]; - tensor var_10406 = reduce_mean(axes = var_10405, keep_dims = var_6867, x = zero_mean_sq_317)[name = tensor("op_10406")]; - tensor var_10407 = const()[name = tensor("op_10407"), val = tensor(0x1.4f8b58p-17)]; - tensor var_10408 = add(x = var_10406, y = var_10407)[name = tensor("op_10408")]; - tensor denom_317_epsilon_0 = const()[name = tensor("denom_317_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_317 = rsqrt(epsilon = denom_317_epsilon_0, x = var_10408)[name = tensor("denom_317")]; - tensor out_317 = mul(x = zero_mean_317, y = denom_317)[name = tensor("out_317")]; - tensor var_10412 = const()[name = tensor("op_10412"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269425024)))]; - tensor var_10413 = add(x = out_317, y = var_10412)[name = tensor("op_10413")]; - tensor var_10415 = const()[name = tensor("op_10415"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269430208)))]; - tensor input_605 = mul(x = var_10413, y = var_10415)[name = tensor("input_605")]; - tensor var_10423 = const()[name = tensor("op_10423"), val = tensor([1, 1])]; - tensor var_10425 = const()[name = tensor("op_10425"), val = tensor([1, 1])]; - tensor var_10427_pad_type_0 = const()[name = tensor("op_10427_pad_type_0"), val = tensor("custom")]; - tensor var_10427_pad_0 = const()[name = tensor("op_10427_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10427 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias, dilations = var_10425, groups = var_6872, pad = var_10427_pad_0, pad_type = var_10427_pad_type_0, strides = var_10423, weight = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight, x = input_605)[name = tensor("op_10427")]; - tensor var_10428_split_sizes_0 = const()[name = tensor("op_10428_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_10428_axis_0 = const()[name = tensor("op_10428_axis_0"), val = tensor(1)]; - tensor var_10428_0, tensor var_10428_1 = split(axis = var_10428_axis_0, split_sizes = var_10428_split_sizes_0, x = var_10427)[name = tensor("op_10428")]; - tensor var_10430_mode_0 = const()[name = tensor("op_10430_mode_0"), val = tensor("EXACT")]; - tensor var_10430 = gelu(mode = var_10430_mode_0, x = var_10428_1)[name = tensor("op_10430")]; - tensor input_607 = mul(x = var_10428_0, y = var_10430)[name = tensor("input_607")]; - tensor var_10434 = const()[name = tensor("op_10434"), val = tensor([1, 1])]; - tensor var_10436 = const()[name = tensor("op_10436"), val = tensor([1, 1])]; - tensor var_10438_pad_type_0 = const()[name = tensor("op_10438_pad_type_0"), val = tensor("custom")]; - tensor var_10438_pad_0 = const()[name = tensor("op_10438_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10438 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias, dilations = var_10436, groups = var_6872, pad = var_10438_pad_0, pad_type = var_10438_pad_type_0, strides = var_10434, weight = up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight, x = input_607)[name = tensor("op_10438")]; - tensor inputs_319 = add(x = var_10438, y = inputs_317)[name = tensor("inputs_319")]; - tensor var_10448 = const()[name = tensor("op_10448"), val = tensor([1])]; - tensor channels_mean_319 = reduce_mean(axes = var_10448, keep_dims = var_6867, x = inputs_319)[name = tensor("channels_mean_319")]; - tensor zero_mean_319 = sub(x = inputs_319, y = channels_mean_319)[name = tensor("zero_mean_319")]; - tensor zero_mean_sq_319 = mul(x = zero_mean_319, y = zero_mean_319)[name = tensor("zero_mean_sq_319")]; - tensor var_10452 = const()[name = tensor("op_10452"), val = tensor([1])]; - tensor var_10453 = reduce_mean(axes = var_10452, keep_dims = var_6867, x = zero_mean_sq_319)[name = tensor("op_10453")]; - tensor var_10454 = const()[name = tensor("op_10454"), val = tensor(0x1.4f8b58p-17)]; - tensor var_10455 = add(x = var_10453, y = var_10454)[name = tensor("op_10455")]; - tensor denom_319_epsilon_0 = const()[name = tensor("denom_319_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_319 = rsqrt(epsilon = denom_319_epsilon_0, x = var_10455)[name = tensor("denom_319")]; - tensor out_319 = mul(x = zero_mean_319, y = denom_319)[name = tensor("out_319")]; - tensor var_10459 = const()[name = tensor("op_10459"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269435392)))]; - tensor var_10460 = add(x = out_319, y = var_10459)[name = tensor("op_10460")]; - tensor var_10462 = const()[name = tensor("op_10462"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269440576)))]; - tensor hidden_states_415 = mul(x = var_10460, y = var_10462)[name = tensor("hidden_states_415")]; - tensor var_10469 = const()[name = tensor("op_10469"), val = tensor([1, 1])]; - tensor var_10471 = const()[name = tensor("op_10471"), val = tensor([1, 1])]; + tensor attn_211_cast = matmul(transpose_x = attn_211_transpose_x_0, transpose_y = attn_211_transpose_y_0, x = var_10281_cast, y = var_10285_cast)[name = tensor("attn_211_cast")]; + tensor var_10289 = const()[name = tensor("op_10289"), val = tensor([2, 1280, 1, -1])]; + tensor input_603_cast = reshape(shape = var_10289, x = attn_211_cast)[name = tensor("input_603_cast")]; + tensor var_10294 = const()[name = tensor("op_10294"), val = tensor([1, 1])]; + tensor var_10296 = const()[name = tensor("op_10296"), val = tensor([1, 1])]; + tensor var_10298_pad_type_0 = const()[name = tensor("op_10298_pad_type_0"), val = tensor("custom")]; + tensor var_10298_pad_0 = const()[name = tensor("op_10298_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3971432576)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3974709440)))]; + tensor var_10298_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_10296, groups = var_31, pad = var_10298_pad_0, pad_type = var_10298_pad_type_0, strides = var_10294, weight = unet_up_blocks_0_attentions_1_transformer_blocks_8_attn2_to_out_0_weight_to_fp16, x = input_603_cast)[name = tensor("op_10298_cast")]; + tensor inputs_317_cast = add(x = var_10298_cast, y = inputs_315_cast)[name = tensor("inputs_317_cast")]; + tensor var_10302 = const()[name = tensor("op_10302"), val = tensor([1])]; + tensor channels_mean_317_cast = reduce_mean(axes = var_10302, keep_dims = var_23, x = inputs_317_cast)[name = tensor("channels_mean_317_cast")]; + tensor zero_mean_317_cast = sub(x = inputs_317_cast, y = channels_mean_317_cast)[name = tensor("zero_mean_317_cast")]; + tensor zero_mean_sq_317_cast = mul(x = zero_mean_317_cast, y = zero_mean_317_cast)[name = tensor("zero_mean_sq_317_cast")]; + tensor var_10306 = const()[name = tensor("op_10306"), val = tensor([1])]; + tensor var_10307_cast = reduce_mean(axes = var_10306, keep_dims = var_23, x = zero_mean_sq_317_cast)[name = tensor("op_10307_cast")]; + tensor var_10308_to_fp16 = const()[name = tensor("op_10308_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10309_cast = add(x = var_10307_cast, y = var_10308_to_fp16)[name = tensor("op_10309_cast")]; + tensor denom_317_epsilon_0_to_fp16 = const()[name = tensor("denom_317_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_317_cast = rsqrt(epsilon = denom_317_epsilon_0_to_fp16, x = var_10309_cast)[name = tensor("denom_317_cast")]; + tensor out_317_cast = mul(x = zero_mean_317_cast, y = denom_317_cast)[name = tensor("out_317_cast")]; + tensor var_10313_to_fp16 = const()[name = tensor("op_10313_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3974712064)))]; + tensor var_10314_cast = add(x = out_317_cast, y = var_10313_to_fp16)[name = tensor("op_10314_cast")]; + tensor var_10316_to_fp16 = const()[name = tensor("op_10316_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3974714688)))]; + tensor input_605_cast = mul(x = var_10314_cast, y = var_10316_to_fp16)[name = tensor("input_605_cast")]; + tensor var_10324 = const()[name = tensor("op_10324"), val = tensor([1, 1])]; + tensor var_10326 = const()[name = tensor("op_10326"), val = tensor([1, 1])]; + tensor var_10328_pad_type_0 = const()[name = tensor("op_10328_pad_type_0"), val = tensor("custom")]; + tensor var_10328_pad_0 = const()[name = tensor("op_10328_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3974717312)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4000931776)))]; + tensor var_10328_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_10326, groups = var_31, pad = var_10328_pad_0, pad_type = var_10328_pad_type_0, strides = var_10324, weight = unet_up_blocks_0_attentions_1_transformer_blocks_8_ff_net_0_proj_weight_to_fp16, x = input_605_cast)[name = tensor("op_10328_cast")]; + tensor var_10329_split_sizes_0 = const()[name = tensor("op_10329_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10329_axis_0 = const()[name = tensor("op_10329_axis_0"), val = tensor(1)]; + tensor var_10329_cast_0, tensor var_10329_cast_1 = split(axis = var_10329_axis_0, split_sizes = var_10329_split_sizes_0, x = var_10328_cast)[name = tensor("op_10329_cast")]; + tensor var_10331_mode_0 = const()[name = tensor("op_10331_mode_0"), val = tensor("EXACT")]; + tensor var_10331_cast = gelu(mode = var_10331_mode_0, x = var_10329_cast_1)[name = tensor("op_10331_cast")]; + tensor input_607_cast = mul(x = var_10329_cast_0, y = var_10331_cast)[name = tensor("input_607_cast")]; + tensor var_10335 = const()[name = tensor("op_10335"), val = tensor([1, 1])]; + tensor var_10337 = const()[name = tensor("op_10337"), val = tensor([1, 1])]; + tensor var_10339_pad_type_0 = const()[name = tensor("op_10339_pad_type_0"), val = tensor("custom")]; + tensor var_10339_pad_0 = const()[name = tensor("op_10339_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4000952320)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4014059584)))]; + tensor var_10339_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_10337, groups = var_31, pad = var_10339_pad_0, pad_type = var_10339_pad_type_0, strides = var_10335, weight = unet_up_blocks_0_attentions_1_transformer_blocks_8_ff_net_2_weight_to_fp16, x = input_607_cast)[name = tensor("op_10339_cast")]; + tensor inputs_319_cast = add(x = var_10339_cast, y = inputs_317_cast)[name = tensor("inputs_319_cast")]; + tensor var_10349 = const()[name = tensor("op_10349"), val = tensor([1])]; + tensor channels_mean_319_cast = reduce_mean(axes = var_10349, keep_dims = var_23, x = inputs_319_cast)[name = tensor("channels_mean_319_cast")]; + tensor zero_mean_319_cast = sub(x = inputs_319_cast, y = channels_mean_319_cast)[name = tensor("zero_mean_319_cast")]; + tensor zero_mean_sq_319_cast = mul(x = zero_mean_319_cast, y = zero_mean_319_cast)[name = tensor("zero_mean_sq_319_cast")]; + tensor var_10353 = const()[name = tensor("op_10353"), val = tensor([1])]; + tensor var_10354_cast = reduce_mean(axes = var_10353, keep_dims = var_23, x = zero_mean_sq_319_cast)[name = tensor("op_10354_cast")]; + tensor var_10355_to_fp16 = const()[name = tensor("op_10355_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10356_cast = add(x = var_10354_cast, y = var_10355_to_fp16)[name = tensor("op_10356_cast")]; + tensor denom_319_epsilon_0_to_fp16 = const()[name = tensor("denom_319_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_319_cast = rsqrt(epsilon = denom_319_epsilon_0_to_fp16, x = var_10356_cast)[name = tensor("denom_319_cast")]; + tensor out_319_cast = mul(x = zero_mean_319_cast, y = denom_319_cast)[name = tensor("out_319_cast")]; + tensor var_10360_to_fp16 = const()[name = tensor("op_10360_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4014062208)))]; + tensor var_10361_cast = add(x = out_319_cast, y = var_10360_to_fp16)[name = tensor("op_10361_cast")]; + tensor var_10363_to_fp16 = const()[name = tensor("op_10363_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4014064832)))]; + tensor hidden_states_415_cast = mul(x = var_10361_cast, y = var_10363_to_fp16)[name = tensor("hidden_states_415_cast")]; + tensor var_10370 = const()[name = tensor("op_10370"), val = tensor([1, 1])]; + tensor var_10372 = const()[name = tensor("op_10372"), val = tensor([1, 1])]; tensor q_213_pad_type_0 = const()[name = tensor("q_213_pad_type_0"), val = tensor("custom")]; tensor q_213_pad_0 = const()[name = tensor("q_213_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_213 = conv(dilations = var_10471, groups = var_6872, pad = q_213_pad_0, pad_type = q_213_pad_type_0, strides = var_10469, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight, x = hidden_states_415)[name = tensor("q_213")]; - tensor var_10475 = const()[name = tensor("op_10475"), val = tensor([1, 1])]; - tensor var_10477 = const()[name = tensor("op_10477"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4014067456)))]; + tensor q_213_cast = conv(dilations = var_10372, groups = var_31, pad = q_213_pad_0, pad_type = q_213_pad_type_0, strides = var_10370, weight = unet_up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_q_weight_to_fp16, x = hidden_states_415_cast)[name = tensor("q_213_cast")]; + tensor var_10376 = const()[name = tensor("op_10376"), val = tensor([1, 1])]; + tensor var_10378 = const()[name = tensor("op_10378"), val = tensor([1, 1])]; tensor k_213_pad_type_0 = const()[name = tensor("k_213_pad_type_0"), val = tensor("custom")]; tensor k_213_pad_0 = const()[name = tensor("k_213_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_213 = conv(dilations = var_10477, groups = var_6872, pad = k_213_pad_0, pad_type = k_213_pad_type_0, strides = var_10475, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight, x = hidden_states_415)[name = tensor("k_213")]; - tensor var_10481 = const()[name = tensor("op_10481"), val = tensor([1, 1])]; - tensor var_10483 = const()[name = tensor("op_10483"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4017344320)))]; + tensor k_213_cast = conv(dilations = var_10378, groups = var_31, pad = k_213_pad_0, pad_type = k_213_pad_type_0, strides = var_10376, weight = unet_up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_k_weight_to_fp16, x = hidden_states_415_cast)[name = tensor("k_213_cast")]; + tensor var_10382 = const()[name = tensor("op_10382"), val = tensor([1, 1])]; + tensor var_10384 = const()[name = tensor("op_10384"), val = tensor([1, 1])]; tensor v_213_pad_type_0 = const()[name = tensor("v_213_pad_type_0"), val = tensor("custom")]; tensor v_213_pad_0 = const()[name = tensor("v_213_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_213 = conv(dilations = var_10483, groups = var_6872, pad = v_213_pad_0, pad_type = v_213_pad_type_0, strides = var_10481, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight, x = hidden_states_415)[name = tensor("v_213")]; - tensor var_10487 = const()[name = tensor("op_10487"), val = tensor([2, 20, 64, -1])]; - tensor var_10488 = reshape(shape = var_10487, x = q_213)[name = tensor("op_10488")]; - tensor var_10489 = const()[name = tensor("op_10489"), val = tensor([2, 20, 64, -1])]; - tensor var_10490 = reshape(shape = var_10489, x = k_213)[name = tensor("op_10490")]; - tensor var_10491 = const()[name = tensor("op_10491"), val = tensor([2, 20, 64, -1])]; - tensor var_10492 = reshape(shape = var_10491, x = v_213)[name = tensor("op_10492")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4020621184)))]; + tensor v_213_cast = conv(dilations = var_10384, groups = var_31, pad = v_213_pad_0, pad_type = v_213_pad_type_0, strides = var_10382, weight = unet_up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_v_weight_to_fp16, x = hidden_states_415_cast)[name = tensor("v_213_cast")]; + tensor var_10388 = const()[name = tensor("op_10388"), val = tensor([2, 20, 64, -1])]; + tensor var_10389_cast = reshape(shape = var_10388, x = q_213_cast)[name = tensor("op_10389_cast")]; + tensor var_10390 = const()[name = tensor("op_10390"), val = tensor([2, 20, 64, -1])]; + tensor var_10391_cast = reshape(shape = var_10390, x = k_213_cast)[name = tensor("op_10391_cast")]; + tensor var_10392 = const()[name = tensor("op_10392"), val = tensor([2, 20, 64, -1])]; + tensor var_10393_cast = reshape(shape = var_10392, x = v_213_cast)[name = tensor("op_10393_cast")]; tensor attn_weights_425_transpose_x_0 = const()[name = tensor("attn_weights_425_transpose_x_0"), val = tensor(true)]; tensor attn_weights_425_transpose_y_0 = const()[name = tensor("attn_weights_425_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_425 = matmul(transpose_x = attn_weights_425_transpose_x_0, transpose_y = attn_weights_425_transpose_y_0, x = var_10488, y = var_10490)[name = tensor("attn_weights_425")]; - tensor attn_weights_427 = mul(x = attn_weights_425, y = var_6863)[name = tensor("attn_weights_427")]; - tensor var_10496 = softmax(axis = var_6856, x = attn_weights_427)[name = tensor("op_10496")]; + tensor attn_weights_425_cast = matmul(transpose_x = attn_weights_425_transpose_x_0, transpose_y = attn_weights_425_transpose_y_0, x = var_10389_cast, y = var_10391_cast)[name = tensor("attn_weights_425_cast")]; + tensor attn_weights_427_cast = mul(x = attn_weights_425_cast, y = var_12_to_fp16)[name = tensor("attn_weights_427_cast")]; + tensor var_10397_cast = softmax(axis = var_18, x = attn_weights_427_cast)[name = tensor("op_10397_cast")]; tensor attn_213_transpose_x_0 = const()[name = tensor("attn_213_transpose_x_0"), val = tensor(false)]; tensor attn_213_transpose_y_0 = const()[name = tensor("attn_213_transpose_y_0"), val = tensor(true)]; - tensor attn_213 = matmul(transpose_x = attn_213_transpose_x_0, transpose_y = attn_213_transpose_y_0, x = var_10492, y = var_10496)[name = tensor("attn_213")]; - tensor var_10500 = const()[name = tensor("op_10500"), val = tensor([2, 1280, 1, -1])]; - tensor input_609 = reshape(shape = var_10500, x = attn_213)[name = tensor("input_609")]; - tensor var_10505 = const()[name = tensor("op_10505"), val = tensor([1, 1])]; - tensor var_10507 = const()[name = tensor("op_10507"), val = tensor([1, 1])]; - tensor var_10509_pad_type_0 = const()[name = tensor("op_10509_pad_type_0"), val = tensor("custom")]; - tensor var_10509_pad_0 = const()[name = tensor("op_10509_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10509 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias, dilations = var_10507, groups = var_6872, pad = var_10509_pad_0, pad_type = var_10509_pad_type_0, strides = var_10505, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight, x = input_609)[name = tensor("op_10509")]; - tensor inputs_321 = add(x = var_10509, y = inputs_319)[name = tensor("inputs_321")]; - tensor var_10513 = const()[name = tensor("op_10513"), val = tensor([1])]; - tensor channels_mean_321 = reduce_mean(axes = var_10513, keep_dims = var_6867, x = inputs_321)[name = tensor("channels_mean_321")]; - tensor zero_mean_321 = sub(x = inputs_321, y = channels_mean_321)[name = tensor("zero_mean_321")]; - tensor zero_mean_sq_321 = mul(x = zero_mean_321, y = zero_mean_321)[name = tensor("zero_mean_sq_321")]; - tensor var_10517 = const()[name = tensor("op_10517"), val = tensor([1])]; - tensor var_10518 = reduce_mean(axes = var_10517, keep_dims = var_6867, x = zero_mean_sq_321)[name = tensor("op_10518")]; - tensor var_10519 = const()[name = tensor("op_10519"), val = tensor(0x1.4f8b58p-17)]; - tensor var_10520 = add(x = var_10518, y = var_10519)[name = tensor("op_10520")]; - tensor denom_321_epsilon_0 = const()[name = tensor("denom_321_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_321 = rsqrt(epsilon = denom_321_epsilon_0, x = var_10520)[name = tensor("denom_321")]; - tensor out_321 = mul(x = zero_mean_321, y = denom_321)[name = tensor("out_321")]; - tensor var_10524 = const()[name = tensor("op_10524"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269445760)))]; - tensor var_10525 = add(x = out_321, y = var_10524)[name = tensor("op_10525")]; - tensor var_10527 = const()[name = tensor("op_10527"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269450944)))]; - tensor hidden_states_417 = mul(x = var_10525, y = var_10527)[name = tensor("hidden_states_417")]; - tensor var_10534 = const()[name = tensor("op_10534"), val = tensor([1, 1])]; - tensor var_10536 = const()[name = tensor("op_10536"), val = tensor([1, 1])]; + tensor attn_213_cast = matmul(transpose_x = attn_213_transpose_x_0, transpose_y = attn_213_transpose_y_0, x = var_10393_cast, y = var_10397_cast)[name = tensor("attn_213_cast")]; + tensor var_10401 = const()[name = tensor("op_10401"), val = tensor([2, 1280, 1, -1])]; + tensor input_609_cast = reshape(shape = var_10401, x = attn_213_cast)[name = tensor("input_609_cast")]; + tensor var_10406 = const()[name = tensor("op_10406"), val = tensor([1, 1])]; + tensor var_10408 = const()[name = tensor("op_10408"), val = tensor([1, 1])]; + tensor var_10410_pad_type_0 = const()[name = tensor("op_10410_pad_type_0"), val = tensor("custom")]; + tensor var_10410_pad_0 = const()[name = tensor("op_10410_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4023898048)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4027174912)))]; + tensor var_10410_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_10408, groups = var_31, pad = var_10410_pad_0, pad_type = var_10410_pad_type_0, strides = var_10406, weight = unet_up_blocks_0_attentions_1_transformer_blocks_9_attn1_to_out_0_weight_to_fp16, x = input_609_cast)[name = tensor("op_10410_cast")]; + tensor inputs_321_cast = add(x = var_10410_cast, y = inputs_319_cast)[name = tensor("inputs_321_cast")]; + tensor var_10414 = const()[name = tensor("op_10414"), val = tensor([1])]; + tensor channels_mean_321_cast = reduce_mean(axes = var_10414, keep_dims = var_23, x = inputs_321_cast)[name = tensor("channels_mean_321_cast")]; + tensor zero_mean_321_cast = sub(x = inputs_321_cast, y = channels_mean_321_cast)[name = tensor("zero_mean_321_cast")]; + tensor zero_mean_sq_321_cast = mul(x = zero_mean_321_cast, y = zero_mean_321_cast)[name = tensor("zero_mean_sq_321_cast")]; + tensor var_10418 = const()[name = tensor("op_10418"), val = tensor([1])]; + tensor var_10419_cast = reduce_mean(axes = var_10418, keep_dims = var_23, x = zero_mean_sq_321_cast)[name = tensor("op_10419_cast")]; + tensor var_10420_to_fp16 = const()[name = tensor("op_10420_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10421_cast = add(x = var_10419_cast, y = var_10420_to_fp16)[name = tensor("op_10421_cast")]; + tensor denom_321_epsilon_0_to_fp16 = const()[name = tensor("denom_321_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_321_cast = rsqrt(epsilon = denom_321_epsilon_0_to_fp16, x = var_10421_cast)[name = tensor("denom_321_cast")]; + tensor out_321_cast = mul(x = zero_mean_321_cast, y = denom_321_cast)[name = tensor("out_321_cast")]; + tensor var_10425_to_fp16 = const()[name = tensor("op_10425_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4027177536)))]; + tensor var_10426_cast = add(x = out_321_cast, y = var_10425_to_fp16)[name = tensor("op_10426_cast")]; + tensor var_10428_to_fp16 = const()[name = tensor("op_10428_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4027180160)))]; + tensor hidden_states_417_cast = mul(x = var_10426_cast, y = var_10428_to_fp16)[name = tensor("hidden_states_417_cast")]; + tensor var_10435 = const()[name = tensor("op_10435"), val = tensor([1, 1])]; + tensor var_10437 = const()[name = tensor("op_10437"), val = tensor([1, 1])]; tensor q_215_pad_type_0 = const()[name = tensor("q_215_pad_type_0"), val = tensor("custom")]; tensor q_215_pad_0 = const()[name = tensor("q_215_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_215 = conv(dilations = var_10536, groups = var_6872, pad = q_215_pad_0, pad_type = q_215_pad_type_0, strides = var_10534, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight, x = hidden_states_417)[name = tensor("q_215")]; - tensor var_10540 = const()[name = tensor("op_10540"), val = tensor([1, 1])]; - tensor var_10542 = const()[name = tensor("op_10542"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4027182784)))]; + tensor q_215_cast = conv(dilations = var_10437, groups = var_31, pad = q_215_pad_0, pad_type = q_215_pad_type_0, strides = var_10435, weight = unet_up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_q_weight_to_fp16, x = hidden_states_417_cast)[name = tensor("q_215_cast")]; + tensor var_10441 = const()[name = tensor("op_10441"), val = tensor([1, 1])]; + tensor var_10443 = const()[name = tensor("op_10443"), val = tensor([1, 1])]; tensor k_215_pad_type_0 = const()[name = tensor("k_215_pad_type_0"), val = tensor("custom")]; tensor k_215_pad_0 = const()[name = tensor("k_215_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_215 = conv(dilations = var_10542, groups = var_6872, pad = k_215_pad_0, pad_type = k_215_pad_type_0, strides = var_10540, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_215")]; - tensor var_10546 = const()[name = tensor("op_10546"), val = tensor([1, 1])]; - tensor var_10548 = const()[name = tensor("op_10548"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4030459648)))]; + tensor k_215_cast = conv(dilations = var_10443, groups = var_31, pad = k_215_pad_0, pad_type = k_215_pad_type_0, strides = var_10441, weight = unet_up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_215_cast")]; + tensor var_10447 = const()[name = tensor("op_10447"), val = tensor([1, 1])]; + tensor var_10449 = const()[name = tensor("op_10449"), val = tensor([1, 1])]; tensor v_215_pad_type_0 = const()[name = tensor("v_215_pad_type_0"), val = tensor("custom")]; tensor v_215_pad_0 = const()[name = tensor("v_215_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_215 = conv(dilations = var_10548, groups = var_6872, pad = v_215_pad_0, pad_type = v_215_pad_type_0, strides = var_10546, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_215")]; - tensor var_10552 = const()[name = tensor("op_10552"), val = tensor([2, 20, 64, -1])]; - tensor var_10553 = reshape(shape = var_10552, x = q_215)[name = tensor("op_10553")]; - tensor var_10554 = const()[name = tensor("op_10554"), val = tensor([2, 20, 64, -1])]; - tensor var_10555 = reshape(shape = var_10554, x = k_215)[name = tensor("op_10555")]; - tensor var_10556 = const()[name = tensor("op_10556"), val = tensor([2, 20, 64, -1])]; - tensor var_10557 = reshape(shape = var_10556, x = v_215)[name = tensor("op_10557")]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4035702592)))]; + tensor v_215_cast = conv(dilations = var_10449, groups = var_31, pad = v_215_pad_0, pad_type = v_215_pad_type_0, strides = var_10447, weight = unet_up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_215_cast")]; + tensor var_10453 = const()[name = tensor("op_10453"), val = tensor([2, 20, 64, -1])]; + tensor var_10454_cast = reshape(shape = var_10453, x = q_215_cast)[name = tensor("op_10454_cast")]; + tensor var_10455 = const()[name = tensor("op_10455"), val = tensor([2, 20, 64, -1])]; + tensor var_10456_cast = reshape(shape = var_10455, x = k_215_cast)[name = tensor("op_10456_cast")]; + tensor var_10457 = const()[name = tensor("op_10457"), val = tensor([2, 20, 64, -1])]; + tensor var_10458_cast = reshape(shape = var_10457, x = v_215_cast)[name = tensor("op_10458_cast")]; tensor attn_weights_429_transpose_x_0 = const()[name = tensor("attn_weights_429_transpose_x_0"), val = tensor(true)]; tensor attn_weights_429_transpose_y_0 = const()[name = tensor("attn_weights_429_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_429 = matmul(transpose_x = attn_weights_429_transpose_x_0, transpose_y = attn_weights_429_transpose_y_0, x = var_10553, y = var_10555)[name = tensor("attn_weights_429")]; - tensor attn_weights_431 = mul(x = attn_weights_429, y = var_6863)[name = tensor("attn_weights_431")]; - tensor var_10561 = softmax(axis = var_6856, x = attn_weights_431)[name = tensor("op_10561")]; + tensor attn_weights_429_cast = matmul(transpose_x = attn_weights_429_transpose_x_0, transpose_y = attn_weights_429_transpose_y_0, x = var_10454_cast, y = var_10456_cast)[name = tensor("attn_weights_429_cast")]; + tensor attn_weights_431_cast = mul(x = attn_weights_429_cast, y = var_12_to_fp16)[name = tensor("attn_weights_431_cast")]; + tensor var_10462_cast = softmax(axis = var_18, x = attn_weights_431_cast)[name = tensor("op_10462_cast")]; tensor attn_215_transpose_x_0 = const()[name = tensor("attn_215_transpose_x_0"), val = tensor(false)]; tensor attn_215_transpose_y_0 = const()[name = tensor("attn_215_transpose_y_0"), val = tensor(true)]; - tensor attn_215 = matmul(transpose_x = attn_215_transpose_x_0, transpose_y = attn_215_transpose_y_0, x = var_10557, y = var_10561)[name = tensor("attn_215")]; - tensor var_10565 = const()[name = tensor("op_10565"), val = tensor([2, 1280, 1, -1])]; - tensor input_611 = reshape(shape = var_10565, x = attn_215)[name = tensor("input_611")]; - tensor var_10570 = const()[name = tensor("op_10570"), val = tensor([1, 1])]; - tensor var_10572 = const()[name = tensor("op_10572"), val = tensor([1, 1])]; - tensor var_10574_pad_type_0 = const()[name = tensor("op_10574_pad_type_0"), val = tensor("custom")]; - tensor var_10574_pad_0 = const()[name = tensor("op_10574_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10574 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias, dilations = var_10572, groups = var_6872, pad = var_10574_pad_0, pad_type = var_10574_pad_type_0, strides = var_10570, weight = up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight, x = input_611)[name = tensor("op_10574")]; - tensor inputs_323 = add(x = var_10574, y = inputs_321)[name = tensor("inputs_323")]; - tensor var_10578 = const()[name = tensor("op_10578"), val = tensor([1])]; - tensor channels_mean_323 = reduce_mean(axes = var_10578, keep_dims = var_6867, x = inputs_323)[name = tensor("channels_mean_323")]; - tensor zero_mean_323 = sub(x = inputs_323, y = channels_mean_323)[name = tensor("zero_mean_323")]; - tensor zero_mean_sq_323 = mul(x = zero_mean_323, y = zero_mean_323)[name = tensor("zero_mean_sq_323")]; - tensor var_10582 = const()[name = tensor("op_10582"), val = tensor([1])]; - tensor var_10583 = reduce_mean(axes = var_10582, keep_dims = var_6867, x = zero_mean_sq_323)[name = tensor("op_10583")]; - tensor var_10584 = const()[name = tensor("op_10584"), val = tensor(0x1.4f8b58p-17)]; - tensor var_10585 = add(x = var_10583, y = var_10584)[name = tensor("op_10585")]; - tensor denom_323_epsilon_0 = const()[name = tensor("denom_323_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_323 = rsqrt(epsilon = denom_323_epsilon_0, x = var_10585)[name = tensor("denom_323")]; - tensor out_323 = mul(x = zero_mean_323, y = denom_323)[name = tensor("out_323")]; - tensor var_10589 = const()[name = tensor("op_10589"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269456128)))]; - tensor var_10590 = add(x = out_323, y = var_10589)[name = tensor("op_10590")]; - tensor var_10592 = const()[name = tensor("op_10592"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269461312)))]; - tensor input_613 = mul(x = var_10590, y = var_10592)[name = tensor("input_613")]; - tensor var_10600 = const()[name = tensor("op_10600"), val = tensor([1, 1])]; - tensor var_10602 = const()[name = tensor("op_10602"), val = tensor([1, 1])]; - tensor var_10604_pad_type_0 = const()[name = tensor("op_10604_pad_type_0"), val = tensor("custom")]; - tensor var_10604_pad_0 = const()[name = tensor("op_10604_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10604 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias, dilations = var_10602, groups = var_6872, pad = var_10604_pad_0, pad_type = var_10604_pad_type_0, strides = var_10600, weight = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight, x = input_613)[name = tensor("op_10604")]; - tensor var_10605_split_sizes_0 = const()[name = tensor("op_10605_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_10605_axis_0 = const()[name = tensor("op_10605_axis_0"), val = tensor(1)]; - tensor var_10605_0, tensor var_10605_1 = split(axis = var_10605_axis_0, split_sizes = var_10605_split_sizes_0, x = var_10604)[name = tensor("op_10605")]; - tensor var_10607_mode_0 = const()[name = tensor("op_10607_mode_0"), val = tensor("EXACT")]; - tensor var_10607 = gelu(mode = var_10607_mode_0, x = var_10605_1)[name = tensor("op_10607")]; - tensor input_615 = mul(x = var_10605_0, y = var_10607)[name = tensor("input_615")]; - tensor var_10611 = const()[name = tensor("op_10611"), val = tensor([1, 1])]; - tensor var_10613 = const()[name = tensor("op_10613"), val = tensor([1, 1])]; - tensor var_10615_pad_type_0 = const()[name = tensor("op_10615_pad_type_0"), val = tensor("custom")]; - tensor var_10615_pad_0 = const()[name = tensor("op_10615_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10615 = conv(bias = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias, dilations = var_10613, groups = var_6872, pad = var_10615_pad_0, pad_type = var_10615_pad_type_0, strides = var_10611, weight = up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight, x = input_615)[name = tensor("op_10615")]; - tensor hidden_states_421 = add(x = var_10615, y = inputs_323)[name = tensor("hidden_states_421")]; - tensor var_10617 = const()[name = tensor("op_10617"), val = tensor([2, 1280, 32, 32])]; - tensor input_617 = reshape(shape = var_10617, x = hidden_states_421)[name = tensor("input_617")]; - tensor var_10621 = const()[name = tensor("op_10621"), val = tensor([1, 1])]; - tensor var_10623 = const()[name = tensor("op_10623"), val = tensor([1, 1])]; + tensor attn_215_cast = matmul(transpose_x = attn_215_transpose_x_0, transpose_y = attn_215_transpose_y_0, x = var_10458_cast, y = var_10462_cast)[name = tensor("attn_215_cast")]; + tensor var_10466 = const()[name = tensor("op_10466"), val = tensor([2, 1280, 1, -1])]; + tensor input_611_cast = reshape(shape = var_10466, x = attn_215_cast)[name = tensor("input_611_cast")]; + tensor var_10471 = const()[name = tensor("op_10471"), val = tensor([1, 1])]; + tensor var_10473 = const()[name = tensor("op_10473"), val = tensor([1, 1])]; + tensor var_10475_pad_type_0 = const()[name = tensor("op_10475_pad_type_0"), val = tensor("custom")]; + tensor var_10475_pad_0 = const()[name = tensor("op_10475_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4040945536)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4044222400)))]; + tensor var_10475_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_10473, groups = var_31, pad = var_10475_pad_0, pad_type = var_10475_pad_type_0, strides = var_10471, weight = unet_up_blocks_0_attentions_1_transformer_blocks_9_attn2_to_out_0_weight_to_fp16, x = input_611_cast)[name = tensor("op_10475_cast")]; + tensor inputs_323_cast = add(x = var_10475_cast, y = inputs_321_cast)[name = tensor("inputs_323_cast")]; + tensor var_10479 = const()[name = tensor("op_10479"), val = tensor([1])]; + tensor channels_mean_323_cast = reduce_mean(axes = var_10479, keep_dims = var_23, x = inputs_323_cast)[name = tensor("channels_mean_323_cast")]; + tensor zero_mean_323_cast = sub(x = inputs_323_cast, y = channels_mean_323_cast)[name = tensor("zero_mean_323_cast")]; + tensor zero_mean_sq_323_cast = mul(x = zero_mean_323_cast, y = zero_mean_323_cast)[name = tensor("zero_mean_sq_323_cast")]; + tensor var_10483 = const()[name = tensor("op_10483"), val = tensor([1])]; + tensor var_10484_cast = reduce_mean(axes = var_10483, keep_dims = var_23, x = zero_mean_sq_323_cast)[name = tensor("op_10484_cast")]; + tensor var_10485_to_fp16 = const()[name = tensor("op_10485_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10486_cast = add(x = var_10484_cast, y = var_10485_to_fp16)[name = tensor("op_10486_cast")]; + tensor denom_323_epsilon_0_to_fp16 = const()[name = tensor("denom_323_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_323_cast = rsqrt(epsilon = denom_323_epsilon_0_to_fp16, x = var_10486_cast)[name = tensor("denom_323_cast")]; + tensor out_323_cast = mul(x = zero_mean_323_cast, y = denom_323_cast)[name = tensor("out_323_cast")]; + tensor var_10490_to_fp16 = const()[name = tensor("op_10490_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4044225024)))]; + tensor var_10491_cast = add(x = out_323_cast, y = var_10490_to_fp16)[name = tensor("op_10491_cast")]; + tensor var_10493_to_fp16 = const()[name = tensor("op_10493_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4044227648)))]; + tensor input_613_cast = mul(x = var_10491_cast, y = var_10493_to_fp16)[name = tensor("input_613_cast")]; + tensor var_10501 = const()[name = tensor("op_10501"), val = tensor([1, 1])]; + tensor var_10503 = const()[name = tensor("op_10503"), val = tensor([1, 1])]; + tensor var_10505_pad_type_0 = const()[name = tensor("op_10505_pad_type_0"), val = tensor("custom")]; + tensor var_10505_pad_0 = const()[name = tensor("op_10505_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4044230272)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4070444736)))]; + tensor var_10505_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_10503, groups = var_31, pad = var_10505_pad_0, pad_type = var_10505_pad_type_0, strides = var_10501, weight = unet_up_blocks_0_attentions_1_transformer_blocks_9_ff_net_0_proj_weight_to_fp16, x = input_613_cast)[name = tensor("op_10505_cast")]; + tensor var_10506_split_sizes_0 = const()[name = tensor("op_10506_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10506_axis_0 = const()[name = tensor("op_10506_axis_0"), val = tensor(1)]; + tensor var_10506_cast_0, tensor var_10506_cast_1 = split(axis = var_10506_axis_0, split_sizes = var_10506_split_sizes_0, x = var_10505_cast)[name = tensor("op_10506_cast")]; + tensor var_10508_mode_0 = const()[name = tensor("op_10508_mode_0"), val = tensor("EXACT")]; + tensor var_10508_cast = gelu(mode = var_10508_mode_0, x = var_10506_cast_1)[name = tensor("op_10508_cast")]; + tensor input_615_cast = mul(x = var_10506_cast_0, y = var_10508_cast)[name = tensor("input_615_cast")]; + tensor var_10512 = const()[name = tensor("op_10512"), val = tensor([1, 1])]; + tensor var_10514 = const()[name = tensor("op_10514"), val = tensor([1, 1])]; + tensor var_10516_pad_type_0 = const()[name = tensor("op_10516_pad_type_0"), val = tensor("custom")]; + tensor var_10516_pad_0 = const()[name = tensor("op_10516_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4070465280)))]; + tensor unet_up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4083572544)))]; + tensor var_10516_cast = conv(bias = unet_up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_10514, groups = var_31, pad = var_10516_pad_0, pad_type = var_10516_pad_type_0, strides = var_10512, weight = unet_up_blocks_0_attentions_1_transformer_blocks_9_ff_net_2_weight_to_fp16, x = input_615_cast)[name = tensor("op_10516_cast")]; + tensor hidden_states_421_cast = add(x = var_10516_cast, y = inputs_323_cast)[name = tensor("hidden_states_421_cast")]; + tensor var_10518 = const()[name = tensor("op_10518"), val = tensor([2, 1280, 32, 32])]; + tensor input_617_cast = reshape(shape = var_10518, x = hidden_states_421_cast)[name = tensor("input_617_cast")]; + tensor var_10522 = const()[name = tensor("op_10522"), val = tensor([1, 1])]; + tensor var_10524 = const()[name = tensor("op_10524"), val = tensor([1, 1])]; tensor hidden_states_423_pad_type_0 = const()[name = tensor("hidden_states_423_pad_type_0"), val = tensor("custom")]; tensor hidden_states_423_pad_0 = const()[name = tensor("hidden_states_423_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_423 = conv(bias = up_blocks_0_attentions_1_proj_out_bias, dilations = var_10623, groups = var_6872, pad = hidden_states_423_pad_0, pad_type = hidden_states_423_pad_type_0, strides = var_10621, weight = up_blocks_0_attentions_1_proj_out_weight, x = input_617)[name = tensor("hidden_states_423")]; - tensor hidden_states_425 = add(x = hidden_states_423, y = hidden_states_357)[name = tensor("hidden_states_425")]; + tensor unet_up_blocks_0_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4083575168)))]; + tensor unet_up_blocks_0_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4086852032)))]; + tensor hidden_states_423_cast = conv(bias = unet_up_blocks_0_attentions_1_proj_out_bias_to_fp16, dilations = var_10524, groups = var_31, pad = hidden_states_423_pad_0, pad_type = hidden_states_423_pad_type_0, strides = var_10522, weight = unet_up_blocks_0_attentions_1_proj_out_weight_to_fp16, x = input_617_cast)[name = tensor("hidden_states_423_cast")]; + tensor hidden_states_425_cast = add(x = hidden_states_423_cast, y = hidden_states_357_cast)[name = tensor("hidden_states_425_cast")]; tensor input_619_interleave_0 = const()[name = tensor("input_619_interleave_0"), val = tensor(false)]; - tensor input_619 = concat(axis = var_6872, interleave = input_619_interleave_0, values = (hidden_states_425, input_115))[name = tensor("input_619")]; + tensor input_619_cast = concat(axis = var_31, interleave = input_619_interleave_0, values = (hidden_states_425_cast, input_115_cast))[name = tensor("input_619_cast")]; tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([2, 32, 60, 32, 32])]; - tensor reshape_108 = reshape(shape = reshape_108_shape_0, x = input_619)[name = tensor("reshape_108")]; + tensor reshape_108_cast = reshape(shape = reshape_108_shape_0, x = input_619_cast)[name = tensor("reshape_108_cast")]; tensor reduce_mean_81_axes_0 = const()[name = tensor("reduce_mean_81_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_81_keep_dims_0 = const()[name = tensor("reduce_mean_81_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_81 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108)[name = tensor("reduce_mean_81")]; - tensor sub_54 = sub(x = reshape_108, y = reduce_mean_81)[name = tensor("sub_54")]; - tensor square_27 = square(x = sub_54)[name = tensor("square_27")]; + tensor reduce_mean_81_cast = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast)[name = tensor("reduce_mean_81_cast")]; + tensor sub_54_cast = sub(x = reshape_108_cast, y = reduce_mean_81_cast)[name = tensor("sub_54_cast")]; + tensor square_27_cast = square(x = sub_54_cast)[name = tensor("square_27_cast")]; tensor reduce_mean_83_axes_0 = const()[name = tensor("reduce_mean_83_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_83_keep_dims_0 = const()[name = tensor("reduce_mean_83_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_83 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27)[name = tensor("reduce_mean_83")]; - tensor add_54_y_0 = const()[name = tensor("add_54_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_54 = add(x = reduce_mean_83, y = add_54_y_0)[name = tensor("add_54")]; - tensor sqrt_27 = sqrt(x = add_54)[name = tensor("sqrt_27")]; - tensor real_div_27 = real_div(x = sub_54, y = sqrt_27)[name = tensor("real_div_27")]; + tensor reduce_mean_83_cast = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast)[name = tensor("reduce_mean_83_cast")]; + tensor add_54_y_0_to_fp16 = const()[name = tensor("add_54_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_54_cast = add(x = reduce_mean_83_cast, y = add_54_y_0_to_fp16)[name = tensor("add_54_cast")]; + tensor sqrt_27_cast = sqrt(x = add_54_cast)[name = tensor("sqrt_27_cast")]; + tensor real_div_27_cast = real_div(x = sub_54_cast, y = sqrt_27_cast)[name = tensor("real_div_27_cast")]; tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([2, 1920, 32, 32])]; - tensor reshape_109 = reshape(shape = reshape_109_shape_0, x = real_div_27)[name = tensor("reshape_109")]; - tensor add_55_mean_0 = const()[name = tensor("add_55_mean_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269466496)))]; - tensor add_55_variance_0 = const()[name = tensor("add_55_variance_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269474240)))]; - tensor add_55_gamma_0 = const()[name = tensor("add_55_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269481984)))]; - tensor add_55_beta_0 = const()[name = tensor("add_55_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269489728)))]; - tensor add_55_epsilon_0 = const()[name = tensor("add_55_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_55 = batch_norm(beta = add_55_beta_0, epsilon = add_55_epsilon_0, gamma = add_55_gamma_0, mean = add_55_mean_0, variance = add_55_variance_0, x = reshape_109)[name = tensor("add_55")]; - tensor input_623 = silu(x = add_55)[name = tensor("input_623")]; - tensor var_10641 = const()[name = tensor("op_10641"), val = tensor([1, 1])]; - tensor var_10643 = const()[name = tensor("op_10643"), val = tensor([1, 1])]; + tensor reshape_109_cast = reshape(shape = reshape_109_shape_0, x = real_div_27_cast)[name = tensor("reshape_109_cast")]; + tensor add_55_mean_0_to_fp16 = const()[name = tensor("add_55_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4086854656)))]; + tensor add_55_variance_0_to_fp16 = const()[name = tensor("add_55_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4086858560)))]; + tensor add_55_gamma_0_to_fp16 = const()[name = tensor("add_55_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4086862464)))]; + tensor add_55_beta_0_to_fp16 = const()[name = tensor("add_55_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4086866368)))]; + tensor add_55_epsilon_0_to_fp16 = const()[name = tensor("add_55_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_55_cast = batch_norm(beta = add_55_beta_0_to_fp16, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_109_cast)[name = tensor("add_55_cast")]; + tensor input_623_cast = silu(x = add_55_cast)[name = tensor("input_623_cast")]; + tensor var_10542 = const()[name = tensor("op_10542"), val = tensor([1, 1])]; + tensor var_10544 = const()[name = tensor("op_10544"), val = tensor([1, 1])]; tensor hidden_states_427_pad_type_0 = const()[name = tensor("hidden_states_427_pad_type_0"), val = tensor("custom")]; tensor hidden_states_427_pad_0 = const()[name = tensor("hidden_states_427_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_427 = conv(bias = up_blocks_0_resnets_2_conv1_bias, dilations = var_10643, groups = var_6872, pad = hidden_states_427_pad_0, pad_type = hidden_states_427_pad_type_0, strides = var_10641, weight = up_blocks_0_resnets_2_conv1_weight, x = input_623)[name = tensor("hidden_states_427")]; - tensor var_10649 = const()[name = tensor("op_10649"), val = tensor([1, 1])]; - tensor var_10651 = const()[name = tensor("op_10651"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4086870272)))]; + tensor unet_up_blocks_0_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4131107136)))]; + tensor hidden_states_427_cast = conv(bias = unet_up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_10544, groups = var_31, pad = hidden_states_427_pad_0, pad_type = hidden_states_427_pad_type_0, strides = var_10542, weight = unet_up_blocks_0_resnets_2_conv1_weight_to_fp16, x = input_623_cast)[name = tensor("hidden_states_427_cast")]; + tensor var_10550 = const()[name = tensor("op_10550"), val = tensor([1, 1])]; + tensor var_10552 = const()[name = tensor("op_10552"), val = tensor([1, 1])]; tensor temb_21_pad_type_0 = const()[name = tensor("temb_21_pad_type_0"), val = tensor("custom")]; tensor temb_21_pad_0 = const()[name = tensor("temb_21_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb_21 = conv(bias = up_blocks_0_resnets_2_time_emb_proj_bias, dilations = var_10651, groups = var_6872, pad = temb_21_pad_0, pad_type = temb_21_pad_type_0, strides = var_10649, weight = up_blocks_0_resnets_2_time_emb_proj_weight, x = input_21)[name = tensor("temb_21")]; - tensor input_627 = add(x = hidden_states_427, y = temb_21)[name = tensor("input_627")]; + tensor unet_up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4131109760)))]; + tensor unet_up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4134386624)))]; + tensor temb_21_cast = conv(bias = unet_up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_10552, groups = var_31, pad = temb_21_pad_0, pad_type = temb_21_pad_type_0, strides = var_10550, weight = unet_up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_21_cast")]; + tensor input_627_cast = add(x = hidden_states_427_cast, y = temb_21_cast)[name = tensor("input_627_cast")]; tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([2, 32, 40, 32, 32])]; - tensor reshape_112 = reshape(shape = reshape_112_shape_0, x = input_627)[name = tensor("reshape_112")]; + tensor reshape_112_cast = reshape(shape = reshape_112_shape_0, x = input_627_cast)[name = tensor("reshape_112_cast")]; tensor reduce_mean_84_axes_0 = const()[name = tensor("reduce_mean_84_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_84_keep_dims_0 = const()[name = tensor("reduce_mean_84_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_84 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112)[name = tensor("reduce_mean_84")]; - tensor sub_56 = sub(x = reshape_112, y = reduce_mean_84)[name = tensor("sub_56")]; - tensor square_28 = square(x = sub_56)[name = tensor("square_28")]; + tensor reduce_mean_84_cast = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast)[name = tensor("reduce_mean_84_cast")]; + tensor sub_56_cast = sub(x = reshape_112_cast, y = reduce_mean_84_cast)[name = tensor("sub_56_cast")]; + tensor square_28_cast = square(x = sub_56_cast)[name = tensor("square_28_cast")]; tensor reduce_mean_86_axes_0 = const()[name = tensor("reduce_mean_86_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_86_keep_dims_0 = const()[name = tensor("reduce_mean_86_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_86 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28)[name = tensor("reduce_mean_86")]; - tensor add_56_y_0 = const()[name = tensor("add_56_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_56 = add(x = reduce_mean_86, y = add_56_y_0)[name = tensor("add_56")]; - tensor sqrt_28 = sqrt(x = add_56)[name = tensor("sqrt_28")]; - tensor real_div_28 = real_div(x = sub_56, y = sqrt_28)[name = tensor("real_div_28")]; + tensor reduce_mean_86_cast = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast)[name = tensor("reduce_mean_86_cast")]; + tensor add_56_y_0_to_fp16 = const()[name = tensor("add_56_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_56_cast = add(x = reduce_mean_86_cast, y = add_56_y_0_to_fp16)[name = tensor("add_56_cast")]; + tensor sqrt_28_cast = sqrt(x = add_56_cast)[name = tensor("sqrt_28_cast")]; + tensor real_div_28_cast = real_div(x = sub_56_cast, y = sqrt_28_cast)[name = tensor("real_div_28_cast")]; tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([2, 1280, 32, 32])]; - tensor reshape_113 = reshape(shape = reshape_113_shape_0, x = real_div_28)[name = tensor("reshape_113")]; - tensor add_57_gamma_0 = const()[name = tensor("add_57_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269497472)))]; - tensor add_57_beta_0 = const()[name = tensor("add_57_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269502656)))]; - tensor add_57_epsilon_0 = const()[name = tensor("add_57_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_57 = batch_norm(beta = add_57_beta_0, epsilon = add_57_epsilon_0, gamma = add_57_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_113)[name = tensor("add_57")]; - tensor input_631 = silu(x = add_57)[name = tensor("input_631")]; - tensor var_10661 = const()[name = tensor("op_10661"), val = tensor([1, 1])]; - tensor var_10663 = const()[name = tensor("op_10663"), val = tensor([1, 1])]; + tensor reshape_113_cast = reshape(shape = reshape_113_shape_0, x = real_div_28_cast)[name = tensor("reshape_113_cast")]; + tensor add_57_gamma_0_to_fp16 = const()[name = tensor("add_57_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4134389248)))]; + tensor add_57_beta_0_to_fp16 = const()[name = tensor("add_57_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4134391872)))]; + tensor add_57_epsilon_0_to_fp16 = const()[name = tensor("add_57_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_57_cast = batch_norm(beta = add_57_beta_0_to_fp16, epsilon = add_57_epsilon_0_to_fp16, gamma = add_57_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_113_cast)[name = tensor("add_57_cast")]; + tensor input_631_cast = silu(x = add_57_cast)[name = tensor("input_631_cast")]; + tensor var_10562 = const()[name = tensor("op_10562"), val = tensor([1, 1])]; + tensor var_10564 = const()[name = tensor("op_10564"), val = tensor([1, 1])]; tensor hidden_states_429_pad_type_0 = const()[name = tensor("hidden_states_429_pad_type_0"), val = tensor("custom")]; tensor hidden_states_429_pad_0 = const()[name = tensor("hidden_states_429_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_429 = conv(bias = up_blocks_0_resnets_2_conv2_bias, dilations = var_10663, groups = var_6872, pad = hidden_states_429_pad_0, pad_type = hidden_states_429_pad_type_0, strides = var_10661, weight = up_blocks_0_resnets_2_conv2_weight, x = input_631)[name = tensor("hidden_states_429")]; - tensor var_10668 = const()[name = tensor("op_10668"), val = tensor([1, 1])]; - tensor var_10670 = const()[name = tensor("op_10670"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4134394496)))]; + tensor unet_up_blocks_0_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4163885760)))]; + tensor hidden_states_429_cast = conv(bias = unet_up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_10564, groups = var_31, pad = hidden_states_429_pad_0, pad_type = hidden_states_429_pad_type_0, strides = var_10562, weight = unet_up_blocks_0_resnets_2_conv2_weight_to_fp16, x = input_631_cast)[name = tensor("hidden_states_429_cast")]; + tensor var_10569 = const()[name = tensor("op_10569"), val = tensor([1, 1])]; + tensor var_10571 = const()[name = tensor("op_10571"), val = tensor([1, 1])]; tensor x_9_pad_type_0 = const()[name = tensor("x_9_pad_type_0"), val = tensor("custom")]; tensor x_9_pad_0 = const()[name = tensor("x_9_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor x_9 = conv(bias = up_blocks_0_resnets_2_conv_shortcut_bias, dilations = var_10670, groups = var_6872, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = var_10668, weight = up_blocks_0_resnets_2_conv_shortcut_weight, x = input_619)[name = tensor("x_9")]; - tensor hidden_states_431 = add(x = x_9, y = hidden_states_429)[name = tensor("hidden_states_431")]; + tensor unet_up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4163888384)))]; + tensor unet_up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4168803648)))]; + tensor x_9_cast = conv(bias = unet_up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_10571, groups = var_31, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = var_10569, weight = unet_up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16, x = input_619_cast)[name = tensor("x_9_cast")]; + tensor hidden_states_431_cast = add(x = x_9_cast, y = hidden_states_429_cast)[name = tensor("hidden_states_431_cast")]; tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([2, 32, 40, 32, 32])]; - tensor reshape_116 = reshape(shape = reshape_116_shape_0, x = hidden_states_431)[name = tensor("reshape_116")]; + tensor reshape_116_cast = reshape(shape = reshape_116_shape_0, x = hidden_states_431_cast)[name = tensor("reshape_116_cast")]; tensor reduce_mean_87_axes_0 = const()[name = tensor("reduce_mean_87_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_87_keep_dims_0 = const()[name = tensor("reduce_mean_87_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_87 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116)[name = tensor("reduce_mean_87")]; - tensor sub_58 = sub(x = reshape_116, y = reduce_mean_87)[name = tensor("sub_58")]; - tensor square_29 = square(x = sub_58)[name = tensor("square_29")]; + tensor reduce_mean_87_cast = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast)[name = tensor("reduce_mean_87_cast")]; + tensor sub_58_cast = sub(x = reshape_116_cast, y = reduce_mean_87_cast)[name = tensor("sub_58_cast")]; + tensor square_29_cast = square(x = sub_58_cast)[name = tensor("square_29_cast")]; tensor reduce_mean_89_axes_0 = const()[name = tensor("reduce_mean_89_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_89_keep_dims_0 = const()[name = tensor("reduce_mean_89_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_89 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29)[name = tensor("reduce_mean_89")]; - tensor add_58_y_0 = const()[name = tensor("add_58_y_0"), val = tensor(0x1.0c6f7ap-20)]; - tensor add_58 = add(x = reduce_mean_89, y = add_58_y_0)[name = tensor("add_58")]; - tensor sqrt_29 = sqrt(x = add_58)[name = tensor("sqrt_29")]; - tensor real_div_29 = real_div(x = sub_58, y = sqrt_29)[name = tensor("real_div_29")]; + tensor reduce_mean_89_cast = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast)[name = tensor("reduce_mean_89_cast")]; + tensor add_58_y_0_to_fp16 = const()[name = tensor("add_58_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_58_cast = add(x = reduce_mean_89_cast, y = add_58_y_0_to_fp16)[name = tensor("add_58_cast")]; + tensor sqrt_29_cast = sqrt(x = add_58_cast)[name = tensor("sqrt_29_cast")]; + tensor real_div_29_cast = real_div(x = sub_58_cast, y = sqrt_29_cast)[name = tensor("real_div_29_cast")]; tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([2, 1280, 32, 32])]; - tensor reshape_117 = reshape(shape = reshape_117_shape_0, x = real_div_29)[name = tensor("reshape_117")]; - tensor add_59_gamma_0 = const()[name = tensor("add_59_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269507840)))]; - tensor add_59_beta_0 = const()[name = tensor("add_59_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269513024)))]; - tensor add_59_epsilon_0 = const()[name = tensor("add_59_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_59 = batch_norm(beta = add_59_beta_0, epsilon = add_59_epsilon_0, gamma = add_59_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_117)[name = tensor("add_59")]; - tensor var_10708 = const()[name = tensor("op_10708"), val = tensor([1, 1])]; - tensor var_10710 = const()[name = tensor("op_10710"), val = tensor([1, 1])]; + tensor reshape_117_cast = reshape(shape = reshape_117_shape_0, x = real_div_29_cast)[name = tensor("reshape_117_cast")]; + tensor add_59_gamma_0_to_fp16 = const()[name = tensor("add_59_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4168806272)))]; + tensor add_59_beta_0_to_fp16 = const()[name = tensor("add_59_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4168808896)))]; + tensor add_59_epsilon_0_to_fp16 = const()[name = tensor("add_59_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_59_cast = batch_norm(beta = add_59_beta_0_to_fp16, epsilon = add_59_epsilon_0_to_fp16, gamma = add_59_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_117_cast)[name = tensor("add_59_cast")]; + tensor var_10609 = const()[name = tensor("op_10609"), val = tensor([1, 1])]; + tensor var_10611 = const()[name = tensor("op_10611"), val = tensor([1, 1])]; tensor hidden_states_433_pad_type_0 = const()[name = tensor("hidden_states_433_pad_type_0"), val = tensor("custom")]; tensor hidden_states_433_pad_0 = const()[name = tensor("hidden_states_433_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_433 = conv(bias = up_blocks_0_attentions_2_proj_in_bias, dilations = var_10710, groups = var_6872, pad = hidden_states_433_pad_0, pad_type = hidden_states_433_pad_type_0, strides = var_10708, weight = up_blocks_0_attentions_2_proj_in_weight, x = add_59)[name = tensor("hidden_states_433")]; - tensor var_10715 = const()[name = tensor("op_10715"), val = tensor([2, 1280, 1, 1024])]; - tensor inputs_325 = reshape(shape = var_10715, x = hidden_states_433)[name = tensor("inputs_325")]; - tensor var_10725 = const()[name = tensor("op_10725"), val = tensor([1])]; - tensor channels_mean_325 = reduce_mean(axes = var_10725, keep_dims = var_6867, x = inputs_325)[name = tensor("channels_mean_325")]; - tensor zero_mean_325 = sub(x = inputs_325, y = channels_mean_325)[name = tensor("zero_mean_325")]; - tensor zero_mean_sq_325 = mul(x = zero_mean_325, y = zero_mean_325)[name = tensor("zero_mean_sq_325")]; - tensor var_10729 = const()[name = tensor("op_10729"), val = tensor([1])]; - tensor var_10730 = reduce_mean(axes = var_10729, keep_dims = var_6867, x = zero_mean_sq_325)[name = tensor("op_10730")]; - tensor var_10731 = const()[name = tensor("op_10731"), val = tensor(0x1.4f8b58p-17)]; - tensor var_10732 = add(x = var_10730, y = var_10731)[name = tensor("op_10732")]; - tensor denom_325_epsilon_0 = const()[name = tensor("denom_325_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_325 = rsqrt(epsilon = denom_325_epsilon_0, x = var_10732)[name = tensor("denom_325")]; - tensor out_325 = mul(x = zero_mean_325, y = denom_325)[name = tensor("out_325")]; - tensor var_10736 = const()[name = tensor("op_10736"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269518208)))]; - tensor var_10737 = add(x = out_325, y = var_10736)[name = tensor("op_10737")]; - tensor var_10739 = const()[name = tensor("op_10739"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269523392)))]; - tensor hidden_states_435 = mul(x = var_10737, y = var_10739)[name = tensor("hidden_states_435")]; - tensor var_10746 = const()[name = tensor("op_10746"), val = tensor([1, 1])]; - tensor var_10748 = const()[name = tensor("op_10748"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_proj_in_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4168811520)))]; + tensor unet_up_blocks_0_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4172088384)))]; + tensor hidden_states_433_cast = conv(bias = unet_up_blocks_0_attentions_2_proj_in_bias_to_fp16, dilations = var_10611, groups = var_31, pad = hidden_states_433_pad_0, pad_type = hidden_states_433_pad_type_0, strides = var_10609, weight = unet_up_blocks_0_attentions_2_proj_in_weight_to_fp16, x = add_59_cast)[name = tensor("hidden_states_433_cast")]; + tensor var_10616 = const()[name = tensor("op_10616"), val = tensor([2, 1280, 1, 1024])]; + tensor inputs_325_cast = reshape(shape = var_10616, x = hidden_states_433_cast)[name = tensor("inputs_325_cast")]; + tensor var_10626 = const()[name = tensor("op_10626"), val = tensor([1])]; + tensor channels_mean_325_cast = reduce_mean(axes = var_10626, keep_dims = var_23, x = inputs_325_cast)[name = tensor("channels_mean_325_cast")]; + tensor zero_mean_325_cast = sub(x = inputs_325_cast, y = channels_mean_325_cast)[name = tensor("zero_mean_325_cast")]; + tensor zero_mean_sq_325_cast = mul(x = zero_mean_325_cast, y = zero_mean_325_cast)[name = tensor("zero_mean_sq_325_cast")]; + tensor var_10630 = const()[name = tensor("op_10630"), val = tensor([1])]; + tensor var_10631_cast = reduce_mean(axes = var_10630, keep_dims = var_23, x = zero_mean_sq_325_cast)[name = tensor("op_10631_cast")]; + tensor var_10632_to_fp16 = const()[name = tensor("op_10632_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10633_cast = add(x = var_10631_cast, y = var_10632_to_fp16)[name = tensor("op_10633_cast")]; + tensor denom_325_epsilon_0_to_fp16 = const()[name = tensor("denom_325_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_325_cast = rsqrt(epsilon = denom_325_epsilon_0_to_fp16, x = var_10633_cast)[name = tensor("denom_325_cast")]; + tensor out_325_cast = mul(x = zero_mean_325_cast, y = denom_325_cast)[name = tensor("out_325_cast")]; + tensor var_10637_to_fp16 = const()[name = tensor("op_10637_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4172091008)))]; + tensor var_10638_cast = add(x = out_325_cast, y = var_10637_to_fp16)[name = tensor("op_10638_cast")]; + tensor var_10640_to_fp16 = const()[name = tensor("op_10640_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4172093632)))]; + tensor hidden_states_435_cast = mul(x = var_10638_cast, y = var_10640_to_fp16)[name = tensor("hidden_states_435_cast")]; + tensor var_10647 = const()[name = tensor("op_10647"), val = tensor([1, 1])]; + tensor var_10649 = const()[name = tensor("op_10649"), val = tensor([1, 1])]; tensor q_217_pad_type_0 = const()[name = tensor("q_217_pad_type_0"), val = tensor("custom")]; tensor q_217_pad_0 = const()[name = tensor("q_217_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_217 = conv(dilations = var_10748, groups = var_6872, pad = q_217_pad_0, pad_type = q_217_pad_type_0, strides = var_10746, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight, x = hidden_states_435)[name = tensor("q_217")]; - tensor var_10752 = const()[name = tensor("op_10752"), val = tensor([1, 1])]; - tensor var_10754 = const()[name = tensor("op_10754"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4172096256)))]; + tensor q_217_cast = conv(dilations = var_10649, groups = var_31, pad = q_217_pad_0, pad_type = q_217_pad_type_0, strides = var_10647, weight = unet_up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_435_cast)[name = tensor("q_217_cast")]; + tensor var_10653 = const()[name = tensor("op_10653"), val = tensor([1, 1])]; + tensor var_10655 = const()[name = tensor("op_10655"), val = tensor([1, 1])]; tensor k_217_pad_type_0 = const()[name = tensor("k_217_pad_type_0"), val = tensor("custom")]; tensor k_217_pad_0 = const()[name = tensor("k_217_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_217 = conv(dilations = var_10754, groups = var_6872, pad = k_217_pad_0, pad_type = k_217_pad_type_0, strides = var_10752, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight, x = hidden_states_435)[name = tensor("k_217")]; - tensor var_10758 = const()[name = tensor("op_10758"), val = tensor([1, 1])]; - tensor var_10760 = const()[name = tensor("op_10760"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4175373120)))]; + tensor k_217_cast = conv(dilations = var_10655, groups = var_31, pad = k_217_pad_0, pad_type = k_217_pad_type_0, strides = var_10653, weight = unet_up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_435_cast)[name = tensor("k_217_cast")]; + tensor var_10659 = const()[name = tensor("op_10659"), val = tensor([1, 1])]; + tensor var_10661 = const()[name = tensor("op_10661"), val = tensor([1, 1])]; tensor v_217_pad_type_0 = const()[name = tensor("v_217_pad_type_0"), val = tensor("custom")]; tensor v_217_pad_0 = const()[name = tensor("v_217_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_217 = conv(dilations = var_10760, groups = var_6872, pad = v_217_pad_0, pad_type = v_217_pad_type_0, strides = var_10758, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight, x = hidden_states_435)[name = tensor("v_217")]; - tensor var_10764 = const()[name = tensor("op_10764"), val = tensor([2, 20, 64, -1])]; - tensor var_10765 = reshape(shape = var_10764, x = q_217)[name = tensor("op_10765")]; - tensor var_10766 = const()[name = tensor("op_10766"), val = tensor([2, 20, 64, -1])]; - tensor var_10767 = reshape(shape = var_10766, x = k_217)[name = tensor("op_10767")]; - tensor var_10768 = const()[name = tensor("op_10768"), val = tensor([2, 20, 64, -1])]; - tensor var_10769 = reshape(shape = var_10768, x = v_217)[name = tensor("op_10769")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4178649984)))]; + tensor v_217_cast = conv(dilations = var_10661, groups = var_31, pad = v_217_pad_0, pad_type = v_217_pad_type_0, strides = var_10659, weight = unet_up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_435_cast)[name = tensor("v_217_cast")]; + tensor var_10665 = const()[name = tensor("op_10665"), val = tensor([2, 20, 64, -1])]; + tensor var_10666_cast = reshape(shape = var_10665, x = q_217_cast)[name = tensor("op_10666_cast")]; + tensor var_10667 = const()[name = tensor("op_10667"), val = tensor([2, 20, 64, -1])]; + tensor var_10668_cast = reshape(shape = var_10667, x = k_217_cast)[name = tensor("op_10668_cast")]; + tensor var_10669 = const()[name = tensor("op_10669"), val = tensor([2, 20, 64, -1])]; + tensor var_10670_cast = reshape(shape = var_10669, x = v_217_cast)[name = tensor("op_10670_cast")]; tensor attn_weights_433_transpose_x_0 = const()[name = tensor("attn_weights_433_transpose_x_0"), val = tensor(true)]; tensor attn_weights_433_transpose_y_0 = const()[name = tensor("attn_weights_433_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_433 = matmul(transpose_x = attn_weights_433_transpose_x_0, transpose_y = attn_weights_433_transpose_y_0, x = var_10765, y = var_10767)[name = tensor("attn_weights_433")]; - tensor attn_weights_435 = mul(x = attn_weights_433, y = var_6863)[name = tensor("attn_weights_435")]; - tensor var_10773 = softmax(axis = var_6856, x = attn_weights_435)[name = tensor("op_10773")]; + tensor attn_weights_433_cast = matmul(transpose_x = attn_weights_433_transpose_x_0, transpose_y = attn_weights_433_transpose_y_0, x = var_10666_cast, y = var_10668_cast)[name = tensor("attn_weights_433_cast")]; + tensor attn_weights_435_cast = mul(x = attn_weights_433_cast, y = var_12_to_fp16)[name = tensor("attn_weights_435_cast")]; + tensor var_10674_cast = softmax(axis = var_18, x = attn_weights_435_cast)[name = tensor("op_10674_cast")]; tensor attn_217_transpose_x_0 = const()[name = tensor("attn_217_transpose_x_0"), val = tensor(false)]; tensor attn_217_transpose_y_0 = const()[name = tensor("attn_217_transpose_y_0"), val = tensor(true)]; - tensor attn_217 = matmul(transpose_x = attn_217_transpose_x_0, transpose_y = attn_217_transpose_y_0, x = var_10769, y = var_10773)[name = tensor("attn_217")]; - tensor var_10777 = const()[name = tensor("op_10777"), val = tensor([2, 1280, 1, -1])]; - tensor input_635 = reshape(shape = var_10777, x = attn_217)[name = tensor("input_635")]; - tensor var_10782 = const()[name = tensor("op_10782"), val = tensor([1, 1])]; - tensor var_10784 = const()[name = tensor("op_10784"), val = tensor([1, 1])]; - tensor var_10786_pad_type_0 = const()[name = tensor("op_10786_pad_type_0"), val = tensor("custom")]; - tensor var_10786_pad_0 = const()[name = tensor("op_10786_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10786 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias, dilations = var_10784, groups = var_6872, pad = var_10786_pad_0, pad_type = var_10786_pad_type_0, strides = var_10782, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight, x = input_635)[name = tensor("op_10786")]; - tensor inputs_327 = add(x = var_10786, y = inputs_325)[name = tensor("inputs_327")]; - tensor var_10790 = const()[name = tensor("op_10790"), val = tensor([1])]; - tensor channels_mean_327 = reduce_mean(axes = var_10790, keep_dims = var_6867, x = inputs_327)[name = tensor("channels_mean_327")]; - tensor zero_mean_327 = sub(x = inputs_327, y = channels_mean_327)[name = tensor("zero_mean_327")]; - tensor zero_mean_sq_327 = mul(x = zero_mean_327, y = zero_mean_327)[name = tensor("zero_mean_sq_327")]; - tensor var_10794 = const()[name = tensor("op_10794"), val = tensor([1])]; - tensor var_10795 = reduce_mean(axes = var_10794, keep_dims = var_6867, x = zero_mean_sq_327)[name = tensor("op_10795")]; - tensor var_10796 = const()[name = tensor("op_10796"), val = tensor(0x1.4f8b58p-17)]; - tensor var_10797 = add(x = var_10795, y = var_10796)[name = tensor("op_10797")]; - tensor denom_327_epsilon_0 = const()[name = tensor("denom_327_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_327 = rsqrt(epsilon = denom_327_epsilon_0, x = var_10797)[name = tensor("denom_327")]; - tensor out_327 = mul(x = zero_mean_327, y = denom_327)[name = tensor("out_327")]; - tensor var_10801 = const()[name = tensor("op_10801"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269528576)))]; - tensor var_10802 = add(x = out_327, y = var_10801)[name = tensor("op_10802")]; - tensor var_10804 = const()[name = tensor("op_10804"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269533760)))]; - tensor hidden_states_437 = mul(x = var_10802, y = var_10804)[name = tensor("hidden_states_437")]; - tensor var_10811 = const()[name = tensor("op_10811"), val = tensor([1, 1])]; - tensor var_10813 = const()[name = tensor("op_10813"), val = tensor([1, 1])]; + tensor attn_217_cast = matmul(transpose_x = attn_217_transpose_x_0, transpose_y = attn_217_transpose_y_0, x = var_10670_cast, y = var_10674_cast)[name = tensor("attn_217_cast")]; + tensor var_10678 = const()[name = tensor("op_10678"), val = tensor([2, 1280, 1, -1])]; + tensor input_635_cast = reshape(shape = var_10678, x = attn_217_cast)[name = tensor("input_635_cast")]; + tensor var_10683 = const()[name = tensor("op_10683"), val = tensor([1, 1])]; + tensor var_10685 = const()[name = tensor("op_10685"), val = tensor([1, 1])]; + tensor var_10687_pad_type_0 = const()[name = tensor("op_10687_pad_type_0"), val = tensor("custom")]; + tensor var_10687_pad_0 = const()[name = tensor("op_10687_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4181926848)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4185203712)))]; + tensor var_10687_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_10685, groups = var_31, pad = var_10687_pad_0, pad_type = var_10687_pad_type_0, strides = var_10683, weight = unet_up_blocks_0_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_635_cast)[name = tensor("op_10687_cast")]; + tensor inputs_327_cast = add(x = var_10687_cast, y = inputs_325_cast)[name = tensor("inputs_327_cast")]; + tensor var_10691 = const()[name = tensor("op_10691"), val = tensor([1])]; + tensor channels_mean_327_cast = reduce_mean(axes = var_10691, keep_dims = var_23, x = inputs_327_cast)[name = tensor("channels_mean_327_cast")]; + tensor zero_mean_327_cast = sub(x = inputs_327_cast, y = channels_mean_327_cast)[name = tensor("zero_mean_327_cast")]; + tensor zero_mean_sq_327_cast = mul(x = zero_mean_327_cast, y = zero_mean_327_cast)[name = tensor("zero_mean_sq_327_cast")]; + tensor var_10695 = const()[name = tensor("op_10695"), val = tensor([1])]; + tensor var_10696_cast = reduce_mean(axes = var_10695, keep_dims = var_23, x = zero_mean_sq_327_cast)[name = tensor("op_10696_cast")]; + tensor var_10697_to_fp16 = const()[name = tensor("op_10697_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10698_cast = add(x = var_10696_cast, y = var_10697_to_fp16)[name = tensor("op_10698_cast")]; + tensor denom_327_epsilon_0_to_fp16 = const()[name = tensor("denom_327_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_327_cast = rsqrt(epsilon = denom_327_epsilon_0_to_fp16, x = var_10698_cast)[name = tensor("denom_327_cast")]; + tensor out_327_cast = mul(x = zero_mean_327_cast, y = denom_327_cast)[name = tensor("out_327_cast")]; + tensor var_10702_to_fp16 = const()[name = tensor("op_10702_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4185206336)))]; + tensor var_10703_cast = add(x = out_327_cast, y = var_10702_to_fp16)[name = tensor("op_10703_cast")]; + tensor var_10705_to_fp16 = const()[name = tensor("op_10705_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4185208960)))]; + tensor hidden_states_437_cast = mul(x = var_10703_cast, y = var_10705_to_fp16)[name = tensor("hidden_states_437_cast")]; + tensor var_10712 = const()[name = tensor("op_10712"), val = tensor([1, 1])]; + tensor var_10714 = const()[name = tensor("op_10714"), val = tensor([1, 1])]; tensor q_219_pad_type_0 = const()[name = tensor("q_219_pad_type_0"), val = tensor("custom")]; tensor q_219_pad_0 = const()[name = tensor("q_219_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_219 = conv(dilations = var_10813, groups = var_6872, pad = q_219_pad_0, pad_type = q_219_pad_type_0, strides = var_10811, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight, x = hidden_states_437)[name = tensor("q_219")]; - tensor var_10817 = const()[name = tensor("op_10817"), val = tensor([1, 1])]; - tensor var_10819 = const()[name = tensor("op_10819"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4185211584)))]; + tensor q_219_cast = conv(dilations = var_10714, groups = var_31, pad = q_219_pad_0, pad_type = q_219_pad_type_0, strides = var_10712, weight = unet_up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_437_cast)[name = tensor("q_219_cast")]; + tensor var_10718 = const()[name = tensor("op_10718"), val = tensor([1, 1])]; + tensor var_10720 = const()[name = tensor("op_10720"), val = tensor([1, 1])]; tensor k_219_pad_type_0 = const()[name = tensor("k_219_pad_type_0"), val = tensor("custom")]; tensor k_219_pad_0 = const()[name = tensor("k_219_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_219 = conv(dilations = var_10819, groups = var_6872, pad = k_219_pad_0, pad_type = k_219_pad_type_0, strides = var_10817, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_219")]; - tensor var_10823 = const()[name = tensor("op_10823"), val = tensor([1, 1])]; - tensor var_10825 = const()[name = tensor("op_10825"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4188488448)))]; + tensor k_219_cast = conv(dilations = var_10720, groups = var_31, pad = k_219_pad_0, pad_type = k_219_pad_type_0, strides = var_10718, weight = unet_up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_219_cast")]; + tensor var_10724 = const()[name = tensor("op_10724"), val = tensor([1, 1])]; + tensor var_10726 = const()[name = tensor("op_10726"), val = tensor([1, 1])]; tensor v_219_pad_type_0 = const()[name = tensor("v_219_pad_type_0"), val = tensor("custom")]; tensor v_219_pad_0 = const()[name = tensor("v_219_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_219 = conv(dilations = var_10825, groups = var_6872, pad = v_219_pad_0, pad_type = v_219_pad_type_0, strides = var_10823, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_219")]; - tensor var_10829 = const()[name = tensor("op_10829"), val = tensor([2, 20, 64, -1])]; - tensor var_10830 = reshape(shape = var_10829, x = q_219)[name = tensor("op_10830")]; - tensor var_10831 = const()[name = tensor("op_10831"), val = tensor([2, 20, 64, -1])]; - tensor var_10832 = reshape(shape = var_10831, x = k_219)[name = tensor("op_10832")]; - tensor var_10833 = const()[name = tensor("op_10833"), val = tensor([2, 20, 64, -1])]; - tensor var_10834 = reshape(shape = var_10833, x = v_219)[name = tensor("op_10834")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4193731392)))]; + tensor v_219_cast = conv(dilations = var_10726, groups = var_31, pad = v_219_pad_0, pad_type = v_219_pad_type_0, strides = var_10724, weight = unet_up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_219_cast")]; + tensor var_10730 = const()[name = tensor("op_10730"), val = tensor([2, 20, 64, -1])]; + tensor var_10731_cast = reshape(shape = var_10730, x = q_219_cast)[name = tensor("op_10731_cast")]; + tensor var_10732 = const()[name = tensor("op_10732"), val = tensor([2, 20, 64, -1])]; + tensor var_10733_cast = reshape(shape = var_10732, x = k_219_cast)[name = tensor("op_10733_cast")]; + tensor var_10734 = const()[name = tensor("op_10734"), val = tensor([2, 20, 64, -1])]; + tensor var_10735_cast = reshape(shape = var_10734, x = v_219_cast)[name = tensor("op_10735_cast")]; tensor attn_weights_437_transpose_x_0 = const()[name = tensor("attn_weights_437_transpose_x_0"), val = tensor(true)]; tensor attn_weights_437_transpose_y_0 = const()[name = tensor("attn_weights_437_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_437 = matmul(transpose_x = attn_weights_437_transpose_x_0, transpose_y = attn_weights_437_transpose_y_0, x = var_10830, y = var_10832)[name = tensor("attn_weights_437")]; - tensor attn_weights_439 = mul(x = attn_weights_437, y = var_6863)[name = tensor("attn_weights_439")]; - tensor var_10838 = softmax(axis = var_6856, x = attn_weights_439)[name = tensor("op_10838")]; + tensor attn_weights_437_cast = matmul(transpose_x = attn_weights_437_transpose_x_0, transpose_y = attn_weights_437_transpose_y_0, x = var_10731_cast, y = var_10733_cast)[name = tensor("attn_weights_437_cast")]; + tensor attn_weights_439_cast = mul(x = attn_weights_437_cast, y = var_12_to_fp16)[name = tensor("attn_weights_439_cast")]; + tensor var_10739_cast = softmax(axis = var_18, x = attn_weights_439_cast)[name = tensor("op_10739_cast")]; tensor attn_219_transpose_x_0 = const()[name = tensor("attn_219_transpose_x_0"), val = tensor(false)]; tensor attn_219_transpose_y_0 = const()[name = tensor("attn_219_transpose_y_0"), val = tensor(true)]; - tensor attn_219 = matmul(transpose_x = attn_219_transpose_x_0, transpose_y = attn_219_transpose_y_0, x = var_10834, y = var_10838)[name = tensor("attn_219")]; - tensor var_10842 = const()[name = tensor("op_10842"), val = tensor([2, 1280, 1, -1])]; - tensor input_637 = reshape(shape = var_10842, x = attn_219)[name = tensor("input_637")]; - tensor var_10847 = const()[name = tensor("op_10847"), val = tensor([1, 1])]; - tensor var_10849 = const()[name = tensor("op_10849"), val = tensor([1, 1])]; - tensor var_10851_pad_type_0 = const()[name = tensor("op_10851_pad_type_0"), val = tensor("custom")]; - tensor var_10851_pad_0 = const()[name = tensor("op_10851_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10851 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias, dilations = var_10849, groups = var_6872, pad = var_10851_pad_0, pad_type = var_10851_pad_type_0, strides = var_10847, weight = up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight, x = input_637)[name = tensor("op_10851")]; - tensor inputs_329 = add(x = var_10851, y = inputs_327)[name = tensor("inputs_329")]; - tensor var_10855 = const()[name = tensor("op_10855"), val = tensor([1])]; - tensor channels_mean_329 = reduce_mean(axes = var_10855, keep_dims = var_6867, x = inputs_329)[name = tensor("channels_mean_329")]; - tensor zero_mean_329 = sub(x = inputs_329, y = channels_mean_329)[name = tensor("zero_mean_329")]; - tensor zero_mean_sq_329 = mul(x = zero_mean_329, y = zero_mean_329)[name = tensor("zero_mean_sq_329")]; - tensor var_10859 = const()[name = tensor("op_10859"), val = tensor([1])]; - tensor var_10860 = reduce_mean(axes = var_10859, keep_dims = var_6867, x = zero_mean_sq_329)[name = tensor("op_10860")]; - tensor var_10861 = const()[name = tensor("op_10861"), val = tensor(0x1.4f8b58p-17)]; - tensor var_10862 = add(x = var_10860, y = var_10861)[name = tensor("op_10862")]; - tensor denom_329_epsilon_0 = const()[name = tensor("denom_329_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_329 = rsqrt(epsilon = denom_329_epsilon_0, x = var_10862)[name = tensor("denom_329")]; - tensor out_329 = mul(x = zero_mean_329, y = denom_329)[name = tensor("out_329")]; - tensor var_10866 = const()[name = tensor("op_10866"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269538944)))]; - tensor var_10867 = add(x = out_329, y = var_10866)[name = tensor("op_10867")]; - tensor var_10869 = const()[name = tensor("op_10869"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269544128)))]; - tensor input_639 = mul(x = var_10867, y = var_10869)[name = tensor("input_639")]; - tensor var_10877 = const()[name = tensor("op_10877"), val = tensor([1, 1])]; - tensor var_10879 = const()[name = tensor("op_10879"), val = tensor([1, 1])]; - tensor var_10881_pad_type_0 = const()[name = tensor("op_10881_pad_type_0"), val = tensor("custom")]; - tensor var_10881_pad_0 = const()[name = tensor("op_10881_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10881 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias, dilations = var_10879, groups = var_6872, pad = var_10881_pad_0, pad_type = var_10881_pad_type_0, strides = var_10877, weight = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight, x = input_639)[name = tensor("op_10881")]; - tensor var_10882_split_sizes_0 = const()[name = tensor("op_10882_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_10882_axis_0 = const()[name = tensor("op_10882_axis_0"), val = tensor(1)]; - tensor var_10882_0, tensor var_10882_1 = split(axis = var_10882_axis_0, split_sizes = var_10882_split_sizes_0, x = var_10881)[name = tensor("op_10882")]; - tensor var_10884_mode_0 = const()[name = tensor("op_10884_mode_0"), val = tensor("EXACT")]; - tensor var_10884 = gelu(mode = var_10884_mode_0, x = var_10882_1)[name = tensor("op_10884")]; - tensor input_641 = mul(x = var_10882_0, y = var_10884)[name = tensor("input_641")]; - tensor var_10888 = const()[name = tensor("op_10888"), val = tensor([1, 1])]; - tensor var_10890 = const()[name = tensor("op_10890"), val = tensor([1, 1])]; - tensor var_10892_pad_type_0 = const()[name = tensor("op_10892_pad_type_0"), val = tensor("custom")]; - tensor var_10892_pad_0 = const()[name = tensor("op_10892_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10892 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias, dilations = var_10890, groups = var_6872, pad = var_10892_pad_0, pad_type = var_10892_pad_type_0, strides = var_10888, weight = up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight, x = input_641)[name = tensor("op_10892")]; - tensor inputs_331 = add(x = var_10892, y = inputs_329)[name = tensor("inputs_331")]; - tensor var_10902 = const()[name = tensor("op_10902"), val = tensor([1])]; - tensor channels_mean_331 = reduce_mean(axes = var_10902, keep_dims = var_6867, x = inputs_331)[name = tensor("channels_mean_331")]; - tensor zero_mean_331 = sub(x = inputs_331, y = channels_mean_331)[name = tensor("zero_mean_331")]; - tensor zero_mean_sq_331 = mul(x = zero_mean_331, y = zero_mean_331)[name = tensor("zero_mean_sq_331")]; - tensor var_10906 = const()[name = tensor("op_10906"), val = tensor([1])]; - tensor var_10907 = reduce_mean(axes = var_10906, keep_dims = var_6867, x = zero_mean_sq_331)[name = tensor("op_10907")]; - tensor var_10908 = const()[name = tensor("op_10908"), val = tensor(0x1.4f8b58p-17)]; - tensor var_10909 = add(x = var_10907, y = var_10908)[name = tensor("op_10909")]; - tensor denom_331_epsilon_0 = const()[name = tensor("denom_331_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_331 = rsqrt(epsilon = denom_331_epsilon_0, x = var_10909)[name = tensor("denom_331")]; - tensor out_331 = mul(x = zero_mean_331, y = denom_331)[name = tensor("out_331")]; - tensor var_10913 = const()[name = tensor("op_10913"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269549312)))]; - tensor var_10914 = add(x = out_331, y = var_10913)[name = tensor("op_10914")]; - tensor var_10916 = const()[name = tensor("op_10916"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269554496)))]; - tensor hidden_states_441 = mul(x = var_10914, y = var_10916)[name = tensor("hidden_states_441")]; - tensor var_10923 = const()[name = tensor("op_10923"), val = tensor([1, 1])]; - tensor var_10925 = const()[name = tensor("op_10925"), val = tensor([1, 1])]; + tensor attn_219_cast = matmul(transpose_x = attn_219_transpose_x_0, transpose_y = attn_219_transpose_y_0, x = var_10735_cast, y = var_10739_cast)[name = tensor("attn_219_cast")]; + tensor var_10743 = const()[name = tensor("op_10743"), val = tensor([2, 1280, 1, -1])]; + tensor input_637_cast = reshape(shape = var_10743, x = attn_219_cast)[name = tensor("input_637_cast")]; + tensor var_10748 = const()[name = tensor("op_10748"), val = tensor([1, 1])]; + tensor var_10750 = const()[name = tensor("op_10750"), val = tensor([1, 1])]; + tensor var_10752_pad_type_0 = const()[name = tensor("op_10752_pad_type_0"), val = tensor("custom")]; + tensor var_10752_pad_0 = const()[name = tensor("op_10752_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4198974336)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4202251200)))]; + tensor var_10752_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_10750, groups = var_31, pad = var_10752_pad_0, pad_type = var_10752_pad_type_0, strides = var_10748, weight = unet_up_blocks_0_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_637_cast)[name = tensor("op_10752_cast")]; + tensor inputs_329_cast = add(x = var_10752_cast, y = inputs_327_cast)[name = tensor("inputs_329_cast")]; + tensor var_10756 = const()[name = tensor("op_10756"), val = tensor([1])]; + tensor channels_mean_329_cast = reduce_mean(axes = var_10756, keep_dims = var_23, x = inputs_329_cast)[name = tensor("channels_mean_329_cast")]; + tensor zero_mean_329_cast = sub(x = inputs_329_cast, y = channels_mean_329_cast)[name = tensor("zero_mean_329_cast")]; + tensor zero_mean_sq_329_cast = mul(x = zero_mean_329_cast, y = zero_mean_329_cast)[name = tensor("zero_mean_sq_329_cast")]; + tensor var_10760 = const()[name = tensor("op_10760"), val = tensor([1])]; + tensor var_10761_cast = reduce_mean(axes = var_10760, keep_dims = var_23, x = zero_mean_sq_329_cast)[name = tensor("op_10761_cast")]; + tensor var_10762_to_fp16 = const()[name = tensor("op_10762_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10763_cast = add(x = var_10761_cast, y = var_10762_to_fp16)[name = tensor("op_10763_cast")]; + tensor denom_329_epsilon_0_to_fp16 = const()[name = tensor("denom_329_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_329_cast = rsqrt(epsilon = denom_329_epsilon_0_to_fp16, x = var_10763_cast)[name = tensor("denom_329_cast")]; + tensor out_329_cast = mul(x = zero_mean_329_cast, y = denom_329_cast)[name = tensor("out_329_cast")]; + tensor var_10767_to_fp16 = const()[name = tensor("op_10767_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4202253824)))]; + tensor var_10768_cast = add(x = out_329_cast, y = var_10767_to_fp16)[name = tensor("op_10768_cast")]; + tensor var_10770_to_fp16 = const()[name = tensor("op_10770_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4202256448)))]; + tensor input_639_cast = mul(x = var_10768_cast, y = var_10770_to_fp16)[name = tensor("input_639_cast")]; + tensor var_10778 = const()[name = tensor("op_10778"), val = tensor([1, 1])]; + tensor var_10780 = const()[name = tensor("op_10780"), val = tensor([1, 1])]; + tensor var_10782_pad_type_0 = const()[name = tensor("op_10782_pad_type_0"), val = tensor("custom")]; + tensor var_10782_pad_0 = const()[name = tensor("op_10782_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4202259072)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4228473536)))]; + tensor var_10782_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_10780, groups = var_31, pad = var_10782_pad_0, pad_type = var_10782_pad_type_0, strides = var_10778, weight = unet_up_blocks_0_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_639_cast)[name = tensor("op_10782_cast")]; + tensor var_10783_split_sizes_0 = const()[name = tensor("op_10783_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10783_axis_0 = const()[name = tensor("op_10783_axis_0"), val = tensor(1)]; + tensor var_10783_cast_0, tensor var_10783_cast_1 = split(axis = var_10783_axis_0, split_sizes = var_10783_split_sizes_0, x = var_10782_cast)[name = tensor("op_10783_cast")]; + tensor var_10785_mode_0 = const()[name = tensor("op_10785_mode_0"), val = tensor("EXACT")]; + tensor var_10785_cast = gelu(mode = var_10785_mode_0, x = var_10783_cast_1)[name = tensor("op_10785_cast")]; + tensor input_641_cast = mul(x = var_10783_cast_0, y = var_10785_cast)[name = tensor("input_641_cast")]; + tensor var_10789 = const()[name = tensor("op_10789"), val = tensor([1, 1])]; + tensor var_10791 = const()[name = tensor("op_10791"), val = tensor([1, 1])]; + tensor var_10793_pad_type_0 = const()[name = tensor("op_10793_pad_type_0"), val = tensor("custom")]; + tensor var_10793_pad_0 = const()[name = tensor("op_10793_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4228494080)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4241601344)))]; + tensor var_10793_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_10791, groups = var_31, pad = var_10793_pad_0, pad_type = var_10793_pad_type_0, strides = var_10789, weight = unet_up_blocks_0_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_641_cast)[name = tensor("op_10793_cast")]; + tensor inputs_331_cast = add(x = var_10793_cast, y = inputs_329_cast)[name = tensor("inputs_331_cast")]; + tensor var_10803 = const()[name = tensor("op_10803"), val = tensor([1])]; + tensor channels_mean_331_cast = reduce_mean(axes = var_10803, keep_dims = var_23, x = inputs_331_cast)[name = tensor("channels_mean_331_cast")]; + tensor zero_mean_331_cast = sub(x = inputs_331_cast, y = channels_mean_331_cast)[name = tensor("zero_mean_331_cast")]; + tensor zero_mean_sq_331_cast = mul(x = zero_mean_331_cast, y = zero_mean_331_cast)[name = tensor("zero_mean_sq_331_cast")]; + tensor var_10807 = const()[name = tensor("op_10807"), val = tensor([1])]; + tensor var_10808_cast = reduce_mean(axes = var_10807, keep_dims = var_23, x = zero_mean_sq_331_cast)[name = tensor("op_10808_cast")]; + tensor var_10809_to_fp16 = const()[name = tensor("op_10809_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10810_cast = add(x = var_10808_cast, y = var_10809_to_fp16)[name = tensor("op_10810_cast")]; + tensor denom_331_epsilon_0_to_fp16 = const()[name = tensor("denom_331_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_331_cast = rsqrt(epsilon = denom_331_epsilon_0_to_fp16, x = var_10810_cast)[name = tensor("denom_331_cast")]; + tensor out_331_cast = mul(x = zero_mean_331_cast, y = denom_331_cast)[name = tensor("out_331_cast")]; + tensor var_10814_to_fp16 = const()[name = tensor("op_10814_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4241603968)))]; + tensor var_10815_cast = add(x = out_331_cast, y = var_10814_to_fp16)[name = tensor("op_10815_cast")]; + tensor var_10817_to_fp16 = const()[name = tensor("op_10817_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4241606592)))]; + tensor hidden_states_441_cast = mul(x = var_10815_cast, y = var_10817_to_fp16)[name = tensor("hidden_states_441_cast")]; + tensor var_10824 = const()[name = tensor("op_10824"), val = tensor([1, 1])]; + tensor var_10826 = const()[name = tensor("op_10826"), val = tensor([1, 1])]; tensor q_221_pad_type_0 = const()[name = tensor("q_221_pad_type_0"), val = tensor("custom")]; tensor q_221_pad_0 = const()[name = tensor("q_221_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_221 = conv(dilations = var_10925, groups = var_6872, pad = q_221_pad_0, pad_type = q_221_pad_type_0, strides = var_10923, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight, x = hidden_states_441)[name = tensor("q_221")]; - tensor var_10929 = const()[name = tensor("op_10929"), val = tensor([1, 1])]; - tensor var_10931 = const()[name = tensor("op_10931"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4241609216)))]; + tensor q_221_cast = conv(dilations = var_10826, groups = var_31, pad = q_221_pad_0, pad_type = q_221_pad_type_0, strides = var_10824, weight = unet_up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_441_cast)[name = tensor("q_221_cast")]; + tensor var_10830 = const()[name = tensor("op_10830"), val = tensor([1, 1])]; + tensor var_10832 = const()[name = tensor("op_10832"), val = tensor([1, 1])]; tensor k_221_pad_type_0 = const()[name = tensor("k_221_pad_type_0"), val = tensor("custom")]; tensor k_221_pad_0 = const()[name = tensor("k_221_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_221 = conv(dilations = var_10931, groups = var_6872, pad = k_221_pad_0, pad_type = k_221_pad_type_0, strides = var_10929, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight, x = hidden_states_441)[name = tensor("k_221")]; - tensor var_10935 = const()[name = tensor("op_10935"), val = tensor([1, 1])]; - tensor var_10937 = const()[name = tensor("op_10937"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4244886080)))]; + tensor k_221_cast = conv(dilations = var_10832, groups = var_31, pad = k_221_pad_0, pad_type = k_221_pad_type_0, strides = var_10830, weight = unet_up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_441_cast)[name = tensor("k_221_cast")]; + tensor var_10836 = const()[name = tensor("op_10836"), val = tensor([1, 1])]; + tensor var_10838 = const()[name = tensor("op_10838"), val = tensor([1, 1])]; tensor v_221_pad_type_0 = const()[name = tensor("v_221_pad_type_0"), val = tensor("custom")]; tensor v_221_pad_0 = const()[name = tensor("v_221_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_221 = conv(dilations = var_10937, groups = var_6872, pad = v_221_pad_0, pad_type = v_221_pad_type_0, strides = var_10935, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight, x = hidden_states_441)[name = tensor("v_221")]; - tensor var_10941 = const()[name = tensor("op_10941"), val = tensor([2, 20, 64, -1])]; - tensor var_10942 = reshape(shape = var_10941, x = q_221)[name = tensor("op_10942")]; - tensor var_10943 = const()[name = tensor("op_10943"), val = tensor([2, 20, 64, -1])]; - tensor var_10944 = reshape(shape = var_10943, x = k_221)[name = tensor("op_10944")]; - tensor var_10945 = const()[name = tensor("op_10945"), val = tensor([2, 20, 64, -1])]; - tensor var_10946 = reshape(shape = var_10945, x = v_221)[name = tensor("op_10946")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4248162944)))]; + tensor v_221_cast = conv(dilations = var_10838, groups = var_31, pad = v_221_pad_0, pad_type = v_221_pad_type_0, strides = var_10836, weight = unet_up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_441_cast)[name = tensor("v_221_cast")]; + tensor var_10842 = const()[name = tensor("op_10842"), val = tensor([2, 20, 64, -1])]; + tensor var_10843_cast = reshape(shape = var_10842, x = q_221_cast)[name = tensor("op_10843_cast")]; + tensor var_10844 = const()[name = tensor("op_10844"), val = tensor([2, 20, 64, -1])]; + tensor var_10845_cast = reshape(shape = var_10844, x = k_221_cast)[name = tensor("op_10845_cast")]; + tensor var_10846 = const()[name = tensor("op_10846"), val = tensor([2, 20, 64, -1])]; + tensor var_10847_cast = reshape(shape = var_10846, x = v_221_cast)[name = tensor("op_10847_cast")]; tensor attn_weights_441_transpose_x_0 = const()[name = tensor("attn_weights_441_transpose_x_0"), val = tensor(true)]; tensor attn_weights_441_transpose_y_0 = const()[name = tensor("attn_weights_441_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_441 = matmul(transpose_x = attn_weights_441_transpose_x_0, transpose_y = attn_weights_441_transpose_y_0, x = var_10942, y = var_10944)[name = tensor("attn_weights_441")]; - tensor attn_weights_443 = mul(x = attn_weights_441, y = var_6863)[name = tensor("attn_weights_443")]; - tensor var_10950 = softmax(axis = var_6856, x = attn_weights_443)[name = tensor("op_10950")]; + tensor attn_weights_441_cast = matmul(transpose_x = attn_weights_441_transpose_x_0, transpose_y = attn_weights_441_transpose_y_0, x = var_10843_cast, y = var_10845_cast)[name = tensor("attn_weights_441_cast")]; + tensor attn_weights_443_cast = mul(x = attn_weights_441_cast, y = var_12_to_fp16)[name = tensor("attn_weights_443_cast")]; + tensor var_10851_cast = softmax(axis = var_18, x = attn_weights_443_cast)[name = tensor("op_10851_cast")]; tensor attn_221_transpose_x_0 = const()[name = tensor("attn_221_transpose_x_0"), val = tensor(false)]; tensor attn_221_transpose_y_0 = const()[name = tensor("attn_221_transpose_y_0"), val = tensor(true)]; - tensor attn_221 = matmul(transpose_x = attn_221_transpose_x_0, transpose_y = attn_221_transpose_y_0, x = var_10946, y = var_10950)[name = tensor("attn_221")]; - tensor var_10954 = const()[name = tensor("op_10954"), val = tensor([2, 1280, 1, -1])]; - tensor input_643 = reshape(shape = var_10954, x = attn_221)[name = tensor("input_643")]; - tensor var_10959 = const()[name = tensor("op_10959"), val = tensor([1, 1])]; - tensor var_10961 = const()[name = tensor("op_10961"), val = tensor([1, 1])]; - tensor var_10963_pad_type_0 = const()[name = tensor("op_10963_pad_type_0"), val = tensor("custom")]; - tensor var_10963_pad_0 = const()[name = tensor("op_10963_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_10963 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias, dilations = var_10961, groups = var_6872, pad = var_10963_pad_0, pad_type = var_10963_pad_type_0, strides = var_10959, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight, x = input_643)[name = tensor("op_10963")]; - tensor inputs_333 = add(x = var_10963, y = inputs_331)[name = tensor("inputs_333")]; - tensor var_10967 = const()[name = tensor("op_10967"), val = tensor([1])]; - tensor channels_mean_333 = reduce_mean(axes = var_10967, keep_dims = var_6867, x = inputs_333)[name = tensor("channels_mean_333")]; - tensor zero_mean_333 = sub(x = inputs_333, y = channels_mean_333)[name = tensor("zero_mean_333")]; - tensor zero_mean_sq_333 = mul(x = zero_mean_333, y = zero_mean_333)[name = tensor("zero_mean_sq_333")]; - tensor var_10971 = const()[name = tensor("op_10971"), val = tensor([1])]; - tensor var_10972 = reduce_mean(axes = var_10971, keep_dims = var_6867, x = zero_mean_sq_333)[name = tensor("op_10972")]; - tensor var_10973 = const()[name = tensor("op_10973"), val = tensor(0x1.4f8b58p-17)]; - tensor var_10974 = add(x = var_10972, y = var_10973)[name = tensor("op_10974")]; - tensor denom_333_epsilon_0 = const()[name = tensor("denom_333_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_333 = rsqrt(epsilon = denom_333_epsilon_0, x = var_10974)[name = tensor("denom_333")]; - tensor out_333 = mul(x = zero_mean_333, y = denom_333)[name = tensor("out_333")]; - tensor var_10978 = const()[name = tensor("op_10978"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269559680)))]; - tensor var_10979 = add(x = out_333, y = var_10978)[name = tensor("op_10979")]; - tensor var_10981 = const()[name = tensor("op_10981"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269564864)))]; - tensor hidden_states_443 = mul(x = var_10979, y = var_10981)[name = tensor("hidden_states_443")]; - tensor var_10988 = const()[name = tensor("op_10988"), val = tensor([1, 1])]; - tensor var_10990 = const()[name = tensor("op_10990"), val = tensor([1, 1])]; + tensor attn_221_cast = matmul(transpose_x = attn_221_transpose_x_0, transpose_y = attn_221_transpose_y_0, x = var_10847_cast, y = var_10851_cast)[name = tensor("attn_221_cast")]; + tensor var_10855 = const()[name = tensor("op_10855"), val = tensor([2, 1280, 1, -1])]; + tensor input_643_cast = reshape(shape = var_10855, x = attn_221_cast)[name = tensor("input_643_cast")]; + tensor var_10860 = const()[name = tensor("op_10860"), val = tensor([1, 1])]; + tensor var_10862 = const()[name = tensor("op_10862"), val = tensor([1, 1])]; + tensor var_10864_pad_type_0 = const()[name = tensor("op_10864_pad_type_0"), val = tensor("custom")]; + tensor var_10864_pad_0 = const()[name = tensor("op_10864_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4251439808)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4254716672)))]; + tensor var_10864_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_10862, groups = var_31, pad = var_10864_pad_0, pad_type = var_10864_pad_type_0, strides = var_10860, weight = unet_up_blocks_0_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_643_cast)[name = tensor("op_10864_cast")]; + tensor inputs_333_cast = add(x = var_10864_cast, y = inputs_331_cast)[name = tensor("inputs_333_cast")]; + tensor var_10868 = const()[name = tensor("op_10868"), val = tensor([1])]; + tensor channels_mean_333_cast = reduce_mean(axes = var_10868, keep_dims = var_23, x = inputs_333_cast)[name = tensor("channels_mean_333_cast")]; + tensor zero_mean_333_cast = sub(x = inputs_333_cast, y = channels_mean_333_cast)[name = tensor("zero_mean_333_cast")]; + tensor zero_mean_sq_333_cast = mul(x = zero_mean_333_cast, y = zero_mean_333_cast)[name = tensor("zero_mean_sq_333_cast")]; + tensor var_10872 = const()[name = tensor("op_10872"), val = tensor([1])]; + tensor var_10873_cast = reduce_mean(axes = var_10872, keep_dims = var_23, x = zero_mean_sq_333_cast)[name = tensor("op_10873_cast")]; + tensor var_10874_to_fp16 = const()[name = tensor("op_10874_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10875_cast = add(x = var_10873_cast, y = var_10874_to_fp16)[name = tensor("op_10875_cast")]; + tensor denom_333_epsilon_0_to_fp16 = const()[name = tensor("denom_333_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_333_cast = rsqrt(epsilon = denom_333_epsilon_0_to_fp16, x = var_10875_cast)[name = tensor("denom_333_cast")]; + tensor out_333_cast = mul(x = zero_mean_333_cast, y = denom_333_cast)[name = tensor("out_333_cast")]; + tensor var_10879_to_fp16 = const()[name = tensor("op_10879_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4254719296)))]; + tensor var_10880_cast = add(x = out_333_cast, y = var_10879_to_fp16)[name = tensor("op_10880_cast")]; + tensor var_10882_to_fp16 = const()[name = tensor("op_10882_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4254721920)))]; + tensor hidden_states_443_cast = mul(x = var_10880_cast, y = var_10882_to_fp16)[name = tensor("hidden_states_443_cast")]; + tensor var_10889 = const()[name = tensor("op_10889"), val = tensor([1, 1])]; + tensor var_10891 = const()[name = tensor("op_10891"), val = tensor([1, 1])]; tensor q_223_pad_type_0 = const()[name = tensor("q_223_pad_type_0"), val = tensor("custom")]; tensor q_223_pad_0 = const()[name = tensor("q_223_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_223 = conv(dilations = var_10990, groups = var_6872, pad = q_223_pad_0, pad_type = q_223_pad_type_0, strides = var_10988, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight, x = hidden_states_443)[name = tensor("q_223")]; - tensor var_10994 = const()[name = tensor("op_10994"), val = tensor([1, 1])]; - tensor var_10996 = const()[name = tensor("op_10996"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4254724544)))]; + tensor q_223_cast = conv(dilations = var_10891, groups = var_31, pad = q_223_pad_0, pad_type = q_223_pad_type_0, strides = var_10889, weight = unet_up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_443_cast)[name = tensor("q_223_cast")]; + tensor var_10895 = const()[name = tensor("op_10895"), val = tensor([1, 1])]; + tensor var_10897 = const()[name = tensor("op_10897"), val = tensor([1, 1])]; tensor k_223_pad_type_0 = const()[name = tensor("k_223_pad_type_0"), val = tensor("custom")]; tensor k_223_pad_0 = const()[name = tensor("k_223_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_223 = conv(dilations = var_10996, groups = var_6872, pad = k_223_pad_0, pad_type = k_223_pad_type_0, strides = var_10994, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_223")]; - tensor var_11000 = const()[name = tensor("op_11000"), val = tensor([1, 1])]; - tensor var_11002 = const()[name = tensor("op_11002"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4258001408)))]; + tensor k_223_cast = conv(dilations = var_10897, groups = var_31, pad = k_223_pad_0, pad_type = k_223_pad_type_0, strides = var_10895, weight = unet_up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_223_cast")]; + tensor var_10901 = const()[name = tensor("op_10901"), val = tensor([1, 1])]; + tensor var_10903 = const()[name = tensor("op_10903"), val = tensor([1, 1])]; tensor v_223_pad_type_0 = const()[name = tensor("v_223_pad_type_0"), val = tensor("custom")]; tensor v_223_pad_0 = const()[name = tensor("v_223_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_223 = conv(dilations = var_11002, groups = var_6872, pad = v_223_pad_0, pad_type = v_223_pad_type_0, strides = var_11000, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_223")]; - tensor var_11006 = const()[name = tensor("op_11006"), val = tensor([2, 20, 64, -1])]; - tensor var_11007 = reshape(shape = var_11006, x = q_223)[name = tensor("op_11007")]; - tensor var_11008 = const()[name = tensor("op_11008"), val = tensor([2, 20, 64, -1])]; - tensor var_11009 = reshape(shape = var_11008, x = k_223)[name = tensor("op_11009")]; - tensor var_11010 = const()[name = tensor("op_11010"), val = tensor([2, 20, 64, -1])]; - tensor var_11011 = reshape(shape = var_11010, x = v_223)[name = tensor("op_11011")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4263244352)))]; + tensor v_223_cast = conv(dilations = var_10903, groups = var_31, pad = v_223_pad_0, pad_type = v_223_pad_type_0, strides = var_10901, weight = unet_up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_223_cast")]; + tensor var_10907 = const()[name = tensor("op_10907"), val = tensor([2, 20, 64, -1])]; + tensor var_10908_cast = reshape(shape = var_10907, x = q_223_cast)[name = tensor("op_10908_cast")]; + tensor var_10909 = const()[name = tensor("op_10909"), val = tensor([2, 20, 64, -1])]; + tensor var_10910_cast = reshape(shape = var_10909, x = k_223_cast)[name = tensor("op_10910_cast")]; + tensor var_10911 = const()[name = tensor("op_10911"), val = tensor([2, 20, 64, -1])]; + tensor var_10912_cast = reshape(shape = var_10911, x = v_223_cast)[name = tensor("op_10912_cast")]; tensor attn_weights_445_transpose_x_0 = const()[name = tensor("attn_weights_445_transpose_x_0"), val = tensor(true)]; tensor attn_weights_445_transpose_y_0 = const()[name = tensor("attn_weights_445_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_445 = matmul(transpose_x = attn_weights_445_transpose_x_0, transpose_y = attn_weights_445_transpose_y_0, x = var_11007, y = var_11009)[name = tensor("attn_weights_445")]; - tensor attn_weights_447 = mul(x = attn_weights_445, y = var_6863)[name = tensor("attn_weights_447")]; - tensor var_11015 = softmax(axis = var_6856, x = attn_weights_447)[name = tensor("op_11015")]; + tensor attn_weights_445_cast = matmul(transpose_x = attn_weights_445_transpose_x_0, transpose_y = attn_weights_445_transpose_y_0, x = var_10908_cast, y = var_10910_cast)[name = tensor("attn_weights_445_cast")]; + tensor attn_weights_447_cast = mul(x = attn_weights_445_cast, y = var_12_to_fp16)[name = tensor("attn_weights_447_cast")]; + tensor var_10916_cast = softmax(axis = var_18, x = attn_weights_447_cast)[name = tensor("op_10916_cast")]; tensor attn_223_transpose_x_0 = const()[name = tensor("attn_223_transpose_x_0"), val = tensor(false)]; tensor attn_223_transpose_y_0 = const()[name = tensor("attn_223_transpose_y_0"), val = tensor(true)]; - tensor attn_223 = matmul(transpose_x = attn_223_transpose_x_0, transpose_y = attn_223_transpose_y_0, x = var_11011, y = var_11015)[name = tensor("attn_223")]; - tensor var_11019 = const()[name = tensor("op_11019"), val = tensor([2, 1280, 1, -1])]; - tensor input_645 = reshape(shape = var_11019, x = attn_223)[name = tensor("input_645")]; - tensor var_11024 = const()[name = tensor("op_11024"), val = tensor([1, 1])]; - tensor var_11026 = const()[name = tensor("op_11026"), val = tensor([1, 1])]; - tensor var_11028_pad_type_0 = const()[name = tensor("op_11028_pad_type_0"), val = tensor("custom")]; - tensor var_11028_pad_0 = const()[name = tensor("op_11028_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11028 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias, dilations = var_11026, groups = var_6872, pad = var_11028_pad_0, pad_type = var_11028_pad_type_0, strides = var_11024, weight = up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight, x = input_645)[name = tensor("op_11028")]; - tensor inputs_335 = add(x = var_11028, y = inputs_333)[name = tensor("inputs_335")]; - tensor var_11032 = const()[name = tensor("op_11032"), val = tensor([1])]; - tensor channels_mean_335 = reduce_mean(axes = var_11032, keep_dims = var_6867, x = inputs_335)[name = tensor("channels_mean_335")]; - tensor zero_mean_335 = sub(x = inputs_335, y = channels_mean_335)[name = tensor("zero_mean_335")]; - tensor zero_mean_sq_335 = mul(x = zero_mean_335, y = zero_mean_335)[name = tensor("zero_mean_sq_335")]; - tensor var_11036 = const()[name = tensor("op_11036"), val = tensor([1])]; - tensor var_11037 = reduce_mean(axes = var_11036, keep_dims = var_6867, x = zero_mean_sq_335)[name = tensor("op_11037")]; - tensor var_11038 = const()[name = tensor("op_11038"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11039 = add(x = var_11037, y = var_11038)[name = tensor("op_11039")]; - tensor denom_335_epsilon_0 = const()[name = tensor("denom_335_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_335 = rsqrt(epsilon = denom_335_epsilon_0, x = var_11039)[name = tensor("denom_335")]; - tensor out_335 = mul(x = zero_mean_335, y = denom_335)[name = tensor("out_335")]; - tensor var_11043 = const()[name = tensor("op_11043"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269570048)))]; - tensor var_11044 = add(x = out_335, y = var_11043)[name = tensor("op_11044")]; - tensor var_11046 = const()[name = tensor("op_11046"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269575232)))]; - tensor input_647 = mul(x = var_11044, y = var_11046)[name = tensor("input_647")]; - tensor var_11054 = const()[name = tensor("op_11054"), val = tensor([1, 1])]; - tensor var_11056 = const()[name = tensor("op_11056"), val = tensor([1, 1])]; - tensor var_11058_pad_type_0 = const()[name = tensor("op_11058_pad_type_0"), val = tensor("custom")]; - tensor var_11058_pad_0 = const()[name = tensor("op_11058_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11058 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias, dilations = var_11056, groups = var_6872, pad = var_11058_pad_0, pad_type = var_11058_pad_type_0, strides = var_11054, weight = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight, x = input_647)[name = tensor("op_11058")]; - tensor var_11059_split_sizes_0 = const()[name = tensor("op_11059_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_11059_axis_0 = const()[name = tensor("op_11059_axis_0"), val = tensor(1)]; - tensor var_11059_0, tensor var_11059_1 = split(axis = var_11059_axis_0, split_sizes = var_11059_split_sizes_0, x = var_11058)[name = tensor("op_11059")]; - tensor var_11061_mode_0 = const()[name = tensor("op_11061_mode_0"), val = tensor("EXACT")]; - tensor var_11061 = gelu(mode = var_11061_mode_0, x = var_11059_1)[name = tensor("op_11061")]; - tensor input_649 = mul(x = var_11059_0, y = var_11061)[name = tensor("input_649")]; - tensor var_11065 = const()[name = tensor("op_11065"), val = tensor([1, 1])]; - tensor var_11067 = const()[name = tensor("op_11067"), val = tensor([1, 1])]; - tensor var_11069_pad_type_0 = const()[name = tensor("op_11069_pad_type_0"), val = tensor("custom")]; - tensor var_11069_pad_0 = const()[name = tensor("op_11069_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11069 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias, dilations = var_11067, groups = var_6872, pad = var_11069_pad_0, pad_type = var_11069_pad_type_0, strides = var_11065, weight = up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight, x = input_649)[name = tensor("op_11069")]; - tensor inputs_337 = add(x = var_11069, y = inputs_335)[name = tensor("inputs_337")]; - tensor var_11079 = const()[name = tensor("op_11079"), val = tensor([1])]; - tensor channels_mean_337 = reduce_mean(axes = var_11079, keep_dims = var_6867, x = inputs_337)[name = tensor("channels_mean_337")]; - tensor zero_mean_337 = sub(x = inputs_337, y = channels_mean_337)[name = tensor("zero_mean_337")]; - tensor zero_mean_sq_337 = mul(x = zero_mean_337, y = zero_mean_337)[name = tensor("zero_mean_sq_337")]; - tensor var_11083 = const()[name = tensor("op_11083"), val = tensor([1])]; - tensor var_11084 = reduce_mean(axes = var_11083, keep_dims = var_6867, x = zero_mean_sq_337)[name = tensor("op_11084")]; - tensor var_11085 = const()[name = tensor("op_11085"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11086 = add(x = var_11084, y = var_11085)[name = tensor("op_11086")]; - tensor denom_337_epsilon_0 = const()[name = tensor("denom_337_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_337 = rsqrt(epsilon = denom_337_epsilon_0, x = var_11086)[name = tensor("denom_337")]; - tensor out_337 = mul(x = zero_mean_337, y = denom_337)[name = tensor("out_337")]; - tensor var_11090 = const()[name = tensor("op_11090"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269580416)))]; - tensor var_11091 = add(x = out_337, y = var_11090)[name = tensor("op_11091")]; - tensor var_11093 = const()[name = tensor("op_11093"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269585600)))]; - tensor hidden_states_447 = mul(x = var_11091, y = var_11093)[name = tensor("hidden_states_447")]; - tensor var_11100 = const()[name = tensor("op_11100"), val = tensor([1, 1])]; - tensor var_11102 = const()[name = tensor("op_11102"), val = tensor([1, 1])]; + tensor attn_223_cast = matmul(transpose_x = attn_223_transpose_x_0, transpose_y = attn_223_transpose_y_0, x = var_10912_cast, y = var_10916_cast)[name = tensor("attn_223_cast")]; + tensor var_10920 = const()[name = tensor("op_10920"), val = tensor([2, 1280, 1, -1])]; + tensor input_645_cast = reshape(shape = var_10920, x = attn_223_cast)[name = tensor("input_645_cast")]; + tensor var_10925 = const()[name = tensor("op_10925"), val = tensor([1, 1])]; + tensor var_10927 = const()[name = tensor("op_10927"), val = tensor([1, 1])]; + tensor var_10929_pad_type_0 = const()[name = tensor("op_10929_pad_type_0"), val = tensor("custom")]; + tensor var_10929_pad_0 = const()[name = tensor("op_10929_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4268487296)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4271764160)))]; + tensor var_10929_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_10927, groups = var_31, pad = var_10929_pad_0, pad_type = var_10929_pad_type_0, strides = var_10925, weight = unet_up_blocks_0_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_645_cast)[name = tensor("op_10929_cast")]; + tensor inputs_335_cast = add(x = var_10929_cast, y = inputs_333_cast)[name = tensor("inputs_335_cast")]; + tensor var_10933 = const()[name = tensor("op_10933"), val = tensor([1])]; + tensor channels_mean_335_cast = reduce_mean(axes = var_10933, keep_dims = var_23, x = inputs_335_cast)[name = tensor("channels_mean_335_cast")]; + tensor zero_mean_335_cast = sub(x = inputs_335_cast, y = channels_mean_335_cast)[name = tensor("zero_mean_335_cast")]; + tensor zero_mean_sq_335_cast = mul(x = zero_mean_335_cast, y = zero_mean_335_cast)[name = tensor("zero_mean_sq_335_cast")]; + tensor var_10937 = const()[name = tensor("op_10937"), val = tensor([1])]; + tensor var_10938_cast = reduce_mean(axes = var_10937, keep_dims = var_23, x = zero_mean_sq_335_cast)[name = tensor("op_10938_cast")]; + tensor var_10939_to_fp16 = const()[name = tensor("op_10939_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10940_cast = add(x = var_10938_cast, y = var_10939_to_fp16)[name = tensor("op_10940_cast")]; + tensor denom_335_epsilon_0_to_fp16 = const()[name = tensor("denom_335_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_335_cast = rsqrt(epsilon = denom_335_epsilon_0_to_fp16, x = var_10940_cast)[name = tensor("denom_335_cast")]; + tensor out_335_cast = mul(x = zero_mean_335_cast, y = denom_335_cast)[name = tensor("out_335_cast")]; + tensor var_10944_to_fp16 = const()[name = tensor("op_10944_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4271766784)))]; + tensor var_10945_cast = add(x = out_335_cast, y = var_10944_to_fp16)[name = tensor("op_10945_cast")]; + tensor var_10947_to_fp16 = const()[name = tensor("op_10947_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4271769408)))]; + tensor input_647_cast = mul(x = var_10945_cast, y = var_10947_to_fp16)[name = tensor("input_647_cast")]; + tensor var_10955 = const()[name = tensor("op_10955"), val = tensor([1, 1])]; + tensor var_10957 = const()[name = tensor("op_10957"), val = tensor([1, 1])]; + tensor var_10959_pad_type_0 = const()[name = tensor("op_10959_pad_type_0"), val = tensor("custom")]; + tensor var_10959_pad_0 = const()[name = tensor("op_10959_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4271772032)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4297986496)))]; + tensor var_10959_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_10957, groups = var_31, pad = var_10959_pad_0, pad_type = var_10959_pad_type_0, strides = var_10955, weight = unet_up_blocks_0_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_647_cast)[name = tensor("op_10959_cast")]; + tensor var_10960_split_sizes_0 = const()[name = tensor("op_10960_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_10960_axis_0 = const()[name = tensor("op_10960_axis_0"), val = tensor(1)]; + tensor var_10960_cast_0, tensor var_10960_cast_1 = split(axis = var_10960_axis_0, split_sizes = var_10960_split_sizes_0, x = var_10959_cast)[name = tensor("op_10960_cast")]; + tensor var_10962_mode_0 = const()[name = tensor("op_10962_mode_0"), val = tensor("EXACT")]; + tensor var_10962_cast = gelu(mode = var_10962_mode_0, x = var_10960_cast_1)[name = tensor("op_10962_cast")]; + tensor input_649_cast = mul(x = var_10960_cast_0, y = var_10962_cast)[name = tensor("input_649_cast")]; + tensor var_10966 = const()[name = tensor("op_10966"), val = tensor([1, 1])]; + tensor var_10968 = const()[name = tensor("op_10968"), val = tensor([1, 1])]; + tensor var_10970_pad_type_0 = const()[name = tensor("op_10970_pad_type_0"), val = tensor("custom")]; + tensor var_10970_pad_0 = const()[name = tensor("op_10970_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4298007040)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4311114304)))]; + tensor var_10970_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_10968, groups = var_31, pad = var_10970_pad_0, pad_type = var_10970_pad_type_0, strides = var_10966, weight = unet_up_blocks_0_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_649_cast)[name = tensor("op_10970_cast")]; + tensor inputs_337_cast = add(x = var_10970_cast, y = inputs_335_cast)[name = tensor("inputs_337_cast")]; + tensor var_10980 = const()[name = tensor("op_10980"), val = tensor([1])]; + tensor channels_mean_337_cast = reduce_mean(axes = var_10980, keep_dims = var_23, x = inputs_337_cast)[name = tensor("channels_mean_337_cast")]; + tensor zero_mean_337_cast = sub(x = inputs_337_cast, y = channels_mean_337_cast)[name = tensor("zero_mean_337_cast")]; + tensor zero_mean_sq_337_cast = mul(x = zero_mean_337_cast, y = zero_mean_337_cast)[name = tensor("zero_mean_sq_337_cast")]; + tensor var_10984 = const()[name = tensor("op_10984"), val = tensor([1])]; + tensor var_10985_cast = reduce_mean(axes = var_10984, keep_dims = var_23, x = zero_mean_sq_337_cast)[name = tensor("op_10985_cast")]; + tensor var_10986_to_fp16 = const()[name = tensor("op_10986_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_10987_cast = add(x = var_10985_cast, y = var_10986_to_fp16)[name = tensor("op_10987_cast")]; + tensor denom_337_epsilon_0_to_fp16 = const()[name = tensor("denom_337_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_337_cast = rsqrt(epsilon = denom_337_epsilon_0_to_fp16, x = var_10987_cast)[name = tensor("denom_337_cast")]; + tensor out_337_cast = mul(x = zero_mean_337_cast, y = denom_337_cast)[name = tensor("out_337_cast")]; + tensor var_10991_to_fp16 = const()[name = tensor("op_10991_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4311116928)))]; + tensor var_10992_cast = add(x = out_337_cast, y = var_10991_to_fp16)[name = tensor("op_10992_cast")]; + tensor var_10994_to_fp16 = const()[name = tensor("op_10994_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4311119552)))]; + tensor hidden_states_447_cast = mul(x = var_10992_cast, y = var_10994_to_fp16)[name = tensor("hidden_states_447_cast")]; + tensor var_11001 = const()[name = tensor("op_11001"), val = tensor([1, 1])]; + tensor var_11003 = const()[name = tensor("op_11003"), val = tensor([1, 1])]; tensor q_225_pad_type_0 = const()[name = tensor("q_225_pad_type_0"), val = tensor("custom")]; tensor q_225_pad_0 = const()[name = tensor("q_225_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_225 = conv(dilations = var_11102, groups = var_6872, pad = q_225_pad_0, pad_type = q_225_pad_type_0, strides = var_11100, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight, x = hidden_states_447)[name = tensor("q_225")]; - tensor var_11106 = const()[name = tensor("op_11106"), val = tensor([1, 1])]; - tensor var_11108 = const()[name = tensor("op_11108"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4311122176)))]; + tensor q_225_cast = conv(dilations = var_11003, groups = var_31, pad = q_225_pad_0, pad_type = q_225_pad_type_0, strides = var_11001, weight = unet_up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_q_weight_to_fp16, x = hidden_states_447_cast)[name = tensor("q_225_cast")]; + tensor var_11007 = const()[name = tensor("op_11007"), val = tensor([1, 1])]; + tensor var_11009 = const()[name = tensor("op_11009"), val = tensor([1, 1])]; tensor k_225_pad_type_0 = const()[name = tensor("k_225_pad_type_0"), val = tensor("custom")]; tensor k_225_pad_0 = const()[name = tensor("k_225_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_225 = conv(dilations = var_11108, groups = var_6872, pad = k_225_pad_0, pad_type = k_225_pad_type_0, strides = var_11106, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight, x = hidden_states_447)[name = tensor("k_225")]; - tensor var_11112 = const()[name = tensor("op_11112"), val = tensor([1, 1])]; - tensor var_11114 = const()[name = tensor("op_11114"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4314399040)))]; + tensor k_225_cast = conv(dilations = var_11009, groups = var_31, pad = k_225_pad_0, pad_type = k_225_pad_type_0, strides = var_11007, weight = unet_up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_k_weight_to_fp16, x = hidden_states_447_cast)[name = tensor("k_225_cast")]; + tensor var_11013 = const()[name = tensor("op_11013"), val = tensor([1, 1])]; + tensor var_11015 = const()[name = tensor("op_11015"), val = tensor([1, 1])]; tensor v_225_pad_type_0 = const()[name = tensor("v_225_pad_type_0"), val = tensor("custom")]; tensor v_225_pad_0 = const()[name = tensor("v_225_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_225 = conv(dilations = var_11114, groups = var_6872, pad = v_225_pad_0, pad_type = v_225_pad_type_0, strides = var_11112, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight, x = hidden_states_447)[name = tensor("v_225")]; - tensor var_11118 = const()[name = tensor("op_11118"), val = tensor([2, 20, 64, -1])]; - tensor var_11119 = reshape(shape = var_11118, x = q_225)[name = tensor("op_11119")]; - tensor var_11120 = const()[name = tensor("op_11120"), val = tensor([2, 20, 64, -1])]; - tensor var_11121 = reshape(shape = var_11120, x = k_225)[name = tensor("op_11121")]; - tensor var_11122 = const()[name = tensor("op_11122"), val = tensor([2, 20, 64, -1])]; - tensor var_11123 = reshape(shape = var_11122, x = v_225)[name = tensor("op_11123")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4317675904)))]; + tensor v_225_cast = conv(dilations = var_11015, groups = var_31, pad = v_225_pad_0, pad_type = v_225_pad_type_0, strides = var_11013, weight = unet_up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_v_weight_to_fp16, x = hidden_states_447_cast)[name = tensor("v_225_cast")]; + tensor var_11019 = const()[name = tensor("op_11019"), val = tensor([2, 20, 64, -1])]; + tensor var_11020_cast = reshape(shape = var_11019, x = q_225_cast)[name = tensor("op_11020_cast")]; + tensor var_11021 = const()[name = tensor("op_11021"), val = tensor([2, 20, 64, -1])]; + tensor var_11022_cast = reshape(shape = var_11021, x = k_225_cast)[name = tensor("op_11022_cast")]; + tensor var_11023 = const()[name = tensor("op_11023"), val = tensor([2, 20, 64, -1])]; + tensor var_11024_cast = reshape(shape = var_11023, x = v_225_cast)[name = tensor("op_11024_cast")]; tensor attn_weights_449_transpose_x_0 = const()[name = tensor("attn_weights_449_transpose_x_0"), val = tensor(true)]; tensor attn_weights_449_transpose_y_0 = const()[name = tensor("attn_weights_449_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_449 = matmul(transpose_x = attn_weights_449_transpose_x_0, transpose_y = attn_weights_449_transpose_y_0, x = var_11119, y = var_11121)[name = tensor("attn_weights_449")]; - tensor attn_weights_451 = mul(x = attn_weights_449, y = var_6863)[name = tensor("attn_weights_451")]; - tensor var_11127 = softmax(axis = var_6856, x = attn_weights_451)[name = tensor("op_11127")]; + tensor attn_weights_449_cast = matmul(transpose_x = attn_weights_449_transpose_x_0, transpose_y = attn_weights_449_transpose_y_0, x = var_11020_cast, y = var_11022_cast)[name = tensor("attn_weights_449_cast")]; + tensor attn_weights_451_cast = mul(x = attn_weights_449_cast, y = var_12_to_fp16)[name = tensor("attn_weights_451_cast")]; + tensor var_11028_cast = softmax(axis = var_18, x = attn_weights_451_cast)[name = tensor("op_11028_cast")]; tensor attn_225_transpose_x_0 = const()[name = tensor("attn_225_transpose_x_0"), val = tensor(false)]; tensor attn_225_transpose_y_0 = const()[name = tensor("attn_225_transpose_y_0"), val = tensor(true)]; - tensor attn_225 = matmul(transpose_x = attn_225_transpose_x_0, transpose_y = attn_225_transpose_y_0, x = var_11123, y = var_11127)[name = tensor("attn_225")]; - tensor var_11131 = const()[name = tensor("op_11131"), val = tensor([2, 1280, 1, -1])]; - tensor input_651 = reshape(shape = var_11131, x = attn_225)[name = tensor("input_651")]; - tensor var_11136 = const()[name = tensor("op_11136"), val = tensor([1, 1])]; - tensor var_11138 = const()[name = tensor("op_11138"), val = tensor([1, 1])]; - tensor var_11140_pad_type_0 = const()[name = tensor("op_11140_pad_type_0"), val = tensor("custom")]; - tensor var_11140_pad_0 = const()[name = tensor("op_11140_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11140 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias, dilations = var_11138, groups = var_6872, pad = var_11140_pad_0, pad_type = var_11140_pad_type_0, strides = var_11136, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight, x = input_651)[name = tensor("op_11140")]; - tensor inputs_339 = add(x = var_11140, y = inputs_337)[name = tensor("inputs_339")]; - tensor var_11144 = const()[name = tensor("op_11144"), val = tensor([1])]; - tensor channels_mean_339 = reduce_mean(axes = var_11144, keep_dims = var_6867, x = inputs_339)[name = tensor("channels_mean_339")]; - tensor zero_mean_339 = sub(x = inputs_339, y = channels_mean_339)[name = tensor("zero_mean_339")]; - tensor zero_mean_sq_339 = mul(x = zero_mean_339, y = zero_mean_339)[name = tensor("zero_mean_sq_339")]; - tensor var_11148 = const()[name = tensor("op_11148"), val = tensor([1])]; - tensor var_11149 = reduce_mean(axes = var_11148, keep_dims = var_6867, x = zero_mean_sq_339)[name = tensor("op_11149")]; - tensor var_11150 = const()[name = tensor("op_11150"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11151 = add(x = var_11149, y = var_11150)[name = tensor("op_11151")]; - tensor denom_339_epsilon_0 = const()[name = tensor("denom_339_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_339 = rsqrt(epsilon = denom_339_epsilon_0, x = var_11151)[name = tensor("denom_339")]; - tensor out_339 = mul(x = zero_mean_339, y = denom_339)[name = tensor("out_339")]; - tensor var_11155 = const()[name = tensor("op_11155"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269590784)))]; - tensor var_11156 = add(x = out_339, y = var_11155)[name = tensor("op_11156")]; - tensor var_11158 = const()[name = tensor("op_11158"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269595968)))]; - tensor hidden_states_449 = mul(x = var_11156, y = var_11158)[name = tensor("hidden_states_449")]; - tensor var_11165 = const()[name = tensor("op_11165"), val = tensor([1, 1])]; - tensor var_11167 = const()[name = tensor("op_11167"), val = tensor([1, 1])]; + tensor attn_225_cast = matmul(transpose_x = attn_225_transpose_x_0, transpose_y = attn_225_transpose_y_0, x = var_11024_cast, y = var_11028_cast)[name = tensor("attn_225_cast")]; + tensor var_11032 = const()[name = tensor("op_11032"), val = tensor([2, 1280, 1, -1])]; + tensor input_651_cast = reshape(shape = var_11032, x = attn_225_cast)[name = tensor("input_651_cast")]; + tensor var_11037 = const()[name = tensor("op_11037"), val = tensor([1, 1])]; + tensor var_11039 = const()[name = tensor("op_11039"), val = tensor([1, 1])]; + tensor var_11041_pad_type_0 = const()[name = tensor("op_11041_pad_type_0"), val = tensor("custom")]; + tensor var_11041_pad_0 = const()[name = tensor("op_11041_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4320952768)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4324229632)))]; + tensor var_11041_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_bias_to_fp16, dilations = var_11039, groups = var_31, pad = var_11041_pad_0, pad_type = var_11041_pad_type_0, strides = var_11037, weight = unet_up_blocks_0_attentions_2_transformer_blocks_2_attn1_to_out_0_weight_to_fp16, x = input_651_cast)[name = tensor("op_11041_cast")]; + tensor inputs_339_cast = add(x = var_11041_cast, y = inputs_337_cast)[name = tensor("inputs_339_cast")]; + tensor var_11045 = const()[name = tensor("op_11045"), val = tensor([1])]; + tensor channels_mean_339_cast = reduce_mean(axes = var_11045, keep_dims = var_23, x = inputs_339_cast)[name = tensor("channels_mean_339_cast")]; + tensor zero_mean_339_cast = sub(x = inputs_339_cast, y = channels_mean_339_cast)[name = tensor("zero_mean_339_cast")]; + tensor zero_mean_sq_339_cast = mul(x = zero_mean_339_cast, y = zero_mean_339_cast)[name = tensor("zero_mean_sq_339_cast")]; + tensor var_11049 = const()[name = tensor("op_11049"), val = tensor([1])]; + tensor var_11050_cast = reduce_mean(axes = var_11049, keep_dims = var_23, x = zero_mean_sq_339_cast)[name = tensor("op_11050_cast")]; + tensor var_11051_to_fp16 = const()[name = tensor("op_11051_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11052_cast = add(x = var_11050_cast, y = var_11051_to_fp16)[name = tensor("op_11052_cast")]; + tensor denom_339_epsilon_0_to_fp16 = const()[name = tensor("denom_339_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_339_cast = rsqrt(epsilon = denom_339_epsilon_0_to_fp16, x = var_11052_cast)[name = tensor("denom_339_cast")]; + tensor out_339_cast = mul(x = zero_mean_339_cast, y = denom_339_cast)[name = tensor("out_339_cast")]; + tensor var_11056_to_fp16 = const()[name = tensor("op_11056_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4324232256)))]; + tensor var_11057_cast = add(x = out_339_cast, y = var_11056_to_fp16)[name = tensor("op_11057_cast")]; + tensor var_11059_to_fp16 = const()[name = tensor("op_11059_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4324234880)))]; + tensor hidden_states_449_cast = mul(x = var_11057_cast, y = var_11059_to_fp16)[name = tensor("hidden_states_449_cast")]; + tensor var_11066 = const()[name = tensor("op_11066"), val = tensor([1, 1])]; + tensor var_11068 = const()[name = tensor("op_11068"), val = tensor([1, 1])]; tensor q_227_pad_type_0 = const()[name = tensor("q_227_pad_type_0"), val = tensor("custom")]; tensor q_227_pad_0 = const()[name = tensor("q_227_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_227 = conv(dilations = var_11167, groups = var_6872, pad = q_227_pad_0, pad_type = q_227_pad_type_0, strides = var_11165, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight, x = hidden_states_449)[name = tensor("q_227")]; - tensor var_11171 = const()[name = tensor("op_11171"), val = tensor([1, 1])]; - tensor var_11173 = const()[name = tensor("op_11173"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4324237504)))]; + tensor q_227_cast = conv(dilations = var_11068, groups = var_31, pad = q_227_pad_0, pad_type = q_227_pad_type_0, strides = var_11066, weight = unet_up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_q_weight_to_fp16, x = hidden_states_449_cast)[name = tensor("q_227_cast")]; + tensor var_11072 = const()[name = tensor("op_11072"), val = tensor([1, 1])]; + tensor var_11074 = const()[name = tensor("op_11074"), val = tensor([1, 1])]; tensor k_227_pad_type_0 = const()[name = tensor("k_227_pad_type_0"), val = tensor("custom")]; tensor k_227_pad_0 = const()[name = tensor("k_227_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_227 = conv(dilations = var_11173, groups = var_6872, pad = k_227_pad_0, pad_type = k_227_pad_type_0, strides = var_11171, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_227")]; - tensor var_11177 = const()[name = tensor("op_11177"), val = tensor([1, 1])]; - tensor var_11179 = const()[name = tensor("op_11179"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4327514368)))]; + tensor k_227_cast = conv(dilations = var_11074, groups = var_31, pad = k_227_pad_0, pad_type = k_227_pad_type_0, strides = var_11072, weight = unet_up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_227_cast")]; + tensor var_11078 = const()[name = tensor("op_11078"), val = tensor([1, 1])]; + tensor var_11080 = const()[name = tensor("op_11080"), val = tensor([1, 1])]; tensor v_227_pad_type_0 = const()[name = tensor("v_227_pad_type_0"), val = tensor("custom")]; tensor v_227_pad_0 = const()[name = tensor("v_227_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_227 = conv(dilations = var_11179, groups = var_6872, pad = v_227_pad_0, pad_type = v_227_pad_type_0, strides = var_11177, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_227")]; - tensor var_11183 = const()[name = tensor("op_11183"), val = tensor([2, 20, 64, -1])]; - tensor var_11184 = reshape(shape = var_11183, x = q_227)[name = tensor("op_11184")]; - tensor var_11185 = const()[name = tensor("op_11185"), val = tensor([2, 20, 64, -1])]; - tensor var_11186 = reshape(shape = var_11185, x = k_227)[name = tensor("op_11186")]; - tensor var_11187 = const()[name = tensor("op_11187"), val = tensor([2, 20, 64, -1])]; - tensor var_11188 = reshape(shape = var_11187, x = v_227)[name = tensor("op_11188")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4332757312)))]; + tensor v_227_cast = conv(dilations = var_11080, groups = var_31, pad = v_227_pad_0, pad_type = v_227_pad_type_0, strides = var_11078, weight = unet_up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_227_cast")]; + tensor var_11084 = const()[name = tensor("op_11084"), val = tensor([2, 20, 64, -1])]; + tensor var_11085_cast = reshape(shape = var_11084, x = q_227_cast)[name = tensor("op_11085_cast")]; + tensor var_11086 = const()[name = tensor("op_11086"), val = tensor([2, 20, 64, -1])]; + tensor var_11087_cast = reshape(shape = var_11086, x = k_227_cast)[name = tensor("op_11087_cast")]; + tensor var_11088 = const()[name = tensor("op_11088"), val = tensor([2, 20, 64, -1])]; + tensor var_11089_cast = reshape(shape = var_11088, x = v_227_cast)[name = tensor("op_11089_cast")]; tensor attn_weights_453_transpose_x_0 = const()[name = tensor("attn_weights_453_transpose_x_0"), val = tensor(true)]; tensor attn_weights_453_transpose_y_0 = const()[name = tensor("attn_weights_453_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_453 = matmul(transpose_x = attn_weights_453_transpose_x_0, transpose_y = attn_weights_453_transpose_y_0, x = var_11184, y = var_11186)[name = tensor("attn_weights_453")]; - tensor attn_weights_455 = mul(x = attn_weights_453, y = var_6863)[name = tensor("attn_weights_455")]; - tensor var_11192 = softmax(axis = var_6856, x = attn_weights_455)[name = tensor("op_11192")]; + tensor attn_weights_453_cast = matmul(transpose_x = attn_weights_453_transpose_x_0, transpose_y = attn_weights_453_transpose_y_0, x = var_11085_cast, y = var_11087_cast)[name = tensor("attn_weights_453_cast")]; + tensor attn_weights_455_cast = mul(x = attn_weights_453_cast, y = var_12_to_fp16)[name = tensor("attn_weights_455_cast")]; + tensor var_11093_cast = softmax(axis = var_18, x = attn_weights_455_cast)[name = tensor("op_11093_cast")]; tensor attn_227_transpose_x_0 = const()[name = tensor("attn_227_transpose_x_0"), val = tensor(false)]; tensor attn_227_transpose_y_0 = const()[name = tensor("attn_227_transpose_y_0"), val = tensor(true)]; - tensor attn_227 = matmul(transpose_x = attn_227_transpose_x_0, transpose_y = attn_227_transpose_y_0, x = var_11188, y = var_11192)[name = tensor("attn_227")]; - tensor var_11196 = const()[name = tensor("op_11196"), val = tensor([2, 1280, 1, -1])]; - tensor input_653 = reshape(shape = var_11196, x = attn_227)[name = tensor("input_653")]; - tensor var_11201 = const()[name = tensor("op_11201"), val = tensor([1, 1])]; - tensor var_11203 = const()[name = tensor("op_11203"), val = tensor([1, 1])]; - tensor var_11205_pad_type_0 = const()[name = tensor("op_11205_pad_type_0"), val = tensor("custom")]; - tensor var_11205_pad_0 = const()[name = tensor("op_11205_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11205 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias, dilations = var_11203, groups = var_6872, pad = var_11205_pad_0, pad_type = var_11205_pad_type_0, strides = var_11201, weight = up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight, x = input_653)[name = tensor("op_11205")]; - tensor inputs_341 = add(x = var_11205, y = inputs_339)[name = tensor("inputs_341")]; - tensor var_11209 = const()[name = tensor("op_11209"), val = tensor([1])]; - tensor channels_mean_341 = reduce_mean(axes = var_11209, keep_dims = var_6867, x = inputs_341)[name = tensor("channels_mean_341")]; - tensor zero_mean_341 = sub(x = inputs_341, y = channels_mean_341)[name = tensor("zero_mean_341")]; - tensor zero_mean_sq_341 = mul(x = zero_mean_341, y = zero_mean_341)[name = tensor("zero_mean_sq_341")]; - tensor var_11213 = const()[name = tensor("op_11213"), val = tensor([1])]; - tensor var_11214 = reduce_mean(axes = var_11213, keep_dims = var_6867, x = zero_mean_sq_341)[name = tensor("op_11214")]; - tensor var_11215 = const()[name = tensor("op_11215"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11216 = add(x = var_11214, y = var_11215)[name = tensor("op_11216")]; - tensor denom_341_epsilon_0 = const()[name = tensor("denom_341_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_341 = rsqrt(epsilon = denom_341_epsilon_0, x = var_11216)[name = tensor("denom_341")]; - tensor out_341 = mul(x = zero_mean_341, y = denom_341)[name = tensor("out_341")]; - tensor var_11220 = const()[name = tensor("op_11220"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269601152)))]; - tensor var_11221 = add(x = out_341, y = var_11220)[name = tensor("op_11221")]; - tensor var_11223 = const()[name = tensor("op_11223"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269606336)))]; - tensor input_655 = mul(x = var_11221, y = var_11223)[name = tensor("input_655")]; - tensor var_11231 = const()[name = tensor("op_11231"), val = tensor([1, 1])]; - tensor var_11233 = const()[name = tensor("op_11233"), val = tensor([1, 1])]; - tensor var_11235_pad_type_0 = const()[name = tensor("op_11235_pad_type_0"), val = tensor("custom")]; - tensor var_11235_pad_0 = const()[name = tensor("op_11235_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11235 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias, dilations = var_11233, groups = var_6872, pad = var_11235_pad_0, pad_type = var_11235_pad_type_0, strides = var_11231, weight = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight, x = input_655)[name = tensor("op_11235")]; - tensor var_11236_split_sizes_0 = const()[name = tensor("op_11236_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_11236_axis_0 = const()[name = tensor("op_11236_axis_0"), val = tensor(1)]; - tensor var_11236_0, tensor var_11236_1 = split(axis = var_11236_axis_0, split_sizes = var_11236_split_sizes_0, x = var_11235)[name = tensor("op_11236")]; - tensor var_11238_mode_0 = const()[name = tensor("op_11238_mode_0"), val = tensor("EXACT")]; - tensor var_11238 = gelu(mode = var_11238_mode_0, x = var_11236_1)[name = tensor("op_11238")]; - tensor input_657 = mul(x = var_11236_0, y = var_11238)[name = tensor("input_657")]; - tensor var_11242 = const()[name = tensor("op_11242"), val = tensor([1, 1])]; - tensor var_11244 = const()[name = tensor("op_11244"), val = tensor([1, 1])]; - tensor var_11246_pad_type_0 = const()[name = tensor("op_11246_pad_type_0"), val = tensor("custom")]; - tensor var_11246_pad_0 = const()[name = tensor("op_11246_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11246 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias, dilations = var_11244, groups = var_6872, pad = var_11246_pad_0, pad_type = var_11246_pad_type_0, strides = var_11242, weight = up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight, x = input_657)[name = tensor("op_11246")]; - tensor inputs_343 = add(x = var_11246, y = inputs_341)[name = tensor("inputs_343")]; - tensor var_11256 = const()[name = tensor("op_11256"), val = tensor([1])]; - tensor channels_mean_343 = reduce_mean(axes = var_11256, keep_dims = var_6867, x = inputs_343)[name = tensor("channels_mean_343")]; - tensor zero_mean_343 = sub(x = inputs_343, y = channels_mean_343)[name = tensor("zero_mean_343")]; - tensor zero_mean_sq_343 = mul(x = zero_mean_343, y = zero_mean_343)[name = tensor("zero_mean_sq_343")]; - tensor var_11260 = const()[name = tensor("op_11260"), val = tensor([1])]; - tensor var_11261 = reduce_mean(axes = var_11260, keep_dims = var_6867, x = zero_mean_sq_343)[name = tensor("op_11261")]; - tensor var_11262 = const()[name = tensor("op_11262"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11263 = add(x = var_11261, y = var_11262)[name = tensor("op_11263")]; - tensor denom_343_epsilon_0 = const()[name = tensor("denom_343_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_343 = rsqrt(epsilon = denom_343_epsilon_0, x = var_11263)[name = tensor("denom_343")]; - tensor out_343 = mul(x = zero_mean_343, y = denom_343)[name = tensor("out_343")]; - tensor var_11267 = const()[name = tensor("op_11267"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269611520)))]; - tensor var_11268 = add(x = out_343, y = var_11267)[name = tensor("op_11268")]; - tensor var_11270 = const()[name = tensor("op_11270"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269616704)))]; - tensor hidden_states_453 = mul(x = var_11268, y = var_11270)[name = tensor("hidden_states_453")]; - tensor var_11277 = const()[name = tensor("op_11277"), val = tensor([1, 1])]; - tensor var_11279 = const()[name = tensor("op_11279"), val = tensor([1, 1])]; + tensor attn_227_cast = matmul(transpose_x = attn_227_transpose_x_0, transpose_y = attn_227_transpose_y_0, x = var_11089_cast, y = var_11093_cast)[name = tensor("attn_227_cast")]; + tensor var_11097 = const()[name = tensor("op_11097"), val = tensor([2, 1280, 1, -1])]; + tensor input_653_cast = reshape(shape = var_11097, x = attn_227_cast)[name = tensor("input_653_cast")]; + tensor var_11102 = const()[name = tensor("op_11102"), val = tensor([1, 1])]; + tensor var_11104 = const()[name = tensor("op_11104"), val = tensor([1, 1])]; + tensor var_11106_pad_type_0 = const()[name = tensor("op_11106_pad_type_0"), val = tensor("custom")]; + tensor var_11106_pad_0 = const()[name = tensor("op_11106_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4338000256)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4341277120)))]; + tensor var_11106_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_bias_to_fp16, dilations = var_11104, groups = var_31, pad = var_11106_pad_0, pad_type = var_11106_pad_type_0, strides = var_11102, weight = unet_up_blocks_0_attentions_2_transformer_blocks_2_attn2_to_out_0_weight_to_fp16, x = input_653_cast)[name = tensor("op_11106_cast")]; + tensor inputs_341_cast = add(x = var_11106_cast, y = inputs_339_cast)[name = tensor("inputs_341_cast")]; + tensor var_11110 = const()[name = tensor("op_11110"), val = tensor([1])]; + tensor channels_mean_341_cast = reduce_mean(axes = var_11110, keep_dims = var_23, x = inputs_341_cast)[name = tensor("channels_mean_341_cast")]; + tensor zero_mean_341_cast = sub(x = inputs_341_cast, y = channels_mean_341_cast)[name = tensor("zero_mean_341_cast")]; + tensor zero_mean_sq_341_cast = mul(x = zero_mean_341_cast, y = zero_mean_341_cast)[name = tensor("zero_mean_sq_341_cast")]; + tensor var_11114 = const()[name = tensor("op_11114"), val = tensor([1])]; + tensor var_11115_cast = reduce_mean(axes = var_11114, keep_dims = var_23, x = zero_mean_sq_341_cast)[name = tensor("op_11115_cast")]; + tensor var_11116_to_fp16 = const()[name = tensor("op_11116_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11117_cast = add(x = var_11115_cast, y = var_11116_to_fp16)[name = tensor("op_11117_cast")]; + tensor denom_341_epsilon_0_to_fp16 = const()[name = tensor("denom_341_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_341_cast = rsqrt(epsilon = denom_341_epsilon_0_to_fp16, x = var_11117_cast)[name = tensor("denom_341_cast")]; + tensor out_341_cast = mul(x = zero_mean_341_cast, y = denom_341_cast)[name = tensor("out_341_cast")]; + tensor var_11121_to_fp16 = const()[name = tensor("op_11121_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4341279744)))]; + tensor var_11122_cast = add(x = out_341_cast, y = var_11121_to_fp16)[name = tensor("op_11122_cast")]; + tensor var_11124_to_fp16 = const()[name = tensor("op_11124_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4341282368)))]; + tensor input_655_cast = mul(x = var_11122_cast, y = var_11124_to_fp16)[name = tensor("input_655_cast")]; + tensor var_11132 = const()[name = tensor("op_11132"), val = tensor([1, 1])]; + tensor var_11134 = const()[name = tensor("op_11134"), val = tensor([1, 1])]; + tensor var_11136_pad_type_0 = const()[name = tensor("op_11136_pad_type_0"), val = tensor("custom")]; + tensor var_11136_pad_0 = const()[name = tensor("op_11136_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4341284992)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4367499456)))]; + tensor var_11136_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_bias_to_fp16, dilations = var_11134, groups = var_31, pad = var_11136_pad_0, pad_type = var_11136_pad_type_0, strides = var_11132, weight = unet_up_blocks_0_attentions_2_transformer_blocks_2_ff_net_0_proj_weight_to_fp16, x = input_655_cast)[name = tensor("op_11136_cast")]; + tensor var_11137_split_sizes_0 = const()[name = tensor("op_11137_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11137_axis_0 = const()[name = tensor("op_11137_axis_0"), val = tensor(1)]; + tensor var_11137_cast_0, tensor var_11137_cast_1 = split(axis = var_11137_axis_0, split_sizes = var_11137_split_sizes_0, x = var_11136_cast)[name = tensor("op_11137_cast")]; + tensor var_11139_mode_0 = const()[name = tensor("op_11139_mode_0"), val = tensor("EXACT")]; + tensor var_11139_cast = gelu(mode = var_11139_mode_0, x = var_11137_cast_1)[name = tensor("op_11139_cast")]; + tensor input_657_cast = mul(x = var_11137_cast_0, y = var_11139_cast)[name = tensor("input_657_cast")]; + tensor var_11143 = const()[name = tensor("op_11143"), val = tensor([1, 1])]; + tensor var_11145 = const()[name = tensor("op_11145"), val = tensor([1, 1])]; + tensor var_11147_pad_type_0 = const()[name = tensor("op_11147_pad_type_0"), val = tensor("custom")]; + tensor var_11147_pad_0 = const()[name = tensor("op_11147_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4367520000)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4380627264)))]; + tensor var_11147_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_bias_to_fp16, dilations = var_11145, groups = var_31, pad = var_11147_pad_0, pad_type = var_11147_pad_type_0, strides = var_11143, weight = unet_up_blocks_0_attentions_2_transformer_blocks_2_ff_net_2_weight_to_fp16, x = input_657_cast)[name = tensor("op_11147_cast")]; + tensor inputs_343_cast = add(x = var_11147_cast, y = inputs_341_cast)[name = tensor("inputs_343_cast")]; + tensor var_11157 = const()[name = tensor("op_11157"), val = tensor([1])]; + tensor channels_mean_343_cast = reduce_mean(axes = var_11157, keep_dims = var_23, x = inputs_343_cast)[name = tensor("channels_mean_343_cast")]; + tensor zero_mean_343_cast = sub(x = inputs_343_cast, y = channels_mean_343_cast)[name = tensor("zero_mean_343_cast")]; + tensor zero_mean_sq_343_cast = mul(x = zero_mean_343_cast, y = zero_mean_343_cast)[name = tensor("zero_mean_sq_343_cast")]; + tensor var_11161 = const()[name = tensor("op_11161"), val = tensor([1])]; + tensor var_11162_cast = reduce_mean(axes = var_11161, keep_dims = var_23, x = zero_mean_sq_343_cast)[name = tensor("op_11162_cast")]; + tensor var_11163_to_fp16 = const()[name = tensor("op_11163_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11164_cast = add(x = var_11162_cast, y = var_11163_to_fp16)[name = tensor("op_11164_cast")]; + tensor denom_343_epsilon_0_to_fp16 = const()[name = tensor("denom_343_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_343_cast = rsqrt(epsilon = denom_343_epsilon_0_to_fp16, x = var_11164_cast)[name = tensor("denom_343_cast")]; + tensor out_343_cast = mul(x = zero_mean_343_cast, y = denom_343_cast)[name = tensor("out_343_cast")]; + tensor var_11168_to_fp16 = const()[name = tensor("op_11168_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4380629888)))]; + tensor var_11169_cast = add(x = out_343_cast, y = var_11168_to_fp16)[name = tensor("op_11169_cast")]; + tensor var_11171_to_fp16 = const()[name = tensor("op_11171_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4380632512)))]; + tensor hidden_states_453_cast = mul(x = var_11169_cast, y = var_11171_to_fp16)[name = tensor("hidden_states_453_cast")]; + tensor var_11178 = const()[name = tensor("op_11178"), val = tensor([1, 1])]; + tensor var_11180 = const()[name = tensor("op_11180"), val = tensor([1, 1])]; tensor q_229_pad_type_0 = const()[name = tensor("q_229_pad_type_0"), val = tensor("custom")]; tensor q_229_pad_0 = const()[name = tensor("q_229_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_229 = conv(dilations = var_11279, groups = var_6872, pad = q_229_pad_0, pad_type = q_229_pad_type_0, strides = var_11277, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight, x = hidden_states_453)[name = tensor("q_229")]; - tensor var_11283 = const()[name = tensor("op_11283"), val = tensor([1, 1])]; - tensor var_11285 = const()[name = tensor("op_11285"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4380635136)))]; + tensor q_229_cast = conv(dilations = var_11180, groups = var_31, pad = q_229_pad_0, pad_type = q_229_pad_type_0, strides = var_11178, weight = unet_up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_q_weight_to_fp16, x = hidden_states_453_cast)[name = tensor("q_229_cast")]; + tensor var_11184 = const()[name = tensor("op_11184"), val = tensor([1, 1])]; + tensor var_11186 = const()[name = tensor("op_11186"), val = tensor([1, 1])]; tensor k_229_pad_type_0 = const()[name = tensor("k_229_pad_type_0"), val = tensor("custom")]; tensor k_229_pad_0 = const()[name = tensor("k_229_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_229 = conv(dilations = var_11285, groups = var_6872, pad = k_229_pad_0, pad_type = k_229_pad_type_0, strides = var_11283, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight, x = hidden_states_453)[name = tensor("k_229")]; - tensor var_11289 = const()[name = tensor("op_11289"), val = tensor([1, 1])]; - tensor var_11291 = const()[name = tensor("op_11291"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4383912000)))]; + tensor k_229_cast = conv(dilations = var_11186, groups = var_31, pad = k_229_pad_0, pad_type = k_229_pad_type_0, strides = var_11184, weight = unet_up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_k_weight_to_fp16, x = hidden_states_453_cast)[name = tensor("k_229_cast")]; + tensor var_11190 = const()[name = tensor("op_11190"), val = tensor([1, 1])]; + tensor var_11192 = const()[name = tensor("op_11192"), val = tensor([1, 1])]; tensor v_229_pad_type_0 = const()[name = tensor("v_229_pad_type_0"), val = tensor("custom")]; tensor v_229_pad_0 = const()[name = tensor("v_229_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_229 = conv(dilations = var_11291, groups = var_6872, pad = v_229_pad_0, pad_type = v_229_pad_type_0, strides = var_11289, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight, x = hidden_states_453)[name = tensor("v_229")]; - tensor var_11295 = const()[name = tensor("op_11295"), val = tensor([2, 20, 64, -1])]; - tensor var_11296 = reshape(shape = var_11295, x = q_229)[name = tensor("op_11296")]; - tensor var_11297 = const()[name = tensor("op_11297"), val = tensor([2, 20, 64, -1])]; - tensor var_11298 = reshape(shape = var_11297, x = k_229)[name = tensor("op_11298")]; - tensor var_11299 = const()[name = tensor("op_11299"), val = tensor([2, 20, 64, -1])]; - tensor var_11300 = reshape(shape = var_11299, x = v_229)[name = tensor("op_11300")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4387188864)))]; + tensor v_229_cast = conv(dilations = var_11192, groups = var_31, pad = v_229_pad_0, pad_type = v_229_pad_type_0, strides = var_11190, weight = unet_up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_v_weight_to_fp16, x = hidden_states_453_cast)[name = tensor("v_229_cast")]; + tensor var_11196 = const()[name = tensor("op_11196"), val = tensor([2, 20, 64, -1])]; + tensor var_11197_cast = reshape(shape = var_11196, x = q_229_cast)[name = tensor("op_11197_cast")]; + tensor var_11198 = const()[name = tensor("op_11198"), val = tensor([2, 20, 64, -1])]; + tensor var_11199_cast = reshape(shape = var_11198, x = k_229_cast)[name = tensor("op_11199_cast")]; + tensor var_11200 = const()[name = tensor("op_11200"), val = tensor([2, 20, 64, -1])]; + tensor var_11201_cast = reshape(shape = var_11200, x = v_229_cast)[name = tensor("op_11201_cast")]; tensor attn_weights_457_transpose_x_0 = const()[name = tensor("attn_weights_457_transpose_x_0"), val = tensor(true)]; tensor attn_weights_457_transpose_y_0 = const()[name = tensor("attn_weights_457_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_457 = matmul(transpose_x = attn_weights_457_transpose_x_0, transpose_y = attn_weights_457_transpose_y_0, x = var_11296, y = var_11298)[name = tensor("attn_weights_457")]; - tensor attn_weights_459 = mul(x = attn_weights_457, y = var_6863)[name = tensor("attn_weights_459")]; - tensor var_11304 = softmax(axis = var_6856, x = attn_weights_459)[name = tensor("op_11304")]; + tensor attn_weights_457_cast = matmul(transpose_x = attn_weights_457_transpose_x_0, transpose_y = attn_weights_457_transpose_y_0, x = var_11197_cast, y = var_11199_cast)[name = tensor("attn_weights_457_cast")]; + tensor attn_weights_459_cast = mul(x = attn_weights_457_cast, y = var_12_to_fp16)[name = tensor("attn_weights_459_cast")]; + tensor var_11205_cast = softmax(axis = var_18, x = attn_weights_459_cast)[name = tensor("op_11205_cast")]; tensor attn_229_transpose_x_0 = const()[name = tensor("attn_229_transpose_x_0"), val = tensor(false)]; tensor attn_229_transpose_y_0 = const()[name = tensor("attn_229_transpose_y_0"), val = tensor(true)]; - tensor attn_229 = matmul(transpose_x = attn_229_transpose_x_0, transpose_y = attn_229_transpose_y_0, x = var_11300, y = var_11304)[name = tensor("attn_229")]; - tensor var_11308 = const()[name = tensor("op_11308"), val = tensor([2, 1280, 1, -1])]; - tensor input_659 = reshape(shape = var_11308, x = attn_229)[name = tensor("input_659")]; - tensor var_11313 = const()[name = tensor("op_11313"), val = tensor([1, 1])]; - tensor var_11315 = const()[name = tensor("op_11315"), val = tensor([1, 1])]; - tensor var_11317_pad_type_0 = const()[name = tensor("op_11317_pad_type_0"), val = tensor("custom")]; - tensor var_11317_pad_0 = const()[name = tensor("op_11317_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11317 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias, dilations = var_11315, groups = var_6872, pad = var_11317_pad_0, pad_type = var_11317_pad_type_0, strides = var_11313, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight, x = input_659)[name = tensor("op_11317")]; - tensor inputs_345 = add(x = var_11317, y = inputs_343)[name = tensor("inputs_345")]; - tensor var_11321 = const()[name = tensor("op_11321"), val = tensor([1])]; - tensor channels_mean_345 = reduce_mean(axes = var_11321, keep_dims = var_6867, x = inputs_345)[name = tensor("channels_mean_345")]; - tensor zero_mean_345 = sub(x = inputs_345, y = channels_mean_345)[name = tensor("zero_mean_345")]; - tensor zero_mean_sq_345 = mul(x = zero_mean_345, y = zero_mean_345)[name = tensor("zero_mean_sq_345")]; - tensor var_11325 = const()[name = tensor("op_11325"), val = tensor([1])]; - tensor var_11326 = reduce_mean(axes = var_11325, keep_dims = var_6867, x = zero_mean_sq_345)[name = tensor("op_11326")]; - tensor var_11327 = const()[name = tensor("op_11327"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11328 = add(x = var_11326, y = var_11327)[name = tensor("op_11328")]; - tensor denom_345_epsilon_0 = const()[name = tensor("denom_345_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_345 = rsqrt(epsilon = denom_345_epsilon_0, x = var_11328)[name = tensor("denom_345")]; - tensor out_345 = mul(x = zero_mean_345, y = denom_345)[name = tensor("out_345")]; - tensor var_11332 = const()[name = tensor("op_11332"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269621888)))]; - tensor var_11333 = add(x = out_345, y = var_11332)[name = tensor("op_11333")]; - tensor var_11335 = const()[name = tensor("op_11335"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269627072)))]; - tensor hidden_states_455 = mul(x = var_11333, y = var_11335)[name = tensor("hidden_states_455")]; - tensor var_11342 = const()[name = tensor("op_11342"), val = tensor([1, 1])]; - tensor var_11344 = const()[name = tensor("op_11344"), val = tensor([1, 1])]; + tensor attn_229_cast = matmul(transpose_x = attn_229_transpose_x_0, transpose_y = attn_229_transpose_y_0, x = var_11201_cast, y = var_11205_cast)[name = tensor("attn_229_cast")]; + tensor var_11209 = const()[name = tensor("op_11209"), val = tensor([2, 1280, 1, -1])]; + tensor input_659_cast = reshape(shape = var_11209, x = attn_229_cast)[name = tensor("input_659_cast")]; + tensor var_11214 = const()[name = tensor("op_11214"), val = tensor([1, 1])]; + tensor var_11216 = const()[name = tensor("op_11216"), val = tensor([1, 1])]; + tensor var_11218_pad_type_0 = const()[name = tensor("op_11218_pad_type_0"), val = tensor("custom")]; + tensor var_11218_pad_0 = const()[name = tensor("op_11218_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4390465728)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4393742592)))]; + tensor var_11218_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_bias_to_fp16, dilations = var_11216, groups = var_31, pad = var_11218_pad_0, pad_type = var_11218_pad_type_0, strides = var_11214, weight = unet_up_blocks_0_attentions_2_transformer_blocks_3_attn1_to_out_0_weight_to_fp16, x = input_659_cast)[name = tensor("op_11218_cast")]; + tensor inputs_345_cast = add(x = var_11218_cast, y = inputs_343_cast)[name = tensor("inputs_345_cast")]; + tensor var_11222 = const()[name = tensor("op_11222"), val = tensor([1])]; + tensor channels_mean_345_cast = reduce_mean(axes = var_11222, keep_dims = var_23, x = inputs_345_cast)[name = tensor("channels_mean_345_cast")]; + tensor zero_mean_345_cast = sub(x = inputs_345_cast, y = channels_mean_345_cast)[name = tensor("zero_mean_345_cast")]; + tensor zero_mean_sq_345_cast = mul(x = zero_mean_345_cast, y = zero_mean_345_cast)[name = tensor("zero_mean_sq_345_cast")]; + tensor var_11226 = const()[name = tensor("op_11226"), val = tensor([1])]; + tensor var_11227_cast = reduce_mean(axes = var_11226, keep_dims = var_23, x = zero_mean_sq_345_cast)[name = tensor("op_11227_cast")]; + tensor var_11228_to_fp16 = const()[name = tensor("op_11228_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11229_cast = add(x = var_11227_cast, y = var_11228_to_fp16)[name = tensor("op_11229_cast")]; + tensor denom_345_epsilon_0_to_fp16 = const()[name = tensor("denom_345_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_345_cast = rsqrt(epsilon = denom_345_epsilon_0_to_fp16, x = var_11229_cast)[name = tensor("denom_345_cast")]; + tensor out_345_cast = mul(x = zero_mean_345_cast, y = denom_345_cast)[name = tensor("out_345_cast")]; + tensor var_11233_to_fp16 = const()[name = tensor("op_11233_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4393745216)))]; + tensor var_11234_cast = add(x = out_345_cast, y = var_11233_to_fp16)[name = tensor("op_11234_cast")]; + tensor var_11236_to_fp16 = const()[name = tensor("op_11236_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4393747840)))]; + tensor hidden_states_455_cast = mul(x = var_11234_cast, y = var_11236_to_fp16)[name = tensor("hidden_states_455_cast")]; + tensor var_11243 = const()[name = tensor("op_11243"), val = tensor([1, 1])]; + tensor var_11245 = const()[name = tensor("op_11245"), val = tensor([1, 1])]; tensor q_231_pad_type_0 = const()[name = tensor("q_231_pad_type_0"), val = tensor("custom")]; tensor q_231_pad_0 = const()[name = tensor("q_231_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_231 = conv(dilations = var_11344, groups = var_6872, pad = q_231_pad_0, pad_type = q_231_pad_type_0, strides = var_11342, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight, x = hidden_states_455)[name = tensor("q_231")]; - tensor var_11348 = const()[name = tensor("op_11348"), val = tensor([1, 1])]; - tensor var_11350 = const()[name = tensor("op_11350"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4393750464)))]; + tensor q_231_cast = conv(dilations = var_11245, groups = var_31, pad = q_231_pad_0, pad_type = q_231_pad_type_0, strides = var_11243, weight = unet_up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_q_weight_to_fp16, x = hidden_states_455_cast)[name = tensor("q_231_cast")]; + tensor var_11249 = const()[name = tensor("op_11249"), val = tensor([1, 1])]; + tensor var_11251 = const()[name = tensor("op_11251"), val = tensor([1, 1])]; tensor k_231_pad_type_0 = const()[name = tensor("k_231_pad_type_0"), val = tensor("custom")]; tensor k_231_pad_0 = const()[name = tensor("k_231_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_231 = conv(dilations = var_11350, groups = var_6872, pad = k_231_pad_0, pad_type = k_231_pad_type_0, strides = var_11348, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_231")]; - tensor var_11354 = const()[name = tensor("op_11354"), val = tensor([1, 1])]; - tensor var_11356 = const()[name = tensor("op_11356"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4397027328)))]; + tensor k_231_cast = conv(dilations = var_11251, groups = var_31, pad = k_231_pad_0, pad_type = k_231_pad_type_0, strides = var_11249, weight = unet_up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_231_cast")]; + tensor var_11255 = const()[name = tensor("op_11255"), val = tensor([1, 1])]; + tensor var_11257 = const()[name = tensor("op_11257"), val = tensor([1, 1])]; tensor v_231_pad_type_0 = const()[name = tensor("v_231_pad_type_0"), val = tensor("custom")]; tensor v_231_pad_0 = const()[name = tensor("v_231_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_231 = conv(dilations = var_11356, groups = var_6872, pad = v_231_pad_0, pad_type = v_231_pad_type_0, strides = var_11354, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_231")]; - tensor var_11360 = const()[name = tensor("op_11360"), val = tensor([2, 20, 64, -1])]; - tensor var_11361 = reshape(shape = var_11360, x = q_231)[name = tensor("op_11361")]; - tensor var_11362 = const()[name = tensor("op_11362"), val = tensor([2, 20, 64, -1])]; - tensor var_11363 = reshape(shape = var_11362, x = k_231)[name = tensor("op_11363")]; - tensor var_11364 = const()[name = tensor("op_11364"), val = tensor([2, 20, 64, -1])]; - tensor var_11365 = reshape(shape = var_11364, x = v_231)[name = tensor("op_11365")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4402270272)))]; + tensor v_231_cast = conv(dilations = var_11257, groups = var_31, pad = v_231_pad_0, pad_type = v_231_pad_type_0, strides = var_11255, weight = unet_up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_231_cast")]; + tensor var_11261 = const()[name = tensor("op_11261"), val = tensor([2, 20, 64, -1])]; + tensor var_11262_cast = reshape(shape = var_11261, x = q_231_cast)[name = tensor("op_11262_cast")]; + tensor var_11263 = const()[name = tensor("op_11263"), val = tensor([2, 20, 64, -1])]; + tensor var_11264_cast = reshape(shape = var_11263, x = k_231_cast)[name = tensor("op_11264_cast")]; + tensor var_11265 = const()[name = tensor("op_11265"), val = tensor([2, 20, 64, -1])]; + tensor var_11266_cast = reshape(shape = var_11265, x = v_231_cast)[name = tensor("op_11266_cast")]; tensor attn_weights_461_transpose_x_0 = const()[name = tensor("attn_weights_461_transpose_x_0"), val = tensor(true)]; tensor attn_weights_461_transpose_y_0 = const()[name = tensor("attn_weights_461_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_461 = matmul(transpose_x = attn_weights_461_transpose_x_0, transpose_y = attn_weights_461_transpose_y_0, x = var_11361, y = var_11363)[name = tensor("attn_weights_461")]; - tensor attn_weights_463 = mul(x = attn_weights_461, y = var_6863)[name = tensor("attn_weights_463")]; - tensor var_11369 = softmax(axis = var_6856, x = attn_weights_463)[name = tensor("op_11369")]; + tensor attn_weights_461_cast = matmul(transpose_x = attn_weights_461_transpose_x_0, transpose_y = attn_weights_461_transpose_y_0, x = var_11262_cast, y = var_11264_cast)[name = tensor("attn_weights_461_cast")]; + tensor attn_weights_463_cast = mul(x = attn_weights_461_cast, y = var_12_to_fp16)[name = tensor("attn_weights_463_cast")]; + tensor var_11270_cast = softmax(axis = var_18, x = attn_weights_463_cast)[name = tensor("op_11270_cast")]; tensor attn_231_transpose_x_0 = const()[name = tensor("attn_231_transpose_x_0"), val = tensor(false)]; tensor attn_231_transpose_y_0 = const()[name = tensor("attn_231_transpose_y_0"), val = tensor(true)]; - tensor attn_231 = matmul(transpose_x = attn_231_transpose_x_0, transpose_y = attn_231_transpose_y_0, x = var_11365, y = var_11369)[name = tensor("attn_231")]; - tensor var_11373 = const()[name = tensor("op_11373"), val = tensor([2, 1280, 1, -1])]; - tensor input_661 = reshape(shape = var_11373, x = attn_231)[name = tensor("input_661")]; - tensor var_11378 = const()[name = tensor("op_11378"), val = tensor([1, 1])]; - tensor var_11380 = const()[name = tensor("op_11380"), val = tensor([1, 1])]; - tensor var_11382_pad_type_0 = const()[name = tensor("op_11382_pad_type_0"), val = tensor("custom")]; - tensor var_11382_pad_0 = const()[name = tensor("op_11382_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11382 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias, dilations = var_11380, groups = var_6872, pad = var_11382_pad_0, pad_type = var_11382_pad_type_0, strides = var_11378, weight = up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight, x = input_661)[name = tensor("op_11382")]; - tensor inputs_347 = add(x = var_11382, y = inputs_345)[name = tensor("inputs_347")]; - tensor var_11386 = const()[name = tensor("op_11386"), val = tensor([1])]; - tensor channels_mean_347 = reduce_mean(axes = var_11386, keep_dims = var_6867, x = inputs_347)[name = tensor("channels_mean_347")]; - tensor zero_mean_347 = sub(x = inputs_347, y = channels_mean_347)[name = tensor("zero_mean_347")]; - tensor zero_mean_sq_347 = mul(x = zero_mean_347, y = zero_mean_347)[name = tensor("zero_mean_sq_347")]; - tensor var_11390 = const()[name = tensor("op_11390"), val = tensor([1])]; - tensor var_11391 = reduce_mean(axes = var_11390, keep_dims = var_6867, x = zero_mean_sq_347)[name = tensor("op_11391")]; - tensor var_11392 = const()[name = tensor("op_11392"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11393 = add(x = var_11391, y = var_11392)[name = tensor("op_11393")]; - tensor denom_347_epsilon_0 = const()[name = tensor("denom_347_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_347 = rsqrt(epsilon = denom_347_epsilon_0, x = var_11393)[name = tensor("denom_347")]; - tensor out_347 = mul(x = zero_mean_347, y = denom_347)[name = tensor("out_347")]; - tensor var_11397 = const()[name = tensor("op_11397"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269632256)))]; - tensor var_11398 = add(x = out_347, y = var_11397)[name = tensor("op_11398")]; - tensor var_11400 = const()[name = tensor("op_11400"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269637440)))]; - tensor input_663 = mul(x = var_11398, y = var_11400)[name = tensor("input_663")]; - tensor var_11408 = const()[name = tensor("op_11408"), val = tensor([1, 1])]; - tensor var_11410 = const()[name = tensor("op_11410"), val = tensor([1, 1])]; - tensor var_11412_pad_type_0 = const()[name = tensor("op_11412_pad_type_0"), val = tensor("custom")]; - tensor var_11412_pad_0 = const()[name = tensor("op_11412_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11412 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias, dilations = var_11410, groups = var_6872, pad = var_11412_pad_0, pad_type = var_11412_pad_type_0, strides = var_11408, weight = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight, x = input_663)[name = tensor("op_11412")]; - tensor var_11413_split_sizes_0 = const()[name = tensor("op_11413_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_11413_axis_0 = const()[name = tensor("op_11413_axis_0"), val = tensor(1)]; - tensor var_11413_0, tensor var_11413_1 = split(axis = var_11413_axis_0, split_sizes = var_11413_split_sizes_0, x = var_11412)[name = tensor("op_11413")]; - tensor var_11415_mode_0 = const()[name = tensor("op_11415_mode_0"), val = tensor("EXACT")]; - tensor var_11415 = gelu(mode = var_11415_mode_0, x = var_11413_1)[name = tensor("op_11415")]; - tensor input_665 = mul(x = var_11413_0, y = var_11415)[name = tensor("input_665")]; - tensor var_11419 = const()[name = tensor("op_11419"), val = tensor([1, 1])]; - tensor var_11421 = const()[name = tensor("op_11421"), val = tensor([1, 1])]; - tensor var_11423_pad_type_0 = const()[name = tensor("op_11423_pad_type_0"), val = tensor("custom")]; - tensor var_11423_pad_0 = const()[name = tensor("op_11423_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11423 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias, dilations = var_11421, groups = var_6872, pad = var_11423_pad_0, pad_type = var_11423_pad_type_0, strides = var_11419, weight = up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight, x = input_665)[name = tensor("op_11423")]; - tensor inputs_349 = add(x = var_11423, y = inputs_347)[name = tensor("inputs_349")]; - tensor var_11433 = const()[name = tensor("op_11433"), val = tensor([1])]; - tensor channels_mean_349 = reduce_mean(axes = var_11433, keep_dims = var_6867, x = inputs_349)[name = tensor("channels_mean_349")]; - tensor zero_mean_349 = sub(x = inputs_349, y = channels_mean_349)[name = tensor("zero_mean_349")]; - tensor zero_mean_sq_349 = mul(x = zero_mean_349, y = zero_mean_349)[name = tensor("zero_mean_sq_349")]; - tensor var_11437 = const()[name = tensor("op_11437"), val = tensor([1])]; - tensor var_11438 = reduce_mean(axes = var_11437, keep_dims = var_6867, x = zero_mean_sq_349)[name = tensor("op_11438")]; - tensor var_11439 = const()[name = tensor("op_11439"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11440 = add(x = var_11438, y = var_11439)[name = tensor("op_11440")]; - tensor denom_349_epsilon_0 = const()[name = tensor("denom_349_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_349 = rsqrt(epsilon = denom_349_epsilon_0, x = var_11440)[name = tensor("denom_349")]; - tensor out_349 = mul(x = zero_mean_349, y = denom_349)[name = tensor("out_349")]; - tensor var_11444 = const()[name = tensor("op_11444"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269642624)))]; - tensor var_11445 = add(x = out_349, y = var_11444)[name = tensor("op_11445")]; - tensor var_11447 = const()[name = tensor("op_11447"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269647808)))]; - tensor hidden_states_459 = mul(x = var_11445, y = var_11447)[name = tensor("hidden_states_459")]; - tensor var_11454 = const()[name = tensor("op_11454"), val = tensor([1, 1])]; - tensor var_11456 = const()[name = tensor("op_11456"), val = tensor([1, 1])]; + tensor attn_231_cast = matmul(transpose_x = attn_231_transpose_x_0, transpose_y = attn_231_transpose_y_0, x = var_11266_cast, y = var_11270_cast)[name = tensor("attn_231_cast")]; + tensor var_11274 = const()[name = tensor("op_11274"), val = tensor([2, 1280, 1, -1])]; + tensor input_661_cast = reshape(shape = var_11274, x = attn_231_cast)[name = tensor("input_661_cast")]; + tensor var_11279 = const()[name = tensor("op_11279"), val = tensor([1, 1])]; + tensor var_11281 = const()[name = tensor("op_11281"), val = tensor([1, 1])]; + tensor var_11283_pad_type_0 = const()[name = tensor("op_11283_pad_type_0"), val = tensor("custom")]; + tensor var_11283_pad_0 = const()[name = tensor("op_11283_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4407513216)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4410790080)))]; + tensor var_11283_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_bias_to_fp16, dilations = var_11281, groups = var_31, pad = var_11283_pad_0, pad_type = var_11283_pad_type_0, strides = var_11279, weight = unet_up_blocks_0_attentions_2_transformer_blocks_3_attn2_to_out_0_weight_to_fp16, x = input_661_cast)[name = tensor("op_11283_cast")]; + tensor inputs_347_cast = add(x = var_11283_cast, y = inputs_345_cast)[name = tensor("inputs_347_cast")]; + tensor var_11287 = const()[name = tensor("op_11287"), val = tensor([1])]; + tensor channels_mean_347_cast = reduce_mean(axes = var_11287, keep_dims = var_23, x = inputs_347_cast)[name = tensor("channels_mean_347_cast")]; + tensor zero_mean_347_cast = sub(x = inputs_347_cast, y = channels_mean_347_cast)[name = tensor("zero_mean_347_cast")]; + tensor zero_mean_sq_347_cast = mul(x = zero_mean_347_cast, y = zero_mean_347_cast)[name = tensor("zero_mean_sq_347_cast")]; + tensor var_11291 = const()[name = tensor("op_11291"), val = tensor([1])]; + tensor var_11292_cast = reduce_mean(axes = var_11291, keep_dims = var_23, x = zero_mean_sq_347_cast)[name = tensor("op_11292_cast")]; + tensor var_11293_to_fp16 = const()[name = tensor("op_11293_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11294_cast = add(x = var_11292_cast, y = var_11293_to_fp16)[name = tensor("op_11294_cast")]; + tensor denom_347_epsilon_0_to_fp16 = const()[name = tensor("denom_347_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_347_cast = rsqrt(epsilon = denom_347_epsilon_0_to_fp16, x = var_11294_cast)[name = tensor("denom_347_cast")]; + tensor out_347_cast = mul(x = zero_mean_347_cast, y = denom_347_cast)[name = tensor("out_347_cast")]; + tensor var_11298_to_fp16 = const()[name = tensor("op_11298_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4410792704)))]; + tensor var_11299_cast = add(x = out_347_cast, y = var_11298_to_fp16)[name = tensor("op_11299_cast")]; + tensor var_11301_to_fp16 = const()[name = tensor("op_11301_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4410795328)))]; + tensor input_663_cast = mul(x = var_11299_cast, y = var_11301_to_fp16)[name = tensor("input_663_cast")]; + tensor var_11309 = const()[name = tensor("op_11309"), val = tensor([1, 1])]; + tensor var_11311 = const()[name = tensor("op_11311"), val = tensor([1, 1])]; + tensor var_11313_pad_type_0 = const()[name = tensor("op_11313_pad_type_0"), val = tensor("custom")]; + tensor var_11313_pad_0 = const()[name = tensor("op_11313_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4410797952)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4437012416)))]; + tensor var_11313_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_bias_to_fp16, dilations = var_11311, groups = var_31, pad = var_11313_pad_0, pad_type = var_11313_pad_type_0, strides = var_11309, weight = unet_up_blocks_0_attentions_2_transformer_blocks_3_ff_net_0_proj_weight_to_fp16, x = input_663_cast)[name = tensor("op_11313_cast")]; + tensor var_11314_split_sizes_0 = const()[name = tensor("op_11314_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11314_axis_0 = const()[name = tensor("op_11314_axis_0"), val = tensor(1)]; + tensor var_11314_cast_0, tensor var_11314_cast_1 = split(axis = var_11314_axis_0, split_sizes = var_11314_split_sizes_0, x = var_11313_cast)[name = tensor("op_11314_cast")]; + tensor var_11316_mode_0 = const()[name = tensor("op_11316_mode_0"), val = tensor("EXACT")]; + tensor var_11316_cast = gelu(mode = var_11316_mode_0, x = var_11314_cast_1)[name = tensor("op_11316_cast")]; + tensor input_665_cast = mul(x = var_11314_cast_0, y = var_11316_cast)[name = tensor("input_665_cast")]; + tensor var_11320 = const()[name = tensor("op_11320"), val = tensor([1, 1])]; + tensor var_11322 = const()[name = tensor("op_11322"), val = tensor([1, 1])]; + tensor var_11324_pad_type_0 = const()[name = tensor("op_11324_pad_type_0"), val = tensor("custom")]; + tensor var_11324_pad_0 = const()[name = tensor("op_11324_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4437032960)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4450140224)))]; + tensor var_11324_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_bias_to_fp16, dilations = var_11322, groups = var_31, pad = var_11324_pad_0, pad_type = var_11324_pad_type_0, strides = var_11320, weight = unet_up_blocks_0_attentions_2_transformer_blocks_3_ff_net_2_weight_to_fp16, x = input_665_cast)[name = tensor("op_11324_cast")]; + tensor inputs_349_cast = add(x = var_11324_cast, y = inputs_347_cast)[name = tensor("inputs_349_cast")]; + tensor var_11334 = const()[name = tensor("op_11334"), val = tensor([1])]; + tensor channels_mean_349_cast = reduce_mean(axes = var_11334, keep_dims = var_23, x = inputs_349_cast)[name = tensor("channels_mean_349_cast")]; + tensor zero_mean_349_cast = sub(x = inputs_349_cast, y = channels_mean_349_cast)[name = tensor("zero_mean_349_cast")]; + tensor zero_mean_sq_349_cast = mul(x = zero_mean_349_cast, y = zero_mean_349_cast)[name = tensor("zero_mean_sq_349_cast")]; + tensor var_11338 = const()[name = tensor("op_11338"), val = tensor([1])]; + tensor var_11339_cast = reduce_mean(axes = var_11338, keep_dims = var_23, x = zero_mean_sq_349_cast)[name = tensor("op_11339_cast")]; + tensor var_11340_to_fp16 = const()[name = tensor("op_11340_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11341_cast = add(x = var_11339_cast, y = var_11340_to_fp16)[name = tensor("op_11341_cast")]; + tensor denom_349_epsilon_0_to_fp16 = const()[name = tensor("denom_349_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_349_cast = rsqrt(epsilon = denom_349_epsilon_0_to_fp16, x = var_11341_cast)[name = tensor("denom_349_cast")]; + tensor out_349_cast = mul(x = zero_mean_349_cast, y = denom_349_cast)[name = tensor("out_349_cast")]; + tensor var_11345_to_fp16 = const()[name = tensor("op_11345_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4450142848)))]; + tensor var_11346_cast = add(x = out_349_cast, y = var_11345_to_fp16)[name = tensor("op_11346_cast")]; + tensor var_11348_to_fp16 = const()[name = tensor("op_11348_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4450145472)))]; + tensor hidden_states_459_cast = mul(x = var_11346_cast, y = var_11348_to_fp16)[name = tensor("hidden_states_459_cast")]; + tensor var_11355 = const()[name = tensor("op_11355"), val = tensor([1, 1])]; + tensor var_11357 = const()[name = tensor("op_11357"), val = tensor([1, 1])]; tensor q_233_pad_type_0 = const()[name = tensor("q_233_pad_type_0"), val = tensor("custom")]; tensor q_233_pad_0 = const()[name = tensor("q_233_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_233 = conv(dilations = var_11456, groups = var_6872, pad = q_233_pad_0, pad_type = q_233_pad_type_0, strides = var_11454, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight, x = hidden_states_459)[name = tensor("q_233")]; - tensor var_11460 = const()[name = tensor("op_11460"), val = tensor([1, 1])]; - tensor var_11462 = const()[name = tensor("op_11462"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4450148096)))]; + tensor q_233_cast = conv(dilations = var_11357, groups = var_31, pad = q_233_pad_0, pad_type = q_233_pad_type_0, strides = var_11355, weight = unet_up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_q_weight_to_fp16, x = hidden_states_459_cast)[name = tensor("q_233_cast")]; + tensor var_11361 = const()[name = tensor("op_11361"), val = tensor([1, 1])]; + tensor var_11363 = const()[name = tensor("op_11363"), val = tensor([1, 1])]; tensor k_233_pad_type_0 = const()[name = tensor("k_233_pad_type_0"), val = tensor("custom")]; tensor k_233_pad_0 = const()[name = tensor("k_233_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_233 = conv(dilations = var_11462, groups = var_6872, pad = k_233_pad_0, pad_type = k_233_pad_type_0, strides = var_11460, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight, x = hidden_states_459)[name = tensor("k_233")]; - tensor var_11466 = const()[name = tensor("op_11466"), val = tensor([1, 1])]; - tensor var_11468 = const()[name = tensor("op_11468"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4453424960)))]; + tensor k_233_cast = conv(dilations = var_11363, groups = var_31, pad = k_233_pad_0, pad_type = k_233_pad_type_0, strides = var_11361, weight = unet_up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_k_weight_to_fp16, x = hidden_states_459_cast)[name = tensor("k_233_cast")]; + tensor var_11367 = const()[name = tensor("op_11367"), val = tensor([1, 1])]; + tensor var_11369 = const()[name = tensor("op_11369"), val = tensor([1, 1])]; tensor v_233_pad_type_0 = const()[name = tensor("v_233_pad_type_0"), val = tensor("custom")]; tensor v_233_pad_0 = const()[name = tensor("v_233_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_233 = conv(dilations = var_11468, groups = var_6872, pad = v_233_pad_0, pad_type = v_233_pad_type_0, strides = var_11466, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight, x = hidden_states_459)[name = tensor("v_233")]; - tensor var_11472 = const()[name = tensor("op_11472"), val = tensor([2, 20, 64, -1])]; - tensor var_11473 = reshape(shape = var_11472, x = q_233)[name = tensor("op_11473")]; - tensor var_11474 = const()[name = tensor("op_11474"), val = tensor([2, 20, 64, -1])]; - tensor var_11475 = reshape(shape = var_11474, x = k_233)[name = tensor("op_11475")]; - tensor var_11476 = const()[name = tensor("op_11476"), val = tensor([2, 20, 64, -1])]; - tensor var_11477 = reshape(shape = var_11476, x = v_233)[name = tensor("op_11477")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4456701824)))]; + tensor v_233_cast = conv(dilations = var_11369, groups = var_31, pad = v_233_pad_0, pad_type = v_233_pad_type_0, strides = var_11367, weight = unet_up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_v_weight_to_fp16, x = hidden_states_459_cast)[name = tensor("v_233_cast")]; + tensor var_11373 = const()[name = tensor("op_11373"), val = tensor([2, 20, 64, -1])]; + tensor var_11374_cast = reshape(shape = var_11373, x = q_233_cast)[name = tensor("op_11374_cast")]; + tensor var_11375 = const()[name = tensor("op_11375"), val = tensor([2, 20, 64, -1])]; + tensor var_11376_cast = reshape(shape = var_11375, x = k_233_cast)[name = tensor("op_11376_cast")]; + tensor var_11377 = const()[name = tensor("op_11377"), val = tensor([2, 20, 64, -1])]; + tensor var_11378_cast = reshape(shape = var_11377, x = v_233_cast)[name = tensor("op_11378_cast")]; tensor attn_weights_465_transpose_x_0 = const()[name = tensor("attn_weights_465_transpose_x_0"), val = tensor(true)]; tensor attn_weights_465_transpose_y_0 = const()[name = tensor("attn_weights_465_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_465 = matmul(transpose_x = attn_weights_465_transpose_x_0, transpose_y = attn_weights_465_transpose_y_0, x = var_11473, y = var_11475)[name = tensor("attn_weights_465")]; - tensor attn_weights_467 = mul(x = attn_weights_465, y = var_6863)[name = tensor("attn_weights_467")]; - tensor var_11481 = softmax(axis = var_6856, x = attn_weights_467)[name = tensor("op_11481")]; + tensor attn_weights_465_cast = matmul(transpose_x = attn_weights_465_transpose_x_0, transpose_y = attn_weights_465_transpose_y_0, x = var_11374_cast, y = var_11376_cast)[name = tensor("attn_weights_465_cast")]; + tensor attn_weights_467_cast = mul(x = attn_weights_465_cast, y = var_12_to_fp16)[name = tensor("attn_weights_467_cast")]; + tensor var_11382_cast = softmax(axis = var_18, x = attn_weights_467_cast)[name = tensor("op_11382_cast")]; tensor attn_233_transpose_x_0 = const()[name = tensor("attn_233_transpose_x_0"), val = tensor(false)]; tensor attn_233_transpose_y_0 = const()[name = tensor("attn_233_transpose_y_0"), val = tensor(true)]; - tensor attn_233 = matmul(transpose_x = attn_233_transpose_x_0, transpose_y = attn_233_transpose_y_0, x = var_11477, y = var_11481)[name = tensor("attn_233")]; - tensor var_11485 = const()[name = tensor("op_11485"), val = tensor([2, 1280, 1, -1])]; - tensor input_667 = reshape(shape = var_11485, x = attn_233)[name = tensor("input_667")]; - tensor var_11490 = const()[name = tensor("op_11490"), val = tensor([1, 1])]; - tensor var_11492 = const()[name = tensor("op_11492"), val = tensor([1, 1])]; - tensor var_11494_pad_type_0 = const()[name = tensor("op_11494_pad_type_0"), val = tensor("custom")]; - tensor var_11494_pad_0 = const()[name = tensor("op_11494_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11494 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias, dilations = var_11492, groups = var_6872, pad = var_11494_pad_0, pad_type = var_11494_pad_type_0, strides = var_11490, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight, x = input_667)[name = tensor("op_11494")]; - tensor inputs_351 = add(x = var_11494, y = inputs_349)[name = tensor("inputs_351")]; - tensor var_11498 = const()[name = tensor("op_11498"), val = tensor([1])]; - tensor channels_mean_351 = reduce_mean(axes = var_11498, keep_dims = var_6867, x = inputs_351)[name = tensor("channels_mean_351")]; - tensor zero_mean_351 = sub(x = inputs_351, y = channels_mean_351)[name = tensor("zero_mean_351")]; - tensor zero_mean_sq_351 = mul(x = zero_mean_351, y = zero_mean_351)[name = tensor("zero_mean_sq_351")]; - tensor var_11502 = const()[name = tensor("op_11502"), val = tensor([1])]; - tensor var_11503 = reduce_mean(axes = var_11502, keep_dims = var_6867, x = zero_mean_sq_351)[name = tensor("op_11503")]; - tensor var_11504 = const()[name = tensor("op_11504"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11505 = add(x = var_11503, y = var_11504)[name = tensor("op_11505")]; - tensor denom_351_epsilon_0 = const()[name = tensor("denom_351_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_351 = rsqrt(epsilon = denom_351_epsilon_0, x = var_11505)[name = tensor("denom_351")]; - tensor out_351 = mul(x = zero_mean_351, y = denom_351)[name = tensor("out_351")]; - tensor var_11509 = const()[name = tensor("op_11509"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269652992)))]; - tensor var_11510 = add(x = out_351, y = var_11509)[name = tensor("op_11510")]; - tensor var_11512 = const()[name = tensor("op_11512"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269658176)))]; - tensor hidden_states_461 = mul(x = var_11510, y = var_11512)[name = tensor("hidden_states_461")]; - tensor var_11519 = const()[name = tensor("op_11519"), val = tensor([1, 1])]; - tensor var_11521 = const()[name = tensor("op_11521"), val = tensor([1, 1])]; + tensor attn_233_cast = matmul(transpose_x = attn_233_transpose_x_0, transpose_y = attn_233_transpose_y_0, x = var_11378_cast, y = var_11382_cast)[name = tensor("attn_233_cast")]; + tensor var_11386 = const()[name = tensor("op_11386"), val = tensor([2, 1280, 1, -1])]; + tensor input_667_cast = reshape(shape = var_11386, x = attn_233_cast)[name = tensor("input_667_cast")]; + tensor var_11391 = const()[name = tensor("op_11391"), val = tensor([1, 1])]; + tensor var_11393 = const()[name = tensor("op_11393"), val = tensor([1, 1])]; + tensor var_11395_pad_type_0 = const()[name = tensor("op_11395_pad_type_0"), val = tensor("custom")]; + tensor var_11395_pad_0 = const()[name = tensor("op_11395_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4459978688)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4463255552)))]; + tensor var_11395_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_bias_to_fp16, dilations = var_11393, groups = var_31, pad = var_11395_pad_0, pad_type = var_11395_pad_type_0, strides = var_11391, weight = unet_up_blocks_0_attentions_2_transformer_blocks_4_attn1_to_out_0_weight_to_fp16, x = input_667_cast)[name = tensor("op_11395_cast")]; + tensor inputs_351_cast = add(x = var_11395_cast, y = inputs_349_cast)[name = tensor("inputs_351_cast")]; + tensor var_11399 = const()[name = tensor("op_11399"), val = tensor([1])]; + tensor channels_mean_351_cast = reduce_mean(axes = var_11399, keep_dims = var_23, x = inputs_351_cast)[name = tensor("channels_mean_351_cast")]; + tensor zero_mean_351_cast = sub(x = inputs_351_cast, y = channels_mean_351_cast)[name = tensor("zero_mean_351_cast")]; + tensor zero_mean_sq_351_cast = mul(x = zero_mean_351_cast, y = zero_mean_351_cast)[name = tensor("zero_mean_sq_351_cast")]; + tensor var_11403 = const()[name = tensor("op_11403"), val = tensor([1])]; + tensor var_11404_cast = reduce_mean(axes = var_11403, keep_dims = var_23, x = zero_mean_sq_351_cast)[name = tensor("op_11404_cast")]; + tensor var_11405_to_fp16 = const()[name = tensor("op_11405_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11406_cast = add(x = var_11404_cast, y = var_11405_to_fp16)[name = tensor("op_11406_cast")]; + tensor denom_351_epsilon_0_to_fp16 = const()[name = tensor("denom_351_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_351_cast = rsqrt(epsilon = denom_351_epsilon_0_to_fp16, x = var_11406_cast)[name = tensor("denom_351_cast")]; + tensor out_351_cast = mul(x = zero_mean_351_cast, y = denom_351_cast)[name = tensor("out_351_cast")]; + tensor var_11410_to_fp16 = const()[name = tensor("op_11410_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4463258176)))]; + tensor var_11411_cast = add(x = out_351_cast, y = var_11410_to_fp16)[name = tensor("op_11411_cast")]; + tensor var_11413_to_fp16 = const()[name = tensor("op_11413_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4463260800)))]; + tensor hidden_states_461_cast = mul(x = var_11411_cast, y = var_11413_to_fp16)[name = tensor("hidden_states_461_cast")]; + tensor var_11420 = const()[name = tensor("op_11420"), val = tensor([1, 1])]; + tensor var_11422 = const()[name = tensor("op_11422"), val = tensor([1, 1])]; tensor q_235_pad_type_0 = const()[name = tensor("q_235_pad_type_0"), val = tensor("custom")]; tensor q_235_pad_0 = const()[name = tensor("q_235_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_235 = conv(dilations = var_11521, groups = var_6872, pad = q_235_pad_0, pad_type = q_235_pad_type_0, strides = var_11519, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight, x = hidden_states_461)[name = tensor("q_235")]; - tensor var_11525 = const()[name = tensor("op_11525"), val = tensor([1, 1])]; - tensor var_11527 = const()[name = tensor("op_11527"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4463263424)))]; + tensor q_235_cast = conv(dilations = var_11422, groups = var_31, pad = q_235_pad_0, pad_type = q_235_pad_type_0, strides = var_11420, weight = unet_up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_q_weight_to_fp16, x = hidden_states_461_cast)[name = tensor("q_235_cast")]; + tensor var_11426 = const()[name = tensor("op_11426"), val = tensor([1, 1])]; + tensor var_11428 = const()[name = tensor("op_11428"), val = tensor([1, 1])]; tensor k_235_pad_type_0 = const()[name = tensor("k_235_pad_type_0"), val = tensor("custom")]; tensor k_235_pad_0 = const()[name = tensor("k_235_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_235 = conv(dilations = var_11527, groups = var_6872, pad = k_235_pad_0, pad_type = k_235_pad_type_0, strides = var_11525, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_235")]; - tensor var_11531 = const()[name = tensor("op_11531"), val = tensor([1, 1])]; - tensor var_11533 = const()[name = tensor("op_11533"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4466540288)))]; + tensor k_235_cast = conv(dilations = var_11428, groups = var_31, pad = k_235_pad_0, pad_type = k_235_pad_type_0, strides = var_11426, weight = unet_up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_235_cast")]; + tensor var_11432 = const()[name = tensor("op_11432"), val = tensor([1, 1])]; + tensor var_11434 = const()[name = tensor("op_11434"), val = tensor([1, 1])]; tensor v_235_pad_type_0 = const()[name = tensor("v_235_pad_type_0"), val = tensor("custom")]; tensor v_235_pad_0 = const()[name = tensor("v_235_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_235 = conv(dilations = var_11533, groups = var_6872, pad = v_235_pad_0, pad_type = v_235_pad_type_0, strides = var_11531, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_235")]; - tensor var_11537 = const()[name = tensor("op_11537"), val = tensor([2, 20, 64, -1])]; - tensor var_11538 = reshape(shape = var_11537, x = q_235)[name = tensor("op_11538")]; - tensor var_11539 = const()[name = tensor("op_11539"), val = tensor([2, 20, 64, -1])]; - tensor var_11540 = reshape(shape = var_11539, x = k_235)[name = tensor("op_11540")]; - tensor var_11541 = const()[name = tensor("op_11541"), val = tensor([2, 20, 64, -1])]; - tensor var_11542 = reshape(shape = var_11541, x = v_235)[name = tensor("op_11542")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4471783232)))]; + tensor v_235_cast = conv(dilations = var_11434, groups = var_31, pad = v_235_pad_0, pad_type = v_235_pad_type_0, strides = var_11432, weight = unet_up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_235_cast")]; + tensor var_11438 = const()[name = tensor("op_11438"), val = tensor([2, 20, 64, -1])]; + tensor var_11439_cast = reshape(shape = var_11438, x = q_235_cast)[name = tensor("op_11439_cast")]; + tensor var_11440 = const()[name = tensor("op_11440"), val = tensor([2, 20, 64, -1])]; + tensor var_11441_cast = reshape(shape = var_11440, x = k_235_cast)[name = tensor("op_11441_cast")]; + tensor var_11442 = const()[name = tensor("op_11442"), val = tensor([2, 20, 64, -1])]; + tensor var_11443_cast = reshape(shape = var_11442, x = v_235_cast)[name = tensor("op_11443_cast")]; tensor attn_weights_469_transpose_x_0 = const()[name = tensor("attn_weights_469_transpose_x_0"), val = tensor(true)]; tensor attn_weights_469_transpose_y_0 = const()[name = tensor("attn_weights_469_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_469 = matmul(transpose_x = attn_weights_469_transpose_x_0, transpose_y = attn_weights_469_transpose_y_0, x = var_11538, y = var_11540)[name = tensor("attn_weights_469")]; - tensor attn_weights_471 = mul(x = attn_weights_469, y = var_6863)[name = tensor("attn_weights_471")]; - tensor var_11546 = softmax(axis = var_6856, x = attn_weights_471)[name = tensor("op_11546")]; + tensor attn_weights_469_cast = matmul(transpose_x = attn_weights_469_transpose_x_0, transpose_y = attn_weights_469_transpose_y_0, x = var_11439_cast, y = var_11441_cast)[name = tensor("attn_weights_469_cast")]; + tensor attn_weights_471_cast = mul(x = attn_weights_469_cast, y = var_12_to_fp16)[name = tensor("attn_weights_471_cast")]; + tensor var_11447_cast = softmax(axis = var_18, x = attn_weights_471_cast)[name = tensor("op_11447_cast")]; tensor attn_235_transpose_x_0 = const()[name = tensor("attn_235_transpose_x_0"), val = tensor(false)]; tensor attn_235_transpose_y_0 = const()[name = tensor("attn_235_transpose_y_0"), val = tensor(true)]; - tensor attn_235 = matmul(transpose_x = attn_235_transpose_x_0, transpose_y = attn_235_transpose_y_0, x = var_11542, y = var_11546)[name = tensor("attn_235")]; - tensor var_11550 = const()[name = tensor("op_11550"), val = tensor([2, 1280, 1, -1])]; - tensor input_669 = reshape(shape = var_11550, x = attn_235)[name = tensor("input_669")]; - tensor var_11555 = const()[name = tensor("op_11555"), val = tensor([1, 1])]; - tensor var_11557 = const()[name = tensor("op_11557"), val = tensor([1, 1])]; - tensor var_11559_pad_type_0 = const()[name = tensor("op_11559_pad_type_0"), val = tensor("custom")]; - tensor var_11559_pad_0 = const()[name = tensor("op_11559_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11559 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias, dilations = var_11557, groups = var_6872, pad = var_11559_pad_0, pad_type = var_11559_pad_type_0, strides = var_11555, weight = up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight, x = input_669)[name = tensor("op_11559")]; - tensor inputs_353 = add(x = var_11559, y = inputs_351)[name = tensor("inputs_353")]; - tensor var_11563 = const()[name = tensor("op_11563"), val = tensor([1])]; - tensor channels_mean_353 = reduce_mean(axes = var_11563, keep_dims = var_6867, x = inputs_353)[name = tensor("channels_mean_353")]; - tensor zero_mean_353 = sub(x = inputs_353, y = channels_mean_353)[name = tensor("zero_mean_353")]; - tensor zero_mean_sq_353 = mul(x = zero_mean_353, y = zero_mean_353)[name = tensor("zero_mean_sq_353")]; - tensor var_11567 = const()[name = tensor("op_11567"), val = tensor([1])]; - tensor var_11568 = reduce_mean(axes = var_11567, keep_dims = var_6867, x = zero_mean_sq_353)[name = tensor("op_11568")]; - tensor var_11569 = const()[name = tensor("op_11569"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11570 = add(x = var_11568, y = var_11569)[name = tensor("op_11570")]; - tensor denom_353_epsilon_0 = const()[name = tensor("denom_353_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_353 = rsqrt(epsilon = denom_353_epsilon_0, x = var_11570)[name = tensor("denom_353")]; - tensor out_353 = mul(x = zero_mean_353, y = denom_353)[name = tensor("out_353")]; - tensor var_11574 = const()[name = tensor("op_11574"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269663360)))]; - tensor var_11575 = add(x = out_353, y = var_11574)[name = tensor("op_11575")]; - tensor var_11577 = const()[name = tensor("op_11577"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269668544)))]; - tensor input_671 = mul(x = var_11575, y = var_11577)[name = tensor("input_671")]; - tensor var_11585 = const()[name = tensor("op_11585"), val = tensor([1, 1])]; - tensor var_11587 = const()[name = tensor("op_11587"), val = tensor([1, 1])]; - tensor var_11589_pad_type_0 = const()[name = tensor("op_11589_pad_type_0"), val = tensor("custom")]; - tensor var_11589_pad_0 = const()[name = tensor("op_11589_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11589 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias, dilations = var_11587, groups = var_6872, pad = var_11589_pad_0, pad_type = var_11589_pad_type_0, strides = var_11585, weight = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight, x = input_671)[name = tensor("op_11589")]; - tensor var_11590_split_sizes_0 = const()[name = tensor("op_11590_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_11590_axis_0 = const()[name = tensor("op_11590_axis_0"), val = tensor(1)]; - tensor var_11590_0, tensor var_11590_1 = split(axis = var_11590_axis_0, split_sizes = var_11590_split_sizes_0, x = var_11589)[name = tensor("op_11590")]; - tensor var_11592_mode_0 = const()[name = tensor("op_11592_mode_0"), val = tensor("EXACT")]; - tensor var_11592 = gelu(mode = var_11592_mode_0, x = var_11590_1)[name = tensor("op_11592")]; - tensor input_673 = mul(x = var_11590_0, y = var_11592)[name = tensor("input_673")]; - tensor var_11596 = const()[name = tensor("op_11596"), val = tensor([1, 1])]; - tensor var_11598 = const()[name = tensor("op_11598"), val = tensor([1, 1])]; - tensor var_11600_pad_type_0 = const()[name = tensor("op_11600_pad_type_0"), val = tensor("custom")]; - tensor var_11600_pad_0 = const()[name = tensor("op_11600_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11600 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias, dilations = var_11598, groups = var_6872, pad = var_11600_pad_0, pad_type = var_11600_pad_type_0, strides = var_11596, weight = up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight, x = input_673)[name = tensor("op_11600")]; - tensor inputs_355 = add(x = var_11600, y = inputs_353)[name = tensor("inputs_355")]; - tensor var_11610 = const()[name = tensor("op_11610"), val = tensor([1])]; - tensor channels_mean_355 = reduce_mean(axes = var_11610, keep_dims = var_6867, x = inputs_355)[name = tensor("channels_mean_355")]; - tensor zero_mean_355 = sub(x = inputs_355, y = channels_mean_355)[name = tensor("zero_mean_355")]; - tensor zero_mean_sq_355 = mul(x = zero_mean_355, y = zero_mean_355)[name = tensor("zero_mean_sq_355")]; - tensor var_11614 = const()[name = tensor("op_11614"), val = tensor([1])]; - tensor var_11615 = reduce_mean(axes = var_11614, keep_dims = var_6867, x = zero_mean_sq_355)[name = tensor("op_11615")]; - tensor var_11616 = const()[name = tensor("op_11616"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11617 = add(x = var_11615, y = var_11616)[name = tensor("op_11617")]; - tensor denom_355_epsilon_0 = const()[name = tensor("denom_355_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_355 = rsqrt(epsilon = denom_355_epsilon_0, x = var_11617)[name = tensor("denom_355")]; - tensor out_355 = mul(x = zero_mean_355, y = denom_355)[name = tensor("out_355")]; - tensor var_11621 = const()[name = tensor("op_11621"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269673728)))]; - tensor var_11622 = add(x = out_355, y = var_11621)[name = tensor("op_11622")]; - tensor var_11624 = const()[name = tensor("op_11624"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269678912)))]; - tensor hidden_states_465 = mul(x = var_11622, y = var_11624)[name = tensor("hidden_states_465")]; - tensor var_11631 = const()[name = tensor("op_11631"), val = tensor([1, 1])]; - tensor var_11633 = const()[name = tensor("op_11633"), val = tensor([1, 1])]; + tensor attn_235_cast = matmul(transpose_x = attn_235_transpose_x_0, transpose_y = attn_235_transpose_y_0, x = var_11443_cast, y = var_11447_cast)[name = tensor("attn_235_cast")]; + tensor var_11451 = const()[name = tensor("op_11451"), val = tensor([2, 1280, 1, -1])]; + tensor input_669_cast = reshape(shape = var_11451, x = attn_235_cast)[name = tensor("input_669_cast")]; + tensor var_11456 = const()[name = tensor("op_11456"), val = tensor([1, 1])]; + tensor var_11458 = const()[name = tensor("op_11458"), val = tensor([1, 1])]; + tensor var_11460_pad_type_0 = const()[name = tensor("op_11460_pad_type_0"), val = tensor("custom")]; + tensor var_11460_pad_0 = const()[name = tensor("op_11460_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4477026176)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4480303040)))]; + tensor var_11460_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_bias_to_fp16, dilations = var_11458, groups = var_31, pad = var_11460_pad_0, pad_type = var_11460_pad_type_0, strides = var_11456, weight = unet_up_blocks_0_attentions_2_transformer_blocks_4_attn2_to_out_0_weight_to_fp16, x = input_669_cast)[name = tensor("op_11460_cast")]; + tensor inputs_353_cast = add(x = var_11460_cast, y = inputs_351_cast)[name = tensor("inputs_353_cast")]; + tensor var_11464 = const()[name = tensor("op_11464"), val = tensor([1])]; + tensor channels_mean_353_cast = reduce_mean(axes = var_11464, keep_dims = var_23, x = inputs_353_cast)[name = tensor("channels_mean_353_cast")]; + tensor zero_mean_353_cast = sub(x = inputs_353_cast, y = channels_mean_353_cast)[name = tensor("zero_mean_353_cast")]; + tensor zero_mean_sq_353_cast = mul(x = zero_mean_353_cast, y = zero_mean_353_cast)[name = tensor("zero_mean_sq_353_cast")]; + tensor var_11468 = const()[name = tensor("op_11468"), val = tensor([1])]; + tensor var_11469_cast = reduce_mean(axes = var_11468, keep_dims = var_23, x = zero_mean_sq_353_cast)[name = tensor("op_11469_cast")]; + tensor var_11470_to_fp16 = const()[name = tensor("op_11470_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11471_cast = add(x = var_11469_cast, y = var_11470_to_fp16)[name = tensor("op_11471_cast")]; + tensor denom_353_epsilon_0_to_fp16 = const()[name = tensor("denom_353_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_353_cast = rsqrt(epsilon = denom_353_epsilon_0_to_fp16, x = var_11471_cast)[name = tensor("denom_353_cast")]; + tensor out_353_cast = mul(x = zero_mean_353_cast, y = denom_353_cast)[name = tensor("out_353_cast")]; + tensor var_11475_to_fp16 = const()[name = tensor("op_11475_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4480305664)))]; + tensor var_11476_cast = add(x = out_353_cast, y = var_11475_to_fp16)[name = tensor("op_11476_cast")]; + tensor var_11478_to_fp16 = const()[name = tensor("op_11478_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4480308288)))]; + tensor input_671_cast = mul(x = var_11476_cast, y = var_11478_to_fp16)[name = tensor("input_671_cast")]; + tensor var_11486 = const()[name = tensor("op_11486"), val = tensor([1, 1])]; + tensor var_11488 = const()[name = tensor("op_11488"), val = tensor([1, 1])]; + tensor var_11490_pad_type_0 = const()[name = tensor("op_11490_pad_type_0"), val = tensor("custom")]; + tensor var_11490_pad_0 = const()[name = tensor("op_11490_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4480310912)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4506525376)))]; + tensor var_11490_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_bias_to_fp16, dilations = var_11488, groups = var_31, pad = var_11490_pad_0, pad_type = var_11490_pad_type_0, strides = var_11486, weight = unet_up_blocks_0_attentions_2_transformer_blocks_4_ff_net_0_proj_weight_to_fp16, x = input_671_cast)[name = tensor("op_11490_cast")]; + tensor var_11491_split_sizes_0 = const()[name = tensor("op_11491_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11491_axis_0 = const()[name = tensor("op_11491_axis_0"), val = tensor(1)]; + tensor var_11491_cast_0, tensor var_11491_cast_1 = split(axis = var_11491_axis_0, split_sizes = var_11491_split_sizes_0, x = var_11490_cast)[name = tensor("op_11491_cast")]; + tensor var_11493_mode_0 = const()[name = tensor("op_11493_mode_0"), val = tensor("EXACT")]; + tensor var_11493_cast = gelu(mode = var_11493_mode_0, x = var_11491_cast_1)[name = tensor("op_11493_cast")]; + tensor input_673_cast = mul(x = var_11491_cast_0, y = var_11493_cast)[name = tensor("input_673_cast")]; + tensor var_11497 = const()[name = tensor("op_11497"), val = tensor([1, 1])]; + tensor var_11499 = const()[name = tensor("op_11499"), val = tensor([1, 1])]; + tensor var_11501_pad_type_0 = const()[name = tensor("op_11501_pad_type_0"), val = tensor("custom")]; + tensor var_11501_pad_0 = const()[name = tensor("op_11501_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4506545920)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4519653184)))]; + tensor var_11501_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_bias_to_fp16, dilations = var_11499, groups = var_31, pad = var_11501_pad_0, pad_type = var_11501_pad_type_0, strides = var_11497, weight = unet_up_blocks_0_attentions_2_transformer_blocks_4_ff_net_2_weight_to_fp16, x = input_673_cast)[name = tensor("op_11501_cast")]; + tensor inputs_355_cast = add(x = var_11501_cast, y = inputs_353_cast)[name = tensor("inputs_355_cast")]; + tensor var_11511 = const()[name = tensor("op_11511"), val = tensor([1])]; + tensor channels_mean_355_cast = reduce_mean(axes = var_11511, keep_dims = var_23, x = inputs_355_cast)[name = tensor("channels_mean_355_cast")]; + tensor zero_mean_355_cast = sub(x = inputs_355_cast, y = channels_mean_355_cast)[name = tensor("zero_mean_355_cast")]; + tensor zero_mean_sq_355_cast = mul(x = zero_mean_355_cast, y = zero_mean_355_cast)[name = tensor("zero_mean_sq_355_cast")]; + tensor var_11515 = const()[name = tensor("op_11515"), val = tensor([1])]; + tensor var_11516_cast = reduce_mean(axes = var_11515, keep_dims = var_23, x = zero_mean_sq_355_cast)[name = tensor("op_11516_cast")]; + tensor var_11517_to_fp16 = const()[name = tensor("op_11517_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11518_cast = add(x = var_11516_cast, y = var_11517_to_fp16)[name = tensor("op_11518_cast")]; + tensor denom_355_epsilon_0_to_fp16 = const()[name = tensor("denom_355_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_355_cast = rsqrt(epsilon = denom_355_epsilon_0_to_fp16, x = var_11518_cast)[name = tensor("denom_355_cast")]; + tensor out_355_cast = mul(x = zero_mean_355_cast, y = denom_355_cast)[name = tensor("out_355_cast")]; + tensor var_11522_to_fp16 = const()[name = tensor("op_11522_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4519655808)))]; + tensor var_11523_cast = add(x = out_355_cast, y = var_11522_to_fp16)[name = tensor("op_11523_cast")]; + tensor var_11525_to_fp16 = const()[name = tensor("op_11525_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4519658432)))]; + tensor hidden_states_465_cast = mul(x = var_11523_cast, y = var_11525_to_fp16)[name = tensor("hidden_states_465_cast")]; + tensor var_11532 = const()[name = tensor("op_11532"), val = tensor([1, 1])]; + tensor var_11534 = const()[name = tensor("op_11534"), val = tensor([1, 1])]; tensor q_237_pad_type_0 = const()[name = tensor("q_237_pad_type_0"), val = tensor("custom")]; tensor q_237_pad_0 = const()[name = tensor("q_237_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_237 = conv(dilations = var_11633, groups = var_6872, pad = q_237_pad_0, pad_type = q_237_pad_type_0, strides = var_11631, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight, x = hidden_states_465)[name = tensor("q_237")]; - tensor var_11637 = const()[name = tensor("op_11637"), val = tensor([1, 1])]; - tensor var_11639 = const()[name = tensor("op_11639"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4519661056)))]; + tensor q_237_cast = conv(dilations = var_11534, groups = var_31, pad = q_237_pad_0, pad_type = q_237_pad_type_0, strides = var_11532, weight = unet_up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_q_weight_to_fp16, x = hidden_states_465_cast)[name = tensor("q_237_cast")]; + tensor var_11538 = const()[name = tensor("op_11538"), val = tensor([1, 1])]; + tensor var_11540 = const()[name = tensor("op_11540"), val = tensor([1, 1])]; tensor k_237_pad_type_0 = const()[name = tensor("k_237_pad_type_0"), val = tensor("custom")]; tensor k_237_pad_0 = const()[name = tensor("k_237_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_237 = conv(dilations = var_11639, groups = var_6872, pad = k_237_pad_0, pad_type = k_237_pad_type_0, strides = var_11637, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight, x = hidden_states_465)[name = tensor("k_237")]; - tensor var_11643 = const()[name = tensor("op_11643"), val = tensor([1, 1])]; - tensor var_11645 = const()[name = tensor("op_11645"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4522937920)))]; + tensor k_237_cast = conv(dilations = var_11540, groups = var_31, pad = k_237_pad_0, pad_type = k_237_pad_type_0, strides = var_11538, weight = unet_up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_k_weight_to_fp16, x = hidden_states_465_cast)[name = tensor("k_237_cast")]; + tensor var_11544 = const()[name = tensor("op_11544"), val = tensor([1, 1])]; + tensor var_11546 = const()[name = tensor("op_11546"), val = tensor([1, 1])]; tensor v_237_pad_type_0 = const()[name = tensor("v_237_pad_type_0"), val = tensor("custom")]; tensor v_237_pad_0 = const()[name = tensor("v_237_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_237 = conv(dilations = var_11645, groups = var_6872, pad = v_237_pad_0, pad_type = v_237_pad_type_0, strides = var_11643, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight, x = hidden_states_465)[name = tensor("v_237")]; - tensor var_11649 = const()[name = tensor("op_11649"), val = tensor([2, 20, 64, -1])]; - tensor var_11650 = reshape(shape = var_11649, x = q_237)[name = tensor("op_11650")]; - tensor var_11651 = const()[name = tensor("op_11651"), val = tensor([2, 20, 64, -1])]; - tensor var_11652 = reshape(shape = var_11651, x = k_237)[name = tensor("op_11652")]; - tensor var_11653 = const()[name = tensor("op_11653"), val = tensor([2, 20, 64, -1])]; - tensor var_11654 = reshape(shape = var_11653, x = v_237)[name = tensor("op_11654")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4526214784)))]; + tensor v_237_cast = conv(dilations = var_11546, groups = var_31, pad = v_237_pad_0, pad_type = v_237_pad_type_0, strides = var_11544, weight = unet_up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_v_weight_to_fp16, x = hidden_states_465_cast)[name = tensor("v_237_cast")]; + tensor var_11550 = const()[name = tensor("op_11550"), val = tensor([2, 20, 64, -1])]; + tensor var_11551_cast = reshape(shape = var_11550, x = q_237_cast)[name = tensor("op_11551_cast")]; + tensor var_11552 = const()[name = tensor("op_11552"), val = tensor([2, 20, 64, -1])]; + tensor var_11553_cast = reshape(shape = var_11552, x = k_237_cast)[name = tensor("op_11553_cast")]; + tensor var_11554 = const()[name = tensor("op_11554"), val = tensor([2, 20, 64, -1])]; + tensor var_11555_cast = reshape(shape = var_11554, x = v_237_cast)[name = tensor("op_11555_cast")]; tensor attn_weights_473_transpose_x_0 = const()[name = tensor("attn_weights_473_transpose_x_0"), val = tensor(true)]; tensor attn_weights_473_transpose_y_0 = const()[name = tensor("attn_weights_473_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_473 = matmul(transpose_x = attn_weights_473_transpose_x_0, transpose_y = attn_weights_473_transpose_y_0, x = var_11650, y = var_11652)[name = tensor("attn_weights_473")]; - tensor attn_weights_475 = mul(x = attn_weights_473, y = var_6863)[name = tensor("attn_weights_475")]; - tensor var_11658 = softmax(axis = var_6856, x = attn_weights_475)[name = tensor("op_11658")]; + tensor attn_weights_473_cast = matmul(transpose_x = attn_weights_473_transpose_x_0, transpose_y = attn_weights_473_transpose_y_0, x = var_11551_cast, y = var_11553_cast)[name = tensor("attn_weights_473_cast")]; + tensor attn_weights_475_cast = mul(x = attn_weights_473_cast, y = var_12_to_fp16)[name = tensor("attn_weights_475_cast")]; + tensor var_11559_cast = softmax(axis = var_18, x = attn_weights_475_cast)[name = tensor("op_11559_cast")]; tensor attn_237_transpose_x_0 = const()[name = tensor("attn_237_transpose_x_0"), val = tensor(false)]; tensor attn_237_transpose_y_0 = const()[name = tensor("attn_237_transpose_y_0"), val = tensor(true)]; - tensor attn_237 = matmul(transpose_x = attn_237_transpose_x_0, transpose_y = attn_237_transpose_y_0, x = var_11654, y = var_11658)[name = tensor("attn_237")]; - tensor var_11662 = const()[name = tensor("op_11662"), val = tensor([2, 1280, 1, -1])]; - tensor input_675 = reshape(shape = var_11662, x = attn_237)[name = tensor("input_675")]; - tensor var_11667 = const()[name = tensor("op_11667"), val = tensor([1, 1])]; - tensor var_11669 = const()[name = tensor("op_11669"), val = tensor([1, 1])]; - tensor var_11671_pad_type_0 = const()[name = tensor("op_11671_pad_type_0"), val = tensor("custom")]; - tensor var_11671_pad_0 = const()[name = tensor("op_11671_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11671 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias, dilations = var_11669, groups = var_6872, pad = var_11671_pad_0, pad_type = var_11671_pad_type_0, strides = var_11667, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight, x = input_675)[name = tensor("op_11671")]; - tensor inputs_357 = add(x = var_11671, y = inputs_355)[name = tensor("inputs_357")]; - tensor var_11675 = const()[name = tensor("op_11675"), val = tensor([1])]; - tensor channels_mean_357 = reduce_mean(axes = var_11675, keep_dims = var_6867, x = inputs_357)[name = tensor("channels_mean_357")]; - tensor zero_mean_357 = sub(x = inputs_357, y = channels_mean_357)[name = tensor("zero_mean_357")]; - tensor zero_mean_sq_357 = mul(x = zero_mean_357, y = zero_mean_357)[name = tensor("zero_mean_sq_357")]; - tensor var_11679 = const()[name = tensor("op_11679"), val = tensor([1])]; - tensor var_11680 = reduce_mean(axes = var_11679, keep_dims = var_6867, x = zero_mean_sq_357)[name = tensor("op_11680")]; - tensor var_11681 = const()[name = tensor("op_11681"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11682 = add(x = var_11680, y = var_11681)[name = tensor("op_11682")]; - tensor denom_357_epsilon_0 = const()[name = tensor("denom_357_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_357 = rsqrt(epsilon = denom_357_epsilon_0, x = var_11682)[name = tensor("denom_357")]; - tensor out_357 = mul(x = zero_mean_357, y = denom_357)[name = tensor("out_357")]; - tensor var_11686 = const()[name = tensor("op_11686"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269684096)))]; - tensor var_11687 = add(x = out_357, y = var_11686)[name = tensor("op_11687")]; - tensor var_11689 = const()[name = tensor("op_11689"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269689280)))]; - tensor hidden_states_467 = mul(x = var_11687, y = var_11689)[name = tensor("hidden_states_467")]; - tensor var_11696 = const()[name = tensor("op_11696"), val = tensor([1, 1])]; - tensor var_11698 = const()[name = tensor("op_11698"), val = tensor([1, 1])]; + tensor attn_237_cast = matmul(transpose_x = attn_237_transpose_x_0, transpose_y = attn_237_transpose_y_0, x = var_11555_cast, y = var_11559_cast)[name = tensor("attn_237_cast")]; + tensor var_11563 = const()[name = tensor("op_11563"), val = tensor([2, 1280, 1, -1])]; + tensor input_675_cast = reshape(shape = var_11563, x = attn_237_cast)[name = tensor("input_675_cast")]; + tensor var_11568 = const()[name = tensor("op_11568"), val = tensor([1, 1])]; + tensor var_11570 = const()[name = tensor("op_11570"), val = tensor([1, 1])]; + tensor var_11572_pad_type_0 = const()[name = tensor("op_11572_pad_type_0"), val = tensor("custom")]; + tensor var_11572_pad_0 = const()[name = tensor("op_11572_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4529491648)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4532768512)))]; + tensor var_11572_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_bias_to_fp16, dilations = var_11570, groups = var_31, pad = var_11572_pad_0, pad_type = var_11572_pad_type_0, strides = var_11568, weight = unet_up_blocks_0_attentions_2_transformer_blocks_5_attn1_to_out_0_weight_to_fp16, x = input_675_cast)[name = tensor("op_11572_cast")]; + tensor inputs_357_cast = add(x = var_11572_cast, y = inputs_355_cast)[name = tensor("inputs_357_cast")]; + tensor var_11576 = const()[name = tensor("op_11576"), val = tensor([1])]; + tensor channels_mean_357_cast = reduce_mean(axes = var_11576, keep_dims = var_23, x = inputs_357_cast)[name = tensor("channels_mean_357_cast")]; + tensor zero_mean_357_cast = sub(x = inputs_357_cast, y = channels_mean_357_cast)[name = tensor("zero_mean_357_cast")]; + tensor zero_mean_sq_357_cast = mul(x = zero_mean_357_cast, y = zero_mean_357_cast)[name = tensor("zero_mean_sq_357_cast")]; + tensor var_11580 = const()[name = tensor("op_11580"), val = tensor([1])]; + tensor var_11581_cast = reduce_mean(axes = var_11580, keep_dims = var_23, x = zero_mean_sq_357_cast)[name = tensor("op_11581_cast")]; + tensor var_11582_to_fp16 = const()[name = tensor("op_11582_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11583_cast = add(x = var_11581_cast, y = var_11582_to_fp16)[name = tensor("op_11583_cast")]; + tensor denom_357_epsilon_0_to_fp16 = const()[name = tensor("denom_357_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_357_cast = rsqrt(epsilon = denom_357_epsilon_0_to_fp16, x = var_11583_cast)[name = tensor("denom_357_cast")]; + tensor out_357_cast = mul(x = zero_mean_357_cast, y = denom_357_cast)[name = tensor("out_357_cast")]; + tensor var_11587_to_fp16 = const()[name = tensor("op_11587_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4532771136)))]; + tensor var_11588_cast = add(x = out_357_cast, y = var_11587_to_fp16)[name = tensor("op_11588_cast")]; + tensor var_11590_to_fp16 = const()[name = tensor("op_11590_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4532773760)))]; + tensor hidden_states_467_cast = mul(x = var_11588_cast, y = var_11590_to_fp16)[name = tensor("hidden_states_467_cast")]; + tensor var_11597 = const()[name = tensor("op_11597"), val = tensor([1, 1])]; + tensor var_11599 = const()[name = tensor("op_11599"), val = tensor([1, 1])]; tensor q_239_pad_type_0 = const()[name = tensor("q_239_pad_type_0"), val = tensor("custom")]; tensor q_239_pad_0 = const()[name = tensor("q_239_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_239 = conv(dilations = var_11698, groups = var_6872, pad = q_239_pad_0, pad_type = q_239_pad_type_0, strides = var_11696, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight, x = hidden_states_467)[name = tensor("q_239")]; - tensor var_11702 = const()[name = tensor("op_11702"), val = tensor([1, 1])]; - tensor var_11704 = const()[name = tensor("op_11704"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4532776384)))]; + tensor q_239_cast = conv(dilations = var_11599, groups = var_31, pad = q_239_pad_0, pad_type = q_239_pad_type_0, strides = var_11597, weight = unet_up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_q_weight_to_fp16, x = hidden_states_467_cast)[name = tensor("q_239_cast")]; + tensor var_11603 = const()[name = tensor("op_11603"), val = tensor([1, 1])]; + tensor var_11605 = const()[name = tensor("op_11605"), val = tensor([1, 1])]; tensor k_239_pad_type_0 = const()[name = tensor("k_239_pad_type_0"), val = tensor("custom")]; tensor k_239_pad_0 = const()[name = tensor("k_239_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_239 = conv(dilations = var_11704, groups = var_6872, pad = k_239_pad_0, pad_type = k_239_pad_type_0, strides = var_11702, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_239")]; - tensor var_11708 = const()[name = tensor("op_11708"), val = tensor([1, 1])]; - tensor var_11710 = const()[name = tensor("op_11710"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4536053248)))]; + tensor k_239_cast = conv(dilations = var_11605, groups = var_31, pad = k_239_pad_0, pad_type = k_239_pad_type_0, strides = var_11603, weight = unet_up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_239_cast")]; + tensor var_11609 = const()[name = tensor("op_11609"), val = tensor([1, 1])]; + tensor var_11611 = const()[name = tensor("op_11611"), val = tensor([1, 1])]; tensor v_239_pad_type_0 = const()[name = tensor("v_239_pad_type_0"), val = tensor("custom")]; tensor v_239_pad_0 = const()[name = tensor("v_239_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_239 = conv(dilations = var_11710, groups = var_6872, pad = v_239_pad_0, pad_type = v_239_pad_type_0, strides = var_11708, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_239")]; - tensor var_11714 = const()[name = tensor("op_11714"), val = tensor([2, 20, 64, -1])]; - tensor var_11715 = reshape(shape = var_11714, x = q_239)[name = tensor("op_11715")]; - tensor var_11716 = const()[name = tensor("op_11716"), val = tensor([2, 20, 64, -1])]; - tensor var_11717 = reshape(shape = var_11716, x = k_239)[name = tensor("op_11717")]; - tensor var_11718 = const()[name = tensor("op_11718"), val = tensor([2, 20, 64, -1])]; - tensor var_11719 = reshape(shape = var_11718, x = v_239)[name = tensor("op_11719")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4541296192)))]; + tensor v_239_cast = conv(dilations = var_11611, groups = var_31, pad = v_239_pad_0, pad_type = v_239_pad_type_0, strides = var_11609, weight = unet_up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_239_cast")]; + tensor var_11615 = const()[name = tensor("op_11615"), val = tensor([2, 20, 64, -1])]; + tensor var_11616_cast = reshape(shape = var_11615, x = q_239_cast)[name = tensor("op_11616_cast")]; + tensor var_11617 = const()[name = tensor("op_11617"), val = tensor([2, 20, 64, -1])]; + tensor var_11618_cast = reshape(shape = var_11617, x = k_239_cast)[name = tensor("op_11618_cast")]; + tensor var_11619 = const()[name = tensor("op_11619"), val = tensor([2, 20, 64, -1])]; + tensor var_11620_cast = reshape(shape = var_11619, x = v_239_cast)[name = tensor("op_11620_cast")]; tensor attn_weights_477_transpose_x_0 = const()[name = tensor("attn_weights_477_transpose_x_0"), val = tensor(true)]; tensor attn_weights_477_transpose_y_0 = const()[name = tensor("attn_weights_477_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_477 = matmul(transpose_x = attn_weights_477_transpose_x_0, transpose_y = attn_weights_477_transpose_y_0, x = var_11715, y = var_11717)[name = tensor("attn_weights_477")]; - tensor attn_weights_479 = mul(x = attn_weights_477, y = var_6863)[name = tensor("attn_weights_479")]; - tensor var_11723 = softmax(axis = var_6856, x = attn_weights_479)[name = tensor("op_11723")]; + tensor attn_weights_477_cast = matmul(transpose_x = attn_weights_477_transpose_x_0, transpose_y = attn_weights_477_transpose_y_0, x = var_11616_cast, y = var_11618_cast)[name = tensor("attn_weights_477_cast")]; + tensor attn_weights_479_cast = mul(x = attn_weights_477_cast, y = var_12_to_fp16)[name = tensor("attn_weights_479_cast")]; + tensor var_11624_cast = softmax(axis = var_18, x = attn_weights_479_cast)[name = tensor("op_11624_cast")]; tensor attn_239_transpose_x_0 = const()[name = tensor("attn_239_transpose_x_0"), val = tensor(false)]; tensor attn_239_transpose_y_0 = const()[name = tensor("attn_239_transpose_y_0"), val = tensor(true)]; - tensor attn_239 = matmul(transpose_x = attn_239_transpose_x_0, transpose_y = attn_239_transpose_y_0, x = var_11719, y = var_11723)[name = tensor("attn_239")]; - tensor var_11727 = const()[name = tensor("op_11727"), val = tensor([2, 1280, 1, -1])]; - tensor input_677 = reshape(shape = var_11727, x = attn_239)[name = tensor("input_677")]; - tensor var_11732 = const()[name = tensor("op_11732"), val = tensor([1, 1])]; - tensor var_11734 = const()[name = tensor("op_11734"), val = tensor([1, 1])]; - tensor var_11736_pad_type_0 = const()[name = tensor("op_11736_pad_type_0"), val = tensor("custom")]; - tensor var_11736_pad_0 = const()[name = tensor("op_11736_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11736 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias, dilations = var_11734, groups = var_6872, pad = var_11736_pad_0, pad_type = var_11736_pad_type_0, strides = var_11732, weight = up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight, x = input_677)[name = tensor("op_11736")]; - tensor inputs_359 = add(x = var_11736, y = inputs_357)[name = tensor("inputs_359")]; - tensor var_11740 = const()[name = tensor("op_11740"), val = tensor([1])]; - tensor channels_mean_359 = reduce_mean(axes = var_11740, keep_dims = var_6867, x = inputs_359)[name = tensor("channels_mean_359")]; - tensor zero_mean_359 = sub(x = inputs_359, y = channels_mean_359)[name = tensor("zero_mean_359")]; - tensor zero_mean_sq_359 = mul(x = zero_mean_359, y = zero_mean_359)[name = tensor("zero_mean_sq_359")]; - tensor var_11744 = const()[name = tensor("op_11744"), val = tensor([1])]; - tensor var_11745 = reduce_mean(axes = var_11744, keep_dims = var_6867, x = zero_mean_sq_359)[name = tensor("op_11745")]; - tensor var_11746 = const()[name = tensor("op_11746"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11747 = add(x = var_11745, y = var_11746)[name = tensor("op_11747")]; - tensor denom_359_epsilon_0 = const()[name = tensor("denom_359_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_359 = rsqrt(epsilon = denom_359_epsilon_0, x = var_11747)[name = tensor("denom_359")]; - tensor out_359 = mul(x = zero_mean_359, y = denom_359)[name = tensor("out_359")]; - tensor var_11751 = const()[name = tensor("op_11751"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269694464)))]; - tensor var_11752 = add(x = out_359, y = var_11751)[name = tensor("op_11752")]; - tensor var_11754 = const()[name = tensor("op_11754"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269699648)))]; - tensor input_679 = mul(x = var_11752, y = var_11754)[name = tensor("input_679")]; - tensor var_11762 = const()[name = tensor("op_11762"), val = tensor([1, 1])]; - tensor var_11764 = const()[name = tensor("op_11764"), val = tensor([1, 1])]; - tensor var_11766_pad_type_0 = const()[name = tensor("op_11766_pad_type_0"), val = tensor("custom")]; - tensor var_11766_pad_0 = const()[name = tensor("op_11766_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11766 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias, dilations = var_11764, groups = var_6872, pad = var_11766_pad_0, pad_type = var_11766_pad_type_0, strides = var_11762, weight = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight, x = input_679)[name = tensor("op_11766")]; - tensor var_11767_split_sizes_0 = const()[name = tensor("op_11767_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_11767_axis_0 = const()[name = tensor("op_11767_axis_0"), val = tensor(1)]; - tensor var_11767_0, tensor var_11767_1 = split(axis = var_11767_axis_0, split_sizes = var_11767_split_sizes_0, x = var_11766)[name = tensor("op_11767")]; - tensor var_11769_mode_0 = const()[name = tensor("op_11769_mode_0"), val = tensor("EXACT")]; - tensor var_11769 = gelu(mode = var_11769_mode_0, x = var_11767_1)[name = tensor("op_11769")]; - tensor input_681 = mul(x = var_11767_0, y = var_11769)[name = tensor("input_681")]; - tensor var_11773 = const()[name = tensor("op_11773"), val = tensor([1, 1])]; - tensor var_11775 = const()[name = tensor("op_11775"), val = tensor([1, 1])]; - tensor var_11777_pad_type_0 = const()[name = tensor("op_11777_pad_type_0"), val = tensor("custom")]; - tensor var_11777_pad_0 = const()[name = tensor("op_11777_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11777 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias, dilations = var_11775, groups = var_6872, pad = var_11777_pad_0, pad_type = var_11777_pad_type_0, strides = var_11773, weight = up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight, x = input_681)[name = tensor("op_11777")]; - tensor inputs_361 = add(x = var_11777, y = inputs_359)[name = tensor("inputs_361")]; - tensor var_11787 = const()[name = tensor("op_11787"), val = tensor([1])]; - tensor channels_mean_361 = reduce_mean(axes = var_11787, keep_dims = var_6867, x = inputs_361)[name = tensor("channels_mean_361")]; - tensor zero_mean_361 = sub(x = inputs_361, y = channels_mean_361)[name = tensor("zero_mean_361")]; - tensor zero_mean_sq_361 = mul(x = zero_mean_361, y = zero_mean_361)[name = tensor("zero_mean_sq_361")]; - tensor var_11791 = const()[name = tensor("op_11791"), val = tensor([1])]; - tensor var_11792 = reduce_mean(axes = var_11791, keep_dims = var_6867, x = zero_mean_sq_361)[name = tensor("op_11792")]; - tensor var_11793 = const()[name = tensor("op_11793"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11794 = add(x = var_11792, y = var_11793)[name = tensor("op_11794")]; - tensor denom_361_epsilon_0 = const()[name = tensor("denom_361_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_361 = rsqrt(epsilon = denom_361_epsilon_0, x = var_11794)[name = tensor("denom_361")]; - tensor out_361 = mul(x = zero_mean_361, y = denom_361)[name = tensor("out_361")]; - tensor var_11798 = const()[name = tensor("op_11798"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269704832)))]; - tensor var_11799 = add(x = out_361, y = var_11798)[name = tensor("op_11799")]; - tensor var_11801 = const()[name = tensor("op_11801"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269710016)))]; - tensor hidden_states_471 = mul(x = var_11799, y = var_11801)[name = tensor("hidden_states_471")]; - tensor var_11808 = const()[name = tensor("op_11808"), val = tensor([1, 1])]; - tensor var_11810 = const()[name = tensor("op_11810"), val = tensor([1, 1])]; + tensor attn_239_cast = matmul(transpose_x = attn_239_transpose_x_0, transpose_y = attn_239_transpose_y_0, x = var_11620_cast, y = var_11624_cast)[name = tensor("attn_239_cast")]; + tensor var_11628 = const()[name = tensor("op_11628"), val = tensor([2, 1280, 1, -1])]; + tensor input_677_cast = reshape(shape = var_11628, x = attn_239_cast)[name = tensor("input_677_cast")]; + tensor var_11633 = const()[name = tensor("op_11633"), val = tensor([1, 1])]; + tensor var_11635 = const()[name = tensor("op_11635"), val = tensor([1, 1])]; + tensor var_11637_pad_type_0 = const()[name = tensor("op_11637_pad_type_0"), val = tensor("custom")]; + tensor var_11637_pad_0 = const()[name = tensor("op_11637_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4546539136)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4549816000)))]; + tensor var_11637_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_bias_to_fp16, dilations = var_11635, groups = var_31, pad = var_11637_pad_0, pad_type = var_11637_pad_type_0, strides = var_11633, weight = unet_up_blocks_0_attentions_2_transformer_blocks_5_attn2_to_out_0_weight_to_fp16, x = input_677_cast)[name = tensor("op_11637_cast")]; + tensor inputs_359_cast = add(x = var_11637_cast, y = inputs_357_cast)[name = tensor("inputs_359_cast")]; + tensor var_11641 = const()[name = tensor("op_11641"), val = tensor([1])]; + tensor channels_mean_359_cast = reduce_mean(axes = var_11641, keep_dims = var_23, x = inputs_359_cast)[name = tensor("channels_mean_359_cast")]; + tensor zero_mean_359_cast = sub(x = inputs_359_cast, y = channels_mean_359_cast)[name = tensor("zero_mean_359_cast")]; + tensor zero_mean_sq_359_cast = mul(x = zero_mean_359_cast, y = zero_mean_359_cast)[name = tensor("zero_mean_sq_359_cast")]; + tensor var_11645 = const()[name = tensor("op_11645"), val = tensor([1])]; + tensor var_11646_cast = reduce_mean(axes = var_11645, keep_dims = var_23, x = zero_mean_sq_359_cast)[name = tensor("op_11646_cast")]; + tensor var_11647_to_fp16 = const()[name = tensor("op_11647_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11648_cast = add(x = var_11646_cast, y = var_11647_to_fp16)[name = tensor("op_11648_cast")]; + tensor denom_359_epsilon_0_to_fp16 = const()[name = tensor("denom_359_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_359_cast = rsqrt(epsilon = denom_359_epsilon_0_to_fp16, x = var_11648_cast)[name = tensor("denom_359_cast")]; + tensor out_359_cast = mul(x = zero_mean_359_cast, y = denom_359_cast)[name = tensor("out_359_cast")]; + tensor var_11652_to_fp16 = const()[name = tensor("op_11652_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4549818624)))]; + tensor var_11653_cast = add(x = out_359_cast, y = var_11652_to_fp16)[name = tensor("op_11653_cast")]; + tensor var_11655_to_fp16 = const()[name = tensor("op_11655_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4549821248)))]; + tensor input_679_cast = mul(x = var_11653_cast, y = var_11655_to_fp16)[name = tensor("input_679_cast")]; + tensor var_11663 = const()[name = tensor("op_11663"), val = tensor([1, 1])]; + tensor var_11665 = const()[name = tensor("op_11665"), val = tensor([1, 1])]; + tensor var_11667_pad_type_0 = const()[name = tensor("op_11667_pad_type_0"), val = tensor("custom")]; + tensor var_11667_pad_0 = const()[name = tensor("op_11667_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4549823872)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4576038336)))]; + tensor var_11667_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_bias_to_fp16, dilations = var_11665, groups = var_31, pad = var_11667_pad_0, pad_type = var_11667_pad_type_0, strides = var_11663, weight = unet_up_blocks_0_attentions_2_transformer_blocks_5_ff_net_0_proj_weight_to_fp16, x = input_679_cast)[name = tensor("op_11667_cast")]; + tensor var_11668_split_sizes_0 = const()[name = tensor("op_11668_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11668_axis_0 = const()[name = tensor("op_11668_axis_0"), val = tensor(1)]; + tensor var_11668_cast_0, tensor var_11668_cast_1 = split(axis = var_11668_axis_0, split_sizes = var_11668_split_sizes_0, x = var_11667_cast)[name = tensor("op_11668_cast")]; + tensor var_11670_mode_0 = const()[name = tensor("op_11670_mode_0"), val = tensor("EXACT")]; + tensor var_11670_cast = gelu(mode = var_11670_mode_0, x = var_11668_cast_1)[name = tensor("op_11670_cast")]; + tensor input_681_cast = mul(x = var_11668_cast_0, y = var_11670_cast)[name = tensor("input_681_cast")]; + tensor var_11674 = const()[name = tensor("op_11674"), val = tensor([1, 1])]; + tensor var_11676 = const()[name = tensor("op_11676"), val = tensor([1, 1])]; + tensor var_11678_pad_type_0 = const()[name = tensor("op_11678_pad_type_0"), val = tensor("custom")]; + tensor var_11678_pad_0 = const()[name = tensor("op_11678_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4576058880)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4589166144)))]; + tensor var_11678_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_bias_to_fp16, dilations = var_11676, groups = var_31, pad = var_11678_pad_0, pad_type = var_11678_pad_type_0, strides = var_11674, weight = unet_up_blocks_0_attentions_2_transformer_blocks_5_ff_net_2_weight_to_fp16, x = input_681_cast)[name = tensor("op_11678_cast")]; + tensor inputs_361_cast = add(x = var_11678_cast, y = inputs_359_cast)[name = tensor("inputs_361_cast")]; + tensor var_11688 = const()[name = tensor("op_11688"), val = tensor([1])]; + tensor channels_mean_361_cast = reduce_mean(axes = var_11688, keep_dims = var_23, x = inputs_361_cast)[name = tensor("channels_mean_361_cast")]; + tensor zero_mean_361_cast = sub(x = inputs_361_cast, y = channels_mean_361_cast)[name = tensor("zero_mean_361_cast")]; + tensor zero_mean_sq_361_cast = mul(x = zero_mean_361_cast, y = zero_mean_361_cast)[name = tensor("zero_mean_sq_361_cast")]; + tensor var_11692 = const()[name = tensor("op_11692"), val = tensor([1])]; + tensor var_11693_cast = reduce_mean(axes = var_11692, keep_dims = var_23, x = zero_mean_sq_361_cast)[name = tensor("op_11693_cast")]; + tensor var_11694_to_fp16 = const()[name = tensor("op_11694_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11695_cast = add(x = var_11693_cast, y = var_11694_to_fp16)[name = tensor("op_11695_cast")]; + tensor denom_361_epsilon_0_to_fp16 = const()[name = tensor("denom_361_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_361_cast = rsqrt(epsilon = denom_361_epsilon_0_to_fp16, x = var_11695_cast)[name = tensor("denom_361_cast")]; + tensor out_361_cast = mul(x = zero_mean_361_cast, y = denom_361_cast)[name = tensor("out_361_cast")]; + tensor var_11699_to_fp16 = const()[name = tensor("op_11699_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4589168768)))]; + tensor var_11700_cast = add(x = out_361_cast, y = var_11699_to_fp16)[name = tensor("op_11700_cast")]; + tensor var_11702_to_fp16 = const()[name = tensor("op_11702_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4589171392)))]; + tensor hidden_states_471_cast = mul(x = var_11700_cast, y = var_11702_to_fp16)[name = tensor("hidden_states_471_cast")]; + tensor var_11709 = const()[name = tensor("op_11709"), val = tensor([1, 1])]; + tensor var_11711 = const()[name = tensor("op_11711"), val = tensor([1, 1])]; tensor q_241_pad_type_0 = const()[name = tensor("q_241_pad_type_0"), val = tensor("custom")]; tensor q_241_pad_0 = const()[name = tensor("q_241_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_241 = conv(dilations = var_11810, groups = var_6872, pad = q_241_pad_0, pad_type = q_241_pad_type_0, strides = var_11808, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight, x = hidden_states_471)[name = tensor("q_241")]; - tensor var_11814 = const()[name = tensor("op_11814"), val = tensor([1, 1])]; - tensor var_11816 = const()[name = tensor("op_11816"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4589174016)))]; + tensor q_241_cast = conv(dilations = var_11711, groups = var_31, pad = q_241_pad_0, pad_type = q_241_pad_type_0, strides = var_11709, weight = unet_up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_q_weight_to_fp16, x = hidden_states_471_cast)[name = tensor("q_241_cast")]; + tensor var_11715 = const()[name = tensor("op_11715"), val = tensor([1, 1])]; + tensor var_11717 = const()[name = tensor("op_11717"), val = tensor([1, 1])]; tensor k_241_pad_type_0 = const()[name = tensor("k_241_pad_type_0"), val = tensor("custom")]; tensor k_241_pad_0 = const()[name = tensor("k_241_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_241 = conv(dilations = var_11816, groups = var_6872, pad = k_241_pad_0, pad_type = k_241_pad_type_0, strides = var_11814, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight, x = hidden_states_471)[name = tensor("k_241")]; - tensor var_11820 = const()[name = tensor("op_11820"), val = tensor([1, 1])]; - tensor var_11822 = const()[name = tensor("op_11822"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4592450880)))]; + tensor k_241_cast = conv(dilations = var_11717, groups = var_31, pad = k_241_pad_0, pad_type = k_241_pad_type_0, strides = var_11715, weight = unet_up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_k_weight_to_fp16, x = hidden_states_471_cast)[name = tensor("k_241_cast")]; + tensor var_11721 = const()[name = tensor("op_11721"), val = tensor([1, 1])]; + tensor var_11723 = const()[name = tensor("op_11723"), val = tensor([1, 1])]; tensor v_241_pad_type_0 = const()[name = tensor("v_241_pad_type_0"), val = tensor("custom")]; tensor v_241_pad_0 = const()[name = tensor("v_241_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_241 = conv(dilations = var_11822, groups = var_6872, pad = v_241_pad_0, pad_type = v_241_pad_type_0, strides = var_11820, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight, x = hidden_states_471)[name = tensor("v_241")]; - tensor var_11826 = const()[name = tensor("op_11826"), val = tensor([2, 20, 64, -1])]; - tensor var_11827 = reshape(shape = var_11826, x = q_241)[name = tensor("op_11827")]; - tensor var_11828 = const()[name = tensor("op_11828"), val = tensor([2, 20, 64, -1])]; - tensor var_11829 = reshape(shape = var_11828, x = k_241)[name = tensor("op_11829")]; - tensor var_11830 = const()[name = tensor("op_11830"), val = tensor([2, 20, 64, -1])]; - tensor var_11831 = reshape(shape = var_11830, x = v_241)[name = tensor("op_11831")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4595727744)))]; + tensor v_241_cast = conv(dilations = var_11723, groups = var_31, pad = v_241_pad_0, pad_type = v_241_pad_type_0, strides = var_11721, weight = unet_up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_v_weight_to_fp16, x = hidden_states_471_cast)[name = tensor("v_241_cast")]; + tensor var_11727 = const()[name = tensor("op_11727"), val = tensor([2, 20, 64, -1])]; + tensor var_11728_cast = reshape(shape = var_11727, x = q_241_cast)[name = tensor("op_11728_cast")]; + tensor var_11729 = const()[name = tensor("op_11729"), val = tensor([2, 20, 64, -1])]; + tensor var_11730_cast = reshape(shape = var_11729, x = k_241_cast)[name = tensor("op_11730_cast")]; + tensor var_11731 = const()[name = tensor("op_11731"), val = tensor([2, 20, 64, -1])]; + tensor var_11732_cast = reshape(shape = var_11731, x = v_241_cast)[name = tensor("op_11732_cast")]; tensor attn_weights_481_transpose_x_0 = const()[name = tensor("attn_weights_481_transpose_x_0"), val = tensor(true)]; tensor attn_weights_481_transpose_y_0 = const()[name = tensor("attn_weights_481_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_481 = matmul(transpose_x = attn_weights_481_transpose_x_0, transpose_y = attn_weights_481_transpose_y_0, x = var_11827, y = var_11829)[name = tensor("attn_weights_481")]; - tensor attn_weights_483 = mul(x = attn_weights_481, y = var_6863)[name = tensor("attn_weights_483")]; - tensor var_11835 = softmax(axis = var_6856, x = attn_weights_483)[name = tensor("op_11835")]; + tensor attn_weights_481_cast = matmul(transpose_x = attn_weights_481_transpose_x_0, transpose_y = attn_weights_481_transpose_y_0, x = var_11728_cast, y = var_11730_cast)[name = tensor("attn_weights_481_cast")]; + tensor attn_weights_483_cast = mul(x = attn_weights_481_cast, y = var_12_to_fp16)[name = tensor("attn_weights_483_cast")]; + tensor var_11736_cast = softmax(axis = var_18, x = attn_weights_483_cast)[name = tensor("op_11736_cast")]; tensor attn_241_transpose_x_0 = const()[name = tensor("attn_241_transpose_x_0"), val = tensor(false)]; tensor attn_241_transpose_y_0 = const()[name = tensor("attn_241_transpose_y_0"), val = tensor(true)]; - tensor attn_241 = matmul(transpose_x = attn_241_transpose_x_0, transpose_y = attn_241_transpose_y_0, x = var_11831, y = var_11835)[name = tensor("attn_241")]; - tensor var_11839 = const()[name = tensor("op_11839"), val = tensor([2, 1280, 1, -1])]; - tensor input_683 = reshape(shape = var_11839, x = attn_241)[name = tensor("input_683")]; - tensor var_11844 = const()[name = tensor("op_11844"), val = tensor([1, 1])]; - tensor var_11846 = const()[name = tensor("op_11846"), val = tensor([1, 1])]; - tensor var_11848_pad_type_0 = const()[name = tensor("op_11848_pad_type_0"), val = tensor("custom")]; - tensor var_11848_pad_0 = const()[name = tensor("op_11848_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11848 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias, dilations = var_11846, groups = var_6872, pad = var_11848_pad_0, pad_type = var_11848_pad_type_0, strides = var_11844, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight, x = input_683)[name = tensor("op_11848")]; - tensor inputs_363 = add(x = var_11848, y = inputs_361)[name = tensor("inputs_363")]; - tensor var_11852 = const()[name = tensor("op_11852"), val = tensor([1])]; - tensor channels_mean_363 = reduce_mean(axes = var_11852, keep_dims = var_6867, x = inputs_363)[name = tensor("channels_mean_363")]; - tensor zero_mean_363 = sub(x = inputs_363, y = channels_mean_363)[name = tensor("zero_mean_363")]; - tensor zero_mean_sq_363 = mul(x = zero_mean_363, y = zero_mean_363)[name = tensor("zero_mean_sq_363")]; - tensor var_11856 = const()[name = tensor("op_11856"), val = tensor([1])]; - tensor var_11857 = reduce_mean(axes = var_11856, keep_dims = var_6867, x = zero_mean_sq_363)[name = tensor("op_11857")]; - tensor var_11858 = const()[name = tensor("op_11858"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11859 = add(x = var_11857, y = var_11858)[name = tensor("op_11859")]; - tensor denom_363_epsilon_0 = const()[name = tensor("denom_363_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_363 = rsqrt(epsilon = denom_363_epsilon_0, x = var_11859)[name = tensor("denom_363")]; - tensor out_363 = mul(x = zero_mean_363, y = denom_363)[name = tensor("out_363")]; - tensor var_11863 = const()[name = tensor("op_11863"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269715200)))]; - tensor var_11864 = add(x = out_363, y = var_11863)[name = tensor("op_11864")]; - tensor var_11866 = const()[name = tensor("op_11866"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269720384)))]; - tensor hidden_states_473 = mul(x = var_11864, y = var_11866)[name = tensor("hidden_states_473")]; - tensor var_11873 = const()[name = tensor("op_11873"), val = tensor([1, 1])]; - tensor var_11875 = const()[name = tensor("op_11875"), val = tensor([1, 1])]; + tensor attn_241_cast = matmul(transpose_x = attn_241_transpose_x_0, transpose_y = attn_241_transpose_y_0, x = var_11732_cast, y = var_11736_cast)[name = tensor("attn_241_cast")]; + tensor var_11740 = const()[name = tensor("op_11740"), val = tensor([2, 1280, 1, -1])]; + tensor input_683_cast = reshape(shape = var_11740, x = attn_241_cast)[name = tensor("input_683_cast")]; + tensor var_11745 = const()[name = tensor("op_11745"), val = tensor([1, 1])]; + tensor var_11747 = const()[name = tensor("op_11747"), val = tensor([1, 1])]; + tensor var_11749_pad_type_0 = const()[name = tensor("op_11749_pad_type_0"), val = tensor("custom")]; + tensor var_11749_pad_0 = const()[name = tensor("op_11749_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4599004608)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4602281472)))]; + tensor var_11749_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_bias_to_fp16, dilations = var_11747, groups = var_31, pad = var_11749_pad_0, pad_type = var_11749_pad_type_0, strides = var_11745, weight = unet_up_blocks_0_attentions_2_transformer_blocks_6_attn1_to_out_0_weight_to_fp16, x = input_683_cast)[name = tensor("op_11749_cast")]; + tensor inputs_363_cast = add(x = var_11749_cast, y = inputs_361_cast)[name = tensor("inputs_363_cast")]; + tensor var_11753 = const()[name = tensor("op_11753"), val = tensor([1])]; + tensor channels_mean_363_cast = reduce_mean(axes = var_11753, keep_dims = var_23, x = inputs_363_cast)[name = tensor("channels_mean_363_cast")]; + tensor zero_mean_363_cast = sub(x = inputs_363_cast, y = channels_mean_363_cast)[name = tensor("zero_mean_363_cast")]; + tensor zero_mean_sq_363_cast = mul(x = zero_mean_363_cast, y = zero_mean_363_cast)[name = tensor("zero_mean_sq_363_cast")]; + tensor var_11757 = const()[name = tensor("op_11757"), val = tensor([1])]; + tensor var_11758_cast = reduce_mean(axes = var_11757, keep_dims = var_23, x = zero_mean_sq_363_cast)[name = tensor("op_11758_cast")]; + tensor var_11759_to_fp16 = const()[name = tensor("op_11759_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11760_cast = add(x = var_11758_cast, y = var_11759_to_fp16)[name = tensor("op_11760_cast")]; + tensor denom_363_epsilon_0_to_fp16 = const()[name = tensor("denom_363_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_363_cast = rsqrt(epsilon = denom_363_epsilon_0_to_fp16, x = var_11760_cast)[name = tensor("denom_363_cast")]; + tensor out_363_cast = mul(x = zero_mean_363_cast, y = denom_363_cast)[name = tensor("out_363_cast")]; + tensor var_11764_to_fp16 = const()[name = tensor("op_11764_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4602284096)))]; + tensor var_11765_cast = add(x = out_363_cast, y = var_11764_to_fp16)[name = tensor("op_11765_cast")]; + tensor var_11767_to_fp16 = const()[name = tensor("op_11767_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4602286720)))]; + tensor hidden_states_473_cast = mul(x = var_11765_cast, y = var_11767_to_fp16)[name = tensor("hidden_states_473_cast")]; + tensor var_11774 = const()[name = tensor("op_11774"), val = tensor([1, 1])]; + tensor var_11776 = const()[name = tensor("op_11776"), val = tensor([1, 1])]; tensor q_243_pad_type_0 = const()[name = tensor("q_243_pad_type_0"), val = tensor("custom")]; tensor q_243_pad_0 = const()[name = tensor("q_243_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_243 = conv(dilations = var_11875, groups = var_6872, pad = q_243_pad_0, pad_type = q_243_pad_type_0, strides = var_11873, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight, x = hidden_states_473)[name = tensor("q_243")]; - tensor var_11879 = const()[name = tensor("op_11879"), val = tensor([1, 1])]; - tensor var_11881 = const()[name = tensor("op_11881"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4602289344)))]; + tensor q_243_cast = conv(dilations = var_11776, groups = var_31, pad = q_243_pad_0, pad_type = q_243_pad_type_0, strides = var_11774, weight = unet_up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_q_weight_to_fp16, x = hidden_states_473_cast)[name = tensor("q_243_cast")]; + tensor var_11780 = const()[name = tensor("op_11780"), val = tensor([1, 1])]; + tensor var_11782 = const()[name = tensor("op_11782"), val = tensor([1, 1])]; tensor k_243_pad_type_0 = const()[name = tensor("k_243_pad_type_0"), val = tensor("custom")]; tensor k_243_pad_0 = const()[name = tensor("k_243_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_243 = conv(dilations = var_11881, groups = var_6872, pad = k_243_pad_0, pad_type = k_243_pad_type_0, strides = var_11879, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_243")]; - tensor var_11885 = const()[name = tensor("op_11885"), val = tensor([1, 1])]; - tensor var_11887 = const()[name = tensor("op_11887"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4605566208)))]; + tensor k_243_cast = conv(dilations = var_11782, groups = var_31, pad = k_243_pad_0, pad_type = k_243_pad_type_0, strides = var_11780, weight = unet_up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_243_cast")]; + tensor var_11786 = const()[name = tensor("op_11786"), val = tensor([1, 1])]; + tensor var_11788 = const()[name = tensor("op_11788"), val = tensor([1, 1])]; tensor v_243_pad_type_0 = const()[name = tensor("v_243_pad_type_0"), val = tensor("custom")]; tensor v_243_pad_0 = const()[name = tensor("v_243_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_243 = conv(dilations = var_11887, groups = var_6872, pad = v_243_pad_0, pad_type = v_243_pad_type_0, strides = var_11885, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_243")]; - tensor var_11891 = const()[name = tensor("op_11891"), val = tensor([2, 20, 64, -1])]; - tensor var_11892 = reshape(shape = var_11891, x = q_243)[name = tensor("op_11892")]; - tensor var_11893 = const()[name = tensor("op_11893"), val = tensor([2, 20, 64, -1])]; - tensor var_11894 = reshape(shape = var_11893, x = k_243)[name = tensor("op_11894")]; - tensor var_11895 = const()[name = tensor("op_11895"), val = tensor([2, 20, 64, -1])]; - tensor var_11896 = reshape(shape = var_11895, x = v_243)[name = tensor("op_11896")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4610809152)))]; + tensor v_243_cast = conv(dilations = var_11788, groups = var_31, pad = v_243_pad_0, pad_type = v_243_pad_type_0, strides = var_11786, weight = unet_up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_243_cast")]; + tensor var_11792 = const()[name = tensor("op_11792"), val = tensor([2, 20, 64, -1])]; + tensor var_11793_cast = reshape(shape = var_11792, x = q_243_cast)[name = tensor("op_11793_cast")]; + tensor var_11794 = const()[name = tensor("op_11794"), val = tensor([2, 20, 64, -1])]; + tensor var_11795_cast = reshape(shape = var_11794, x = k_243_cast)[name = tensor("op_11795_cast")]; + tensor var_11796 = const()[name = tensor("op_11796"), val = tensor([2, 20, 64, -1])]; + tensor var_11797_cast = reshape(shape = var_11796, x = v_243_cast)[name = tensor("op_11797_cast")]; tensor attn_weights_485_transpose_x_0 = const()[name = tensor("attn_weights_485_transpose_x_0"), val = tensor(true)]; tensor attn_weights_485_transpose_y_0 = const()[name = tensor("attn_weights_485_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_485 = matmul(transpose_x = attn_weights_485_transpose_x_0, transpose_y = attn_weights_485_transpose_y_0, x = var_11892, y = var_11894)[name = tensor("attn_weights_485")]; - tensor attn_weights_487 = mul(x = attn_weights_485, y = var_6863)[name = tensor("attn_weights_487")]; - tensor var_11900 = softmax(axis = var_6856, x = attn_weights_487)[name = tensor("op_11900")]; + tensor attn_weights_485_cast = matmul(transpose_x = attn_weights_485_transpose_x_0, transpose_y = attn_weights_485_transpose_y_0, x = var_11793_cast, y = var_11795_cast)[name = tensor("attn_weights_485_cast")]; + tensor attn_weights_487_cast = mul(x = attn_weights_485_cast, y = var_12_to_fp16)[name = tensor("attn_weights_487_cast")]; + tensor var_11801_cast = softmax(axis = var_18, x = attn_weights_487_cast)[name = tensor("op_11801_cast")]; tensor attn_243_transpose_x_0 = const()[name = tensor("attn_243_transpose_x_0"), val = tensor(false)]; tensor attn_243_transpose_y_0 = const()[name = tensor("attn_243_transpose_y_0"), val = tensor(true)]; - tensor attn_243 = matmul(transpose_x = attn_243_transpose_x_0, transpose_y = attn_243_transpose_y_0, x = var_11896, y = var_11900)[name = tensor("attn_243")]; - tensor var_11904 = const()[name = tensor("op_11904"), val = tensor([2, 1280, 1, -1])]; - tensor input_685 = reshape(shape = var_11904, x = attn_243)[name = tensor("input_685")]; - tensor var_11909 = const()[name = tensor("op_11909"), val = tensor([1, 1])]; - tensor var_11911 = const()[name = tensor("op_11911"), val = tensor([1, 1])]; - tensor var_11913_pad_type_0 = const()[name = tensor("op_11913_pad_type_0"), val = tensor("custom")]; - tensor var_11913_pad_0 = const()[name = tensor("op_11913_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11913 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias, dilations = var_11911, groups = var_6872, pad = var_11913_pad_0, pad_type = var_11913_pad_type_0, strides = var_11909, weight = up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight, x = input_685)[name = tensor("op_11913")]; - tensor inputs_365 = add(x = var_11913, y = inputs_363)[name = tensor("inputs_365")]; - tensor var_11917 = const()[name = tensor("op_11917"), val = tensor([1])]; - tensor channels_mean_365 = reduce_mean(axes = var_11917, keep_dims = var_6867, x = inputs_365)[name = tensor("channels_mean_365")]; - tensor zero_mean_365 = sub(x = inputs_365, y = channels_mean_365)[name = tensor("zero_mean_365")]; - tensor zero_mean_sq_365 = mul(x = zero_mean_365, y = zero_mean_365)[name = tensor("zero_mean_sq_365")]; - tensor var_11921 = const()[name = tensor("op_11921"), val = tensor([1])]; - tensor var_11922 = reduce_mean(axes = var_11921, keep_dims = var_6867, x = zero_mean_sq_365)[name = tensor("op_11922")]; - tensor var_11923 = const()[name = tensor("op_11923"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11924 = add(x = var_11922, y = var_11923)[name = tensor("op_11924")]; - tensor denom_365_epsilon_0 = const()[name = tensor("denom_365_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_365 = rsqrt(epsilon = denom_365_epsilon_0, x = var_11924)[name = tensor("denom_365")]; - tensor out_365 = mul(x = zero_mean_365, y = denom_365)[name = tensor("out_365")]; - tensor var_11928 = const()[name = tensor("op_11928"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269725568)))]; - tensor var_11929 = add(x = out_365, y = var_11928)[name = tensor("op_11929")]; - tensor var_11931 = const()[name = tensor("op_11931"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269730752)))]; - tensor input_687 = mul(x = var_11929, y = var_11931)[name = tensor("input_687")]; - tensor var_11939 = const()[name = tensor("op_11939"), val = tensor([1, 1])]; - tensor var_11941 = const()[name = tensor("op_11941"), val = tensor([1, 1])]; - tensor var_11943_pad_type_0 = const()[name = tensor("op_11943_pad_type_0"), val = tensor("custom")]; - tensor var_11943_pad_0 = const()[name = tensor("op_11943_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11943 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias, dilations = var_11941, groups = var_6872, pad = var_11943_pad_0, pad_type = var_11943_pad_type_0, strides = var_11939, weight = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight, x = input_687)[name = tensor("op_11943")]; - tensor var_11944_split_sizes_0 = const()[name = tensor("op_11944_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_11944_axis_0 = const()[name = tensor("op_11944_axis_0"), val = tensor(1)]; - tensor var_11944_0, tensor var_11944_1 = split(axis = var_11944_axis_0, split_sizes = var_11944_split_sizes_0, x = var_11943)[name = tensor("op_11944")]; - tensor var_11946_mode_0 = const()[name = tensor("op_11946_mode_0"), val = tensor("EXACT")]; - tensor var_11946 = gelu(mode = var_11946_mode_0, x = var_11944_1)[name = tensor("op_11946")]; - tensor input_689 = mul(x = var_11944_0, y = var_11946)[name = tensor("input_689")]; - tensor var_11950 = const()[name = tensor("op_11950"), val = tensor([1, 1])]; - tensor var_11952 = const()[name = tensor("op_11952"), val = tensor([1, 1])]; - tensor var_11954_pad_type_0 = const()[name = tensor("op_11954_pad_type_0"), val = tensor("custom")]; - tensor var_11954_pad_0 = const()[name = tensor("op_11954_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_11954 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias, dilations = var_11952, groups = var_6872, pad = var_11954_pad_0, pad_type = var_11954_pad_type_0, strides = var_11950, weight = up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight, x = input_689)[name = tensor("op_11954")]; - tensor inputs_367 = add(x = var_11954, y = inputs_365)[name = tensor("inputs_367")]; - tensor var_11964 = const()[name = tensor("op_11964"), val = tensor([1])]; - tensor channels_mean_367 = reduce_mean(axes = var_11964, keep_dims = var_6867, x = inputs_367)[name = tensor("channels_mean_367")]; - tensor zero_mean_367 = sub(x = inputs_367, y = channels_mean_367)[name = tensor("zero_mean_367")]; - tensor zero_mean_sq_367 = mul(x = zero_mean_367, y = zero_mean_367)[name = tensor("zero_mean_sq_367")]; - tensor var_11968 = const()[name = tensor("op_11968"), val = tensor([1])]; - tensor var_11969 = reduce_mean(axes = var_11968, keep_dims = var_6867, x = zero_mean_sq_367)[name = tensor("op_11969")]; - tensor var_11970 = const()[name = tensor("op_11970"), val = tensor(0x1.4f8b58p-17)]; - tensor var_11971 = add(x = var_11969, y = var_11970)[name = tensor("op_11971")]; - tensor denom_367_epsilon_0 = const()[name = tensor("denom_367_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_367 = rsqrt(epsilon = denom_367_epsilon_0, x = var_11971)[name = tensor("denom_367")]; - tensor out_367 = mul(x = zero_mean_367, y = denom_367)[name = tensor("out_367")]; - tensor var_11975 = const()[name = tensor("op_11975"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269735936)))]; - tensor var_11976 = add(x = out_367, y = var_11975)[name = tensor("op_11976")]; - tensor var_11978 = const()[name = tensor("op_11978"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269741120)))]; - tensor hidden_states_477 = mul(x = var_11976, y = var_11978)[name = tensor("hidden_states_477")]; - tensor var_11985 = const()[name = tensor("op_11985"), val = tensor([1, 1])]; - tensor var_11987 = const()[name = tensor("op_11987"), val = tensor([1, 1])]; + tensor attn_243_cast = matmul(transpose_x = attn_243_transpose_x_0, transpose_y = attn_243_transpose_y_0, x = var_11797_cast, y = var_11801_cast)[name = tensor("attn_243_cast")]; + tensor var_11805 = const()[name = tensor("op_11805"), val = tensor([2, 1280, 1, -1])]; + tensor input_685_cast = reshape(shape = var_11805, x = attn_243_cast)[name = tensor("input_685_cast")]; + tensor var_11810 = const()[name = tensor("op_11810"), val = tensor([1, 1])]; + tensor var_11812 = const()[name = tensor("op_11812"), val = tensor([1, 1])]; + tensor var_11814_pad_type_0 = const()[name = tensor("op_11814_pad_type_0"), val = tensor("custom")]; + tensor var_11814_pad_0 = const()[name = tensor("op_11814_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4616052096)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4619328960)))]; + tensor var_11814_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_bias_to_fp16, dilations = var_11812, groups = var_31, pad = var_11814_pad_0, pad_type = var_11814_pad_type_0, strides = var_11810, weight = unet_up_blocks_0_attentions_2_transformer_blocks_6_attn2_to_out_0_weight_to_fp16, x = input_685_cast)[name = tensor("op_11814_cast")]; + tensor inputs_365_cast = add(x = var_11814_cast, y = inputs_363_cast)[name = tensor("inputs_365_cast")]; + tensor var_11818 = const()[name = tensor("op_11818"), val = tensor([1])]; + tensor channels_mean_365_cast = reduce_mean(axes = var_11818, keep_dims = var_23, x = inputs_365_cast)[name = tensor("channels_mean_365_cast")]; + tensor zero_mean_365_cast = sub(x = inputs_365_cast, y = channels_mean_365_cast)[name = tensor("zero_mean_365_cast")]; + tensor zero_mean_sq_365_cast = mul(x = zero_mean_365_cast, y = zero_mean_365_cast)[name = tensor("zero_mean_sq_365_cast")]; + tensor var_11822 = const()[name = tensor("op_11822"), val = tensor([1])]; + tensor var_11823_cast = reduce_mean(axes = var_11822, keep_dims = var_23, x = zero_mean_sq_365_cast)[name = tensor("op_11823_cast")]; + tensor var_11824_to_fp16 = const()[name = tensor("op_11824_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11825_cast = add(x = var_11823_cast, y = var_11824_to_fp16)[name = tensor("op_11825_cast")]; + tensor denom_365_epsilon_0_to_fp16 = const()[name = tensor("denom_365_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_365_cast = rsqrt(epsilon = denom_365_epsilon_0_to_fp16, x = var_11825_cast)[name = tensor("denom_365_cast")]; + tensor out_365_cast = mul(x = zero_mean_365_cast, y = denom_365_cast)[name = tensor("out_365_cast")]; + tensor var_11829_to_fp16 = const()[name = tensor("op_11829_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4619331584)))]; + tensor var_11830_cast = add(x = out_365_cast, y = var_11829_to_fp16)[name = tensor("op_11830_cast")]; + tensor var_11832_to_fp16 = const()[name = tensor("op_11832_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4619334208)))]; + tensor input_687_cast = mul(x = var_11830_cast, y = var_11832_to_fp16)[name = tensor("input_687_cast")]; + tensor var_11840 = const()[name = tensor("op_11840"), val = tensor([1, 1])]; + tensor var_11842 = const()[name = tensor("op_11842"), val = tensor([1, 1])]; + tensor var_11844_pad_type_0 = const()[name = tensor("op_11844_pad_type_0"), val = tensor("custom")]; + tensor var_11844_pad_0 = const()[name = tensor("op_11844_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4619336832)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4645551296)))]; + tensor var_11844_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_bias_to_fp16, dilations = var_11842, groups = var_31, pad = var_11844_pad_0, pad_type = var_11844_pad_type_0, strides = var_11840, weight = unet_up_blocks_0_attentions_2_transformer_blocks_6_ff_net_0_proj_weight_to_fp16, x = input_687_cast)[name = tensor("op_11844_cast")]; + tensor var_11845_split_sizes_0 = const()[name = tensor("op_11845_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_11845_axis_0 = const()[name = tensor("op_11845_axis_0"), val = tensor(1)]; + tensor var_11845_cast_0, tensor var_11845_cast_1 = split(axis = var_11845_axis_0, split_sizes = var_11845_split_sizes_0, x = var_11844_cast)[name = tensor("op_11845_cast")]; + tensor var_11847_mode_0 = const()[name = tensor("op_11847_mode_0"), val = tensor("EXACT")]; + tensor var_11847_cast = gelu(mode = var_11847_mode_0, x = var_11845_cast_1)[name = tensor("op_11847_cast")]; + tensor input_689_cast = mul(x = var_11845_cast_0, y = var_11847_cast)[name = tensor("input_689_cast")]; + tensor var_11851 = const()[name = tensor("op_11851"), val = tensor([1, 1])]; + tensor var_11853 = const()[name = tensor("op_11853"), val = tensor([1, 1])]; + tensor var_11855_pad_type_0 = const()[name = tensor("op_11855_pad_type_0"), val = tensor("custom")]; + tensor var_11855_pad_0 = const()[name = tensor("op_11855_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4645571840)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4658679104)))]; + tensor var_11855_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_bias_to_fp16, dilations = var_11853, groups = var_31, pad = var_11855_pad_0, pad_type = var_11855_pad_type_0, strides = var_11851, weight = unet_up_blocks_0_attentions_2_transformer_blocks_6_ff_net_2_weight_to_fp16, x = input_689_cast)[name = tensor("op_11855_cast")]; + tensor inputs_367_cast = add(x = var_11855_cast, y = inputs_365_cast)[name = tensor("inputs_367_cast")]; + tensor var_11865 = const()[name = tensor("op_11865"), val = tensor([1])]; + tensor channels_mean_367_cast = reduce_mean(axes = var_11865, keep_dims = var_23, x = inputs_367_cast)[name = tensor("channels_mean_367_cast")]; + tensor zero_mean_367_cast = sub(x = inputs_367_cast, y = channels_mean_367_cast)[name = tensor("zero_mean_367_cast")]; + tensor zero_mean_sq_367_cast = mul(x = zero_mean_367_cast, y = zero_mean_367_cast)[name = tensor("zero_mean_sq_367_cast")]; + tensor var_11869 = const()[name = tensor("op_11869"), val = tensor([1])]; + tensor var_11870_cast = reduce_mean(axes = var_11869, keep_dims = var_23, x = zero_mean_sq_367_cast)[name = tensor("op_11870_cast")]; + tensor var_11871_to_fp16 = const()[name = tensor("op_11871_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11872_cast = add(x = var_11870_cast, y = var_11871_to_fp16)[name = tensor("op_11872_cast")]; + tensor denom_367_epsilon_0_to_fp16 = const()[name = tensor("denom_367_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_367_cast = rsqrt(epsilon = denom_367_epsilon_0_to_fp16, x = var_11872_cast)[name = tensor("denom_367_cast")]; + tensor out_367_cast = mul(x = zero_mean_367_cast, y = denom_367_cast)[name = tensor("out_367_cast")]; + tensor var_11876_to_fp16 = const()[name = tensor("op_11876_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4658681728)))]; + tensor var_11877_cast = add(x = out_367_cast, y = var_11876_to_fp16)[name = tensor("op_11877_cast")]; + tensor var_11879_to_fp16 = const()[name = tensor("op_11879_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4658684352)))]; + tensor hidden_states_477_cast = mul(x = var_11877_cast, y = var_11879_to_fp16)[name = tensor("hidden_states_477_cast")]; + tensor var_11886 = const()[name = tensor("op_11886"), val = tensor([1, 1])]; + tensor var_11888 = const()[name = tensor("op_11888"), val = tensor([1, 1])]; tensor q_245_pad_type_0 = const()[name = tensor("q_245_pad_type_0"), val = tensor("custom")]; tensor q_245_pad_0 = const()[name = tensor("q_245_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_245 = conv(dilations = var_11987, groups = var_6872, pad = q_245_pad_0, pad_type = q_245_pad_type_0, strides = var_11985, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight, x = hidden_states_477)[name = tensor("q_245")]; - tensor var_11991 = const()[name = tensor("op_11991"), val = tensor([1, 1])]; - tensor var_11993 = const()[name = tensor("op_11993"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4658686976)))]; + tensor q_245_cast = conv(dilations = var_11888, groups = var_31, pad = q_245_pad_0, pad_type = q_245_pad_type_0, strides = var_11886, weight = unet_up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_q_weight_to_fp16, x = hidden_states_477_cast)[name = tensor("q_245_cast")]; + tensor var_11892 = const()[name = tensor("op_11892"), val = tensor([1, 1])]; + tensor var_11894 = const()[name = tensor("op_11894"), val = tensor([1, 1])]; tensor k_245_pad_type_0 = const()[name = tensor("k_245_pad_type_0"), val = tensor("custom")]; tensor k_245_pad_0 = const()[name = tensor("k_245_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_245 = conv(dilations = var_11993, groups = var_6872, pad = k_245_pad_0, pad_type = k_245_pad_type_0, strides = var_11991, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight, x = hidden_states_477)[name = tensor("k_245")]; - tensor var_11997 = const()[name = tensor("op_11997"), val = tensor([1, 1])]; - tensor var_11999 = const()[name = tensor("op_11999"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4661963840)))]; + tensor k_245_cast = conv(dilations = var_11894, groups = var_31, pad = k_245_pad_0, pad_type = k_245_pad_type_0, strides = var_11892, weight = unet_up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_k_weight_to_fp16, x = hidden_states_477_cast)[name = tensor("k_245_cast")]; + tensor var_11898 = const()[name = tensor("op_11898"), val = tensor([1, 1])]; + tensor var_11900 = const()[name = tensor("op_11900"), val = tensor([1, 1])]; tensor v_245_pad_type_0 = const()[name = tensor("v_245_pad_type_0"), val = tensor("custom")]; tensor v_245_pad_0 = const()[name = tensor("v_245_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_245 = conv(dilations = var_11999, groups = var_6872, pad = v_245_pad_0, pad_type = v_245_pad_type_0, strides = var_11997, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight, x = hidden_states_477)[name = tensor("v_245")]; - tensor var_12003 = const()[name = tensor("op_12003"), val = tensor([2, 20, 64, -1])]; - tensor var_12004 = reshape(shape = var_12003, x = q_245)[name = tensor("op_12004")]; - tensor var_12005 = const()[name = tensor("op_12005"), val = tensor([2, 20, 64, -1])]; - tensor var_12006 = reshape(shape = var_12005, x = k_245)[name = tensor("op_12006")]; - tensor var_12007 = const()[name = tensor("op_12007"), val = tensor([2, 20, 64, -1])]; - tensor var_12008 = reshape(shape = var_12007, x = v_245)[name = tensor("op_12008")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4665240704)))]; + tensor v_245_cast = conv(dilations = var_11900, groups = var_31, pad = v_245_pad_0, pad_type = v_245_pad_type_0, strides = var_11898, weight = unet_up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_v_weight_to_fp16, x = hidden_states_477_cast)[name = tensor("v_245_cast")]; + tensor var_11904 = const()[name = tensor("op_11904"), val = tensor([2, 20, 64, -1])]; + tensor var_11905_cast = reshape(shape = var_11904, x = q_245_cast)[name = tensor("op_11905_cast")]; + tensor var_11906 = const()[name = tensor("op_11906"), val = tensor([2, 20, 64, -1])]; + tensor var_11907_cast = reshape(shape = var_11906, x = k_245_cast)[name = tensor("op_11907_cast")]; + tensor var_11908 = const()[name = tensor("op_11908"), val = tensor([2, 20, 64, -1])]; + tensor var_11909_cast = reshape(shape = var_11908, x = v_245_cast)[name = tensor("op_11909_cast")]; tensor attn_weights_489_transpose_x_0 = const()[name = tensor("attn_weights_489_transpose_x_0"), val = tensor(true)]; tensor attn_weights_489_transpose_y_0 = const()[name = tensor("attn_weights_489_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_489 = matmul(transpose_x = attn_weights_489_transpose_x_0, transpose_y = attn_weights_489_transpose_y_0, x = var_12004, y = var_12006)[name = tensor("attn_weights_489")]; - tensor attn_weights_491 = mul(x = attn_weights_489, y = var_6863)[name = tensor("attn_weights_491")]; - tensor var_12012 = softmax(axis = var_6856, x = attn_weights_491)[name = tensor("op_12012")]; + tensor attn_weights_489_cast = matmul(transpose_x = attn_weights_489_transpose_x_0, transpose_y = attn_weights_489_transpose_y_0, x = var_11905_cast, y = var_11907_cast)[name = tensor("attn_weights_489_cast")]; + tensor attn_weights_491_cast = mul(x = attn_weights_489_cast, y = var_12_to_fp16)[name = tensor("attn_weights_491_cast")]; + tensor var_11913_cast = softmax(axis = var_18, x = attn_weights_491_cast)[name = tensor("op_11913_cast")]; tensor attn_245_transpose_x_0 = const()[name = tensor("attn_245_transpose_x_0"), val = tensor(false)]; tensor attn_245_transpose_y_0 = const()[name = tensor("attn_245_transpose_y_0"), val = tensor(true)]; - tensor attn_245 = matmul(transpose_x = attn_245_transpose_x_0, transpose_y = attn_245_transpose_y_0, x = var_12008, y = var_12012)[name = tensor("attn_245")]; - tensor var_12016 = const()[name = tensor("op_12016"), val = tensor([2, 1280, 1, -1])]; - tensor input_691 = reshape(shape = var_12016, x = attn_245)[name = tensor("input_691")]; - tensor var_12021 = const()[name = tensor("op_12021"), val = tensor([1, 1])]; - tensor var_12023 = const()[name = tensor("op_12023"), val = tensor([1, 1])]; - tensor var_12025_pad_type_0 = const()[name = tensor("op_12025_pad_type_0"), val = tensor("custom")]; - tensor var_12025_pad_0 = const()[name = tensor("op_12025_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12025 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias, dilations = var_12023, groups = var_6872, pad = var_12025_pad_0, pad_type = var_12025_pad_type_0, strides = var_12021, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight, x = input_691)[name = tensor("op_12025")]; - tensor inputs_369 = add(x = var_12025, y = inputs_367)[name = tensor("inputs_369")]; - tensor var_12029 = const()[name = tensor("op_12029"), val = tensor([1])]; - tensor channels_mean_369 = reduce_mean(axes = var_12029, keep_dims = var_6867, x = inputs_369)[name = tensor("channels_mean_369")]; - tensor zero_mean_369 = sub(x = inputs_369, y = channels_mean_369)[name = tensor("zero_mean_369")]; - tensor zero_mean_sq_369 = mul(x = zero_mean_369, y = zero_mean_369)[name = tensor("zero_mean_sq_369")]; - tensor var_12033 = const()[name = tensor("op_12033"), val = tensor([1])]; - tensor var_12034 = reduce_mean(axes = var_12033, keep_dims = var_6867, x = zero_mean_sq_369)[name = tensor("op_12034")]; - tensor var_12035 = const()[name = tensor("op_12035"), val = tensor(0x1.4f8b58p-17)]; - tensor var_12036 = add(x = var_12034, y = var_12035)[name = tensor("op_12036")]; - tensor denom_369_epsilon_0 = const()[name = tensor("denom_369_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_369 = rsqrt(epsilon = denom_369_epsilon_0, x = var_12036)[name = tensor("denom_369")]; - tensor out_369 = mul(x = zero_mean_369, y = denom_369)[name = tensor("out_369")]; - tensor var_12040 = const()[name = tensor("op_12040"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269746304)))]; - tensor var_12041 = add(x = out_369, y = var_12040)[name = tensor("op_12041")]; - tensor var_12043 = const()[name = tensor("op_12043"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269751488)))]; - tensor hidden_states_479 = mul(x = var_12041, y = var_12043)[name = tensor("hidden_states_479")]; - tensor var_12050 = const()[name = tensor("op_12050"), val = tensor([1, 1])]; - tensor var_12052 = const()[name = tensor("op_12052"), val = tensor([1, 1])]; + tensor attn_245_cast = matmul(transpose_x = attn_245_transpose_x_0, transpose_y = attn_245_transpose_y_0, x = var_11909_cast, y = var_11913_cast)[name = tensor("attn_245_cast")]; + tensor var_11917 = const()[name = tensor("op_11917"), val = tensor([2, 1280, 1, -1])]; + tensor input_691_cast = reshape(shape = var_11917, x = attn_245_cast)[name = tensor("input_691_cast")]; + tensor var_11922 = const()[name = tensor("op_11922"), val = tensor([1, 1])]; + tensor var_11924 = const()[name = tensor("op_11924"), val = tensor([1, 1])]; + tensor var_11926_pad_type_0 = const()[name = tensor("op_11926_pad_type_0"), val = tensor("custom")]; + tensor var_11926_pad_0 = const()[name = tensor("op_11926_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4668517568)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4671794432)))]; + tensor var_11926_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_bias_to_fp16, dilations = var_11924, groups = var_31, pad = var_11926_pad_0, pad_type = var_11926_pad_type_0, strides = var_11922, weight = unet_up_blocks_0_attentions_2_transformer_blocks_7_attn1_to_out_0_weight_to_fp16, x = input_691_cast)[name = tensor("op_11926_cast")]; + tensor inputs_369_cast = add(x = var_11926_cast, y = inputs_367_cast)[name = tensor("inputs_369_cast")]; + tensor var_11930 = const()[name = tensor("op_11930"), val = tensor([1])]; + tensor channels_mean_369_cast = reduce_mean(axes = var_11930, keep_dims = var_23, x = inputs_369_cast)[name = tensor("channels_mean_369_cast")]; + tensor zero_mean_369_cast = sub(x = inputs_369_cast, y = channels_mean_369_cast)[name = tensor("zero_mean_369_cast")]; + tensor zero_mean_sq_369_cast = mul(x = zero_mean_369_cast, y = zero_mean_369_cast)[name = tensor("zero_mean_sq_369_cast")]; + tensor var_11934 = const()[name = tensor("op_11934"), val = tensor([1])]; + tensor var_11935_cast = reduce_mean(axes = var_11934, keep_dims = var_23, x = zero_mean_sq_369_cast)[name = tensor("op_11935_cast")]; + tensor var_11936_to_fp16 = const()[name = tensor("op_11936_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_11937_cast = add(x = var_11935_cast, y = var_11936_to_fp16)[name = tensor("op_11937_cast")]; + tensor denom_369_epsilon_0_to_fp16 = const()[name = tensor("denom_369_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_369_cast = rsqrt(epsilon = denom_369_epsilon_0_to_fp16, x = var_11937_cast)[name = tensor("denom_369_cast")]; + tensor out_369_cast = mul(x = zero_mean_369_cast, y = denom_369_cast)[name = tensor("out_369_cast")]; + tensor var_11941_to_fp16 = const()[name = tensor("op_11941_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4671797056)))]; + tensor var_11942_cast = add(x = out_369_cast, y = var_11941_to_fp16)[name = tensor("op_11942_cast")]; + tensor var_11944_to_fp16 = const()[name = tensor("op_11944_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4671799680)))]; + tensor hidden_states_479_cast = mul(x = var_11942_cast, y = var_11944_to_fp16)[name = tensor("hidden_states_479_cast")]; + tensor var_11951 = const()[name = tensor("op_11951"), val = tensor([1, 1])]; + tensor var_11953 = const()[name = tensor("op_11953"), val = tensor([1, 1])]; tensor q_247_pad_type_0 = const()[name = tensor("q_247_pad_type_0"), val = tensor("custom")]; tensor q_247_pad_0 = const()[name = tensor("q_247_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_247 = conv(dilations = var_12052, groups = var_6872, pad = q_247_pad_0, pad_type = q_247_pad_type_0, strides = var_12050, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight, x = hidden_states_479)[name = tensor("q_247")]; - tensor var_12056 = const()[name = tensor("op_12056"), val = tensor([1, 1])]; - tensor var_12058 = const()[name = tensor("op_12058"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4671802304)))]; + tensor q_247_cast = conv(dilations = var_11953, groups = var_31, pad = q_247_pad_0, pad_type = q_247_pad_type_0, strides = var_11951, weight = unet_up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_q_weight_to_fp16, x = hidden_states_479_cast)[name = tensor("q_247_cast")]; + tensor var_11957 = const()[name = tensor("op_11957"), val = tensor([1, 1])]; + tensor var_11959 = const()[name = tensor("op_11959"), val = tensor([1, 1])]; tensor k_247_pad_type_0 = const()[name = tensor("k_247_pad_type_0"), val = tensor("custom")]; tensor k_247_pad_0 = const()[name = tensor("k_247_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_247 = conv(dilations = var_12058, groups = var_6872, pad = k_247_pad_0, pad_type = k_247_pad_type_0, strides = var_12056, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_247")]; - tensor var_12062 = const()[name = tensor("op_12062"), val = tensor([1, 1])]; - tensor var_12064 = const()[name = tensor("op_12064"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4675079168)))]; + tensor k_247_cast = conv(dilations = var_11959, groups = var_31, pad = k_247_pad_0, pad_type = k_247_pad_type_0, strides = var_11957, weight = unet_up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_247_cast")]; + tensor var_11963 = const()[name = tensor("op_11963"), val = tensor([1, 1])]; + tensor var_11965 = const()[name = tensor("op_11965"), val = tensor([1, 1])]; tensor v_247_pad_type_0 = const()[name = tensor("v_247_pad_type_0"), val = tensor("custom")]; tensor v_247_pad_0 = const()[name = tensor("v_247_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_247 = conv(dilations = var_12064, groups = var_6872, pad = v_247_pad_0, pad_type = v_247_pad_type_0, strides = var_12062, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_247")]; - tensor var_12068 = const()[name = tensor("op_12068"), val = tensor([2, 20, 64, -1])]; - tensor var_12069 = reshape(shape = var_12068, x = q_247)[name = tensor("op_12069")]; - tensor var_12070 = const()[name = tensor("op_12070"), val = tensor([2, 20, 64, -1])]; - tensor var_12071 = reshape(shape = var_12070, x = k_247)[name = tensor("op_12071")]; - tensor var_12072 = const()[name = tensor("op_12072"), val = tensor([2, 20, 64, -1])]; - tensor var_12073 = reshape(shape = var_12072, x = v_247)[name = tensor("op_12073")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4680322112)))]; + tensor v_247_cast = conv(dilations = var_11965, groups = var_31, pad = v_247_pad_0, pad_type = v_247_pad_type_0, strides = var_11963, weight = unet_up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_247_cast")]; + tensor var_11969 = const()[name = tensor("op_11969"), val = tensor([2, 20, 64, -1])]; + tensor var_11970_cast = reshape(shape = var_11969, x = q_247_cast)[name = tensor("op_11970_cast")]; + tensor var_11971 = const()[name = tensor("op_11971"), val = tensor([2, 20, 64, -1])]; + tensor var_11972_cast = reshape(shape = var_11971, x = k_247_cast)[name = tensor("op_11972_cast")]; + tensor var_11973 = const()[name = tensor("op_11973"), val = tensor([2, 20, 64, -1])]; + tensor var_11974_cast = reshape(shape = var_11973, x = v_247_cast)[name = tensor("op_11974_cast")]; tensor attn_weights_493_transpose_x_0 = const()[name = tensor("attn_weights_493_transpose_x_0"), val = tensor(true)]; tensor attn_weights_493_transpose_y_0 = const()[name = tensor("attn_weights_493_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_493 = matmul(transpose_x = attn_weights_493_transpose_x_0, transpose_y = attn_weights_493_transpose_y_0, x = var_12069, y = var_12071)[name = tensor("attn_weights_493")]; - tensor attn_weights_495 = mul(x = attn_weights_493, y = var_6863)[name = tensor("attn_weights_495")]; - tensor var_12077 = softmax(axis = var_6856, x = attn_weights_495)[name = tensor("op_12077")]; + tensor attn_weights_493_cast = matmul(transpose_x = attn_weights_493_transpose_x_0, transpose_y = attn_weights_493_transpose_y_0, x = var_11970_cast, y = var_11972_cast)[name = tensor("attn_weights_493_cast")]; + tensor attn_weights_495_cast = mul(x = attn_weights_493_cast, y = var_12_to_fp16)[name = tensor("attn_weights_495_cast")]; + tensor var_11978_cast = softmax(axis = var_18, x = attn_weights_495_cast)[name = tensor("op_11978_cast")]; tensor attn_247_transpose_x_0 = const()[name = tensor("attn_247_transpose_x_0"), val = tensor(false)]; tensor attn_247_transpose_y_0 = const()[name = tensor("attn_247_transpose_y_0"), val = tensor(true)]; - tensor attn_247 = matmul(transpose_x = attn_247_transpose_x_0, transpose_y = attn_247_transpose_y_0, x = var_12073, y = var_12077)[name = tensor("attn_247")]; - tensor var_12081 = const()[name = tensor("op_12081"), val = tensor([2, 1280, 1, -1])]; - tensor input_693 = reshape(shape = var_12081, x = attn_247)[name = tensor("input_693")]; - tensor var_12086 = const()[name = tensor("op_12086"), val = tensor([1, 1])]; - tensor var_12088 = const()[name = tensor("op_12088"), val = tensor([1, 1])]; - tensor var_12090_pad_type_0 = const()[name = tensor("op_12090_pad_type_0"), val = tensor("custom")]; - tensor var_12090_pad_0 = const()[name = tensor("op_12090_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12090 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias, dilations = var_12088, groups = var_6872, pad = var_12090_pad_0, pad_type = var_12090_pad_type_0, strides = var_12086, weight = up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight, x = input_693)[name = tensor("op_12090")]; - tensor inputs_371 = add(x = var_12090, y = inputs_369)[name = tensor("inputs_371")]; - tensor var_12094 = const()[name = tensor("op_12094"), val = tensor([1])]; - tensor channels_mean_371 = reduce_mean(axes = var_12094, keep_dims = var_6867, x = inputs_371)[name = tensor("channels_mean_371")]; - tensor zero_mean_371 = sub(x = inputs_371, y = channels_mean_371)[name = tensor("zero_mean_371")]; - tensor zero_mean_sq_371 = mul(x = zero_mean_371, y = zero_mean_371)[name = tensor("zero_mean_sq_371")]; - tensor var_12098 = const()[name = tensor("op_12098"), val = tensor([1])]; - tensor var_12099 = reduce_mean(axes = var_12098, keep_dims = var_6867, x = zero_mean_sq_371)[name = tensor("op_12099")]; - tensor var_12100 = const()[name = tensor("op_12100"), val = tensor(0x1.4f8b58p-17)]; - tensor var_12101 = add(x = var_12099, y = var_12100)[name = tensor("op_12101")]; - tensor denom_371_epsilon_0 = const()[name = tensor("denom_371_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_371 = rsqrt(epsilon = denom_371_epsilon_0, x = var_12101)[name = tensor("denom_371")]; - tensor out_371 = mul(x = zero_mean_371, y = denom_371)[name = tensor("out_371")]; - tensor var_12105 = const()[name = tensor("op_12105"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269756672)))]; - tensor var_12106 = add(x = out_371, y = var_12105)[name = tensor("op_12106")]; - tensor var_12108 = const()[name = tensor("op_12108"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269761856)))]; - tensor input_695 = mul(x = var_12106, y = var_12108)[name = tensor("input_695")]; - tensor var_12116 = const()[name = tensor("op_12116"), val = tensor([1, 1])]; - tensor var_12118 = const()[name = tensor("op_12118"), val = tensor([1, 1])]; - tensor var_12120_pad_type_0 = const()[name = tensor("op_12120_pad_type_0"), val = tensor("custom")]; - tensor var_12120_pad_0 = const()[name = tensor("op_12120_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12120 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias, dilations = var_12118, groups = var_6872, pad = var_12120_pad_0, pad_type = var_12120_pad_type_0, strides = var_12116, weight = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight, x = input_695)[name = tensor("op_12120")]; - tensor var_12121_split_sizes_0 = const()[name = tensor("op_12121_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_12121_axis_0 = const()[name = tensor("op_12121_axis_0"), val = tensor(1)]; - tensor var_12121_0, tensor var_12121_1 = split(axis = var_12121_axis_0, split_sizes = var_12121_split_sizes_0, x = var_12120)[name = tensor("op_12121")]; - tensor var_12123_mode_0 = const()[name = tensor("op_12123_mode_0"), val = tensor("EXACT")]; - tensor var_12123 = gelu(mode = var_12123_mode_0, x = var_12121_1)[name = tensor("op_12123")]; - tensor input_697 = mul(x = var_12121_0, y = var_12123)[name = tensor("input_697")]; - tensor var_12127 = const()[name = tensor("op_12127"), val = tensor([1, 1])]; - tensor var_12129 = const()[name = tensor("op_12129"), val = tensor([1, 1])]; - tensor var_12131_pad_type_0 = const()[name = tensor("op_12131_pad_type_0"), val = tensor("custom")]; - tensor var_12131_pad_0 = const()[name = tensor("op_12131_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12131 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias, dilations = var_12129, groups = var_6872, pad = var_12131_pad_0, pad_type = var_12131_pad_type_0, strides = var_12127, weight = up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight, x = input_697)[name = tensor("op_12131")]; - tensor inputs_373 = add(x = var_12131, y = inputs_371)[name = tensor("inputs_373")]; - tensor var_12141 = const()[name = tensor("op_12141"), val = tensor([1])]; - tensor channels_mean_373 = reduce_mean(axes = var_12141, keep_dims = var_6867, x = inputs_373)[name = tensor("channels_mean_373")]; - tensor zero_mean_373 = sub(x = inputs_373, y = channels_mean_373)[name = tensor("zero_mean_373")]; - tensor zero_mean_sq_373 = mul(x = zero_mean_373, y = zero_mean_373)[name = tensor("zero_mean_sq_373")]; - tensor var_12145 = const()[name = tensor("op_12145"), val = tensor([1])]; - tensor var_12146 = reduce_mean(axes = var_12145, keep_dims = var_6867, x = zero_mean_sq_373)[name = tensor("op_12146")]; - tensor var_12147 = const()[name = tensor("op_12147"), val = tensor(0x1.4f8b58p-17)]; - tensor var_12148 = add(x = var_12146, y = var_12147)[name = tensor("op_12148")]; - tensor denom_373_epsilon_0 = const()[name = tensor("denom_373_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_373 = rsqrt(epsilon = denom_373_epsilon_0, x = var_12148)[name = tensor("denom_373")]; - tensor out_373 = mul(x = zero_mean_373, y = denom_373)[name = tensor("out_373")]; - tensor var_12152 = const()[name = tensor("op_12152"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269767040)))]; - tensor var_12153 = add(x = out_373, y = var_12152)[name = tensor("op_12153")]; - tensor var_12155 = const()[name = tensor("op_12155"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269772224)))]; - tensor hidden_states_483 = mul(x = var_12153, y = var_12155)[name = tensor("hidden_states_483")]; - tensor var_12162 = const()[name = tensor("op_12162"), val = tensor([1, 1])]; - tensor var_12164 = const()[name = tensor("op_12164"), val = tensor([1, 1])]; + tensor attn_247_cast = matmul(transpose_x = attn_247_transpose_x_0, transpose_y = attn_247_transpose_y_0, x = var_11974_cast, y = var_11978_cast)[name = tensor("attn_247_cast")]; + tensor var_11982 = const()[name = tensor("op_11982"), val = tensor([2, 1280, 1, -1])]; + tensor input_693_cast = reshape(shape = var_11982, x = attn_247_cast)[name = tensor("input_693_cast")]; + tensor var_11987 = const()[name = tensor("op_11987"), val = tensor([1, 1])]; + tensor var_11989 = const()[name = tensor("op_11989"), val = tensor([1, 1])]; + tensor var_11991_pad_type_0 = const()[name = tensor("op_11991_pad_type_0"), val = tensor("custom")]; + tensor var_11991_pad_0 = const()[name = tensor("op_11991_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4685565056)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4688841920)))]; + tensor var_11991_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_bias_to_fp16, dilations = var_11989, groups = var_31, pad = var_11991_pad_0, pad_type = var_11991_pad_type_0, strides = var_11987, weight = unet_up_blocks_0_attentions_2_transformer_blocks_7_attn2_to_out_0_weight_to_fp16, x = input_693_cast)[name = tensor("op_11991_cast")]; + tensor inputs_371_cast = add(x = var_11991_cast, y = inputs_369_cast)[name = tensor("inputs_371_cast")]; + tensor var_11995 = const()[name = tensor("op_11995"), val = tensor([1])]; + tensor channels_mean_371_cast = reduce_mean(axes = var_11995, keep_dims = var_23, x = inputs_371_cast)[name = tensor("channels_mean_371_cast")]; + tensor zero_mean_371_cast = sub(x = inputs_371_cast, y = channels_mean_371_cast)[name = tensor("zero_mean_371_cast")]; + tensor zero_mean_sq_371_cast = mul(x = zero_mean_371_cast, y = zero_mean_371_cast)[name = tensor("zero_mean_sq_371_cast")]; + tensor var_11999 = const()[name = tensor("op_11999"), val = tensor([1])]; + tensor var_12000_cast = reduce_mean(axes = var_11999, keep_dims = var_23, x = zero_mean_sq_371_cast)[name = tensor("op_12000_cast")]; + tensor var_12001_to_fp16 = const()[name = tensor("op_12001_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12002_cast = add(x = var_12000_cast, y = var_12001_to_fp16)[name = tensor("op_12002_cast")]; + tensor denom_371_epsilon_0_to_fp16 = const()[name = tensor("denom_371_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_371_cast = rsqrt(epsilon = denom_371_epsilon_0_to_fp16, x = var_12002_cast)[name = tensor("denom_371_cast")]; + tensor out_371_cast = mul(x = zero_mean_371_cast, y = denom_371_cast)[name = tensor("out_371_cast")]; + tensor var_12006_to_fp16 = const()[name = tensor("op_12006_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4688844544)))]; + tensor var_12007_cast = add(x = out_371_cast, y = var_12006_to_fp16)[name = tensor("op_12007_cast")]; + tensor var_12009_to_fp16 = const()[name = tensor("op_12009_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4688847168)))]; + tensor input_695_cast = mul(x = var_12007_cast, y = var_12009_to_fp16)[name = tensor("input_695_cast")]; + tensor var_12017 = const()[name = tensor("op_12017"), val = tensor([1, 1])]; + tensor var_12019 = const()[name = tensor("op_12019"), val = tensor([1, 1])]; + tensor var_12021_pad_type_0 = const()[name = tensor("op_12021_pad_type_0"), val = tensor("custom")]; + tensor var_12021_pad_0 = const()[name = tensor("op_12021_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4688849792)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4715064256)))]; + tensor var_12021_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_bias_to_fp16, dilations = var_12019, groups = var_31, pad = var_12021_pad_0, pad_type = var_12021_pad_type_0, strides = var_12017, weight = unet_up_blocks_0_attentions_2_transformer_blocks_7_ff_net_0_proj_weight_to_fp16, x = input_695_cast)[name = tensor("op_12021_cast")]; + tensor var_12022_split_sizes_0 = const()[name = tensor("op_12022_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_12022_axis_0 = const()[name = tensor("op_12022_axis_0"), val = tensor(1)]; + tensor var_12022_cast_0, tensor var_12022_cast_1 = split(axis = var_12022_axis_0, split_sizes = var_12022_split_sizes_0, x = var_12021_cast)[name = tensor("op_12022_cast")]; + tensor var_12024_mode_0 = const()[name = tensor("op_12024_mode_0"), val = tensor("EXACT")]; + tensor var_12024_cast = gelu(mode = var_12024_mode_0, x = var_12022_cast_1)[name = tensor("op_12024_cast")]; + tensor input_697_cast = mul(x = var_12022_cast_0, y = var_12024_cast)[name = tensor("input_697_cast")]; + tensor var_12028 = const()[name = tensor("op_12028"), val = tensor([1, 1])]; + tensor var_12030 = const()[name = tensor("op_12030"), val = tensor([1, 1])]; + tensor var_12032_pad_type_0 = const()[name = tensor("op_12032_pad_type_0"), val = tensor("custom")]; + tensor var_12032_pad_0 = const()[name = tensor("op_12032_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4715084800)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4728192064)))]; + tensor var_12032_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_bias_to_fp16, dilations = var_12030, groups = var_31, pad = var_12032_pad_0, pad_type = var_12032_pad_type_0, strides = var_12028, weight = unet_up_blocks_0_attentions_2_transformer_blocks_7_ff_net_2_weight_to_fp16, x = input_697_cast)[name = tensor("op_12032_cast")]; + tensor inputs_373_cast = add(x = var_12032_cast, y = inputs_371_cast)[name = tensor("inputs_373_cast")]; + tensor var_12042 = const()[name = tensor("op_12042"), val = tensor([1])]; + tensor channels_mean_373_cast = reduce_mean(axes = var_12042, keep_dims = var_23, x = inputs_373_cast)[name = tensor("channels_mean_373_cast")]; + tensor zero_mean_373_cast = sub(x = inputs_373_cast, y = channels_mean_373_cast)[name = tensor("zero_mean_373_cast")]; + tensor zero_mean_sq_373_cast = mul(x = zero_mean_373_cast, y = zero_mean_373_cast)[name = tensor("zero_mean_sq_373_cast")]; + tensor var_12046 = const()[name = tensor("op_12046"), val = tensor([1])]; + tensor var_12047_cast = reduce_mean(axes = var_12046, keep_dims = var_23, x = zero_mean_sq_373_cast)[name = tensor("op_12047_cast")]; + tensor var_12048_to_fp16 = const()[name = tensor("op_12048_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12049_cast = add(x = var_12047_cast, y = var_12048_to_fp16)[name = tensor("op_12049_cast")]; + tensor denom_373_epsilon_0_to_fp16 = const()[name = tensor("denom_373_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_373_cast = rsqrt(epsilon = denom_373_epsilon_0_to_fp16, x = var_12049_cast)[name = tensor("denom_373_cast")]; + tensor out_373_cast = mul(x = zero_mean_373_cast, y = denom_373_cast)[name = tensor("out_373_cast")]; + tensor var_12053_to_fp16 = const()[name = tensor("op_12053_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4728194688)))]; + tensor var_12054_cast = add(x = out_373_cast, y = var_12053_to_fp16)[name = tensor("op_12054_cast")]; + tensor var_12056_to_fp16 = const()[name = tensor("op_12056_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4728197312)))]; + tensor hidden_states_483_cast = mul(x = var_12054_cast, y = var_12056_to_fp16)[name = tensor("hidden_states_483_cast")]; + tensor var_12063 = const()[name = tensor("op_12063"), val = tensor([1, 1])]; + tensor var_12065 = const()[name = tensor("op_12065"), val = tensor([1, 1])]; tensor q_249_pad_type_0 = const()[name = tensor("q_249_pad_type_0"), val = tensor("custom")]; tensor q_249_pad_0 = const()[name = tensor("q_249_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_249 = conv(dilations = var_12164, groups = var_6872, pad = q_249_pad_0, pad_type = q_249_pad_type_0, strides = var_12162, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight, x = hidden_states_483)[name = tensor("q_249")]; - tensor var_12168 = const()[name = tensor("op_12168"), val = tensor([1, 1])]; - tensor var_12170 = const()[name = tensor("op_12170"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4728199936)))]; + tensor q_249_cast = conv(dilations = var_12065, groups = var_31, pad = q_249_pad_0, pad_type = q_249_pad_type_0, strides = var_12063, weight = unet_up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_q_weight_to_fp16, x = hidden_states_483_cast)[name = tensor("q_249_cast")]; + tensor var_12069 = const()[name = tensor("op_12069"), val = tensor([1, 1])]; + tensor var_12071 = const()[name = tensor("op_12071"), val = tensor([1, 1])]; tensor k_249_pad_type_0 = const()[name = tensor("k_249_pad_type_0"), val = tensor("custom")]; tensor k_249_pad_0 = const()[name = tensor("k_249_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_249 = conv(dilations = var_12170, groups = var_6872, pad = k_249_pad_0, pad_type = k_249_pad_type_0, strides = var_12168, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight, x = hidden_states_483)[name = tensor("k_249")]; - tensor var_12174 = const()[name = tensor("op_12174"), val = tensor([1, 1])]; - tensor var_12176 = const()[name = tensor("op_12176"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4731476800)))]; + tensor k_249_cast = conv(dilations = var_12071, groups = var_31, pad = k_249_pad_0, pad_type = k_249_pad_type_0, strides = var_12069, weight = unet_up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_k_weight_to_fp16, x = hidden_states_483_cast)[name = tensor("k_249_cast")]; + tensor var_12075 = const()[name = tensor("op_12075"), val = tensor([1, 1])]; + tensor var_12077 = const()[name = tensor("op_12077"), val = tensor([1, 1])]; tensor v_249_pad_type_0 = const()[name = tensor("v_249_pad_type_0"), val = tensor("custom")]; tensor v_249_pad_0 = const()[name = tensor("v_249_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_249 = conv(dilations = var_12176, groups = var_6872, pad = v_249_pad_0, pad_type = v_249_pad_type_0, strides = var_12174, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight, x = hidden_states_483)[name = tensor("v_249")]; - tensor var_12180 = const()[name = tensor("op_12180"), val = tensor([2, 20, 64, -1])]; - tensor var_12181 = reshape(shape = var_12180, x = q_249)[name = tensor("op_12181")]; - tensor var_12182 = const()[name = tensor("op_12182"), val = tensor([2, 20, 64, -1])]; - tensor var_12183 = reshape(shape = var_12182, x = k_249)[name = tensor("op_12183")]; - tensor var_12184 = const()[name = tensor("op_12184"), val = tensor([2, 20, 64, -1])]; - tensor var_12185 = reshape(shape = var_12184, x = v_249)[name = tensor("op_12185")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4734753664)))]; + tensor v_249_cast = conv(dilations = var_12077, groups = var_31, pad = v_249_pad_0, pad_type = v_249_pad_type_0, strides = var_12075, weight = unet_up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_v_weight_to_fp16, x = hidden_states_483_cast)[name = tensor("v_249_cast")]; + tensor var_12081 = const()[name = tensor("op_12081"), val = tensor([2, 20, 64, -1])]; + tensor var_12082_cast = reshape(shape = var_12081, x = q_249_cast)[name = tensor("op_12082_cast")]; + tensor var_12083 = const()[name = tensor("op_12083"), val = tensor([2, 20, 64, -1])]; + tensor var_12084_cast = reshape(shape = var_12083, x = k_249_cast)[name = tensor("op_12084_cast")]; + tensor var_12085 = const()[name = tensor("op_12085"), val = tensor([2, 20, 64, -1])]; + tensor var_12086_cast = reshape(shape = var_12085, x = v_249_cast)[name = tensor("op_12086_cast")]; tensor attn_weights_497_transpose_x_0 = const()[name = tensor("attn_weights_497_transpose_x_0"), val = tensor(true)]; tensor attn_weights_497_transpose_y_0 = const()[name = tensor("attn_weights_497_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_497 = matmul(transpose_x = attn_weights_497_transpose_x_0, transpose_y = attn_weights_497_transpose_y_0, x = var_12181, y = var_12183)[name = tensor("attn_weights_497")]; - tensor attn_weights_499 = mul(x = attn_weights_497, y = var_6863)[name = tensor("attn_weights_499")]; - tensor var_12189 = softmax(axis = var_6856, x = attn_weights_499)[name = tensor("op_12189")]; + tensor attn_weights_497_cast = matmul(transpose_x = attn_weights_497_transpose_x_0, transpose_y = attn_weights_497_transpose_y_0, x = var_12082_cast, y = var_12084_cast)[name = tensor("attn_weights_497_cast")]; + tensor attn_weights_499_cast = mul(x = attn_weights_497_cast, y = var_12_to_fp16)[name = tensor("attn_weights_499_cast")]; + tensor var_12090_cast = softmax(axis = var_18, x = attn_weights_499_cast)[name = tensor("op_12090_cast")]; tensor attn_249_transpose_x_0 = const()[name = tensor("attn_249_transpose_x_0"), val = tensor(false)]; tensor attn_249_transpose_y_0 = const()[name = tensor("attn_249_transpose_y_0"), val = tensor(true)]; - tensor attn_249 = matmul(transpose_x = attn_249_transpose_x_0, transpose_y = attn_249_transpose_y_0, x = var_12185, y = var_12189)[name = tensor("attn_249")]; - tensor var_12193 = const()[name = tensor("op_12193"), val = tensor([2, 1280, 1, -1])]; - tensor input_699 = reshape(shape = var_12193, x = attn_249)[name = tensor("input_699")]; - tensor var_12198 = const()[name = tensor("op_12198"), val = tensor([1, 1])]; - tensor var_12200 = const()[name = tensor("op_12200"), val = tensor([1, 1])]; - tensor var_12202_pad_type_0 = const()[name = tensor("op_12202_pad_type_0"), val = tensor("custom")]; - tensor var_12202_pad_0 = const()[name = tensor("op_12202_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12202 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias, dilations = var_12200, groups = var_6872, pad = var_12202_pad_0, pad_type = var_12202_pad_type_0, strides = var_12198, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight, x = input_699)[name = tensor("op_12202")]; - tensor inputs_375 = add(x = var_12202, y = inputs_373)[name = tensor("inputs_375")]; - tensor var_12206 = const()[name = tensor("op_12206"), val = tensor([1])]; - tensor channels_mean_375 = reduce_mean(axes = var_12206, keep_dims = var_6867, x = inputs_375)[name = tensor("channels_mean_375")]; - tensor zero_mean_375 = sub(x = inputs_375, y = channels_mean_375)[name = tensor("zero_mean_375")]; - tensor zero_mean_sq_375 = mul(x = zero_mean_375, y = zero_mean_375)[name = tensor("zero_mean_sq_375")]; - tensor var_12210 = const()[name = tensor("op_12210"), val = tensor([1])]; - tensor var_12211 = reduce_mean(axes = var_12210, keep_dims = var_6867, x = zero_mean_sq_375)[name = tensor("op_12211")]; - tensor var_12212 = const()[name = tensor("op_12212"), val = tensor(0x1.4f8b58p-17)]; - tensor var_12213 = add(x = var_12211, y = var_12212)[name = tensor("op_12213")]; - tensor denom_375_epsilon_0 = const()[name = tensor("denom_375_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_375 = rsqrt(epsilon = denom_375_epsilon_0, x = var_12213)[name = tensor("denom_375")]; - tensor out_375 = mul(x = zero_mean_375, y = denom_375)[name = tensor("out_375")]; - tensor var_12217 = const()[name = tensor("op_12217"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269777408)))]; - tensor var_12218 = add(x = out_375, y = var_12217)[name = tensor("op_12218")]; - tensor var_12220 = const()[name = tensor("op_12220"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269782592)))]; - tensor hidden_states_485 = mul(x = var_12218, y = var_12220)[name = tensor("hidden_states_485")]; - tensor var_12227 = const()[name = tensor("op_12227"), val = tensor([1, 1])]; - tensor var_12229 = const()[name = tensor("op_12229"), val = tensor([1, 1])]; + tensor attn_249_cast = matmul(transpose_x = attn_249_transpose_x_0, transpose_y = attn_249_transpose_y_0, x = var_12086_cast, y = var_12090_cast)[name = tensor("attn_249_cast")]; + tensor var_12094 = const()[name = tensor("op_12094"), val = tensor([2, 1280, 1, -1])]; + tensor input_699_cast = reshape(shape = var_12094, x = attn_249_cast)[name = tensor("input_699_cast")]; + tensor var_12099 = const()[name = tensor("op_12099"), val = tensor([1, 1])]; + tensor var_12101 = const()[name = tensor("op_12101"), val = tensor([1, 1])]; + tensor var_12103_pad_type_0 = const()[name = tensor("op_12103_pad_type_0"), val = tensor("custom")]; + tensor var_12103_pad_0 = const()[name = tensor("op_12103_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4738030528)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4741307392)))]; + tensor var_12103_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_bias_to_fp16, dilations = var_12101, groups = var_31, pad = var_12103_pad_0, pad_type = var_12103_pad_type_0, strides = var_12099, weight = unet_up_blocks_0_attentions_2_transformer_blocks_8_attn1_to_out_0_weight_to_fp16, x = input_699_cast)[name = tensor("op_12103_cast")]; + tensor inputs_375_cast = add(x = var_12103_cast, y = inputs_373_cast)[name = tensor("inputs_375_cast")]; + tensor var_12107 = const()[name = tensor("op_12107"), val = tensor([1])]; + tensor channels_mean_375_cast = reduce_mean(axes = var_12107, keep_dims = var_23, x = inputs_375_cast)[name = tensor("channels_mean_375_cast")]; + tensor zero_mean_375_cast = sub(x = inputs_375_cast, y = channels_mean_375_cast)[name = tensor("zero_mean_375_cast")]; + tensor zero_mean_sq_375_cast = mul(x = zero_mean_375_cast, y = zero_mean_375_cast)[name = tensor("zero_mean_sq_375_cast")]; + tensor var_12111 = const()[name = tensor("op_12111"), val = tensor([1])]; + tensor var_12112_cast = reduce_mean(axes = var_12111, keep_dims = var_23, x = zero_mean_sq_375_cast)[name = tensor("op_12112_cast")]; + tensor var_12113_to_fp16 = const()[name = tensor("op_12113_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12114_cast = add(x = var_12112_cast, y = var_12113_to_fp16)[name = tensor("op_12114_cast")]; + tensor denom_375_epsilon_0_to_fp16 = const()[name = tensor("denom_375_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_375_cast = rsqrt(epsilon = denom_375_epsilon_0_to_fp16, x = var_12114_cast)[name = tensor("denom_375_cast")]; + tensor out_375_cast = mul(x = zero_mean_375_cast, y = denom_375_cast)[name = tensor("out_375_cast")]; + tensor var_12118_to_fp16 = const()[name = tensor("op_12118_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4741310016)))]; + tensor var_12119_cast = add(x = out_375_cast, y = var_12118_to_fp16)[name = tensor("op_12119_cast")]; + tensor var_12121_to_fp16 = const()[name = tensor("op_12121_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4741312640)))]; + tensor hidden_states_485_cast = mul(x = var_12119_cast, y = var_12121_to_fp16)[name = tensor("hidden_states_485_cast")]; + tensor var_12128 = const()[name = tensor("op_12128"), val = tensor([1, 1])]; + tensor var_12130 = const()[name = tensor("op_12130"), val = tensor([1, 1])]; tensor q_251_pad_type_0 = const()[name = tensor("q_251_pad_type_0"), val = tensor("custom")]; tensor q_251_pad_0 = const()[name = tensor("q_251_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_251 = conv(dilations = var_12229, groups = var_6872, pad = q_251_pad_0, pad_type = q_251_pad_type_0, strides = var_12227, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight, x = hidden_states_485)[name = tensor("q_251")]; - tensor var_12233 = const()[name = tensor("op_12233"), val = tensor([1, 1])]; - tensor var_12235 = const()[name = tensor("op_12235"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4741315264)))]; + tensor q_251_cast = conv(dilations = var_12130, groups = var_31, pad = q_251_pad_0, pad_type = q_251_pad_type_0, strides = var_12128, weight = unet_up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_q_weight_to_fp16, x = hidden_states_485_cast)[name = tensor("q_251_cast")]; + tensor var_12134 = const()[name = tensor("op_12134"), val = tensor([1, 1])]; + tensor var_12136 = const()[name = tensor("op_12136"), val = tensor([1, 1])]; tensor k_251_pad_type_0 = const()[name = tensor("k_251_pad_type_0"), val = tensor("custom")]; tensor k_251_pad_0 = const()[name = tensor("k_251_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_251 = conv(dilations = var_12235, groups = var_6872, pad = k_251_pad_0, pad_type = k_251_pad_type_0, strides = var_12233, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_251")]; - tensor var_12239 = const()[name = tensor("op_12239"), val = tensor([1, 1])]; - tensor var_12241 = const()[name = tensor("op_12241"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4744592128)))]; + tensor k_251_cast = conv(dilations = var_12136, groups = var_31, pad = k_251_pad_0, pad_type = k_251_pad_type_0, strides = var_12134, weight = unet_up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_251_cast")]; + tensor var_12140 = const()[name = tensor("op_12140"), val = tensor([1, 1])]; + tensor var_12142 = const()[name = tensor("op_12142"), val = tensor([1, 1])]; tensor v_251_pad_type_0 = const()[name = tensor("v_251_pad_type_0"), val = tensor("custom")]; tensor v_251_pad_0 = const()[name = tensor("v_251_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_251 = conv(dilations = var_12241, groups = var_6872, pad = v_251_pad_0, pad_type = v_251_pad_type_0, strides = var_12239, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_251")]; - tensor var_12245 = const()[name = tensor("op_12245"), val = tensor([2, 20, 64, -1])]; - tensor var_12246 = reshape(shape = var_12245, x = q_251)[name = tensor("op_12246")]; - tensor var_12247 = const()[name = tensor("op_12247"), val = tensor([2, 20, 64, -1])]; - tensor var_12248 = reshape(shape = var_12247, x = k_251)[name = tensor("op_12248")]; - tensor var_12249 = const()[name = tensor("op_12249"), val = tensor([2, 20, 64, -1])]; - tensor var_12250 = reshape(shape = var_12249, x = v_251)[name = tensor("op_12250")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4749835072)))]; + tensor v_251_cast = conv(dilations = var_12142, groups = var_31, pad = v_251_pad_0, pad_type = v_251_pad_type_0, strides = var_12140, weight = unet_up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_251_cast")]; + tensor var_12146 = const()[name = tensor("op_12146"), val = tensor([2, 20, 64, -1])]; + tensor var_12147_cast = reshape(shape = var_12146, x = q_251_cast)[name = tensor("op_12147_cast")]; + tensor var_12148 = const()[name = tensor("op_12148"), val = tensor([2, 20, 64, -1])]; + tensor var_12149_cast = reshape(shape = var_12148, x = k_251_cast)[name = tensor("op_12149_cast")]; + tensor var_12150 = const()[name = tensor("op_12150"), val = tensor([2, 20, 64, -1])]; + tensor var_12151_cast = reshape(shape = var_12150, x = v_251_cast)[name = tensor("op_12151_cast")]; tensor attn_weights_501_transpose_x_0 = const()[name = tensor("attn_weights_501_transpose_x_0"), val = tensor(true)]; tensor attn_weights_501_transpose_y_0 = const()[name = tensor("attn_weights_501_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_501 = matmul(transpose_x = attn_weights_501_transpose_x_0, transpose_y = attn_weights_501_transpose_y_0, x = var_12246, y = var_12248)[name = tensor("attn_weights_501")]; - tensor attn_weights_503 = mul(x = attn_weights_501, y = var_6863)[name = tensor("attn_weights_503")]; - tensor var_12254 = softmax(axis = var_6856, x = attn_weights_503)[name = tensor("op_12254")]; + tensor attn_weights_501_cast = matmul(transpose_x = attn_weights_501_transpose_x_0, transpose_y = attn_weights_501_transpose_y_0, x = var_12147_cast, y = var_12149_cast)[name = tensor("attn_weights_501_cast")]; + tensor attn_weights_503_cast = mul(x = attn_weights_501_cast, y = var_12_to_fp16)[name = tensor("attn_weights_503_cast")]; + tensor var_12155_cast = softmax(axis = var_18, x = attn_weights_503_cast)[name = tensor("op_12155_cast")]; tensor attn_251_transpose_x_0 = const()[name = tensor("attn_251_transpose_x_0"), val = tensor(false)]; tensor attn_251_transpose_y_0 = const()[name = tensor("attn_251_transpose_y_0"), val = tensor(true)]; - tensor attn_251 = matmul(transpose_x = attn_251_transpose_x_0, transpose_y = attn_251_transpose_y_0, x = var_12250, y = var_12254)[name = tensor("attn_251")]; - tensor var_12258 = const()[name = tensor("op_12258"), val = tensor([2, 1280, 1, -1])]; - tensor input_701 = reshape(shape = var_12258, x = attn_251)[name = tensor("input_701")]; - tensor var_12263 = const()[name = tensor("op_12263"), val = tensor([1, 1])]; - tensor var_12265 = const()[name = tensor("op_12265"), val = tensor([1, 1])]; - tensor var_12267_pad_type_0 = const()[name = tensor("op_12267_pad_type_0"), val = tensor("custom")]; - tensor var_12267_pad_0 = const()[name = tensor("op_12267_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12267 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias, dilations = var_12265, groups = var_6872, pad = var_12267_pad_0, pad_type = var_12267_pad_type_0, strides = var_12263, weight = up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight, x = input_701)[name = tensor("op_12267")]; - tensor inputs_377 = add(x = var_12267, y = inputs_375)[name = tensor("inputs_377")]; - tensor var_12271 = const()[name = tensor("op_12271"), val = tensor([1])]; - tensor channels_mean_377 = reduce_mean(axes = var_12271, keep_dims = var_6867, x = inputs_377)[name = tensor("channels_mean_377")]; - tensor zero_mean_377 = sub(x = inputs_377, y = channels_mean_377)[name = tensor("zero_mean_377")]; - tensor zero_mean_sq_377 = mul(x = zero_mean_377, y = zero_mean_377)[name = tensor("zero_mean_sq_377")]; - tensor var_12275 = const()[name = tensor("op_12275"), val = tensor([1])]; - tensor var_12276 = reduce_mean(axes = var_12275, keep_dims = var_6867, x = zero_mean_sq_377)[name = tensor("op_12276")]; - tensor var_12277 = const()[name = tensor("op_12277"), val = tensor(0x1.4f8b58p-17)]; - tensor var_12278 = add(x = var_12276, y = var_12277)[name = tensor("op_12278")]; - tensor denom_377_epsilon_0 = const()[name = tensor("denom_377_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_377 = rsqrt(epsilon = denom_377_epsilon_0, x = var_12278)[name = tensor("denom_377")]; - tensor out_377 = mul(x = zero_mean_377, y = denom_377)[name = tensor("out_377")]; - tensor var_12282 = const()[name = tensor("op_12282"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269787776)))]; - tensor var_12283 = add(x = out_377, y = var_12282)[name = tensor("op_12283")]; - tensor var_12285 = const()[name = tensor("op_12285"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269792960)))]; - tensor input_703 = mul(x = var_12283, y = var_12285)[name = tensor("input_703")]; - tensor var_12293 = const()[name = tensor("op_12293"), val = tensor([1, 1])]; - tensor var_12295 = const()[name = tensor("op_12295"), val = tensor([1, 1])]; - tensor var_12297_pad_type_0 = const()[name = tensor("op_12297_pad_type_0"), val = tensor("custom")]; - tensor var_12297_pad_0 = const()[name = tensor("op_12297_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12297 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias, dilations = var_12295, groups = var_6872, pad = var_12297_pad_0, pad_type = var_12297_pad_type_0, strides = var_12293, weight = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight, x = input_703)[name = tensor("op_12297")]; - tensor var_12298_split_sizes_0 = const()[name = tensor("op_12298_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_12298_axis_0 = const()[name = tensor("op_12298_axis_0"), val = tensor(1)]; - tensor var_12298_0, tensor var_12298_1 = split(axis = var_12298_axis_0, split_sizes = var_12298_split_sizes_0, x = var_12297)[name = tensor("op_12298")]; - tensor var_12300_mode_0 = const()[name = tensor("op_12300_mode_0"), val = tensor("EXACT")]; - tensor var_12300 = gelu(mode = var_12300_mode_0, x = var_12298_1)[name = tensor("op_12300")]; - tensor input_705 = mul(x = var_12298_0, y = var_12300)[name = tensor("input_705")]; - tensor var_12304 = const()[name = tensor("op_12304"), val = tensor([1, 1])]; - tensor var_12306 = const()[name = tensor("op_12306"), val = tensor([1, 1])]; - tensor var_12308_pad_type_0 = const()[name = tensor("op_12308_pad_type_0"), val = tensor("custom")]; - tensor var_12308_pad_0 = const()[name = tensor("op_12308_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12308 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias, dilations = var_12306, groups = var_6872, pad = var_12308_pad_0, pad_type = var_12308_pad_type_0, strides = var_12304, weight = up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight, x = input_705)[name = tensor("op_12308")]; - tensor inputs_379 = add(x = var_12308, y = inputs_377)[name = tensor("inputs_379")]; - tensor var_12318 = const()[name = tensor("op_12318"), val = tensor([1])]; - tensor channels_mean_379 = reduce_mean(axes = var_12318, keep_dims = var_6867, x = inputs_379)[name = tensor("channels_mean_379")]; - tensor zero_mean_379 = sub(x = inputs_379, y = channels_mean_379)[name = tensor("zero_mean_379")]; - tensor zero_mean_sq_379 = mul(x = zero_mean_379, y = zero_mean_379)[name = tensor("zero_mean_sq_379")]; - tensor var_12322 = const()[name = tensor("op_12322"), val = tensor([1])]; - tensor var_12323 = reduce_mean(axes = var_12322, keep_dims = var_6867, x = zero_mean_sq_379)[name = tensor("op_12323")]; - tensor var_12324 = const()[name = tensor("op_12324"), val = tensor(0x1.4f8b58p-17)]; - tensor var_12325 = add(x = var_12323, y = var_12324)[name = tensor("op_12325")]; - tensor denom_379_epsilon_0 = const()[name = tensor("denom_379_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_379 = rsqrt(epsilon = denom_379_epsilon_0, x = var_12325)[name = tensor("denom_379")]; - tensor out_379 = mul(x = zero_mean_379, y = denom_379)[name = tensor("out_379")]; - tensor var_12329 = const()[name = tensor("op_12329"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269798144)))]; - tensor var_12330 = add(x = out_379, y = var_12329)[name = tensor("op_12330")]; - tensor var_12332 = const()[name = tensor("op_12332"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269803328)))]; - tensor hidden_states_489 = mul(x = var_12330, y = var_12332)[name = tensor("hidden_states_489")]; - tensor var_12339 = const()[name = tensor("op_12339"), val = tensor([1, 1])]; - tensor var_12341 = const()[name = tensor("op_12341"), val = tensor([1, 1])]; + tensor attn_251_cast = matmul(transpose_x = attn_251_transpose_x_0, transpose_y = attn_251_transpose_y_0, x = var_12151_cast, y = var_12155_cast)[name = tensor("attn_251_cast")]; + tensor var_12159 = const()[name = tensor("op_12159"), val = tensor([2, 1280, 1, -1])]; + tensor input_701_cast = reshape(shape = var_12159, x = attn_251_cast)[name = tensor("input_701_cast")]; + tensor var_12164 = const()[name = tensor("op_12164"), val = tensor([1, 1])]; + tensor var_12166 = const()[name = tensor("op_12166"), val = tensor([1, 1])]; + tensor var_12168_pad_type_0 = const()[name = tensor("op_12168_pad_type_0"), val = tensor("custom")]; + tensor var_12168_pad_0 = const()[name = tensor("op_12168_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4755078016)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4758354880)))]; + tensor var_12168_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_bias_to_fp16, dilations = var_12166, groups = var_31, pad = var_12168_pad_0, pad_type = var_12168_pad_type_0, strides = var_12164, weight = unet_up_blocks_0_attentions_2_transformer_blocks_8_attn2_to_out_0_weight_to_fp16, x = input_701_cast)[name = tensor("op_12168_cast")]; + tensor inputs_377_cast = add(x = var_12168_cast, y = inputs_375_cast)[name = tensor("inputs_377_cast")]; + tensor var_12172 = const()[name = tensor("op_12172"), val = tensor([1])]; + tensor channels_mean_377_cast = reduce_mean(axes = var_12172, keep_dims = var_23, x = inputs_377_cast)[name = tensor("channels_mean_377_cast")]; + tensor zero_mean_377_cast = sub(x = inputs_377_cast, y = channels_mean_377_cast)[name = tensor("zero_mean_377_cast")]; + tensor zero_mean_sq_377_cast = mul(x = zero_mean_377_cast, y = zero_mean_377_cast)[name = tensor("zero_mean_sq_377_cast")]; + tensor var_12176 = const()[name = tensor("op_12176"), val = tensor([1])]; + tensor var_12177_cast = reduce_mean(axes = var_12176, keep_dims = var_23, x = zero_mean_sq_377_cast)[name = tensor("op_12177_cast")]; + tensor var_12178_to_fp16 = const()[name = tensor("op_12178_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12179_cast = add(x = var_12177_cast, y = var_12178_to_fp16)[name = tensor("op_12179_cast")]; + tensor denom_377_epsilon_0_to_fp16 = const()[name = tensor("denom_377_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_377_cast = rsqrt(epsilon = denom_377_epsilon_0_to_fp16, x = var_12179_cast)[name = tensor("denom_377_cast")]; + tensor out_377_cast = mul(x = zero_mean_377_cast, y = denom_377_cast)[name = tensor("out_377_cast")]; + tensor var_12183_to_fp16 = const()[name = tensor("op_12183_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4758357504)))]; + tensor var_12184_cast = add(x = out_377_cast, y = var_12183_to_fp16)[name = tensor("op_12184_cast")]; + tensor var_12186_to_fp16 = const()[name = tensor("op_12186_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4758360128)))]; + tensor input_703_cast = mul(x = var_12184_cast, y = var_12186_to_fp16)[name = tensor("input_703_cast")]; + tensor var_12194 = const()[name = tensor("op_12194"), val = tensor([1, 1])]; + tensor var_12196 = const()[name = tensor("op_12196"), val = tensor([1, 1])]; + tensor var_12198_pad_type_0 = const()[name = tensor("op_12198_pad_type_0"), val = tensor("custom")]; + tensor var_12198_pad_0 = const()[name = tensor("op_12198_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4758362752)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4784577216)))]; + tensor var_12198_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_bias_to_fp16, dilations = var_12196, groups = var_31, pad = var_12198_pad_0, pad_type = var_12198_pad_type_0, strides = var_12194, weight = unet_up_blocks_0_attentions_2_transformer_blocks_8_ff_net_0_proj_weight_to_fp16, x = input_703_cast)[name = tensor("op_12198_cast")]; + tensor var_12199_split_sizes_0 = const()[name = tensor("op_12199_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_12199_axis_0 = const()[name = tensor("op_12199_axis_0"), val = tensor(1)]; + tensor var_12199_cast_0, tensor var_12199_cast_1 = split(axis = var_12199_axis_0, split_sizes = var_12199_split_sizes_0, x = var_12198_cast)[name = tensor("op_12199_cast")]; + tensor var_12201_mode_0 = const()[name = tensor("op_12201_mode_0"), val = tensor("EXACT")]; + tensor var_12201_cast = gelu(mode = var_12201_mode_0, x = var_12199_cast_1)[name = tensor("op_12201_cast")]; + tensor input_705_cast = mul(x = var_12199_cast_0, y = var_12201_cast)[name = tensor("input_705_cast")]; + tensor var_12205 = const()[name = tensor("op_12205"), val = tensor([1, 1])]; + tensor var_12207 = const()[name = tensor("op_12207"), val = tensor([1, 1])]; + tensor var_12209_pad_type_0 = const()[name = tensor("op_12209_pad_type_0"), val = tensor("custom")]; + tensor var_12209_pad_0 = const()[name = tensor("op_12209_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4784597760)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4797705024)))]; + tensor var_12209_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_bias_to_fp16, dilations = var_12207, groups = var_31, pad = var_12209_pad_0, pad_type = var_12209_pad_type_0, strides = var_12205, weight = unet_up_blocks_0_attentions_2_transformer_blocks_8_ff_net_2_weight_to_fp16, x = input_705_cast)[name = tensor("op_12209_cast")]; + tensor inputs_379_cast = add(x = var_12209_cast, y = inputs_377_cast)[name = tensor("inputs_379_cast")]; + tensor var_12219 = const()[name = tensor("op_12219"), val = tensor([1])]; + tensor channels_mean_379_cast = reduce_mean(axes = var_12219, keep_dims = var_23, x = inputs_379_cast)[name = tensor("channels_mean_379_cast")]; + tensor zero_mean_379_cast = sub(x = inputs_379_cast, y = channels_mean_379_cast)[name = tensor("zero_mean_379_cast")]; + tensor zero_mean_sq_379_cast = mul(x = zero_mean_379_cast, y = zero_mean_379_cast)[name = tensor("zero_mean_sq_379_cast")]; + tensor var_12223 = const()[name = tensor("op_12223"), val = tensor([1])]; + tensor var_12224_cast = reduce_mean(axes = var_12223, keep_dims = var_23, x = zero_mean_sq_379_cast)[name = tensor("op_12224_cast")]; + tensor var_12225_to_fp16 = const()[name = tensor("op_12225_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12226_cast = add(x = var_12224_cast, y = var_12225_to_fp16)[name = tensor("op_12226_cast")]; + tensor denom_379_epsilon_0_to_fp16 = const()[name = tensor("denom_379_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_379_cast = rsqrt(epsilon = denom_379_epsilon_0_to_fp16, x = var_12226_cast)[name = tensor("denom_379_cast")]; + tensor out_379_cast = mul(x = zero_mean_379_cast, y = denom_379_cast)[name = tensor("out_379_cast")]; + tensor var_12230_to_fp16 = const()[name = tensor("op_12230_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4797707648)))]; + tensor var_12231_cast = add(x = out_379_cast, y = var_12230_to_fp16)[name = tensor("op_12231_cast")]; + tensor var_12233_to_fp16 = const()[name = tensor("op_12233_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4797710272)))]; + tensor hidden_states_489_cast = mul(x = var_12231_cast, y = var_12233_to_fp16)[name = tensor("hidden_states_489_cast")]; + tensor var_12240 = const()[name = tensor("op_12240"), val = tensor([1, 1])]; + tensor var_12242 = const()[name = tensor("op_12242"), val = tensor([1, 1])]; tensor q_253_pad_type_0 = const()[name = tensor("q_253_pad_type_0"), val = tensor("custom")]; tensor q_253_pad_0 = const()[name = tensor("q_253_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_253 = conv(dilations = var_12341, groups = var_6872, pad = q_253_pad_0, pad_type = q_253_pad_type_0, strides = var_12339, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight, x = hidden_states_489)[name = tensor("q_253")]; - tensor var_12345 = const()[name = tensor("op_12345"), val = tensor([1, 1])]; - tensor var_12347 = const()[name = tensor("op_12347"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4797712896)))]; + tensor q_253_cast = conv(dilations = var_12242, groups = var_31, pad = q_253_pad_0, pad_type = q_253_pad_type_0, strides = var_12240, weight = unet_up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_q_weight_to_fp16, x = hidden_states_489_cast)[name = tensor("q_253_cast")]; + tensor var_12246 = const()[name = tensor("op_12246"), val = tensor([1, 1])]; + tensor var_12248 = const()[name = tensor("op_12248"), val = tensor([1, 1])]; tensor k_253_pad_type_0 = const()[name = tensor("k_253_pad_type_0"), val = tensor("custom")]; tensor k_253_pad_0 = const()[name = tensor("k_253_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_253 = conv(dilations = var_12347, groups = var_6872, pad = k_253_pad_0, pad_type = k_253_pad_type_0, strides = var_12345, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight, x = hidden_states_489)[name = tensor("k_253")]; - tensor var_12351 = const()[name = tensor("op_12351"), val = tensor([1, 1])]; - tensor var_12353 = const()[name = tensor("op_12353"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4800989760)))]; + tensor k_253_cast = conv(dilations = var_12248, groups = var_31, pad = k_253_pad_0, pad_type = k_253_pad_type_0, strides = var_12246, weight = unet_up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_k_weight_to_fp16, x = hidden_states_489_cast)[name = tensor("k_253_cast")]; + tensor var_12252 = const()[name = tensor("op_12252"), val = tensor([1, 1])]; + tensor var_12254 = const()[name = tensor("op_12254"), val = tensor([1, 1])]; tensor v_253_pad_type_0 = const()[name = tensor("v_253_pad_type_0"), val = tensor("custom")]; tensor v_253_pad_0 = const()[name = tensor("v_253_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_253 = conv(dilations = var_12353, groups = var_6872, pad = v_253_pad_0, pad_type = v_253_pad_type_0, strides = var_12351, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight, x = hidden_states_489)[name = tensor("v_253")]; - tensor var_12357 = const()[name = tensor("op_12357"), val = tensor([2, 20, 64, -1])]; - tensor var_12358 = reshape(shape = var_12357, x = q_253)[name = tensor("op_12358")]; - tensor var_12359 = const()[name = tensor("op_12359"), val = tensor([2, 20, 64, -1])]; - tensor var_12360 = reshape(shape = var_12359, x = k_253)[name = tensor("op_12360")]; - tensor var_12361 = const()[name = tensor("op_12361"), val = tensor([2, 20, 64, -1])]; - tensor var_12362 = reshape(shape = var_12361, x = v_253)[name = tensor("op_12362")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4804266624)))]; + tensor v_253_cast = conv(dilations = var_12254, groups = var_31, pad = v_253_pad_0, pad_type = v_253_pad_type_0, strides = var_12252, weight = unet_up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_v_weight_to_fp16, x = hidden_states_489_cast)[name = tensor("v_253_cast")]; + tensor var_12258 = const()[name = tensor("op_12258"), val = tensor([2, 20, 64, -1])]; + tensor var_12259_cast = reshape(shape = var_12258, x = q_253_cast)[name = tensor("op_12259_cast")]; + tensor var_12260 = const()[name = tensor("op_12260"), val = tensor([2, 20, 64, -1])]; + tensor var_12261_cast = reshape(shape = var_12260, x = k_253_cast)[name = tensor("op_12261_cast")]; + tensor var_12262 = const()[name = tensor("op_12262"), val = tensor([2, 20, 64, -1])]; + tensor var_12263_cast = reshape(shape = var_12262, x = v_253_cast)[name = tensor("op_12263_cast")]; tensor attn_weights_505_transpose_x_0 = const()[name = tensor("attn_weights_505_transpose_x_0"), val = tensor(true)]; tensor attn_weights_505_transpose_y_0 = const()[name = tensor("attn_weights_505_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_505 = matmul(transpose_x = attn_weights_505_transpose_x_0, transpose_y = attn_weights_505_transpose_y_0, x = var_12358, y = var_12360)[name = tensor("attn_weights_505")]; - tensor attn_weights_507 = mul(x = attn_weights_505, y = var_6863)[name = tensor("attn_weights_507")]; - tensor var_12366 = softmax(axis = var_6856, x = attn_weights_507)[name = tensor("op_12366")]; + tensor attn_weights_505_cast = matmul(transpose_x = attn_weights_505_transpose_x_0, transpose_y = attn_weights_505_transpose_y_0, x = var_12259_cast, y = var_12261_cast)[name = tensor("attn_weights_505_cast")]; + tensor attn_weights_507_cast = mul(x = attn_weights_505_cast, y = var_12_to_fp16)[name = tensor("attn_weights_507_cast")]; + tensor var_12267_cast = softmax(axis = var_18, x = attn_weights_507_cast)[name = tensor("op_12267_cast")]; tensor attn_253_transpose_x_0 = const()[name = tensor("attn_253_transpose_x_0"), val = tensor(false)]; tensor attn_253_transpose_y_0 = const()[name = tensor("attn_253_transpose_y_0"), val = tensor(true)]; - tensor attn_253 = matmul(transpose_x = attn_253_transpose_x_0, transpose_y = attn_253_transpose_y_0, x = var_12362, y = var_12366)[name = tensor("attn_253")]; - tensor var_12370 = const()[name = tensor("op_12370"), val = tensor([2, 1280, 1, -1])]; - tensor input_707 = reshape(shape = var_12370, x = attn_253)[name = tensor("input_707")]; - tensor var_12375 = const()[name = tensor("op_12375"), val = tensor([1, 1])]; - tensor var_12377 = const()[name = tensor("op_12377"), val = tensor([1, 1])]; - tensor var_12379_pad_type_0 = const()[name = tensor("op_12379_pad_type_0"), val = tensor("custom")]; - tensor var_12379_pad_0 = const()[name = tensor("op_12379_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12379 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias, dilations = var_12377, groups = var_6872, pad = var_12379_pad_0, pad_type = var_12379_pad_type_0, strides = var_12375, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight, x = input_707)[name = tensor("op_12379")]; - tensor inputs_381 = add(x = var_12379, y = inputs_379)[name = tensor("inputs_381")]; - tensor var_12383 = const()[name = tensor("op_12383"), val = tensor([1])]; - tensor channels_mean_381 = reduce_mean(axes = var_12383, keep_dims = var_6867, x = inputs_381)[name = tensor("channels_mean_381")]; - tensor zero_mean_381 = sub(x = inputs_381, y = channels_mean_381)[name = tensor("zero_mean_381")]; - tensor zero_mean_sq_381 = mul(x = zero_mean_381, y = zero_mean_381)[name = tensor("zero_mean_sq_381")]; - tensor var_12387 = const()[name = tensor("op_12387"), val = tensor([1])]; - tensor var_12388 = reduce_mean(axes = var_12387, keep_dims = var_6867, x = zero_mean_sq_381)[name = tensor("op_12388")]; - tensor var_12389 = const()[name = tensor("op_12389"), val = tensor(0x1.4f8b58p-17)]; - tensor var_12390 = add(x = var_12388, y = var_12389)[name = tensor("op_12390")]; - tensor denom_381_epsilon_0 = const()[name = tensor("denom_381_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_381 = rsqrt(epsilon = denom_381_epsilon_0, x = var_12390)[name = tensor("denom_381")]; - tensor out_381 = mul(x = zero_mean_381, y = denom_381)[name = tensor("out_381")]; - tensor var_12394 = const()[name = tensor("op_12394"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269808512)))]; - tensor var_12395 = add(x = out_381, y = var_12394)[name = tensor("op_12395")]; - tensor var_12397 = const()[name = tensor("op_12397"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269813696)))]; - tensor hidden_states_491 = mul(x = var_12395, y = var_12397)[name = tensor("hidden_states_491")]; - tensor var_12404 = const()[name = tensor("op_12404"), val = tensor([1, 1])]; - tensor var_12406 = const()[name = tensor("op_12406"), val = tensor([1, 1])]; + tensor attn_253_cast = matmul(transpose_x = attn_253_transpose_x_0, transpose_y = attn_253_transpose_y_0, x = var_12263_cast, y = var_12267_cast)[name = tensor("attn_253_cast")]; + tensor var_12271 = const()[name = tensor("op_12271"), val = tensor([2, 1280, 1, -1])]; + tensor input_707_cast = reshape(shape = var_12271, x = attn_253_cast)[name = tensor("input_707_cast")]; + tensor var_12276 = const()[name = tensor("op_12276"), val = tensor([1, 1])]; + tensor var_12278 = const()[name = tensor("op_12278"), val = tensor([1, 1])]; + tensor var_12280_pad_type_0 = const()[name = tensor("op_12280_pad_type_0"), val = tensor("custom")]; + tensor var_12280_pad_0 = const()[name = tensor("op_12280_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4807543488)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4810820352)))]; + tensor var_12280_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_bias_to_fp16, dilations = var_12278, groups = var_31, pad = var_12280_pad_0, pad_type = var_12280_pad_type_0, strides = var_12276, weight = unet_up_blocks_0_attentions_2_transformer_blocks_9_attn1_to_out_0_weight_to_fp16, x = input_707_cast)[name = tensor("op_12280_cast")]; + tensor inputs_381_cast = add(x = var_12280_cast, y = inputs_379_cast)[name = tensor("inputs_381_cast")]; + tensor var_12284 = const()[name = tensor("op_12284"), val = tensor([1])]; + tensor channels_mean_381_cast = reduce_mean(axes = var_12284, keep_dims = var_23, x = inputs_381_cast)[name = tensor("channels_mean_381_cast")]; + tensor zero_mean_381_cast = sub(x = inputs_381_cast, y = channels_mean_381_cast)[name = tensor("zero_mean_381_cast")]; + tensor zero_mean_sq_381_cast = mul(x = zero_mean_381_cast, y = zero_mean_381_cast)[name = tensor("zero_mean_sq_381_cast")]; + tensor var_12288 = const()[name = tensor("op_12288"), val = tensor([1])]; + tensor var_12289_cast = reduce_mean(axes = var_12288, keep_dims = var_23, x = zero_mean_sq_381_cast)[name = tensor("op_12289_cast")]; + tensor var_12290_to_fp16 = const()[name = tensor("op_12290_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12291_cast = add(x = var_12289_cast, y = var_12290_to_fp16)[name = tensor("op_12291_cast")]; + tensor denom_381_epsilon_0_to_fp16 = const()[name = tensor("denom_381_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_381_cast = rsqrt(epsilon = denom_381_epsilon_0_to_fp16, x = var_12291_cast)[name = tensor("denom_381_cast")]; + tensor out_381_cast = mul(x = zero_mean_381_cast, y = denom_381_cast)[name = tensor("out_381_cast")]; + tensor var_12295_to_fp16 = const()[name = tensor("op_12295_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4810822976)))]; + tensor var_12296_cast = add(x = out_381_cast, y = var_12295_to_fp16)[name = tensor("op_12296_cast")]; + tensor var_12298_to_fp16 = const()[name = tensor("op_12298_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4810825600)))]; + tensor hidden_states_491_cast = mul(x = var_12296_cast, y = var_12298_to_fp16)[name = tensor("hidden_states_491_cast")]; + tensor var_12305 = const()[name = tensor("op_12305"), val = tensor([1, 1])]; + tensor var_12307 = const()[name = tensor("op_12307"), val = tensor([1, 1])]; tensor q_255_pad_type_0 = const()[name = tensor("q_255_pad_type_0"), val = tensor("custom")]; tensor q_255_pad_0 = const()[name = tensor("q_255_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_255 = conv(dilations = var_12406, groups = var_6872, pad = q_255_pad_0, pad_type = q_255_pad_type_0, strides = var_12404, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight, x = hidden_states_491)[name = tensor("q_255")]; - tensor var_12410 = const()[name = tensor("op_12410"), val = tensor([1, 1])]; - tensor var_12412 = const()[name = tensor("op_12412"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4810828224)))]; + tensor q_255_cast = conv(dilations = var_12307, groups = var_31, pad = q_255_pad_0, pad_type = q_255_pad_type_0, strides = var_12305, weight = unet_up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_q_weight_to_fp16, x = hidden_states_491_cast)[name = tensor("q_255_cast")]; + tensor var_12311 = const()[name = tensor("op_12311"), val = tensor([1, 1])]; + tensor var_12313 = const()[name = tensor("op_12313"), val = tensor([1, 1])]; tensor k_255_pad_type_0 = const()[name = tensor("k_255_pad_type_0"), val = tensor("custom")]; tensor k_255_pad_0 = const()[name = tensor("k_255_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_255 = conv(dilations = var_12412, groups = var_6872, pad = k_255_pad_0, pad_type = k_255_pad_type_0, strides = var_12410, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_255")]; - tensor var_12416 = const()[name = tensor("op_12416"), val = tensor([1, 1])]; - tensor var_12418 = const()[name = tensor("op_12418"), val = tensor([1, 1])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4814105088)))]; + tensor k_255_cast = conv(dilations = var_12313, groups = var_31, pad = k_255_pad_0, pad_type = k_255_pad_type_0, strides = var_12311, weight = unet_up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_255_cast")]; + tensor var_12317 = const()[name = tensor("op_12317"), val = tensor([1, 1])]; + tensor var_12319 = const()[name = tensor("op_12319"), val = tensor([1, 1])]; tensor v_255_pad_type_0 = const()[name = tensor("v_255_pad_type_0"), val = tensor("custom")]; tensor v_255_pad_0 = const()[name = tensor("v_255_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_255 = conv(dilations = var_12418, groups = var_6872, pad = v_255_pad_0, pad_type = v_255_pad_type_0, strides = var_12416, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_255")]; - tensor var_12422 = const()[name = tensor("op_12422"), val = tensor([2, 20, 64, -1])]; - tensor var_12423 = reshape(shape = var_12422, x = q_255)[name = tensor("op_12423")]; - tensor var_12424 = const()[name = tensor("op_12424"), val = tensor([2, 20, 64, -1])]; - tensor var_12425 = reshape(shape = var_12424, x = k_255)[name = tensor("op_12425")]; - tensor var_12426 = const()[name = tensor("op_12426"), val = tensor([2, 20, 64, -1])]; - tensor var_12427 = reshape(shape = var_12426, x = v_255)[name = tensor("op_12427")]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4819348032)))]; + tensor v_255_cast = conv(dilations = var_12319, groups = var_31, pad = v_255_pad_0, pad_type = v_255_pad_type_0, strides = var_12317, weight = unet_up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_255_cast")]; + tensor var_12323 = const()[name = tensor("op_12323"), val = tensor([2, 20, 64, -1])]; + tensor var_12324_cast = reshape(shape = var_12323, x = q_255_cast)[name = tensor("op_12324_cast")]; + tensor var_12325 = const()[name = tensor("op_12325"), val = tensor([2, 20, 64, -1])]; + tensor var_12326_cast = reshape(shape = var_12325, x = k_255_cast)[name = tensor("op_12326_cast")]; + tensor var_12327 = const()[name = tensor("op_12327"), val = tensor([2, 20, 64, -1])]; + tensor var_12328_cast = reshape(shape = var_12327, x = v_255_cast)[name = tensor("op_12328_cast")]; tensor attn_weights_509_transpose_x_0 = const()[name = tensor("attn_weights_509_transpose_x_0"), val = tensor(true)]; tensor attn_weights_509_transpose_y_0 = const()[name = tensor("attn_weights_509_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_509 = matmul(transpose_x = attn_weights_509_transpose_x_0, transpose_y = attn_weights_509_transpose_y_0, x = var_12423, y = var_12425)[name = tensor("attn_weights_509")]; - tensor attn_weights_511 = mul(x = attn_weights_509, y = var_6863)[name = tensor("attn_weights_511")]; - tensor var_12431 = softmax(axis = var_6856, x = attn_weights_511)[name = tensor("op_12431")]; + tensor attn_weights_509_cast = matmul(transpose_x = attn_weights_509_transpose_x_0, transpose_y = attn_weights_509_transpose_y_0, x = var_12324_cast, y = var_12326_cast)[name = tensor("attn_weights_509_cast")]; + tensor attn_weights_511_cast = mul(x = attn_weights_509_cast, y = var_12_to_fp16)[name = tensor("attn_weights_511_cast")]; + tensor var_12332_cast = softmax(axis = var_18, x = attn_weights_511_cast)[name = tensor("op_12332_cast")]; tensor attn_255_transpose_x_0 = const()[name = tensor("attn_255_transpose_x_0"), val = tensor(false)]; tensor attn_255_transpose_y_0 = const()[name = tensor("attn_255_transpose_y_0"), val = tensor(true)]; - tensor attn_255 = matmul(transpose_x = attn_255_transpose_x_0, transpose_y = attn_255_transpose_y_0, x = var_12427, y = var_12431)[name = tensor("attn_255")]; - tensor var_12435 = const()[name = tensor("op_12435"), val = tensor([2, 1280, 1, -1])]; - tensor input_709 = reshape(shape = var_12435, x = attn_255)[name = tensor("input_709")]; - tensor var_12440 = const()[name = tensor("op_12440"), val = tensor([1, 1])]; - tensor var_12442 = const()[name = tensor("op_12442"), val = tensor([1, 1])]; - tensor var_12444_pad_type_0 = const()[name = tensor("op_12444_pad_type_0"), val = tensor("custom")]; - tensor var_12444_pad_0 = const()[name = tensor("op_12444_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12444 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias, dilations = var_12442, groups = var_6872, pad = var_12444_pad_0, pad_type = var_12444_pad_type_0, strides = var_12440, weight = up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight, x = input_709)[name = tensor("op_12444")]; - tensor inputs_383 = add(x = var_12444, y = inputs_381)[name = tensor("inputs_383")]; - tensor var_12448 = const()[name = tensor("op_12448"), val = tensor([1])]; - tensor channels_mean_383 = reduce_mean(axes = var_12448, keep_dims = var_6867, x = inputs_383)[name = tensor("channels_mean_383")]; - tensor zero_mean_383 = sub(x = inputs_383, y = channels_mean_383)[name = tensor("zero_mean_383")]; - tensor zero_mean_sq_383 = mul(x = zero_mean_383, y = zero_mean_383)[name = tensor("zero_mean_sq_383")]; - tensor var_12452 = const()[name = tensor("op_12452"), val = tensor([1])]; - tensor var_12453 = reduce_mean(axes = var_12452, keep_dims = var_6867, x = zero_mean_sq_383)[name = tensor("op_12453")]; - tensor var_12454 = const()[name = tensor("op_12454"), val = tensor(0x1.4f8b58p-17)]; - tensor var_12455 = add(x = var_12453, y = var_12454)[name = tensor("op_12455")]; - tensor denom_383_epsilon_0 = const()[name = tensor("denom_383_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_383 = rsqrt(epsilon = denom_383_epsilon_0, x = var_12455)[name = tensor("denom_383")]; - tensor out_383 = mul(x = zero_mean_383, y = denom_383)[name = tensor("out_383")]; - tensor var_12459 = const()[name = tensor("op_12459"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269818880)))]; - tensor var_12460 = add(x = out_383, y = var_12459)[name = tensor("op_12460")]; - tensor var_12462 = const()[name = tensor("op_12462"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269824064)))]; - tensor input_711 = mul(x = var_12460, y = var_12462)[name = tensor("input_711")]; - tensor var_12470 = const()[name = tensor("op_12470"), val = tensor([1, 1])]; - tensor var_12472 = const()[name = tensor("op_12472"), val = tensor([1, 1])]; - tensor var_12474_pad_type_0 = const()[name = tensor("op_12474_pad_type_0"), val = tensor("custom")]; - tensor var_12474_pad_0 = const()[name = tensor("op_12474_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12474 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias, dilations = var_12472, groups = var_6872, pad = var_12474_pad_0, pad_type = var_12474_pad_type_0, strides = var_12470, weight = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight, x = input_711)[name = tensor("op_12474")]; - tensor var_12475_split_sizes_0 = const()[name = tensor("op_12475_split_sizes_0"), val = tensor([5120, 5120])]; - tensor var_12475_axis_0 = const()[name = tensor("op_12475_axis_0"), val = tensor(1)]; - tensor var_12475_0, tensor var_12475_1 = split(axis = var_12475_axis_0, split_sizes = var_12475_split_sizes_0, x = var_12474)[name = tensor("op_12475")]; - tensor var_12477_mode_0 = const()[name = tensor("op_12477_mode_0"), val = tensor("EXACT")]; - tensor var_12477 = gelu(mode = var_12477_mode_0, x = var_12475_1)[name = tensor("op_12477")]; - tensor input_713 = mul(x = var_12475_0, y = var_12477)[name = tensor("input_713")]; - tensor var_12481 = const()[name = tensor("op_12481"), val = tensor([1, 1])]; - tensor var_12483 = const()[name = tensor("op_12483"), val = tensor([1, 1])]; - tensor var_12485_pad_type_0 = const()[name = tensor("op_12485_pad_type_0"), val = tensor("custom")]; - tensor var_12485_pad_0 = const()[name = tensor("op_12485_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12485 = conv(bias = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias, dilations = var_12483, groups = var_6872, pad = var_12485_pad_0, pad_type = var_12485_pad_type_0, strides = var_12481, weight = up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight, x = input_713)[name = tensor("op_12485")]; - tensor hidden_states_495 = add(x = var_12485, y = inputs_383)[name = tensor("hidden_states_495")]; - tensor var_12487 = const()[name = tensor("op_12487"), val = tensor([2, 1280, 32, 32])]; - tensor input_715 = reshape(shape = var_12487, x = hidden_states_495)[name = tensor("input_715")]; - tensor var_12491 = const()[name = tensor("op_12491"), val = tensor([1, 1])]; - tensor var_12493 = const()[name = tensor("op_12493"), val = tensor([1, 1])]; + tensor attn_255_cast = matmul(transpose_x = attn_255_transpose_x_0, transpose_y = attn_255_transpose_y_0, x = var_12328_cast, y = var_12332_cast)[name = tensor("attn_255_cast")]; + tensor var_12336 = const()[name = tensor("op_12336"), val = tensor([2, 1280, 1, -1])]; + tensor input_709_cast = reshape(shape = var_12336, x = attn_255_cast)[name = tensor("input_709_cast")]; + tensor var_12341 = const()[name = tensor("op_12341"), val = tensor([1, 1])]; + tensor var_12343 = const()[name = tensor("op_12343"), val = tensor([1, 1])]; + tensor var_12345_pad_type_0 = const()[name = tensor("op_12345_pad_type_0"), val = tensor("custom")]; + tensor var_12345_pad_0 = const()[name = tensor("op_12345_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4824590976)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4827867840)))]; + tensor var_12345_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_bias_to_fp16, dilations = var_12343, groups = var_31, pad = var_12345_pad_0, pad_type = var_12345_pad_type_0, strides = var_12341, weight = unet_up_blocks_0_attentions_2_transformer_blocks_9_attn2_to_out_0_weight_to_fp16, x = input_709_cast)[name = tensor("op_12345_cast")]; + tensor inputs_383_cast = add(x = var_12345_cast, y = inputs_381_cast)[name = tensor("inputs_383_cast")]; + tensor var_12349 = const()[name = tensor("op_12349"), val = tensor([1])]; + tensor channels_mean_383_cast = reduce_mean(axes = var_12349, keep_dims = var_23, x = inputs_383_cast)[name = tensor("channels_mean_383_cast")]; + tensor zero_mean_383_cast = sub(x = inputs_383_cast, y = channels_mean_383_cast)[name = tensor("zero_mean_383_cast")]; + tensor zero_mean_sq_383_cast = mul(x = zero_mean_383_cast, y = zero_mean_383_cast)[name = tensor("zero_mean_sq_383_cast")]; + tensor var_12353 = const()[name = tensor("op_12353"), val = tensor([1])]; + tensor var_12354_cast = reduce_mean(axes = var_12353, keep_dims = var_23, x = zero_mean_sq_383_cast)[name = tensor("op_12354_cast")]; + tensor var_12355_to_fp16 = const()[name = tensor("op_12355_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12356_cast = add(x = var_12354_cast, y = var_12355_to_fp16)[name = tensor("op_12356_cast")]; + tensor denom_383_epsilon_0_to_fp16 = const()[name = tensor("denom_383_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_383_cast = rsqrt(epsilon = denom_383_epsilon_0_to_fp16, x = var_12356_cast)[name = tensor("denom_383_cast")]; + tensor out_383_cast = mul(x = zero_mean_383_cast, y = denom_383_cast)[name = tensor("out_383_cast")]; + tensor var_12360_to_fp16 = const()[name = tensor("op_12360_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4827870464)))]; + tensor var_12361_cast = add(x = out_383_cast, y = var_12360_to_fp16)[name = tensor("op_12361_cast")]; + tensor var_12363_to_fp16 = const()[name = tensor("op_12363_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4827873088)))]; + tensor input_711_cast = mul(x = var_12361_cast, y = var_12363_to_fp16)[name = tensor("input_711_cast")]; + tensor var_12371 = const()[name = tensor("op_12371"), val = tensor([1, 1])]; + tensor var_12373 = const()[name = tensor("op_12373"), val = tensor([1, 1])]; + tensor var_12375_pad_type_0 = const()[name = tensor("op_12375_pad_type_0"), val = tensor("custom")]; + tensor var_12375_pad_0 = const()[name = tensor("op_12375_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4827875712)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4854090176)))]; + tensor var_12375_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_bias_to_fp16, dilations = var_12373, groups = var_31, pad = var_12375_pad_0, pad_type = var_12375_pad_type_0, strides = var_12371, weight = unet_up_blocks_0_attentions_2_transformer_blocks_9_ff_net_0_proj_weight_to_fp16, x = input_711_cast)[name = tensor("op_12375_cast")]; + tensor var_12376_split_sizes_0 = const()[name = tensor("op_12376_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_12376_axis_0 = const()[name = tensor("op_12376_axis_0"), val = tensor(1)]; + tensor var_12376_cast_0, tensor var_12376_cast_1 = split(axis = var_12376_axis_0, split_sizes = var_12376_split_sizes_0, x = var_12375_cast)[name = tensor("op_12376_cast")]; + tensor var_12378_mode_0 = const()[name = tensor("op_12378_mode_0"), val = tensor("EXACT")]; + tensor var_12378_cast = gelu(mode = var_12378_mode_0, x = var_12376_cast_1)[name = tensor("op_12378_cast")]; + tensor input_713_cast = mul(x = var_12376_cast_0, y = var_12378_cast)[name = tensor("input_713_cast")]; + tensor var_12382 = const()[name = tensor("op_12382"), val = tensor([1, 1])]; + tensor var_12384 = const()[name = tensor("op_12384"), val = tensor([1, 1])]; + tensor var_12386_pad_type_0 = const()[name = tensor("op_12386_pad_type_0"), val = tensor("custom")]; + tensor var_12386_pad_0 = const()[name = tensor("op_12386_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4854110720)))]; + tensor unet_up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4867217984)))]; + tensor var_12386_cast = conv(bias = unet_up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_bias_to_fp16, dilations = var_12384, groups = var_31, pad = var_12386_pad_0, pad_type = var_12386_pad_type_0, strides = var_12382, weight = unet_up_blocks_0_attentions_2_transformer_blocks_9_ff_net_2_weight_to_fp16, x = input_713_cast)[name = tensor("op_12386_cast")]; + tensor hidden_states_495_cast = add(x = var_12386_cast, y = inputs_383_cast)[name = tensor("hidden_states_495_cast")]; + tensor var_12388 = const()[name = tensor("op_12388"), val = tensor([2, 1280, 32, 32])]; + tensor input_715_cast = reshape(shape = var_12388, x = hidden_states_495_cast)[name = tensor("input_715_cast")]; + tensor var_12392 = const()[name = tensor("op_12392"), val = tensor([1, 1])]; + tensor var_12394 = const()[name = tensor("op_12394"), val = tensor([1, 1])]; tensor hidden_states_497_pad_type_0 = const()[name = tensor("hidden_states_497_pad_type_0"), val = tensor("custom")]; tensor hidden_states_497_pad_0 = const()[name = tensor("hidden_states_497_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_497 = conv(bias = up_blocks_0_attentions_2_proj_out_bias, dilations = var_12493, groups = var_6872, pad = hidden_states_497_pad_0, pad_type = hidden_states_497_pad_type_0, strides = var_12491, weight = up_blocks_0_attentions_2_proj_out_weight, x = input_715)[name = tensor("hidden_states_497")]; - tensor input_717 = add(x = hidden_states_497, y = hidden_states_431)[name = tensor("input_717")]; + tensor unet_up_blocks_0_attentions_2_proj_out_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4867220608)))]; + tensor unet_up_blocks_0_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4870497472)))]; + tensor hidden_states_497_cast = conv(bias = unet_up_blocks_0_attentions_2_proj_out_bias_to_fp16, dilations = var_12394, groups = var_31, pad = hidden_states_497_pad_0, pad_type = hidden_states_497_pad_type_0, strides = var_12392, weight = unet_up_blocks_0_attentions_2_proj_out_weight_to_fp16, x = input_715_cast)[name = tensor("hidden_states_497_cast")]; + tensor input_717_cast = add(x = hidden_states_497_cast, y = hidden_states_431_cast)[name = tensor("input_717_cast")]; tensor input_719_scale_factor_height_0 = const()[name = tensor("input_719_scale_factor_height_0"), val = tensor(0x1p+1)]; tensor input_719_scale_factor_width_0 = const()[name = tensor("input_719_scale_factor_width_0"), val = tensor(0x1p+1)]; - tensor input_719 = upsample_nearest_neighbor(scale_factor_height = input_719_scale_factor_height_0, scale_factor_width = input_719_scale_factor_width_0, x = input_717)[name = tensor("input_719")]; - tensor var_12502 = const()[name = tensor("op_12502"), val = tensor([1, 1])]; - tensor var_12504 = const()[name = tensor("op_12504"), val = tensor([1, 1])]; + tensor input_719_cast = upsample_nearest_neighbor(scale_factor_height = input_719_scale_factor_height_0, scale_factor_width = input_719_scale_factor_width_0, x = input_717_cast)[name = tensor("input_719_cast")]; + tensor var_12403 = const()[name = tensor("op_12403"), val = tensor([1, 1])]; + tensor var_12405 = const()[name = tensor("op_12405"), val = tensor([1, 1])]; tensor hidden_states_499_pad_type_0 = const()[name = tensor("hidden_states_499_pad_type_0"), val = tensor("custom")]; tensor hidden_states_499_pad_0 = const()[name = tensor("hidden_states_499_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_499 = conv(bias = up_blocks_0_upsamplers_0_conv_bias, dilations = var_12504, groups = var_6872, pad = hidden_states_499_pad_0, pad_type = hidden_states_499_pad_type_0, strides = var_12502, weight = up_blocks_0_upsamplers_0_conv_weight, x = input_719)[name = tensor("hidden_states_499")]; - tensor var_12509 = const()[name = tensor("op_12509"), val = tensor(3)]; - tensor var_12516 = const()[name = tensor("op_12516"), val = tensor(0x1p-3)]; - tensor var_12520 = const()[name = tensor("op_12520"), val = tensor(true)]; - tensor var_12525 = const()[name = tensor("op_12525"), val = tensor(1)]; + tensor unet_up_blocks_0_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("unet_up_blocks_0_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4870500096)))]; + tensor unet_up_blocks_0_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("unet_up_blocks_0_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4899991360)))]; + tensor hidden_states_499_cast = conv(bias = unet_up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_12405, groups = var_31, pad = hidden_states_499_pad_0, pad_type = hidden_states_499_pad_type_0, strides = var_12403, weight = unet_up_blocks_0_upsamplers_0_conv_weight_to_fp16, x = input_719_cast)[name = tensor("hidden_states_499_cast")]; tensor input_721_interleave_0 = const()[name = tensor("input_721_interleave_0"), val = tensor(false)]; - tensor input_721 = concat(axis = var_12525, interleave = input_721_interleave_0, values = (hidden_states_499, input_113))[name = tensor("input_721")]; + tensor input_721_cast = concat(axis = var_31, interleave = input_721_interleave_0, values = (hidden_states_499_cast, input_113_cast))[name = tensor("input_721_cast")]; tensor reshape_120_shape_0 = const()[name = tensor("reshape_120_shape_0"), val = tensor([2, 32, 60, 64, 64])]; - tensor reshape_120 = reshape(shape = reshape_120_shape_0, x = input_721)[name = tensor("reshape_120")]; + tensor reshape_120_cast = reshape(shape = reshape_120_shape_0, x = input_721_cast)[name = tensor("reshape_120_cast")]; tensor reduce_mean_90_axes_0 = const()[name = tensor("reduce_mean_90_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_90_keep_dims_0 = const()[name = tensor("reduce_mean_90_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_90 = reduce_mean(axes = reduce_mean_90_axes_0, keep_dims = reduce_mean_90_keep_dims_0, x = reshape_120)[name = tensor("reduce_mean_90")]; - tensor sub_60 = sub(x = reshape_120, y = reduce_mean_90)[name = tensor("sub_60")]; - tensor square_30 = square(x = sub_60)[name = tensor("square_30")]; + tensor reduce_mean_90_cast = reduce_mean(axes = reduce_mean_90_axes_0, keep_dims = reduce_mean_90_keep_dims_0, x = reshape_120_cast)[name = tensor("reduce_mean_90_cast")]; + tensor sub_60_cast = sub(x = reshape_120_cast, y = reduce_mean_90_cast)[name = tensor("sub_60_cast")]; + tensor square_30_cast = square(x = sub_60_cast)[name = tensor("square_30_cast")]; tensor reduce_mean_92_axes_0 = const()[name = tensor("reduce_mean_92_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_92_keep_dims_0 = const()[name = tensor("reduce_mean_92_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_92 = reduce_mean(axes = reduce_mean_92_axes_0, keep_dims = reduce_mean_92_keep_dims_0, x = square_30)[name = tensor("reduce_mean_92")]; - tensor add_60_y_0 = const()[name = tensor("add_60_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_60 = add(x = reduce_mean_92, y = add_60_y_0)[name = tensor("add_60")]; - tensor sqrt_30 = sqrt(x = add_60)[name = tensor("sqrt_30")]; - tensor real_div_30 = real_div(x = sub_60, y = sqrt_30)[name = tensor("real_div_30")]; + tensor reduce_mean_92_cast = reduce_mean(axes = reduce_mean_92_axes_0, keep_dims = reduce_mean_92_keep_dims_0, x = square_30_cast)[name = tensor("reduce_mean_92_cast")]; + tensor add_60_y_0_to_fp16 = const()[name = tensor("add_60_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_60_cast = add(x = reduce_mean_92_cast, y = add_60_y_0_to_fp16)[name = tensor("add_60_cast")]; + tensor sqrt_30_cast = sqrt(x = add_60_cast)[name = tensor("sqrt_30_cast")]; + tensor real_div_30_cast = real_div(x = sub_60_cast, y = sqrt_30_cast)[name = tensor("real_div_30_cast")]; tensor reshape_121_shape_0 = const()[name = tensor("reshape_121_shape_0"), val = tensor([2, 1920, 64, 64])]; - tensor reshape_121 = reshape(shape = reshape_121_shape_0, x = real_div_30)[name = tensor("reshape_121")]; - tensor add_61_gamma_0 = const()[name = tensor("add_61_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269829248)))]; - tensor add_61_beta_0 = const()[name = tensor("add_61_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269836992)))]; - tensor add_61_epsilon_0 = const()[name = tensor("add_61_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_61 = batch_norm(beta = add_61_beta_0, epsilon = add_61_epsilon_0, gamma = add_61_gamma_0, mean = add_55_mean_0, variance = add_55_variance_0, x = reshape_121)[name = tensor("add_61")]; - tensor input_725 = silu(x = add_61)[name = tensor("input_725")]; - tensor var_12554 = const()[name = tensor("op_12554"), val = tensor([1, 1])]; - tensor var_12556 = const()[name = tensor("op_12556"), val = tensor([1, 1])]; + tensor reshape_121_cast = reshape(shape = reshape_121_shape_0, x = real_div_30_cast)[name = tensor("reshape_121_cast")]; + tensor add_61_gamma_0_to_fp16 = const()[name = tensor("add_61_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4899993984)))]; + tensor add_61_beta_0_to_fp16 = const()[name = tensor("add_61_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4899997888)))]; + tensor add_61_epsilon_0_to_fp16 = const()[name = tensor("add_61_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_61_cast = batch_norm(beta = add_61_beta_0_to_fp16, epsilon = add_61_epsilon_0_to_fp16, gamma = add_61_gamma_0_to_fp16, mean = add_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_121_cast)[name = tensor("add_61_cast")]; + tensor input_725_cast = silu(x = add_61_cast)[name = tensor("input_725_cast")]; + tensor var_12436 = const()[name = tensor("op_12436"), val = tensor([1, 1])]; + tensor var_12438 = const()[name = tensor("op_12438"), val = tensor([1, 1])]; tensor hidden_states_501_pad_type_0 = const()[name = tensor("hidden_states_501_pad_type_0"), val = tensor("custom")]; tensor hidden_states_501_pad_0 = const()[name = tensor("hidden_states_501_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_501 = conv(bias = up_blocks_1_resnets_0_conv1_bias, dilations = var_12556, groups = var_12525, pad = hidden_states_501_pad_0, pad_type = hidden_states_501_pad_type_0, strides = var_12554, weight = up_blocks_1_resnets_0_conv1_weight, x = input_725)[name = tensor("hidden_states_501")]; - tensor var_12562 = const()[name = tensor("op_12562"), val = tensor([1, 1])]; - tensor var_12564 = const()[name = tensor("op_12564"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4900001792)))]; + tensor unet_up_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4922120256)))]; + tensor hidden_states_501_cast = conv(bias = unet_up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_12438, groups = var_31, pad = hidden_states_501_pad_0, pad_type = hidden_states_501_pad_type_0, strides = var_12436, weight = unet_up_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_725_cast)[name = tensor("hidden_states_501_cast")]; + tensor var_12444 = const()[name = tensor("op_12444"), val = tensor([1, 1])]; + tensor var_12446 = const()[name = tensor("op_12446"), val = tensor([1, 1])]; tensor temb_23_pad_type_0 = const()[name = tensor("temb_23_pad_type_0"), val = tensor("custom")]; tensor temb_23_pad_0 = const()[name = tensor("temb_23_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb_23 = conv(bias = up_blocks_1_resnets_0_time_emb_proj_bias, dilations = var_12564, groups = var_12525, pad = temb_23_pad_0, pad_type = temb_23_pad_type_0, strides = var_12562, weight = up_blocks_1_resnets_0_time_emb_proj_weight, x = input_21)[name = tensor("temb_23")]; - tensor input_729 = add(x = hidden_states_501, y = temb_23)[name = tensor("input_729")]; + tensor unet_up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4922121600)))]; + tensor unet_up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4923760064)))]; + tensor temb_23_cast = conv(bias = unet_up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_12446, groups = var_31, pad = temb_23_pad_0, pad_type = temb_23_pad_type_0, strides = var_12444, weight = unet_up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_23_cast")]; + tensor input_729_cast = add(x = hidden_states_501_cast, y = temb_23_cast)[name = tensor("input_729_cast")]; tensor reshape_124_shape_0 = const()[name = tensor("reshape_124_shape_0"), val = tensor([2, 32, 20, 64, 64])]; - tensor reshape_124 = reshape(shape = reshape_124_shape_0, x = input_729)[name = tensor("reshape_124")]; + tensor reshape_124_cast = reshape(shape = reshape_124_shape_0, x = input_729_cast)[name = tensor("reshape_124_cast")]; tensor reduce_mean_93_axes_0 = const()[name = tensor("reduce_mean_93_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_93_keep_dims_0 = const()[name = tensor("reduce_mean_93_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_93 = reduce_mean(axes = reduce_mean_93_axes_0, keep_dims = reduce_mean_93_keep_dims_0, x = reshape_124)[name = tensor("reduce_mean_93")]; - tensor sub_62 = sub(x = reshape_124, y = reduce_mean_93)[name = tensor("sub_62")]; - tensor square_31 = square(x = sub_62)[name = tensor("square_31")]; + tensor reduce_mean_93_cast = reduce_mean(axes = reduce_mean_93_axes_0, keep_dims = reduce_mean_93_keep_dims_0, x = reshape_124_cast)[name = tensor("reduce_mean_93_cast")]; + tensor sub_62_cast = sub(x = reshape_124_cast, y = reduce_mean_93_cast)[name = tensor("sub_62_cast")]; + tensor square_31_cast = square(x = sub_62_cast)[name = tensor("square_31_cast")]; tensor reduce_mean_95_axes_0 = const()[name = tensor("reduce_mean_95_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_95_keep_dims_0 = const()[name = tensor("reduce_mean_95_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_95 = reduce_mean(axes = reduce_mean_95_axes_0, keep_dims = reduce_mean_95_keep_dims_0, x = square_31)[name = tensor("reduce_mean_95")]; - tensor add_62_y_0 = const()[name = tensor("add_62_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_62 = add(x = reduce_mean_95, y = add_62_y_0)[name = tensor("add_62")]; - tensor sqrt_31 = sqrt(x = add_62)[name = tensor("sqrt_31")]; - tensor real_div_31 = real_div(x = sub_62, y = sqrt_31)[name = tensor("real_div_31")]; + tensor reduce_mean_95_cast = reduce_mean(axes = reduce_mean_95_axes_0, keep_dims = reduce_mean_95_keep_dims_0, x = square_31_cast)[name = tensor("reduce_mean_95_cast")]; + tensor add_62_y_0_to_fp16 = const()[name = tensor("add_62_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_62_cast = add(x = reduce_mean_95_cast, y = add_62_y_0_to_fp16)[name = tensor("add_62_cast")]; + tensor sqrt_31_cast = sqrt(x = add_62_cast)[name = tensor("sqrt_31_cast")]; + tensor real_div_31_cast = real_div(x = sub_62_cast, y = sqrt_31_cast)[name = tensor("real_div_31_cast")]; tensor reshape_125_shape_0 = const()[name = tensor("reshape_125_shape_0"), val = tensor([2, 640, 64, 64])]; - tensor reshape_125 = reshape(shape = reshape_125_shape_0, x = real_div_31)[name = tensor("reshape_125")]; - tensor add_63_gamma_0 = const()[name = tensor("add_63_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269844736)))]; - tensor add_63_beta_0 = const()[name = tensor("add_63_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269847360)))]; - tensor add_63_epsilon_0 = const()[name = tensor("add_63_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_63 = batch_norm(beta = add_63_beta_0, epsilon = add_63_epsilon_0, gamma = add_63_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_125)[name = tensor("add_63")]; - tensor input_733 = silu(x = add_63)[name = tensor("input_733")]; - tensor var_12574 = const()[name = tensor("op_12574"), val = tensor([1, 1])]; - tensor var_12576 = const()[name = tensor("op_12576"), val = tensor([1, 1])]; + tensor reshape_125_cast = reshape(shape = reshape_125_shape_0, x = real_div_31_cast)[name = tensor("reshape_125_cast")]; + tensor add_63_gamma_0_to_fp16 = const()[name = tensor("add_63_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4923761408)))]; + tensor add_63_beta_0_to_fp16 = const()[name = tensor("add_63_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4923762752)))]; + tensor add_63_epsilon_0_to_fp16 = const()[name = tensor("add_63_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_63_cast = batch_norm(beta = add_63_beta_0_to_fp16, epsilon = add_63_epsilon_0_to_fp16, gamma = add_63_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_125_cast)[name = tensor("add_63_cast")]; + tensor input_733_cast = silu(x = add_63_cast)[name = tensor("input_733_cast")]; + tensor var_12456 = const()[name = tensor("op_12456"), val = tensor([1, 1])]; + tensor var_12458 = const()[name = tensor("op_12458"), val = tensor([1, 1])]; tensor hidden_states_503_pad_type_0 = const()[name = tensor("hidden_states_503_pad_type_0"), val = tensor("custom")]; tensor hidden_states_503_pad_0 = const()[name = tensor("hidden_states_503_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_503 = conv(bias = up_blocks_1_resnets_0_conv2_bias, dilations = var_12576, groups = var_12525, pad = hidden_states_503_pad_0, pad_type = hidden_states_503_pad_type_0, strides = var_12574, weight = up_blocks_1_resnets_0_conv2_weight, x = input_733)[name = tensor("hidden_states_503")]; - tensor var_12581 = const()[name = tensor("op_12581"), val = tensor([1, 1])]; - tensor var_12583 = const()[name = tensor("op_12583"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4923764096)))]; + tensor unet_up_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4931136960)))]; + tensor hidden_states_503_cast = conv(bias = unet_up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_12458, groups = var_31, pad = hidden_states_503_pad_0, pad_type = hidden_states_503_pad_type_0, strides = var_12456, weight = unet_up_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_733_cast)[name = tensor("hidden_states_503_cast")]; + tensor var_12463 = const()[name = tensor("op_12463"), val = tensor([1, 1])]; + tensor var_12465 = const()[name = tensor("op_12465"), val = tensor([1, 1])]; tensor x_11_pad_type_0 = const()[name = tensor("x_11_pad_type_0"), val = tensor("custom")]; tensor x_11_pad_0 = const()[name = tensor("x_11_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor x_11 = conv(bias = up_blocks_1_resnets_0_conv_shortcut_bias, dilations = var_12583, groups = var_12525, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = var_12581, weight = up_blocks_1_resnets_0_conv_shortcut_weight, x = input_721)[name = tensor("x_11")]; - tensor hidden_states_505 = add(x = x_11, y = hidden_states_503)[name = tensor("hidden_states_505")]; + tensor unet_up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4931138304)))]; + tensor unet_up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4933595968)))]; + tensor x_11_cast = conv(bias = unet_up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_12465, groups = var_31, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = var_12463, weight = unet_up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16, x = input_721_cast)[name = tensor("x_11_cast")]; + tensor hidden_states_505_cast = add(x = x_11_cast, y = hidden_states_503_cast)[name = tensor("hidden_states_505_cast")]; tensor reshape_128_shape_0 = const()[name = tensor("reshape_128_shape_0"), val = tensor([2, 32, 20, 64, 64])]; - tensor reshape_128 = reshape(shape = reshape_128_shape_0, x = hidden_states_505)[name = tensor("reshape_128")]; + tensor reshape_128_cast = reshape(shape = reshape_128_shape_0, x = hidden_states_505_cast)[name = tensor("reshape_128_cast")]; tensor reduce_mean_96_axes_0 = const()[name = tensor("reduce_mean_96_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_96_keep_dims_0 = const()[name = tensor("reduce_mean_96_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_96 = reduce_mean(axes = reduce_mean_96_axes_0, keep_dims = reduce_mean_96_keep_dims_0, x = reshape_128)[name = tensor("reduce_mean_96")]; - tensor sub_64 = sub(x = reshape_128, y = reduce_mean_96)[name = tensor("sub_64")]; - tensor square_32 = square(x = sub_64)[name = tensor("square_32")]; + tensor reduce_mean_96_cast = reduce_mean(axes = reduce_mean_96_axes_0, keep_dims = reduce_mean_96_keep_dims_0, x = reshape_128_cast)[name = tensor("reduce_mean_96_cast")]; + tensor sub_64_cast = sub(x = reshape_128_cast, y = reduce_mean_96_cast)[name = tensor("sub_64_cast")]; + tensor square_32_cast = square(x = sub_64_cast)[name = tensor("square_32_cast")]; tensor reduce_mean_98_axes_0 = const()[name = tensor("reduce_mean_98_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_98_keep_dims_0 = const()[name = tensor("reduce_mean_98_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_98 = reduce_mean(axes = reduce_mean_98_axes_0, keep_dims = reduce_mean_98_keep_dims_0, x = square_32)[name = tensor("reduce_mean_98")]; - tensor add_64_y_0 = const()[name = tensor("add_64_y_0"), val = tensor(0x1.0c6f7ap-20)]; - tensor add_64 = add(x = reduce_mean_98, y = add_64_y_0)[name = tensor("add_64")]; - tensor sqrt_32 = sqrt(x = add_64)[name = tensor("sqrt_32")]; - tensor real_div_32 = real_div(x = sub_64, y = sqrt_32)[name = tensor("real_div_32")]; + tensor reduce_mean_98_cast = reduce_mean(axes = reduce_mean_98_axes_0, keep_dims = reduce_mean_98_keep_dims_0, x = square_32_cast)[name = tensor("reduce_mean_98_cast")]; + tensor add_64_y_0_to_fp16 = const()[name = tensor("add_64_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_64_cast = add(x = reduce_mean_98_cast, y = add_64_y_0_to_fp16)[name = tensor("add_64_cast")]; + tensor sqrt_32_cast = sqrt(x = add_64_cast)[name = tensor("sqrt_32_cast")]; + tensor real_div_32_cast = real_div(x = sub_64_cast, y = sqrt_32_cast)[name = tensor("real_div_32_cast")]; tensor reshape_129_shape_0 = const()[name = tensor("reshape_129_shape_0"), val = tensor([2, 640, 64, 64])]; - tensor reshape_129 = reshape(shape = reshape_129_shape_0, x = real_div_32)[name = tensor("reshape_129")]; - tensor add_65_gamma_0 = const()[name = tensor("add_65_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269849984)))]; - tensor add_65_beta_0 = const()[name = tensor("add_65_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269852608)))]; - tensor add_65_epsilon_0 = const()[name = tensor("add_65_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_65 = batch_norm(beta = add_65_beta_0, epsilon = add_65_epsilon_0, gamma = add_65_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_129)[name = tensor("add_65")]; - tensor var_12605 = const()[name = tensor("op_12605"), val = tensor([1, 1])]; - tensor var_12607 = const()[name = tensor("op_12607"), val = tensor([1, 1])]; + tensor reshape_129_cast = reshape(shape = reshape_129_shape_0, x = real_div_32_cast)[name = tensor("reshape_129_cast")]; + tensor add_65_gamma_0_to_fp16 = const()[name = tensor("add_65_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4933597312)))]; + tensor add_65_beta_0_to_fp16 = const()[name = tensor("add_65_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4933598656)))]; + tensor add_65_epsilon_0_to_fp16 = const()[name = tensor("add_65_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_65_cast = batch_norm(beta = add_65_beta_0_to_fp16, epsilon = add_65_epsilon_0_to_fp16, gamma = add_65_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_129_cast)[name = tensor("add_65_cast")]; + tensor var_12487 = const()[name = tensor("op_12487"), val = tensor([1, 1])]; + tensor var_12489 = const()[name = tensor("op_12489"), val = tensor([1, 1])]; tensor hidden_states_507_pad_type_0 = const()[name = tensor("hidden_states_507_pad_type_0"), val = tensor("custom")]; tensor hidden_states_507_pad_0 = const()[name = tensor("hidden_states_507_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_507 = conv(bias = up_blocks_1_attentions_0_proj_in_bias, dilations = var_12607, groups = var_12525, pad = hidden_states_507_pad_0, pad_type = hidden_states_507_pad_type_0, strides = var_12605, weight = up_blocks_1_attentions_0_proj_in_weight, x = add_65)[name = tensor("hidden_states_507")]; - tensor var_12612 = const()[name = tensor("op_12612"), val = tensor([2, 640, 1, 4096])]; - tensor inputs_385 = reshape(shape = var_12612, x = hidden_states_507)[name = tensor("inputs_385")]; - tensor var_12622 = const()[name = tensor("op_12622"), val = tensor([1])]; - tensor channels_mean_385 = reduce_mean(axes = var_12622, keep_dims = var_12520, x = inputs_385)[name = tensor("channels_mean_385")]; - tensor zero_mean_385 = sub(x = inputs_385, y = channels_mean_385)[name = tensor("zero_mean_385")]; - tensor zero_mean_sq_385 = mul(x = zero_mean_385, y = zero_mean_385)[name = tensor("zero_mean_sq_385")]; - tensor var_12626 = const()[name = tensor("op_12626"), val = tensor([1])]; - tensor var_12627 = reduce_mean(axes = var_12626, keep_dims = var_12520, x = zero_mean_sq_385)[name = tensor("op_12627")]; - tensor var_12628 = const()[name = tensor("op_12628"), val = tensor(0x1.4f8b58p-17)]; - tensor var_12629 = add(x = var_12627, y = var_12628)[name = tensor("op_12629")]; - tensor denom_385_epsilon_0 = const()[name = tensor("denom_385_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_385 = rsqrt(epsilon = denom_385_epsilon_0, x = var_12629)[name = tensor("denom_385")]; - tensor out_385 = mul(x = zero_mean_385, y = denom_385)[name = tensor("out_385")]; - tensor var_12633 = const()[name = tensor("op_12633"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269855232)))]; - tensor var_12634 = add(x = out_385, y = var_12633)[name = tensor("op_12634")]; - tensor var_12636 = const()[name = tensor("op_12636"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269857856)))]; - tensor hidden_states_509 = mul(x = var_12634, y = var_12636)[name = tensor("hidden_states_509")]; - tensor var_12643 = const()[name = tensor("op_12643"), val = tensor([1, 1])]; - tensor var_12645 = const()[name = tensor("op_12645"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_0_proj_in_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4933600000)))]; + tensor unet_up_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4934419264)))]; + tensor hidden_states_507_cast = conv(bias = unet_up_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_12489, groups = var_31, pad = hidden_states_507_pad_0, pad_type = hidden_states_507_pad_type_0, strides = var_12487, weight = unet_up_blocks_1_attentions_0_proj_in_weight_to_fp16, x = add_65_cast)[name = tensor("hidden_states_507_cast")]; + tensor var_12494 = const()[name = tensor("op_12494"), val = tensor([2, 640, 1, 4096])]; + tensor inputs_385_cast = reshape(shape = var_12494, x = hidden_states_507_cast)[name = tensor("inputs_385_cast")]; + tensor var_12504 = const()[name = tensor("op_12504"), val = tensor([1])]; + tensor channels_mean_385_cast = reduce_mean(axes = var_12504, keep_dims = var_23, x = inputs_385_cast)[name = tensor("channels_mean_385_cast")]; + tensor zero_mean_385_cast = sub(x = inputs_385_cast, y = channels_mean_385_cast)[name = tensor("zero_mean_385_cast")]; + tensor zero_mean_sq_385_cast = mul(x = zero_mean_385_cast, y = zero_mean_385_cast)[name = tensor("zero_mean_sq_385_cast")]; + tensor var_12508 = const()[name = tensor("op_12508"), val = tensor([1])]; + tensor var_12509_cast = reduce_mean(axes = var_12508, keep_dims = var_23, x = zero_mean_sq_385_cast)[name = tensor("op_12509_cast")]; + tensor var_12510_to_fp16 = const()[name = tensor("op_12510_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12511_cast = add(x = var_12509_cast, y = var_12510_to_fp16)[name = tensor("op_12511_cast")]; + tensor denom_385_epsilon_0_to_fp16 = const()[name = tensor("denom_385_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_385_cast = rsqrt(epsilon = denom_385_epsilon_0_to_fp16, x = var_12511_cast)[name = tensor("denom_385_cast")]; + tensor out_385_cast = mul(x = zero_mean_385_cast, y = denom_385_cast)[name = tensor("out_385_cast")]; + tensor var_12515_to_fp16 = const()[name = tensor("op_12515_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4934420608)))]; + tensor var_12516_cast = add(x = out_385_cast, y = var_12515_to_fp16)[name = tensor("op_12516_cast")]; + tensor var_12518_to_fp16 = const()[name = tensor("op_12518_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4934421952)))]; + tensor hidden_states_509_cast = mul(x = var_12516_cast, y = var_12518_to_fp16)[name = tensor("hidden_states_509_cast")]; + tensor var_12525 = const()[name = tensor("op_12525"), val = tensor([1, 1])]; + tensor var_12527 = const()[name = tensor("op_12527"), val = tensor([1, 1])]; tensor q_257_pad_type_0 = const()[name = tensor("q_257_pad_type_0"), val = tensor("custom")]; tensor q_257_pad_0 = const()[name = tensor("q_257_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_257 = conv(dilations = var_12645, groups = var_12525, pad = q_257_pad_0, pad_type = q_257_pad_type_0, strides = var_12643, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight, x = hidden_states_509)[name = tensor("q_257")]; - tensor var_12649 = const()[name = tensor("op_12649"), val = tensor([1, 1])]; - tensor var_12651 = const()[name = tensor("op_12651"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4934423296)))]; + tensor q_257_cast = conv(dilations = var_12527, groups = var_31, pad = q_257_pad_0, pad_type = q_257_pad_type_0, strides = var_12525, weight = unet_up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_509_cast)[name = tensor("q_257_cast")]; + tensor var_12531 = const()[name = tensor("op_12531"), val = tensor([1, 1])]; + tensor var_12533 = const()[name = tensor("op_12533"), val = tensor([1, 1])]; tensor k_257_pad_type_0 = const()[name = tensor("k_257_pad_type_0"), val = tensor("custom")]; tensor k_257_pad_0 = const()[name = tensor("k_257_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_257 = conv(dilations = var_12651, groups = var_12525, pad = k_257_pad_0, pad_type = k_257_pad_type_0, strides = var_12649, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight, x = hidden_states_509)[name = tensor("k_257")]; - tensor var_12655 = const()[name = tensor("op_12655"), val = tensor([1, 1])]; - tensor var_12657 = const()[name = tensor("op_12657"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4935242560)))]; + tensor k_257_cast = conv(dilations = var_12533, groups = var_31, pad = k_257_pad_0, pad_type = k_257_pad_type_0, strides = var_12531, weight = unet_up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_509_cast)[name = tensor("k_257_cast")]; + tensor var_12537 = const()[name = tensor("op_12537"), val = tensor([1, 1])]; + tensor var_12539 = const()[name = tensor("op_12539"), val = tensor([1, 1])]; tensor v_257_pad_type_0 = const()[name = tensor("v_257_pad_type_0"), val = tensor("custom")]; tensor v_257_pad_0 = const()[name = tensor("v_257_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_257 = conv(dilations = var_12657, groups = var_12525, pad = v_257_pad_0, pad_type = v_257_pad_type_0, strides = var_12655, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight, x = hidden_states_509)[name = tensor("v_257")]; - tensor var_12661 = const()[name = tensor("op_12661"), val = tensor([2, 10, 64, -1])]; - tensor var_12662 = reshape(shape = var_12661, x = q_257)[name = tensor("op_12662")]; - tensor var_12663 = const()[name = tensor("op_12663"), val = tensor([2, 10, 64, -1])]; - tensor var_12664 = reshape(shape = var_12663, x = k_257)[name = tensor("op_12664")]; - tensor var_12665 = const()[name = tensor("op_12665"), val = tensor([2, 10, 64, -1])]; - tensor var_12666 = reshape(shape = var_12665, x = v_257)[name = tensor("op_12666")]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4936061824)))]; + tensor v_257_cast = conv(dilations = var_12539, groups = var_31, pad = v_257_pad_0, pad_type = v_257_pad_type_0, strides = var_12537, weight = unet_up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_509_cast)[name = tensor("v_257_cast")]; + tensor var_12543 = const()[name = tensor("op_12543"), val = tensor([2, 10, 64, -1])]; + tensor var_12544_cast = reshape(shape = var_12543, x = q_257_cast)[name = tensor("op_12544_cast")]; + tensor var_12545 = const()[name = tensor("op_12545"), val = tensor([2, 10, 64, -1])]; + tensor var_12546_cast = reshape(shape = var_12545, x = k_257_cast)[name = tensor("op_12546_cast")]; + tensor var_12547 = const()[name = tensor("op_12547"), val = tensor([2, 10, 64, -1])]; + tensor var_12548_cast = reshape(shape = var_12547, x = v_257_cast)[name = tensor("op_12548_cast")]; tensor attn_weights_513_transpose_x_0 = const()[name = tensor("attn_weights_513_transpose_x_0"), val = tensor(true)]; tensor attn_weights_513_transpose_y_0 = const()[name = tensor("attn_weights_513_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_513 = matmul(transpose_x = attn_weights_513_transpose_x_0, transpose_y = attn_weights_513_transpose_y_0, x = var_12662, y = var_12664)[name = tensor("attn_weights_513")]; - tensor attn_weights_515 = mul(x = attn_weights_513, y = var_12516)[name = tensor("attn_weights_515")]; - tensor var_12670 = softmax(axis = var_12509, x = attn_weights_515)[name = tensor("op_12670")]; + tensor attn_weights_513_cast = matmul(transpose_x = attn_weights_513_transpose_x_0, transpose_y = attn_weights_513_transpose_y_0, x = var_12544_cast, y = var_12546_cast)[name = tensor("attn_weights_513_cast")]; + tensor attn_weights_515_cast = mul(x = attn_weights_513_cast, y = var_12_to_fp16)[name = tensor("attn_weights_515_cast")]; + tensor var_12552_cast = softmax(axis = var_18, x = attn_weights_515_cast)[name = tensor("op_12552_cast")]; tensor attn_257_transpose_x_0 = const()[name = tensor("attn_257_transpose_x_0"), val = tensor(false)]; tensor attn_257_transpose_y_0 = const()[name = tensor("attn_257_transpose_y_0"), val = tensor(true)]; - tensor attn_257 = matmul(transpose_x = attn_257_transpose_x_0, transpose_y = attn_257_transpose_y_0, x = var_12666, y = var_12670)[name = tensor("attn_257")]; - tensor var_12674 = const()[name = tensor("op_12674"), val = tensor([2, 640, 1, -1])]; - tensor input_737 = reshape(shape = var_12674, x = attn_257)[name = tensor("input_737")]; - tensor var_12679 = const()[name = tensor("op_12679"), val = tensor([1, 1])]; - tensor var_12681 = const()[name = tensor("op_12681"), val = tensor([1, 1])]; - tensor var_12683_pad_type_0 = const()[name = tensor("op_12683_pad_type_0"), val = tensor("custom")]; - tensor var_12683_pad_0 = const()[name = tensor("op_12683_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12683 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias, dilations = var_12681, groups = var_12525, pad = var_12683_pad_0, pad_type = var_12683_pad_type_0, strides = var_12679, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight, x = input_737)[name = tensor("op_12683")]; - tensor inputs_387 = add(x = var_12683, y = inputs_385)[name = tensor("inputs_387")]; - tensor var_12687 = const()[name = tensor("op_12687"), val = tensor([1])]; - tensor channels_mean_387 = reduce_mean(axes = var_12687, keep_dims = var_12520, x = inputs_387)[name = tensor("channels_mean_387")]; - tensor zero_mean_387 = sub(x = inputs_387, y = channels_mean_387)[name = tensor("zero_mean_387")]; - tensor zero_mean_sq_387 = mul(x = zero_mean_387, y = zero_mean_387)[name = tensor("zero_mean_sq_387")]; - tensor var_12691 = const()[name = tensor("op_12691"), val = tensor([1])]; - tensor var_12692 = reduce_mean(axes = var_12691, keep_dims = var_12520, x = zero_mean_sq_387)[name = tensor("op_12692")]; - tensor var_12693 = const()[name = tensor("op_12693"), val = tensor(0x1.4f8b58p-17)]; - tensor var_12694 = add(x = var_12692, y = var_12693)[name = tensor("op_12694")]; - tensor denom_387_epsilon_0 = const()[name = tensor("denom_387_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_387 = rsqrt(epsilon = denom_387_epsilon_0, x = var_12694)[name = tensor("denom_387")]; - tensor out_387 = mul(x = zero_mean_387, y = denom_387)[name = tensor("out_387")]; - tensor var_12698 = const()[name = tensor("op_12698"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269860480)))]; - tensor var_12699 = add(x = out_387, y = var_12698)[name = tensor("op_12699")]; - tensor var_12701 = const()[name = tensor("op_12701"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269863104)))]; - tensor hidden_states_511 = mul(x = var_12699, y = var_12701)[name = tensor("hidden_states_511")]; - tensor var_12708 = const()[name = tensor("op_12708"), val = tensor([1, 1])]; - tensor var_12710 = const()[name = tensor("op_12710"), val = tensor([1, 1])]; + tensor attn_257_cast = matmul(transpose_x = attn_257_transpose_x_0, transpose_y = attn_257_transpose_y_0, x = var_12548_cast, y = var_12552_cast)[name = tensor("attn_257_cast")]; + tensor var_12556 = const()[name = tensor("op_12556"), val = tensor([2, 640, 1, -1])]; + tensor input_737_cast = reshape(shape = var_12556, x = attn_257_cast)[name = tensor("input_737_cast")]; + tensor var_12561 = const()[name = tensor("op_12561"), val = tensor([1, 1])]; + tensor var_12563 = const()[name = tensor("op_12563"), val = tensor([1, 1])]; + tensor var_12565_pad_type_0 = const()[name = tensor("op_12565_pad_type_0"), val = tensor("custom")]; + tensor var_12565_pad_0 = const()[name = tensor("op_12565_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4936881088)))]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4937700352)))]; + tensor var_12565_cast = conv(bias = unet_up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_12563, groups = var_31, pad = var_12565_pad_0, pad_type = var_12565_pad_type_0, strides = var_12561, weight = unet_up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_737_cast)[name = tensor("op_12565_cast")]; + tensor inputs_387_cast = add(x = var_12565_cast, y = inputs_385_cast)[name = tensor("inputs_387_cast")]; + tensor var_12569 = const()[name = tensor("op_12569"), val = tensor([1])]; + tensor channels_mean_387_cast = reduce_mean(axes = var_12569, keep_dims = var_23, x = inputs_387_cast)[name = tensor("channels_mean_387_cast")]; + tensor zero_mean_387_cast = sub(x = inputs_387_cast, y = channels_mean_387_cast)[name = tensor("zero_mean_387_cast")]; + tensor zero_mean_sq_387_cast = mul(x = zero_mean_387_cast, y = zero_mean_387_cast)[name = tensor("zero_mean_sq_387_cast")]; + tensor var_12573 = const()[name = tensor("op_12573"), val = tensor([1])]; + tensor var_12574_cast = reduce_mean(axes = var_12573, keep_dims = var_23, x = zero_mean_sq_387_cast)[name = tensor("op_12574_cast")]; + tensor var_12575_to_fp16 = const()[name = tensor("op_12575_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12576_cast = add(x = var_12574_cast, y = var_12575_to_fp16)[name = tensor("op_12576_cast")]; + tensor denom_387_epsilon_0_to_fp16 = const()[name = tensor("denom_387_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_387_cast = rsqrt(epsilon = denom_387_epsilon_0_to_fp16, x = var_12576_cast)[name = tensor("denom_387_cast")]; + tensor out_387_cast = mul(x = zero_mean_387_cast, y = denom_387_cast)[name = tensor("out_387_cast")]; + tensor var_12580_to_fp16 = const()[name = tensor("op_12580_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4937701696)))]; + tensor var_12581_cast = add(x = out_387_cast, y = var_12580_to_fp16)[name = tensor("op_12581_cast")]; + tensor var_12583_to_fp16 = const()[name = tensor("op_12583_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4937703040)))]; + tensor hidden_states_511_cast = mul(x = var_12581_cast, y = var_12583_to_fp16)[name = tensor("hidden_states_511_cast")]; + tensor var_12590 = const()[name = tensor("op_12590"), val = tensor([1, 1])]; + tensor var_12592 = const()[name = tensor("op_12592"), val = tensor([1, 1])]; tensor q_259_pad_type_0 = const()[name = tensor("q_259_pad_type_0"), val = tensor("custom")]; tensor q_259_pad_0 = const()[name = tensor("q_259_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_259 = conv(dilations = var_12710, groups = var_12525, pad = q_259_pad_0, pad_type = q_259_pad_type_0, strides = var_12708, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight, x = hidden_states_511)[name = tensor("q_259")]; - tensor var_12714 = const()[name = tensor("op_12714"), val = tensor([1, 1])]; - tensor var_12716 = const()[name = tensor("op_12716"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4937704384)))]; + tensor q_259_cast = conv(dilations = var_12592, groups = var_31, pad = q_259_pad_0, pad_type = q_259_pad_type_0, strides = var_12590, weight = unet_up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_511_cast)[name = tensor("q_259_cast")]; + tensor var_12596 = const()[name = tensor("op_12596"), val = tensor([1, 1])]; + tensor var_12598 = const()[name = tensor("op_12598"), val = tensor([1, 1])]; tensor k_259_pad_type_0 = const()[name = tensor("k_259_pad_type_0"), val = tensor("custom")]; tensor k_259_pad_0 = const()[name = tensor("k_259_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_259 = conv(dilations = var_12716, groups = var_12525, pad = k_259_pad_0, pad_type = k_259_pad_type_0, strides = var_12714, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_259")]; - tensor var_12720 = const()[name = tensor("op_12720"), val = tensor([1, 1])]; - tensor var_12722 = const()[name = tensor("op_12722"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4938523648)))]; + tensor k_259_cast = conv(dilations = var_12598, groups = var_31, pad = k_259_pad_0, pad_type = k_259_pad_type_0, strides = var_12596, weight = unet_up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_259_cast")]; + tensor var_12602 = const()[name = tensor("op_12602"), val = tensor([1, 1])]; + tensor var_12604 = const()[name = tensor("op_12604"), val = tensor([1, 1])]; tensor v_259_pad_type_0 = const()[name = tensor("v_259_pad_type_0"), val = tensor("custom")]; tensor v_259_pad_0 = const()[name = tensor("v_259_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_259 = conv(dilations = var_12722, groups = var_12525, pad = v_259_pad_0, pad_type = v_259_pad_type_0, strides = var_12720, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_259")]; - tensor var_12726 = const()[name = tensor("op_12726"), val = tensor([2, 10, 64, -1])]; - tensor var_12727 = reshape(shape = var_12726, x = q_259)[name = tensor("op_12727")]; - tensor var_12728 = const()[name = tensor("op_12728"), val = tensor([2, 10, 64, -1])]; - tensor var_12729 = reshape(shape = var_12728, x = k_259)[name = tensor("op_12729")]; - tensor var_12730 = const()[name = tensor("op_12730"), val = tensor([2, 10, 64, -1])]; - tensor var_12731 = reshape(shape = var_12730, x = v_259)[name = tensor("op_12731")]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4941145152)))]; + tensor v_259_cast = conv(dilations = var_12604, groups = var_31, pad = v_259_pad_0, pad_type = v_259_pad_type_0, strides = var_12602, weight = unet_up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_259_cast")]; + tensor var_12608 = const()[name = tensor("op_12608"), val = tensor([2, 10, 64, -1])]; + tensor var_12609_cast = reshape(shape = var_12608, x = q_259_cast)[name = tensor("op_12609_cast")]; + tensor var_12610 = const()[name = tensor("op_12610"), val = tensor([2, 10, 64, -1])]; + tensor var_12611_cast = reshape(shape = var_12610, x = k_259_cast)[name = tensor("op_12611_cast")]; + tensor var_12612 = const()[name = tensor("op_12612"), val = tensor([2, 10, 64, -1])]; + tensor var_12613_cast = reshape(shape = var_12612, x = v_259_cast)[name = tensor("op_12613_cast")]; tensor attn_weights_517_transpose_x_0 = const()[name = tensor("attn_weights_517_transpose_x_0"), val = tensor(true)]; tensor attn_weights_517_transpose_y_0 = const()[name = tensor("attn_weights_517_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_517 = matmul(transpose_x = attn_weights_517_transpose_x_0, transpose_y = attn_weights_517_transpose_y_0, x = var_12727, y = var_12729)[name = tensor("attn_weights_517")]; - tensor attn_weights_519 = mul(x = attn_weights_517, y = var_12516)[name = tensor("attn_weights_519")]; - tensor var_12735 = softmax(axis = var_12509, x = attn_weights_519)[name = tensor("op_12735")]; + tensor attn_weights_517_cast = matmul(transpose_x = attn_weights_517_transpose_x_0, transpose_y = attn_weights_517_transpose_y_0, x = var_12609_cast, y = var_12611_cast)[name = tensor("attn_weights_517_cast")]; + tensor attn_weights_519_cast = mul(x = attn_weights_517_cast, y = var_12_to_fp16)[name = tensor("attn_weights_519_cast")]; + tensor var_12617_cast = softmax(axis = var_18, x = attn_weights_519_cast)[name = tensor("op_12617_cast")]; tensor attn_259_transpose_x_0 = const()[name = tensor("attn_259_transpose_x_0"), val = tensor(false)]; tensor attn_259_transpose_y_0 = const()[name = tensor("attn_259_transpose_y_0"), val = tensor(true)]; - tensor attn_259 = matmul(transpose_x = attn_259_transpose_x_0, transpose_y = attn_259_transpose_y_0, x = var_12731, y = var_12735)[name = tensor("attn_259")]; - tensor var_12739 = const()[name = tensor("op_12739"), val = tensor([2, 640, 1, -1])]; - tensor input_739 = reshape(shape = var_12739, x = attn_259)[name = tensor("input_739")]; - tensor var_12744 = const()[name = tensor("op_12744"), val = tensor([1, 1])]; - tensor var_12746 = const()[name = tensor("op_12746"), val = tensor([1, 1])]; - tensor var_12748_pad_type_0 = const()[name = tensor("op_12748_pad_type_0"), val = tensor("custom")]; - tensor var_12748_pad_0 = const()[name = tensor("op_12748_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12748 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias, dilations = var_12746, groups = var_12525, pad = var_12748_pad_0, pad_type = var_12748_pad_type_0, strides = var_12744, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight, x = input_739)[name = tensor("op_12748")]; - tensor inputs_389 = add(x = var_12748, y = inputs_387)[name = tensor("inputs_389")]; - tensor var_12752 = const()[name = tensor("op_12752"), val = tensor([1])]; - tensor channels_mean_389 = reduce_mean(axes = var_12752, keep_dims = var_12520, x = inputs_389)[name = tensor("channels_mean_389")]; - tensor zero_mean_389 = sub(x = inputs_389, y = channels_mean_389)[name = tensor("zero_mean_389")]; - tensor zero_mean_sq_389 = mul(x = zero_mean_389, y = zero_mean_389)[name = tensor("zero_mean_sq_389")]; - tensor var_12756 = const()[name = tensor("op_12756"), val = tensor([1])]; - tensor var_12757 = reduce_mean(axes = var_12756, keep_dims = var_12520, x = zero_mean_sq_389)[name = tensor("op_12757")]; - tensor var_12758 = const()[name = tensor("op_12758"), val = tensor(0x1.4f8b58p-17)]; - tensor var_12759 = add(x = var_12757, y = var_12758)[name = tensor("op_12759")]; - tensor denom_389_epsilon_0 = const()[name = tensor("denom_389_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_389 = rsqrt(epsilon = denom_389_epsilon_0, x = var_12759)[name = tensor("denom_389")]; - tensor out_389 = mul(x = zero_mean_389, y = denom_389)[name = tensor("out_389")]; - tensor var_12763 = const()[name = tensor("op_12763"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269865728)))]; - tensor var_12764 = add(x = out_389, y = var_12763)[name = tensor("op_12764")]; - tensor var_12766 = const()[name = tensor("op_12766"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269868352)))]; - tensor input_741 = mul(x = var_12764, y = var_12766)[name = tensor("input_741")]; - tensor var_12774 = const()[name = tensor("op_12774"), val = tensor([1, 1])]; - tensor var_12776 = const()[name = tensor("op_12776"), val = tensor([1, 1])]; - tensor var_12778_pad_type_0 = const()[name = tensor("op_12778_pad_type_0"), val = tensor("custom")]; - tensor var_12778_pad_0 = const()[name = tensor("op_12778_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12778 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias, dilations = var_12776, groups = var_12525, pad = var_12778_pad_0, pad_type = var_12778_pad_type_0, strides = var_12774, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight, x = input_741)[name = tensor("op_12778")]; - tensor var_12779_split_sizes_0 = const()[name = tensor("op_12779_split_sizes_0"), val = tensor([2560, 2560])]; - tensor var_12779_axis_0 = const()[name = tensor("op_12779_axis_0"), val = tensor(1)]; - tensor var_12779_0, tensor var_12779_1 = split(axis = var_12779_axis_0, split_sizes = var_12779_split_sizes_0, x = var_12778)[name = tensor("op_12779")]; - tensor var_12781_mode_0 = const()[name = tensor("op_12781_mode_0"), val = tensor("EXACT")]; - tensor var_12781 = gelu(mode = var_12781_mode_0, x = var_12779_1)[name = tensor("op_12781")]; - tensor input_743 = mul(x = var_12779_0, y = var_12781)[name = tensor("input_743")]; - tensor var_12785 = const()[name = tensor("op_12785"), val = tensor([1, 1])]; - tensor var_12787 = const()[name = tensor("op_12787"), val = tensor([1, 1])]; - tensor var_12789_pad_type_0 = const()[name = tensor("op_12789_pad_type_0"), val = tensor("custom")]; - tensor var_12789_pad_0 = const()[name = tensor("op_12789_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12789 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias, dilations = var_12787, groups = var_12525, pad = var_12789_pad_0, pad_type = var_12789_pad_type_0, strides = var_12785, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight, x = input_743)[name = tensor("op_12789")]; - tensor inputs_391 = add(x = var_12789, y = inputs_389)[name = tensor("inputs_391")]; - tensor var_12799 = const()[name = tensor("op_12799"), val = tensor([1])]; - tensor channels_mean_391 = reduce_mean(axes = var_12799, keep_dims = var_12520, x = inputs_391)[name = tensor("channels_mean_391")]; - tensor zero_mean_391 = sub(x = inputs_391, y = channels_mean_391)[name = tensor("zero_mean_391")]; - tensor zero_mean_sq_391 = mul(x = zero_mean_391, y = zero_mean_391)[name = tensor("zero_mean_sq_391")]; - tensor var_12803 = const()[name = tensor("op_12803"), val = tensor([1])]; - tensor var_12804 = reduce_mean(axes = var_12803, keep_dims = var_12520, x = zero_mean_sq_391)[name = tensor("op_12804")]; - tensor var_12805 = const()[name = tensor("op_12805"), val = tensor(0x1.4f8b58p-17)]; - tensor var_12806 = add(x = var_12804, y = var_12805)[name = tensor("op_12806")]; - tensor denom_391_epsilon_0 = const()[name = tensor("denom_391_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_391 = rsqrt(epsilon = denom_391_epsilon_0, x = var_12806)[name = tensor("denom_391")]; - tensor out_391 = mul(x = zero_mean_391, y = denom_391)[name = tensor("out_391")]; - tensor var_12810 = const()[name = tensor("op_12810"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269870976)))]; - tensor var_12811 = add(x = out_391, y = var_12810)[name = tensor("op_12811")]; - tensor var_12813 = const()[name = tensor("op_12813"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269873600)))]; - tensor hidden_states_515 = mul(x = var_12811, y = var_12813)[name = tensor("hidden_states_515")]; - tensor var_12820 = const()[name = tensor("op_12820"), val = tensor([1, 1])]; - tensor var_12822 = const()[name = tensor("op_12822"), val = tensor([1, 1])]; + tensor attn_259_cast = matmul(transpose_x = attn_259_transpose_x_0, transpose_y = attn_259_transpose_y_0, x = var_12613_cast, y = var_12617_cast)[name = tensor("attn_259_cast")]; + tensor var_12621 = const()[name = tensor("op_12621"), val = tensor([2, 640, 1, -1])]; + tensor input_739_cast = reshape(shape = var_12621, x = attn_259_cast)[name = tensor("input_739_cast")]; + tensor var_12626 = const()[name = tensor("op_12626"), val = tensor([1, 1])]; + tensor var_12628 = const()[name = tensor("op_12628"), val = tensor([1, 1])]; + tensor var_12630_pad_type_0 = const()[name = tensor("op_12630_pad_type_0"), val = tensor("custom")]; + tensor var_12630_pad_0 = const()[name = tensor("op_12630_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4943766656)))]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4944585920)))]; + tensor var_12630_cast = conv(bias = unet_up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_12628, groups = var_31, pad = var_12630_pad_0, pad_type = var_12630_pad_type_0, strides = var_12626, weight = unet_up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_739_cast)[name = tensor("op_12630_cast")]; + tensor inputs_389_cast = add(x = var_12630_cast, y = inputs_387_cast)[name = tensor("inputs_389_cast")]; + tensor var_12634 = const()[name = tensor("op_12634"), val = tensor([1])]; + tensor channels_mean_389_cast = reduce_mean(axes = var_12634, keep_dims = var_23, x = inputs_389_cast)[name = tensor("channels_mean_389_cast")]; + tensor zero_mean_389_cast = sub(x = inputs_389_cast, y = channels_mean_389_cast)[name = tensor("zero_mean_389_cast")]; + tensor zero_mean_sq_389_cast = mul(x = zero_mean_389_cast, y = zero_mean_389_cast)[name = tensor("zero_mean_sq_389_cast")]; + tensor var_12638 = const()[name = tensor("op_12638"), val = tensor([1])]; + tensor var_12639_cast = reduce_mean(axes = var_12638, keep_dims = var_23, x = zero_mean_sq_389_cast)[name = tensor("op_12639_cast")]; + tensor var_12640_to_fp16 = const()[name = tensor("op_12640_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12641_cast = add(x = var_12639_cast, y = var_12640_to_fp16)[name = tensor("op_12641_cast")]; + tensor denom_389_epsilon_0_to_fp16 = const()[name = tensor("denom_389_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_389_cast = rsqrt(epsilon = denom_389_epsilon_0_to_fp16, x = var_12641_cast)[name = tensor("denom_389_cast")]; + tensor out_389_cast = mul(x = zero_mean_389_cast, y = denom_389_cast)[name = tensor("out_389_cast")]; + tensor var_12645_to_fp16 = const()[name = tensor("op_12645_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4944587264)))]; + tensor var_12646_cast = add(x = out_389_cast, y = var_12645_to_fp16)[name = tensor("op_12646_cast")]; + tensor var_12648_to_fp16 = const()[name = tensor("op_12648_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4944588608)))]; + tensor input_741_cast = mul(x = var_12646_cast, y = var_12648_to_fp16)[name = tensor("input_741_cast")]; + tensor var_12656 = const()[name = tensor("op_12656"), val = tensor([1, 1])]; + tensor var_12658 = const()[name = tensor("op_12658"), val = tensor([1, 1])]; + tensor var_12660_pad_type_0 = const()[name = tensor("op_12660_pad_type_0"), val = tensor("custom")]; + tensor var_12660_pad_0 = const()[name = tensor("op_12660_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4944589952)))]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4951143616)))]; + tensor var_12660_cast = conv(bias = unet_up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_12658, groups = var_31, pad = var_12660_pad_0, pad_type = var_12660_pad_type_0, strides = var_12656, weight = unet_up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_741_cast)[name = tensor("op_12660_cast")]; + tensor var_12661_split_sizes_0 = const()[name = tensor("op_12661_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_12661_axis_0 = const()[name = tensor("op_12661_axis_0"), val = tensor(1)]; + tensor var_12661_cast_0, tensor var_12661_cast_1 = split(axis = var_12661_axis_0, split_sizes = var_12661_split_sizes_0, x = var_12660_cast)[name = tensor("op_12661_cast")]; + tensor var_12663_mode_0 = const()[name = tensor("op_12663_mode_0"), val = tensor("EXACT")]; + tensor var_12663_cast = gelu(mode = var_12663_mode_0, x = var_12661_cast_1)[name = tensor("op_12663_cast")]; + tensor input_743_cast = mul(x = var_12661_cast_0, y = var_12663_cast)[name = tensor("input_743_cast")]; + tensor var_12667 = const()[name = tensor("op_12667"), val = tensor([1, 1])]; + tensor var_12669 = const()[name = tensor("op_12669"), val = tensor([1, 1])]; + tensor var_12671_pad_type_0 = const()[name = tensor("op_12671_pad_type_0"), val = tensor("custom")]; + tensor var_12671_pad_0 = const()[name = tensor("op_12671_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4951153920)))]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4954430784)))]; + tensor var_12671_cast = conv(bias = unet_up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_12669, groups = var_31, pad = var_12671_pad_0, pad_type = var_12671_pad_type_0, strides = var_12667, weight = unet_up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_743_cast)[name = tensor("op_12671_cast")]; + tensor inputs_391_cast = add(x = var_12671_cast, y = inputs_389_cast)[name = tensor("inputs_391_cast")]; + tensor var_12681 = const()[name = tensor("op_12681"), val = tensor([1])]; + tensor channels_mean_391_cast = reduce_mean(axes = var_12681, keep_dims = var_23, x = inputs_391_cast)[name = tensor("channels_mean_391_cast")]; + tensor zero_mean_391_cast = sub(x = inputs_391_cast, y = channels_mean_391_cast)[name = tensor("zero_mean_391_cast")]; + tensor zero_mean_sq_391_cast = mul(x = zero_mean_391_cast, y = zero_mean_391_cast)[name = tensor("zero_mean_sq_391_cast")]; + tensor var_12685 = const()[name = tensor("op_12685"), val = tensor([1])]; + tensor var_12686_cast = reduce_mean(axes = var_12685, keep_dims = var_23, x = zero_mean_sq_391_cast)[name = tensor("op_12686_cast")]; + tensor var_12687_to_fp16 = const()[name = tensor("op_12687_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12688_cast = add(x = var_12686_cast, y = var_12687_to_fp16)[name = tensor("op_12688_cast")]; + tensor denom_391_epsilon_0_to_fp16 = const()[name = tensor("denom_391_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_391_cast = rsqrt(epsilon = denom_391_epsilon_0_to_fp16, x = var_12688_cast)[name = tensor("denom_391_cast")]; + tensor out_391_cast = mul(x = zero_mean_391_cast, y = denom_391_cast)[name = tensor("out_391_cast")]; + tensor var_12692_to_fp16 = const()[name = tensor("op_12692_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4954432128)))]; + tensor var_12693_cast = add(x = out_391_cast, y = var_12692_to_fp16)[name = tensor("op_12693_cast")]; + tensor var_12695_to_fp16 = const()[name = tensor("op_12695_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4954433472)))]; + tensor hidden_states_515_cast = mul(x = var_12693_cast, y = var_12695_to_fp16)[name = tensor("hidden_states_515_cast")]; + tensor var_12702 = const()[name = tensor("op_12702"), val = tensor([1, 1])]; + tensor var_12704 = const()[name = tensor("op_12704"), val = tensor([1, 1])]; tensor q_261_pad_type_0 = const()[name = tensor("q_261_pad_type_0"), val = tensor("custom")]; tensor q_261_pad_0 = const()[name = tensor("q_261_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_261 = conv(dilations = var_12822, groups = var_12525, pad = q_261_pad_0, pad_type = q_261_pad_type_0, strides = var_12820, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight, x = hidden_states_515)[name = tensor("q_261")]; - tensor var_12826 = const()[name = tensor("op_12826"), val = tensor([1, 1])]; - tensor var_12828 = const()[name = tensor("op_12828"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4954434816)))]; + tensor q_261_cast = conv(dilations = var_12704, groups = var_31, pad = q_261_pad_0, pad_type = q_261_pad_type_0, strides = var_12702, weight = unet_up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_515_cast)[name = tensor("q_261_cast")]; + tensor var_12708 = const()[name = tensor("op_12708"), val = tensor([1, 1])]; + tensor var_12710 = const()[name = tensor("op_12710"), val = tensor([1, 1])]; tensor k_261_pad_type_0 = const()[name = tensor("k_261_pad_type_0"), val = tensor("custom")]; tensor k_261_pad_0 = const()[name = tensor("k_261_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_261 = conv(dilations = var_12828, groups = var_12525, pad = k_261_pad_0, pad_type = k_261_pad_type_0, strides = var_12826, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight, x = hidden_states_515)[name = tensor("k_261")]; - tensor var_12832 = const()[name = tensor("op_12832"), val = tensor([1, 1])]; - tensor var_12834 = const()[name = tensor("op_12834"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4955254080)))]; + tensor k_261_cast = conv(dilations = var_12710, groups = var_31, pad = k_261_pad_0, pad_type = k_261_pad_type_0, strides = var_12708, weight = unet_up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_515_cast)[name = tensor("k_261_cast")]; + tensor var_12714 = const()[name = tensor("op_12714"), val = tensor([1, 1])]; + tensor var_12716 = const()[name = tensor("op_12716"), val = tensor([1, 1])]; tensor v_261_pad_type_0 = const()[name = tensor("v_261_pad_type_0"), val = tensor("custom")]; tensor v_261_pad_0 = const()[name = tensor("v_261_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_261 = conv(dilations = var_12834, groups = var_12525, pad = v_261_pad_0, pad_type = v_261_pad_type_0, strides = var_12832, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight, x = hidden_states_515)[name = tensor("v_261")]; - tensor var_12838 = const()[name = tensor("op_12838"), val = tensor([2, 10, 64, -1])]; - tensor var_12839 = reshape(shape = var_12838, x = q_261)[name = tensor("op_12839")]; - tensor var_12840 = const()[name = tensor("op_12840"), val = tensor([2, 10, 64, -1])]; - tensor var_12841 = reshape(shape = var_12840, x = k_261)[name = tensor("op_12841")]; - tensor var_12842 = const()[name = tensor("op_12842"), val = tensor([2, 10, 64, -1])]; - tensor var_12843 = reshape(shape = var_12842, x = v_261)[name = tensor("op_12843")]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4956073344)))]; + tensor v_261_cast = conv(dilations = var_12716, groups = var_31, pad = v_261_pad_0, pad_type = v_261_pad_type_0, strides = var_12714, weight = unet_up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_515_cast)[name = tensor("v_261_cast")]; + tensor var_12720 = const()[name = tensor("op_12720"), val = tensor([2, 10, 64, -1])]; + tensor var_12721_cast = reshape(shape = var_12720, x = q_261_cast)[name = tensor("op_12721_cast")]; + tensor var_12722 = const()[name = tensor("op_12722"), val = tensor([2, 10, 64, -1])]; + tensor var_12723_cast = reshape(shape = var_12722, x = k_261_cast)[name = tensor("op_12723_cast")]; + tensor var_12724 = const()[name = tensor("op_12724"), val = tensor([2, 10, 64, -1])]; + tensor var_12725_cast = reshape(shape = var_12724, x = v_261_cast)[name = tensor("op_12725_cast")]; tensor attn_weights_521_transpose_x_0 = const()[name = tensor("attn_weights_521_transpose_x_0"), val = tensor(true)]; tensor attn_weights_521_transpose_y_0 = const()[name = tensor("attn_weights_521_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_521 = matmul(transpose_x = attn_weights_521_transpose_x_0, transpose_y = attn_weights_521_transpose_y_0, x = var_12839, y = var_12841)[name = tensor("attn_weights_521")]; - tensor attn_weights_523 = mul(x = attn_weights_521, y = var_12516)[name = tensor("attn_weights_523")]; - tensor var_12847 = softmax(axis = var_12509, x = attn_weights_523)[name = tensor("op_12847")]; + tensor attn_weights_521_cast = matmul(transpose_x = attn_weights_521_transpose_x_0, transpose_y = attn_weights_521_transpose_y_0, x = var_12721_cast, y = var_12723_cast)[name = tensor("attn_weights_521_cast")]; + tensor attn_weights_523_cast = mul(x = attn_weights_521_cast, y = var_12_to_fp16)[name = tensor("attn_weights_523_cast")]; + tensor var_12729_cast = softmax(axis = var_18, x = attn_weights_523_cast)[name = tensor("op_12729_cast")]; tensor attn_261_transpose_x_0 = const()[name = tensor("attn_261_transpose_x_0"), val = tensor(false)]; tensor attn_261_transpose_y_0 = const()[name = tensor("attn_261_transpose_y_0"), val = tensor(true)]; - tensor attn_261 = matmul(transpose_x = attn_261_transpose_x_0, transpose_y = attn_261_transpose_y_0, x = var_12843, y = var_12847)[name = tensor("attn_261")]; - tensor var_12851 = const()[name = tensor("op_12851"), val = tensor([2, 640, 1, -1])]; - tensor input_745 = reshape(shape = var_12851, x = attn_261)[name = tensor("input_745")]; - tensor var_12856 = const()[name = tensor("op_12856"), val = tensor([1, 1])]; - tensor var_12858 = const()[name = tensor("op_12858"), val = tensor([1, 1])]; - tensor var_12860_pad_type_0 = const()[name = tensor("op_12860_pad_type_0"), val = tensor("custom")]; - tensor var_12860_pad_0 = const()[name = tensor("op_12860_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12860 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias, dilations = var_12858, groups = var_12525, pad = var_12860_pad_0, pad_type = var_12860_pad_type_0, strides = var_12856, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight, x = input_745)[name = tensor("op_12860")]; - tensor inputs_393 = add(x = var_12860, y = inputs_391)[name = tensor("inputs_393")]; - tensor var_12864 = const()[name = tensor("op_12864"), val = tensor([1])]; - tensor channels_mean_393 = reduce_mean(axes = var_12864, keep_dims = var_12520, x = inputs_393)[name = tensor("channels_mean_393")]; - tensor zero_mean_393 = sub(x = inputs_393, y = channels_mean_393)[name = tensor("zero_mean_393")]; - tensor zero_mean_sq_393 = mul(x = zero_mean_393, y = zero_mean_393)[name = tensor("zero_mean_sq_393")]; - tensor var_12868 = const()[name = tensor("op_12868"), val = tensor([1])]; - tensor var_12869 = reduce_mean(axes = var_12868, keep_dims = var_12520, x = zero_mean_sq_393)[name = tensor("op_12869")]; - tensor var_12870 = const()[name = tensor("op_12870"), val = tensor(0x1.4f8b58p-17)]; - tensor var_12871 = add(x = var_12869, y = var_12870)[name = tensor("op_12871")]; - tensor denom_393_epsilon_0 = const()[name = tensor("denom_393_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_393 = rsqrt(epsilon = denom_393_epsilon_0, x = var_12871)[name = tensor("denom_393")]; - tensor out_393 = mul(x = zero_mean_393, y = denom_393)[name = tensor("out_393")]; - tensor var_12875 = const()[name = tensor("op_12875"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269876224)))]; - tensor var_12876 = add(x = out_393, y = var_12875)[name = tensor("op_12876")]; - tensor var_12878 = const()[name = tensor("op_12878"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269878848)))]; - tensor hidden_states_517 = mul(x = var_12876, y = var_12878)[name = tensor("hidden_states_517")]; - tensor var_12885 = const()[name = tensor("op_12885"), val = tensor([1, 1])]; - tensor var_12887 = const()[name = tensor("op_12887"), val = tensor([1, 1])]; + tensor attn_261_cast = matmul(transpose_x = attn_261_transpose_x_0, transpose_y = attn_261_transpose_y_0, x = var_12725_cast, y = var_12729_cast)[name = tensor("attn_261_cast")]; + tensor var_12733 = const()[name = tensor("op_12733"), val = tensor([2, 640, 1, -1])]; + tensor input_745_cast = reshape(shape = var_12733, x = attn_261_cast)[name = tensor("input_745_cast")]; + tensor var_12738 = const()[name = tensor("op_12738"), val = tensor([1, 1])]; + tensor var_12740 = const()[name = tensor("op_12740"), val = tensor([1, 1])]; + tensor var_12742_pad_type_0 = const()[name = tensor("op_12742_pad_type_0"), val = tensor("custom")]; + tensor var_12742_pad_0 = const()[name = tensor("op_12742_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4956892608)))]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4957711872)))]; + tensor var_12742_cast = conv(bias = unet_up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_12740, groups = var_31, pad = var_12742_pad_0, pad_type = var_12742_pad_type_0, strides = var_12738, weight = unet_up_blocks_1_attentions_0_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_745_cast)[name = tensor("op_12742_cast")]; + tensor inputs_393_cast = add(x = var_12742_cast, y = inputs_391_cast)[name = tensor("inputs_393_cast")]; + tensor var_12746 = const()[name = tensor("op_12746"), val = tensor([1])]; + tensor channels_mean_393_cast = reduce_mean(axes = var_12746, keep_dims = var_23, x = inputs_393_cast)[name = tensor("channels_mean_393_cast")]; + tensor zero_mean_393_cast = sub(x = inputs_393_cast, y = channels_mean_393_cast)[name = tensor("zero_mean_393_cast")]; + tensor zero_mean_sq_393_cast = mul(x = zero_mean_393_cast, y = zero_mean_393_cast)[name = tensor("zero_mean_sq_393_cast")]; + tensor var_12750 = const()[name = tensor("op_12750"), val = tensor([1])]; + tensor var_12751_cast = reduce_mean(axes = var_12750, keep_dims = var_23, x = zero_mean_sq_393_cast)[name = tensor("op_12751_cast")]; + tensor var_12752_to_fp16 = const()[name = tensor("op_12752_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12753_cast = add(x = var_12751_cast, y = var_12752_to_fp16)[name = tensor("op_12753_cast")]; + tensor denom_393_epsilon_0_to_fp16 = const()[name = tensor("denom_393_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_393_cast = rsqrt(epsilon = denom_393_epsilon_0_to_fp16, x = var_12753_cast)[name = tensor("denom_393_cast")]; + tensor out_393_cast = mul(x = zero_mean_393_cast, y = denom_393_cast)[name = tensor("out_393_cast")]; + tensor var_12757_to_fp16 = const()[name = tensor("op_12757_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4957713216)))]; + tensor var_12758_cast = add(x = out_393_cast, y = var_12757_to_fp16)[name = tensor("op_12758_cast")]; + tensor var_12760_to_fp16 = const()[name = tensor("op_12760_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4957714560)))]; + tensor hidden_states_517_cast = mul(x = var_12758_cast, y = var_12760_to_fp16)[name = tensor("hidden_states_517_cast")]; + tensor var_12767 = const()[name = tensor("op_12767"), val = tensor([1, 1])]; + tensor var_12769 = const()[name = tensor("op_12769"), val = tensor([1, 1])]; tensor q_263_pad_type_0 = const()[name = tensor("q_263_pad_type_0"), val = tensor("custom")]; tensor q_263_pad_0 = const()[name = tensor("q_263_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_263 = conv(dilations = var_12887, groups = var_12525, pad = q_263_pad_0, pad_type = q_263_pad_type_0, strides = var_12885, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight, x = hidden_states_517)[name = tensor("q_263")]; - tensor var_12891 = const()[name = tensor("op_12891"), val = tensor([1, 1])]; - tensor var_12893 = const()[name = tensor("op_12893"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4957715904)))]; + tensor q_263_cast = conv(dilations = var_12769, groups = var_31, pad = q_263_pad_0, pad_type = q_263_pad_type_0, strides = var_12767, weight = unet_up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_517_cast)[name = tensor("q_263_cast")]; + tensor var_12773 = const()[name = tensor("op_12773"), val = tensor([1, 1])]; + tensor var_12775 = const()[name = tensor("op_12775"), val = tensor([1, 1])]; tensor k_263_pad_type_0 = const()[name = tensor("k_263_pad_type_0"), val = tensor("custom")]; tensor k_263_pad_0 = const()[name = tensor("k_263_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_263 = conv(dilations = var_12893, groups = var_12525, pad = k_263_pad_0, pad_type = k_263_pad_type_0, strides = var_12891, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_263")]; - tensor var_12897 = const()[name = tensor("op_12897"), val = tensor([1, 1])]; - tensor var_12899 = const()[name = tensor("op_12899"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4958535168)))]; + tensor k_263_cast = conv(dilations = var_12775, groups = var_31, pad = k_263_pad_0, pad_type = k_263_pad_type_0, strides = var_12773, weight = unet_up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_263_cast")]; + tensor var_12779 = const()[name = tensor("op_12779"), val = tensor([1, 1])]; + tensor var_12781 = const()[name = tensor("op_12781"), val = tensor([1, 1])]; tensor v_263_pad_type_0 = const()[name = tensor("v_263_pad_type_0"), val = tensor("custom")]; tensor v_263_pad_0 = const()[name = tensor("v_263_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_263 = conv(dilations = var_12899, groups = var_12525, pad = v_263_pad_0, pad_type = v_263_pad_type_0, strides = var_12897, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_263")]; - tensor var_12903 = const()[name = tensor("op_12903"), val = tensor([2, 10, 64, -1])]; - tensor var_12904 = reshape(shape = var_12903, x = q_263)[name = tensor("op_12904")]; - tensor var_12905 = const()[name = tensor("op_12905"), val = tensor([2, 10, 64, -1])]; - tensor var_12906 = reshape(shape = var_12905, x = k_263)[name = tensor("op_12906")]; - tensor var_12907 = const()[name = tensor("op_12907"), val = tensor([2, 10, 64, -1])]; - tensor var_12908 = reshape(shape = var_12907, x = v_263)[name = tensor("op_12908")]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4961156672)))]; + tensor v_263_cast = conv(dilations = var_12781, groups = var_31, pad = v_263_pad_0, pad_type = v_263_pad_type_0, strides = var_12779, weight = unet_up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_263_cast")]; + tensor var_12785 = const()[name = tensor("op_12785"), val = tensor([2, 10, 64, -1])]; + tensor var_12786_cast = reshape(shape = var_12785, x = q_263_cast)[name = tensor("op_12786_cast")]; + tensor var_12787 = const()[name = tensor("op_12787"), val = tensor([2, 10, 64, -1])]; + tensor var_12788_cast = reshape(shape = var_12787, x = k_263_cast)[name = tensor("op_12788_cast")]; + tensor var_12789 = const()[name = tensor("op_12789"), val = tensor([2, 10, 64, -1])]; + tensor var_12790_cast = reshape(shape = var_12789, x = v_263_cast)[name = tensor("op_12790_cast")]; tensor attn_weights_525_transpose_x_0 = const()[name = tensor("attn_weights_525_transpose_x_0"), val = tensor(true)]; tensor attn_weights_525_transpose_y_0 = const()[name = tensor("attn_weights_525_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_525 = matmul(transpose_x = attn_weights_525_transpose_x_0, transpose_y = attn_weights_525_transpose_y_0, x = var_12904, y = var_12906)[name = tensor("attn_weights_525")]; - tensor attn_weights_527 = mul(x = attn_weights_525, y = var_12516)[name = tensor("attn_weights_527")]; - tensor var_12912 = softmax(axis = var_12509, x = attn_weights_527)[name = tensor("op_12912")]; + tensor attn_weights_525_cast = matmul(transpose_x = attn_weights_525_transpose_x_0, transpose_y = attn_weights_525_transpose_y_0, x = var_12786_cast, y = var_12788_cast)[name = tensor("attn_weights_525_cast")]; + tensor attn_weights_527_cast = mul(x = attn_weights_525_cast, y = var_12_to_fp16)[name = tensor("attn_weights_527_cast")]; + tensor var_12794_cast = softmax(axis = var_18, x = attn_weights_527_cast)[name = tensor("op_12794_cast")]; tensor attn_263_transpose_x_0 = const()[name = tensor("attn_263_transpose_x_0"), val = tensor(false)]; tensor attn_263_transpose_y_0 = const()[name = tensor("attn_263_transpose_y_0"), val = tensor(true)]; - tensor attn_263 = matmul(transpose_x = attn_263_transpose_x_0, transpose_y = attn_263_transpose_y_0, x = var_12908, y = var_12912)[name = tensor("attn_263")]; - tensor var_12916 = const()[name = tensor("op_12916"), val = tensor([2, 640, 1, -1])]; - tensor input_747 = reshape(shape = var_12916, x = attn_263)[name = tensor("input_747")]; - tensor var_12921 = const()[name = tensor("op_12921"), val = tensor([1, 1])]; - tensor var_12923 = const()[name = tensor("op_12923"), val = tensor([1, 1])]; - tensor var_12925_pad_type_0 = const()[name = tensor("op_12925_pad_type_0"), val = tensor("custom")]; - tensor var_12925_pad_0 = const()[name = tensor("op_12925_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12925 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias, dilations = var_12923, groups = var_12525, pad = var_12925_pad_0, pad_type = var_12925_pad_type_0, strides = var_12921, weight = up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight, x = input_747)[name = tensor("op_12925")]; - tensor inputs_395 = add(x = var_12925, y = inputs_393)[name = tensor("inputs_395")]; - tensor var_12929 = const()[name = tensor("op_12929"), val = tensor([1])]; - tensor channels_mean_395 = reduce_mean(axes = var_12929, keep_dims = var_12520, x = inputs_395)[name = tensor("channels_mean_395")]; - tensor zero_mean_395 = sub(x = inputs_395, y = channels_mean_395)[name = tensor("zero_mean_395")]; - tensor zero_mean_sq_395 = mul(x = zero_mean_395, y = zero_mean_395)[name = tensor("zero_mean_sq_395")]; - tensor var_12933 = const()[name = tensor("op_12933"), val = tensor([1])]; - tensor var_12934 = reduce_mean(axes = var_12933, keep_dims = var_12520, x = zero_mean_sq_395)[name = tensor("op_12934")]; - tensor var_12935 = const()[name = tensor("op_12935"), val = tensor(0x1.4f8b58p-17)]; - tensor var_12936 = add(x = var_12934, y = var_12935)[name = tensor("op_12936")]; - tensor denom_395_epsilon_0 = const()[name = tensor("denom_395_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_395 = rsqrt(epsilon = denom_395_epsilon_0, x = var_12936)[name = tensor("denom_395")]; - tensor out_395 = mul(x = zero_mean_395, y = denom_395)[name = tensor("out_395")]; - tensor var_12940 = const()[name = tensor("op_12940"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269881472)))]; - tensor var_12941 = add(x = out_395, y = var_12940)[name = tensor("op_12941")]; - tensor var_12943 = const()[name = tensor("op_12943"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269884096)))]; - tensor input_749 = mul(x = var_12941, y = var_12943)[name = tensor("input_749")]; - tensor var_12951 = const()[name = tensor("op_12951"), val = tensor([1, 1])]; - tensor var_12953 = const()[name = tensor("op_12953"), val = tensor([1, 1])]; - tensor var_12955_pad_type_0 = const()[name = tensor("op_12955_pad_type_0"), val = tensor("custom")]; - tensor var_12955_pad_0 = const()[name = tensor("op_12955_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12955 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias, dilations = var_12953, groups = var_12525, pad = var_12955_pad_0, pad_type = var_12955_pad_type_0, strides = var_12951, weight = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight, x = input_749)[name = tensor("op_12955")]; - tensor var_12956_split_sizes_0 = const()[name = tensor("op_12956_split_sizes_0"), val = tensor([2560, 2560])]; - tensor var_12956_axis_0 = const()[name = tensor("op_12956_axis_0"), val = tensor(1)]; - tensor var_12956_0, tensor var_12956_1 = split(axis = var_12956_axis_0, split_sizes = var_12956_split_sizes_0, x = var_12955)[name = tensor("op_12956")]; - tensor var_12958_mode_0 = const()[name = tensor("op_12958_mode_0"), val = tensor("EXACT")]; - tensor var_12958 = gelu(mode = var_12958_mode_0, x = var_12956_1)[name = tensor("op_12958")]; - tensor input_751 = mul(x = var_12956_0, y = var_12958)[name = tensor("input_751")]; - tensor var_12962 = const()[name = tensor("op_12962"), val = tensor([1, 1])]; - tensor var_12964 = const()[name = tensor("op_12964"), val = tensor([1, 1])]; - tensor var_12966_pad_type_0 = const()[name = tensor("op_12966_pad_type_0"), val = tensor("custom")]; - tensor var_12966_pad_0 = const()[name = tensor("op_12966_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_12966 = conv(bias = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias, dilations = var_12964, groups = var_12525, pad = var_12966_pad_0, pad_type = var_12966_pad_type_0, strides = var_12962, weight = up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight, x = input_751)[name = tensor("op_12966")]; - tensor hidden_states_521 = add(x = var_12966, y = inputs_395)[name = tensor("hidden_states_521")]; - tensor var_12968 = const()[name = tensor("op_12968"), val = tensor([2, 640, 64, 64])]; - tensor input_753 = reshape(shape = var_12968, x = hidden_states_521)[name = tensor("input_753")]; - tensor var_12972 = const()[name = tensor("op_12972"), val = tensor([1, 1])]; - tensor var_12974 = const()[name = tensor("op_12974"), val = tensor([1, 1])]; + tensor attn_263_cast = matmul(transpose_x = attn_263_transpose_x_0, transpose_y = attn_263_transpose_y_0, x = var_12790_cast, y = var_12794_cast)[name = tensor("attn_263_cast")]; + tensor var_12798 = const()[name = tensor("op_12798"), val = tensor([2, 640, 1, -1])]; + tensor input_747_cast = reshape(shape = var_12798, x = attn_263_cast)[name = tensor("input_747_cast")]; + tensor var_12803 = const()[name = tensor("op_12803"), val = tensor([1, 1])]; + tensor var_12805 = const()[name = tensor("op_12805"), val = tensor([1, 1])]; + tensor var_12807_pad_type_0 = const()[name = tensor("op_12807_pad_type_0"), val = tensor("custom")]; + tensor var_12807_pad_0 = const()[name = tensor("op_12807_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4963778176)))]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4964597440)))]; + tensor var_12807_cast = conv(bias = unet_up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_12805, groups = var_31, pad = var_12807_pad_0, pad_type = var_12807_pad_type_0, strides = var_12803, weight = unet_up_blocks_1_attentions_0_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_747_cast)[name = tensor("op_12807_cast")]; + tensor inputs_395_cast = add(x = var_12807_cast, y = inputs_393_cast)[name = tensor("inputs_395_cast")]; + tensor var_12811 = const()[name = tensor("op_12811"), val = tensor([1])]; + tensor channels_mean_395_cast = reduce_mean(axes = var_12811, keep_dims = var_23, x = inputs_395_cast)[name = tensor("channels_mean_395_cast")]; + tensor zero_mean_395_cast = sub(x = inputs_395_cast, y = channels_mean_395_cast)[name = tensor("zero_mean_395_cast")]; + tensor zero_mean_sq_395_cast = mul(x = zero_mean_395_cast, y = zero_mean_395_cast)[name = tensor("zero_mean_sq_395_cast")]; + tensor var_12815 = const()[name = tensor("op_12815"), val = tensor([1])]; + tensor var_12816_cast = reduce_mean(axes = var_12815, keep_dims = var_23, x = zero_mean_sq_395_cast)[name = tensor("op_12816_cast")]; + tensor var_12817_to_fp16 = const()[name = tensor("op_12817_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12818_cast = add(x = var_12816_cast, y = var_12817_to_fp16)[name = tensor("op_12818_cast")]; + tensor denom_395_epsilon_0_to_fp16 = const()[name = tensor("denom_395_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_395_cast = rsqrt(epsilon = denom_395_epsilon_0_to_fp16, x = var_12818_cast)[name = tensor("denom_395_cast")]; + tensor out_395_cast = mul(x = zero_mean_395_cast, y = denom_395_cast)[name = tensor("out_395_cast")]; + tensor var_12822_to_fp16 = const()[name = tensor("op_12822_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4964598784)))]; + tensor var_12823_cast = add(x = out_395_cast, y = var_12822_to_fp16)[name = tensor("op_12823_cast")]; + tensor var_12825_to_fp16 = const()[name = tensor("op_12825_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4964600128)))]; + tensor input_749_cast = mul(x = var_12823_cast, y = var_12825_to_fp16)[name = tensor("input_749_cast")]; + tensor var_12833 = const()[name = tensor("op_12833"), val = tensor([1, 1])]; + tensor var_12835 = const()[name = tensor("op_12835"), val = tensor([1, 1])]; + tensor var_12837_pad_type_0 = const()[name = tensor("op_12837_pad_type_0"), val = tensor("custom")]; + tensor var_12837_pad_0 = const()[name = tensor("op_12837_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4964601472)))]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4971155136)))]; + tensor var_12837_cast = conv(bias = unet_up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_12835, groups = var_31, pad = var_12837_pad_0, pad_type = var_12837_pad_type_0, strides = var_12833, weight = unet_up_blocks_1_attentions_0_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_749_cast)[name = tensor("op_12837_cast")]; + tensor var_12838_split_sizes_0 = const()[name = tensor("op_12838_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_12838_axis_0 = const()[name = tensor("op_12838_axis_0"), val = tensor(1)]; + tensor var_12838_cast_0, tensor var_12838_cast_1 = split(axis = var_12838_axis_0, split_sizes = var_12838_split_sizes_0, x = var_12837_cast)[name = tensor("op_12838_cast")]; + tensor var_12840_mode_0 = const()[name = tensor("op_12840_mode_0"), val = tensor("EXACT")]; + tensor var_12840_cast = gelu(mode = var_12840_mode_0, x = var_12838_cast_1)[name = tensor("op_12840_cast")]; + tensor input_751_cast = mul(x = var_12838_cast_0, y = var_12840_cast)[name = tensor("input_751_cast")]; + tensor var_12844 = const()[name = tensor("op_12844"), val = tensor([1, 1])]; + tensor var_12846 = const()[name = tensor("op_12846"), val = tensor([1, 1])]; + tensor var_12848_pad_type_0 = const()[name = tensor("op_12848_pad_type_0"), val = tensor("custom")]; + tensor var_12848_pad_0 = const()[name = tensor("op_12848_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4971165440)))]; + tensor unet_up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4974442304)))]; + tensor var_12848_cast = conv(bias = unet_up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_12846, groups = var_31, pad = var_12848_pad_0, pad_type = var_12848_pad_type_0, strides = var_12844, weight = unet_up_blocks_1_attentions_0_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_751_cast)[name = tensor("op_12848_cast")]; + tensor hidden_states_521_cast = add(x = var_12848_cast, y = inputs_395_cast)[name = tensor("hidden_states_521_cast")]; + tensor var_12850 = const()[name = tensor("op_12850"), val = tensor([2, 640, 64, 64])]; + tensor input_753_cast = reshape(shape = var_12850, x = hidden_states_521_cast)[name = tensor("input_753_cast")]; + tensor var_12854 = const()[name = tensor("op_12854"), val = tensor([1, 1])]; + tensor var_12856 = const()[name = tensor("op_12856"), val = tensor([1, 1])]; tensor hidden_states_523_pad_type_0 = const()[name = tensor("hidden_states_523_pad_type_0"), val = tensor("custom")]; tensor hidden_states_523_pad_0 = const()[name = tensor("hidden_states_523_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_523 = conv(bias = up_blocks_1_attentions_0_proj_out_bias, dilations = var_12974, groups = var_12525, pad = hidden_states_523_pad_0, pad_type = hidden_states_523_pad_type_0, strides = var_12972, weight = up_blocks_1_attentions_0_proj_out_weight, x = input_753)[name = tensor("hidden_states_523")]; - tensor hidden_states_525 = add(x = hidden_states_523, y = hidden_states_505)[name = tensor("hidden_states_525")]; + tensor unet_up_blocks_1_attentions_0_proj_out_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4974443648)))]; + tensor unet_up_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4975262912)))]; + tensor hidden_states_523_cast = conv(bias = unet_up_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_12856, groups = var_31, pad = hidden_states_523_pad_0, pad_type = hidden_states_523_pad_type_0, strides = var_12854, weight = unet_up_blocks_1_attentions_0_proj_out_weight_to_fp16, x = input_753_cast)[name = tensor("hidden_states_523_cast")]; + tensor hidden_states_525_cast = add(x = hidden_states_523_cast, y = hidden_states_505_cast)[name = tensor("hidden_states_525_cast")]; tensor input_755_interleave_0 = const()[name = tensor("input_755_interleave_0"), val = tensor(false)]; - tensor input_755 = concat(axis = var_12525, interleave = input_755_interleave_0, values = (hidden_states_525, input_79))[name = tensor("input_755")]; + tensor input_755_cast = concat(axis = var_31, interleave = input_755_interleave_0, values = (hidden_states_525_cast, input_79_cast))[name = tensor("input_755_cast")]; tensor reshape_132_shape_0 = const()[name = tensor("reshape_132_shape_0"), val = tensor([2, 32, 40, 64, 64])]; - tensor reshape_132 = reshape(shape = reshape_132_shape_0, x = input_755)[name = tensor("reshape_132")]; + tensor reshape_132_cast = reshape(shape = reshape_132_shape_0, x = input_755_cast)[name = tensor("reshape_132_cast")]; tensor reduce_mean_99_axes_0 = const()[name = tensor("reduce_mean_99_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_99_keep_dims_0 = const()[name = tensor("reduce_mean_99_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_99 = reduce_mean(axes = reduce_mean_99_axes_0, keep_dims = reduce_mean_99_keep_dims_0, x = reshape_132)[name = tensor("reduce_mean_99")]; - tensor sub_66 = sub(x = reshape_132, y = reduce_mean_99)[name = tensor("sub_66")]; - tensor square_33 = square(x = sub_66)[name = tensor("square_33")]; + tensor reduce_mean_99_cast = reduce_mean(axes = reduce_mean_99_axes_0, keep_dims = reduce_mean_99_keep_dims_0, x = reshape_132_cast)[name = tensor("reduce_mean_99_cast")]; + tensor sub_66_cast = sub(x = reshape_132_cast, y = reduce_mean_99_cast)[name = tensor("sub_66_cast")]; + tensor square_33_cast = square(x = sub_66_cast)[name = tensor("square_33_cast")]; tensor reduce_mean_101_axes_0 = const()[name = tensor("reduce_mean_101_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_101_keep_dims_0 = const()[name = tensor("reduce_mean_101_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_101 = reduce_mean(axes = reduce_mean_101_axes_0, keep_dims = reduce_mean_101_keep_dims_0, x = square_33)[name = tensor("reduce_mean_101")]; - tensor add_66_y_0 = const()[name = tensor("add_66_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_66 = add(x = reduce_mean_101, y = add_66_y_0)[name = tensor("add_66")]; - tensor sqrt_33 = sqrt(x = add_66)[name = tensor("sqrt_33")]; - tensor real_div_33 = real_div(x = sub_66, y = sqrt_33)[name = tensor("real_div_33")]; + tensor reduce_mean_101_cast = reduce_mean(axes = reduce_mean_101_axes_0, keep_dims = reduce_mean_101_keep_dims_0, x = square_33_cast)[name = tensor("reduce_mean_101_cast")]; + tensor add_66_y_0_to_fp16 = const()[name = tensor("add_66_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_66_cast = add(x = reduce_mean_101_cast, y = add_66_y_0_to_fp16)[name = tensor("add_66_cast")]; + tensor sqrt_33_cast = sqrt(x = add_66_cast)[name = tensor("sqrt_33_cast")]; + tensor real_div_33_cast = real_div(x = sub_66_cast, y = sqrt_33_cast)[name = tensor("real_div_33_cast")]; tensor reshape_133_shape_0 = const()[name = tensor("reshape_133_shape_0"), val = tensor([2, 1280, 64, 64])]; - tensor reshape_133 = reshape(shape = reshape_133_shape_0, x = real_div_33)[name = tensor("reshape_133")]; - tensor add_67_gamma_0 = const()[name = tensor("add_67_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269886720)))]; - tensor add_67_beta_0 = const()[name = tensor("add_67_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269891904)))]; - tensor add_67_epsilon_0 = const()[name = tensor("add_67_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_67 = batch_norm(beta = add_67_beta_0, epsilon = add_67_epsilon_0, gamma = add_67_gamma_0, mean = add_23_mean_0, variance = add_23_variance_0, x = reshape_133)[name = tensor("add_67")]; - tensor input_759 = silu(x = add_67)[name = tensor("input_759")]; - tensor var_12992 = const()[name = tensor("op_12992"), val = tensor([1, 1])]; - tensor var_12994 = const()[name = tensor("op_12994"), val = tensor([1, 1])]; + tensor reshape_133_cast = reshape(shape = reshape_133_shape_0, x = real_div_33_cast)[name = tensor("reshape_133_cast")]; + tensor add_67_gamma_0_to_fp16 = const()[name = tensor("add_67_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4975264256)))]; + tensor add_67_beta_0_to_fp16 = const()[name = tensor("add_67_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4975266880)))]; + tensor add_67_epsilon_0_to_fp16 = const()[name = tensor("add_67_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_67_cast = batch_norm(beta = add_67_beta_0_to_fp16, epsilon = add_67_epsilon_0_to_fp16, gamma = add_67_gamma_0_to_fp16, mean = add_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_133_cast)[name = tensor("add_67_cast")]; + tensor input_759_cast = silu(x = add_67_cast)[name = tensor("input_759_cast")]; + tensor var_12874 = const()[name = tensor("op_12874"), val = tensor([1, 1])]; + tensor var_12876 = const()[name = tensor("op_12876"), val = tensor([1, 1])]; tensor hidden_states_527_pad_type_0 = const()[name = tensor("hidden_states_527_pad_type_0"), val = tensor("custom")]; tensor hidden_states_527_pad_0 = const()[name = tensor("hidden_states_527_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_527 = conv(bias = up_blocks_1_resnets_1_conv1_bias, dilations = var_12994, groups = var_12525, pad = hidden_states_527_pad_0, pad_type = hidden_states_527_pad_type_0, strides = var_12992, weight = up_blocks_1_resnets_1_conv1_weight, x = input_759)[name = tensor("hidden_states_527")]; - tensor var_13000 = const()[name = tensor("op_13000"), val = tensor([1, 1])]; - tensor var_13002 = const()[name = tensor("op_13002"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4975269504)))]; + tensor unet_up_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4990015168)))]; + tensor hidden_states_527_cast = conv(bias = unet_up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_12876, groups = var_31, pad = hidden_states_527_pad_0, pad_type = hidden_states_527_pad_type_0, strides = var_12874, weight = unet_up_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_759_cast)[name = tensor("hidden_states_527_cast")]; + tensor var_12882 = const()[name = tensor("op_12882"), val = tensor([1, 1])]; + tensor var_12884 = const()[name = tensor("op_12884"), val = tensor([1, 1])]; tensor temb_25_pad_type_0 = const()[name = tensor("temb_25_pad_type_0"), val = tensor("custom")]; tensor temb_25_pad_0 = const()[name = tensor("temb_25_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb_25 = conv(bias = up_blocks_1_resnets_1_time_emb_proj_bias, dilations = var_13002, groups = var_12525, pad = temb_25_pad_0, pad_type = temb_25_pad_type_0, strides = var_13000, weight = up_blocks_1_resnets_1_time_emb_proj_weight, x = input_21)[name = tensor("temb_25")]; - tensor input_763 = add(x = hidden_states_527, y = temb_25)[name = tensor("input_763")]; + tensor unet_up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4990016512)))]; + tensor unet_up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4991654976)))]; + tensor temb_25_cast = conv(bias = unet_up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_12884, groups = var_31, pad = temb_25_pad_0, pad_type = temb_25_pad_type_0, strides = var_12882, weight = unet_up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_25_cast")]; + tensor input_763_cast = add(x = hidden_states_527_cast, y = temb_25_cast)[name = tensor("input_763_cast")]; tensor reshape_136_shape_0 = const()[name = tensor("reshape_136_shape_0"), val = tensor([2, 32, 20, 64, 64])]; - tensor reshape_136 = reshape(shape = reshape_136_shape_0, x = input_763)[name = tensor("reshape_136")]; + tensor reshape_136_cast = reshape(shape = reshape_136_shape_0, x = input_763_cast)[name = tensor("reshape_136_cast")]; tensor reduce_mean_102_axes_0 = const()[name = tensor("reduce_mean_102_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_102_keep_dims_0 = const()[name = tensor("reduce_mean_102_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_102 = reduce_mean(axes = reduce_mean_102_axes_0, keep_dims = reduce_mean_102_keep_dims_0, x = reshape_136)[name = tensor("reduce_mean_102")]; - tensor sub_68 = sub(x = reshape_136, y = reduce_mean_102)[name = tensor("sub_68")]; - tensor square_34 = square(x = sub_68)[name = tensor("square_34")]; + tensor reduce_mean_102_cast = reduce_mean(axes = reduce_mean_102_axes_0, keep_dims = reduce_mean_102_keep_dims_0, x = reshape_136_cast)[name = tensor("reduce_mean_102_cast")]; + tensor sub_68_cast = sub(x = reshape_136_cast, y = reduce_mean_102_cast)[name = tensor("sub_68_cast")]; + tensor square_34_cast = square(x = sub_68_cast)[name = tensor("square_34_cast")]; tensor reduce_mean_104_axes_0 = const()[name = tensor("reduce_mean_104_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_104_keep_dims_0 = const()[name = tensor("reduce_mean_104_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_104 = reduce_mean(axes = reduce_mean_104_axes_0, keep_dims = reduce_mean_104_keep_dims_0, x = square_34)[name = tensor("reduce_mean_104")]; - tensor add_68_y_0 = const()[name = tensor("add_68_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_68 = add(x = reduce_mean_104, y = add_68_y_0)[name = tensor("add_68")]; - tensor sqrt_34 = sqrt(x = add_68)[name = tensor("sqrt_34")]; - tensor real_div_34 = real_div(x = sub_68, y = sqrt_34)[name = tensor("real_div_34")]; + tensor reduce_mean_104_cast = reduce_mean(axes = reduce_mean_104_axes_0, keep_dims = reduce_mean_104_keep_dims_0, x = square_34_cast)[name = tensor("reduce_mean_104_cast")]; + tensor add_68_y_0_to_fp16 = const()[name = tensor("add_68_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_68_cast = add(x = reduce_mean_104_cast, y = add_68_y_0_to_fp16)[name = tensor("add_68_cast")]; + tensor sqrt_34_cast = sqrt(x = add_68_cast)[name = tensor("sqrt_34_cast")]; + tensor real_div_34_cast = real_div(x = sub_68_cast, y = sqrt_34_cast)[name = tensor("real_div_34_cast")]; tensor reshape_137_shape_0 = const()[name = tensor("reshape_137_shape_0"), val = tensor([2, 640, 64, 64])]; - tensor reshape_137 = reshape(shape = reshape_137_shape_0, x = real_div_34)[name = tensor("reshape_137")]; - tensor add_69_gamma_0 = const()[name = tensor("add_69_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269897088)))]; - tensor add_69_beta_0 = const()[name = tensor("add_69_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269899712)))]; - tensor add_69_epsilon_0 = const()[name = tensor("add_69_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_69 = batch_norm(beta = add_69_beta_0, epsilon = add_69_epsilon_0, gamma = add_69_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_137)[name = tensor("add_69")]; - tensor input_767 = silu(x = add_69)[name = tensor("input_767")]; - tensor var_13012 = const()[name = tensor("op_13012"), val = tensor([1, 1])]; - tensor var_13014 = const()[name = tensor("op_13014"), val = tensor([1, 1])]; + tensor reshape_137_cast = reshape(shape = reshape_137_shape_0, x = real_div_34_cast)[name = tensor("reshape_137_cast")]; + tensor add_69_gamma_0_to_fp16 = const()[name = tensor("add_69_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4991656320)))]; + tensor add_69_beta_0_to_fp16 = const()[name = tensor("add_69_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4991657664)))]; + tensor add_69_epsilon_0_to_fp16 = const()[name = tensor("add_69_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_69_cast = batch_norm(beta = add_69_beta_0_to_fp16, epsilon = add_69_epsilon_0_to_fp16, gamma = add_69_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_137_cast)[name = tensor("add_69_cast")]; + tensor input_767_cast = silu(x = add_69_cast)[name = tensor("input_767_cast")]; + tensor var_12894 = const()[name = tensor("op_12894"), val = tensor([1, 1])]; + tensor var_12896 = const()[name = tensor("op_12896"), val = tensor([1, 1])]; tensor hidden_states_529_pad_type_0 = const()[name = tensor("hidden_states_529_pad_type_0"), val = tensor("custom")]; tensor hidden_states_529_pad_0 = const()[name = tensor("hidden_states_529_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_529 = conv(bias = up_blocks_1_resnets_1_conv2_bias, dilations = var_13014, groups = var_12525, pad = hidden_states_529_pad_0, pad_type = hidden_states_529_pad_type_0, strides = var_13012, weight = up_blocks_1_resnets_1_conv2_weight, x = input_767)[name = tensor("hidden_states_529")]; - tensor var_13019 = const()[name = tensor("op_13019"), val = tensor([1, 1])]; - tensor var_13021 = const()[name = tensor("op_13021"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4991659008)))]; + tensor unet_up_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4999031872)))]; + tensor hidden_states_529_cast = conv(bias = unet_up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_12896, groups = var_31, pad = hidden_states_529_pad_0, pad_type = hidden_states_529_pad_type_0, strides = var_12894, weight = unet_up_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_767_cast)[name = tensor("hidden_states_529_cast")]; + tensor var_12901 = const()[name = tensor("op_12901"), val = tensor([1, 1])]; + tensor var_12903 = const()[name = tensor("op_12903"), val = tensor([1, 1])]; tensor x_13_pad_type_0 = const()[name = tensor("x_13_pad_type_0"), val = tensor("custom")]; tensor x_13_pad_0 = const()[name = tensor("x_13_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor x_13 = conv(bias = up_blocks_1_resnets_1_conv_shortcut_bias, dilations = var_13021, groups = var_12525, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = var_13019, weight = up_blocks_1_resnets_1_conv_shortcut_weight, x = input_755)[name = tensor("x_13")]; - tensor hidden_states_531 = add(x = x_13, y = hidden_states_529)[name = tensor("hidden_states_531")]; + tensor unet_up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4999033216)))]; + tensor unet_up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5000671680)))]; + tensor x_13_cast = conv(bias = unet_up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_12903, groups = var_31, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = var_12901, weight = unet_up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16, x = input_755_cast)[name = tensor("x_13_cast")]; + tensor hidden_states_531_cast = add(x = x_13_cast, y = hidden_states_529_cast)[name = tensor("hidden_states_531_cast")]; tensor reshape_140_shape_0 = const()[name = tensor("reshape_140_shape_0"), val = tensor([2, 32, 20, 64, 64])]; - tensor reshape_140 = reshape(shape = reshape_140_shape_0, x = hidden_states_531)[name = tensor("reshape_140")]; + tensor reshape_140_cast = reshape(shape = reshape_140_shape_0, x = hidden_states_531_cast)[name = tensor("reshape_140_cast")]; tensor reduce_mean_105_axes_0 = const()[name = tensor("reduce_mean_105_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_105_keep_dims_0 = const()[name = tensor("reduce_mean_105_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_105 = reduce_mean(axes = reduce_mean_105_axes_0, keep_dims = reduce_mean_105_keep_dims_0, x = reshape_140)[name = tensor("reduce_mean_105")]; - tensor sub_70 = sub(x = reshape_140, y = reduce_mean_105)[name = tensor("sub_70")]; - tensor square_35 = square(x = sub_70)[name = tensor("square_35")]; + tensor reduce_mean_105_cast = reduce_mean(axes = reduce_mean_105_axes_0, keep_dims = reduce_mean_105_keep_dims_0, x = reshape_140_cast)[name = tensor("reduce_mean_105_cast")]; + tensor sub_70_cast = sub(x = reshape_140_cast, y = reduce_mean_105_cast)[name = tensor("sub_70_cast")]; + tensor square_35_cast = square(x = sub_70_cast)[name = tensor("square_35_cast")]; tensor reduce_mean_107_axes_0 = const()[name = tensor("reduce_mean_107_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_107_keep_dims_0 = const()[name = tensor("reduce_mean_107_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_107 = reduce_mean(axes = reduce_mean_107_axes_0, keep_dims = reduce_mean_107_keep_dims_0, x = square_35)[name = tensor("reduce_mean_107")]; - tensor add_70_y_0 = const()[name = tensor("add_70_y_0"), val = tensor(0x1.0c6f7ap-20)]; - tensor add_70 = add(x = reduce_mean_107, y = add_70_y_0)[name = tensor("add_70")]; - tensor sqrt_35 = sqrt(x = add_70)[name = tensor("sqrt_35")]; - tensor real_div_35 = real_div(x = sub_70, y = sqrt_35)[name = tensor("real_div_35")]; + tensor reduce_mean_107_cast = reduce_mean(axes = reduce_mean_107_axes_0, keep_dims = reduce_mean_107_keep_dims_0, x = square_35_cast)[name = tensor("reduce_mean_107_cast")]; + tensor add_70_y_0_to_fp16 = const()[name = tensor("add_70_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_70_cast = add(x = reduce_mean_107_cast, y = add_70_y_0_to_fp16)[name = tensor("add_70_cast")]; + tensor sqrt_35_cast = sqrt(x = add_70_cast)[name = tensor("sqrt_35_cast")]; + tensor real_div_35_cast = real_div(x = sub_70_cast, y = sqrt_35_cast)[name = tensor("real_div_35_cast")]; tensor reshape_141_shape_0 = const()[name = tensor("reshape_141_shape_0"), val = tensor([2, 640, 64, 64])]; - tensor reshape_141 = reshape(shape = reshape_141_shape_0, x = real_div_35)[name = tensor("reshape_141")]; - tensor add_71_gamma_0 = const()[name = tensor("add_71_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269902336)))]; - tensor add_71_beta_0 = const()[name = tensor("add_71_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269904960)))]; - tensor add_71_epsilon_0 = const()[name = tensor("add_71_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_71 = batch_norm(beta = add_71_beta_0, epsilon = add_71_epsilon_0, gamma = add_71_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_141)[name = tensor("add_71")]; - tensor var_13043 = const()[name = tensor("op_13043"), val = tensor([1, 1])]; - tensor var_13045 = const()[name = tensor("op_13045"), val = tensor([1, 1])]; + tensor reshape_141_cast = reshape(shape = reshape_141_shape_0, x = real_div_35_cast)[name = tensor("reshape_141_cast")]; + tensor add_71_gamma_0_to_fp16 = const()[name = tensor("add_71_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5000673024)))]; + tensor add_71_beta_0_to_fp16 = const()[name = tensor("add_71_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5000674368)))]; + tensor add_71_epsilon_0_to_fp16 = const()[name = tensor("add_71_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_71_cast = batch_norm(beta = add_71_beta_0_to_fp16, epsilon = add_71_epsilon_0_to_fp16, gamma = add_71_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_141_cast)[name = tensor("add_71_cast")]; + tensor var_12925 = const()[name = tensor("op_12925"), val = tensor([1, 1])]; + tensor var_12927 = const()[name = tensor("op_12927"), val = tensor([1, 1])]; tensor hidden_states_533_pad_type_0 = const()[name = tensor("hidden_states_533_pad_type_0"), val = tensor("custom")]; tensor hidden_states_533_pad_0 = const()[name = tensor("hidden_states_533_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_533 = conv(bias = up_blocks_1_attentions_1_proj_in_bias, dilations = var_13045, groups = var_12525, pad = hidden_states_533_pad_0, pad_type = hidden_states_533_pad_type_0, strides = var_13043, weight = up_blocks_1_attentions_1_proj_in_weight, x = add_71)[name = tensor("hidden_states_533")]; - tensor var_13050 = const()[name = tensor("op_13050"), val = tensor([2, 640, 1, 4096])]; - tensor inputs_397 = reshape(shape = var_13050, x = hidden_states_533)[name = tensor("inputs_397")]; - tensor var_13060 = const()[name = tensor("op_13060"), val = tensor([1])]; - tensor channels_mean_397 = reduce_mean(axes = var_13060, keep_dims = var_12520, x = inputs_397)[name = tensor("channels_mean_397")]; - tensor zero_mean_397 = sub(x = inputs_397, y = channels_mean_397)[name = tensor("zero_mean_397")]; - tensor zero_mean_sq_397 = mul(x = zero_mean_397, y = zero_mean_397)[name = tensor("zero_mean_sq_397")]; - tensor var_13064 = const()[name = tensor("op_13064"), val = tensor([1])]; - tensor var_13065 = reduce_mean(axes = var_13064, keep_dims = var_12520, x = zero_mean_sq_397)[name = tensor("op_13065")]; - tensor var_13066 = const()[name = tensor("op_13066"), val = tensor(0x1.4f8b58p-17)]; - tensor var_13067 = add(x = var_13065, y = var_13066)[name = tensor("op_13067")]; - tensor denom_397_epsilon_0 = const()[name = tensor("denom_397_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_397 = rsqrt(epsilon = denom_397_epsilon_0, x = var_13067)[name = tensor("denom_397")]; - tensor out_397 = mul(x = zero_mean_397, y = denom_397)[name = tensor("out_397")]; - tensor var_13071 = const()[name = tensor("op_13071"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269907584)))]; - tensor var_13072 = add(x = out_397, y = var_13071)[name = tensor("op_13072")]; - tensor var_13074 = const()[name = tensor("op_13074"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269910208)))]; - tensor hidden_states_535 = mul(x = var_13072, y = var_13074)[name = tensor("hidden_states_535")]; - tensor var_13081 = const()[name = tensor("op_13081"), val = tensor([1, 1])]; - tensor var_13083 = const()[name = tensor("op_13083"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_1_proj_in_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5000675712)))]; + tensor unet_up_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5001494976)))]; + tensor hidden_states_533_cast = conv(bias = unet_up_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_12927, groups = var_31, pad = hidden_states_533_pad_0, pad_type = hidden_states_533_pad_type_0, strides = var_12925, weight = unet_up_blocks_1_attentions_1_proj_in_weight_to_fp16, x = add_71_cast)[name = tensor("hidden_states_533_cast")]; + tensor var_12932 = const()[name = tensor("op_12932"), val = tensor([2, 640, 1, 4096])]; + tensor inputs_397_cast = reshape(shape = var_12932, x = hidden_states_533_cast)[name = tensor("inputs_397_cast")]; + tensor var_12942 = const()[name = tensor("op_12942"), val = tensor([1])]; + tensor channels_mean_397_cast = reduce_mean(axes = var_12942, keep_dims = var_23, x = inputs_397_cast)[name = tensor("channels_mean_397_cast")]; + tensor zero_mean_397_cast = sub(x = inputs_397_cast, y = channels_mean_397_cast)[name = tensor("zero_mean_397_cast")]; + tensor zero_mean_sq_397_cast = mul(x = zero_mean_397_cast, y = zero_mean_397_cast)[name = tensor("zero_mean_sq_397_cast")]; + tensor var_12946 = const()[name = tensor("op_12946"), val = tensor([1])]; + tensor var_12947_cast = reduce_mean(axes = var_12946, keep_dims = var_23, x = zero_mean_sq_397_cast)[name = tensor("op_12947_cast")]; + tensor var_12948_to_fp16 = const()[name = tensor("op_12948_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_12949_cast = add(x = var_12947_cast, y = var_12948_to_fp16)[name = tensor("op_12949_cast")]; + tensor denom_397_epsilon_0_to_fp16 = const()[name = tensor("denom_397_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_397_cast = rsqrt(epsilon = denom_397_epsilon_0_to_fp16, x = var_12949_cast)[name = tensor("denom_397_cast")]; + tensor out_397_cast = mul(x = zero_mean_397_cast, y = denom_397_cast)[name = tensor("out_397_cast")]; + tensor var_12953_to_fp16 = const()[name = tensor("op_12953_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5001496320)))]; + tensor var_12954_cast = add(x = out_397_cast, y = var_12953_to_fp16)[name = tensor("op_12954_cast")]; + tensor var_12956_to_fp16 = const()[name = tensor("op_12956_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5001497664)))]; + tensor hidden_states_535_cast = mul(x = var_12954_cast, y = var_12956_to_fp16)[name = tensor("hidden_states_535_cast")]; + tensor var_12963 = const()[name = tensor("op_12963"), val = tensor([1, 1])]; + tensor var_12965 = const()[name = tensor("op_12965"), val = tensor([1, 1])]; tensor q_265_pad_type_0 = const()[name = tensor("q_265_pad_type_0"), val = tensor("custom")]; tensor q_265_pad_0 = const()[name = tensor("q_265_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_265 = conv(dilations = var_13083, groups = var_12525, pad = q_265_pad_0, pad_type = q_265_pad_type_0, strides = var_13081, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight, x = hidden_states_535)[name = tensor("q_265")]; - tensor var_13087 = const()[name = tensor("op_13087"), val = tensor([1, 1])]; - tensor var_13089 = const()[name = tensor("op_13089"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5001499008)))]; + tensor q_265_cast = conv(dilations = var_12965, groups = var_31, pad = q_265_pad_0, pad_type = q_265_pad_type_0, strides = var_12963, weight = unet_up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_535_cast)[name = tensor("q_265_cast")]; + tensor var_12969 = const()[name = tensor("op_12969"), val = tensor([1, 1])]; + tensor var_12971 = const()[name = tensor("op_12971"), val = tensor([1, 1])]; tensor k_265_pad_type_0 = const()[name = tensor("k_265_pad_type_0"), val = tensor("custom")]; tensor k_265_pad_0 = const()[name = tensor("k_265_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_265 = conv(dilations = var_13089, groups = var_12525, pad = k_265_pad_0, pad_type = k_265_pad_type_0, strides = var_13087, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight, x = hidden_states_535)[name = tensor("k_265")]; - tensor var_13093 = const()[name = tensor("op_13093"), val = tensor([1, 1])]; - tensor var_13095 = const()[name = tensor("op_13095"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5002318272)))]; + tensor k_265_cast = conv(dilations = var_12971, groups = var_31, pad = k_265_pad_0, pad_type = k_265_pad_type_0, strides = var_12969, weight = unet_up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_535_cast)[name = tensor("k_265_cast")]; + tensor var_12975 = const()[name = tensor("op_12975"), val = tensor([1, 1])]; + tensor var_12977 = const()[name = tensor("op_12977"), val = tensor([1, 1])]; tensor v_265_pad_type_0 = const()[name = tensor("v_265_pad_type_0"), val = tensor("custom")]; tensor v_265_pad_0 = const()[name = tensor("v_265_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_265 = conv(dilations = var_13095, groups = var_12525, pad = v_265_pad_0, pad_type = v_265_pad_type_0, strides = var_13093, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight, x = hidden_states_535)[name = tensor("v_265")]; - tensor var_13099 = const()[name = tensor("op_13099"), val = tensor([2, 10, 64, -1])]; - tensor var_13100 = reshape(shape = var_13099, x = q_265)[name = tensor("op_13100")]; - tensor var_13101 = const()[name = tensor("op_13101"), val = tensor([2, 10, 64, -1])]; - tensor var_13102 = reshape(shape = var_13101, x = k_265)[name = tensor("op_13102")]; - tensor var_13103 = const()[name = tensor("op_13103"), val = tensor([2, 10, 64, -1])]; - tensor var_13104 = reshape(shape = var_13103, x = v_265)[name = tensor("op_13104")]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5003137536)))]; + tensor v_265_cast = conv(dilations = var_12977, groups = var_31, pad = v_265_pad_0, pad_type = v_265_pad_type_0, strides = var_12975, weight = unet_up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_535_cast)[name = tensor("v_265_cast")]; + tensor var_12981 = const()[name = tensor("op_12981"), val = tensor([2, 10, 64, -1])]; + tensor var_12982_cast = reshape(shape = var_12981, x = q_265_cast)[name = tensor("op_12982_cast")]; + tensor var_12983 = const()[name = tensor("op_12983"), val = tensor([2, 10, 64, -1])]; + tensor var_12984_cast = reshape(shape = var_12983, x = k_265_cast)[name = tensor("op_12984_cast")]; + tensor var_12985 = const()[name = tensor("op_12985"), val = tensor([2, 10, 64, -1])]; + tensor var_12986_cast = reshape(shape = var_12985, x = v_265_cast)[name = tensor("op_12986_cast")]; tensor attn_weights_529_transpose_x_0 = const()[name = tensor("attn_weights_529_transpose_x_0"), val = tensor(true)]; tensor attn_weights_529_transpose_y_0 = const()[name = tensor("attn_weights_529_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_529 = matmul(transpose_x = attn_weights_529_transpose_x_0, transpose_y = attn_weights_529_transpose_y_0, x = var_13100, y = var_13102)[name = tensor("attn_weights_529")]; - tensor attn_weights_531 = mul(x = attn_weights_529, y = var_12516)[name = tensor("attn_weights_531")]; - tensor var_13108 = softmax(axis = var_12509, x = attn_weights_531)[name = tensor("op_13108")]; + tensor attn_weights_529_cast = matmul(transpose_x = attn_weights_529_transpose_x_0, transpose_y = attn_weights_529_transpose_y_0, x = var_12982_cast, y = var_12984_cast)[name = tensor("attn_weights_529_cast")]; + tensor attn_weights_531_cast = mul(x = attn_weights_529_cast, y = var_12_to_fp16)[name = tensor("attn_weights_531_cast")]; + tensor var_12990_cast = softmax(axis = var_18, x = attn_weights_531_cast)[name = tensor("op_12990_cast")]; tensor attn_265_transpose_x_0 = const()[name = tensor("attn_265_transpose_x_0"), val = tensor(false)]; tensor attn_265_transpose_y_0 = const()[name = tensor("attn_265_transpose_y_0"), val = tensor(true)]; - tensor attn_265 = matmul(transpose_x = attn_265_transpose_x_0, transpose_y = attn_265_transpose_y_0, x = var_13104, y = var_13108)[name = tensor("attn_265")]; - tensor var_13112 = const()[name = tensor("op_13112"), val = tensor([2, 640, 1, -1])]; - tensor input_771 = reshape(shape = var_13112, x = attn_265)[name = tensor("input_771")]; - tensor var_13117 = const()[name = tensor("op_13117"), val = tensor([1, 1])]; - tensor var_13119 = const()[name = tensor("op_13119"), val = tensor([1, 1])]; - tensor var_13121_pad_type_0 = const()[name = tensor("op_13121_pad_type_0"), val = tensor("custom")]; - tensor var_13121_pad_0 = const()[name = tensor("op_13121_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_13121 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias, dilations = var_13119, groups = var_12525, pad = var_13121_pad_0, pad_type = var_13121_pad_type_0, strides = var_13117, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight, x = input_771)[name = tensor("op_13121")]; - tensor inputs_399 = add(x = var_13121, y = inputs_397)[name = tensor("inputs_399")]; - tensor var_13125 = const()[name = tensor("op_13125"), val = tensor([1])]; - tensor channels_mean_399 = reduce_mean(axes = var_13125, keep_dims = var_12520, x = inputs_399)[name = tensor("channels_mean_399")]; - tensor zero_mean_399 = sub(x = inputs_399, y = channels_mean_399)[name = tensor("zero_mean_399")]; - tensor zero_mean_sq_399 = mul(x = zero_mean_399, y = zero_mean_399)[name = tensor("zero_mean_sq_399")]; - tensor var_13129 = const()[name = tensor("op_13129"), val = tensor([1])]; - tensor var_13130 = reduce_mean(axes = var_13129, keep_dims = var_12520, x = zero_mean_sq_399)[name = tensor("op_13130")]; - tensor var_13131 = const()[name = tensor("op_13131"), val = tensor(0x1.4f8b58p-17)]; - tensor var_13132 = add(x = var_13130, y = var_13131)[name = tensor("op_13132")]; - tensor denom_399_epsilon_0 = const()[name = tensor("denom_399_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_399 = rsqrt(epsilon = denom_399_epsilon_0, x = var_13132)[name = tensor("denom_399")]; - tensor out_399 = mul(x = zero_mean_399, y = denom_399)[name = tensor("out_399")]; - tensor var_13136 = const()[name = tensor("op_13136"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269912832)))]; - tensor var_13137 = add(x = out_399, y = var_13136)[name = tensor("op_13137")]; - tensor var_13139 = const()[name = tensor("op_13139"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269915456)))]; - tensor hidden_states_537 = mul(x = var_13137, y = var_13139)[name = tensor("hidden_states_537")]; - tensor var_13146 = const()[name = tensor("op_13146"), val = tensor([1, 1])]; - tensor var_13148 = const()[name = tensor("op_13148"), val = tensor([1, 1])]; + tensor attn_265_cast = matmul(transpose_x = attn_265_transpose_x_0, transpose_y = attn_265_transpose_y_0, x = var_12986_cast, y = var_12990_cast)[name = tensor("attn_265_cast")]; + tensor var_12994 = const()[name = tensor("op_12994"), val = tensor([2, 640, 1, -1])]; + tensor input_771_cast = reshape(shape = var_12994, x = attn_265_cast)[name = tensor("input_771_cast")]; + tensor var_12999 = const()[name = tensor("op_12999"), val = tensor([1, 1])]; + tensor var_13001 = const()[name = tensor("op_13001"), val = tensor([1, 1])]; + tensor var_13003_pad_type_0 = const()[name = tensor("op_13003_pad_type_0"), val = tensor("custom")]; + tensor var_13003_pad_0 = const()[name = tensor("op_13003_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5003956800)))]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5004776064)))]; + tensor var_13003_cast = conv(bias = unet_up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_13001, groups = var_31, pad = var_13003_pad_0, pad_type = var_13003_pad_type_0, strides = var_12999, weight = unet_up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_771_cast)[name = tensor("op_13003_cast")]; + tensor inputs_399_cast = add(x = var_13003_cast, y = inputs_397_cast)[name = tensor("inputs_399_cast")]; + tensor var_13007 = const()[name = tensor("op_13007"), val = tensor([1])]; + tensor channels_mean_399_cast = reduce_mean(axes = var_13007, keep_dims = var_23, x = inputs_399_cast)[name = tensor("channels_mean_399_cast")]; + tensor zero_mean_399_cast = sub(x = inputs_399_cast, y = channels_mean_399_cast)[name = tensor("zero_mean_399_cast")]; + tensor zero_mean_sq_399_cast = mul(x = zero_mean_399_cast, y = zero_mean_399_cast)[name = tensor("zero_mean_sq_399_cast")]; + tensor var_13011 = const()[name = tensor("op_13011"), val = tensor([1])]; + tensor var_13012_cast = reduce_mean(axes = var_13011, keep_dims = var_23, x = zero_mean_sq_399_cast)[name = tensor("op_13012_cast")]; + tensor var_13013_to_fp16 = const()[name = tensor("op_13013_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13014_cast = add(x = var_13012_cast, y = var_13013_to_fp16)[name = tensor("op_13014_cast")]; + tensor denom_399_epsilon_0_to_fp16 = const()[name = tensor("denom_399_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_399_cast = rsqrt(epsilon = denom_399_epsilon_0_to_fp16, x = var_13014_cast)[name = tensor("denom_399_cast")]; + tensor out_399_cast = mul(x = zero_mean_399_cast, y = denom_399_cast)[name = tensor("out_399_cast")]; + tensor var_13018_to_fp16 = const()[name = tensor("op_13018_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5004777408)))]; + tensor var_13019_cast = add(x = out_399_cast, y = var_13018_to_fp16)[name = tensor("op_13019_cast")]; + tensor var_13021_to_fp16 = const()[name = tensor("op_13021_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5004778752)))]; + tensor hidden_states_537_cast = mul(x = var_13019_cast, y = var_13021_to_fp16)[name = tensor("hidden_states_537_cast")]; + tensor var_13028 = const()[name = tensor("op_13028"), val = tensor([1, 1])]; + tensor var_13030 = const()[name = tensor("op_13030"), val = tensor([1, 1])]; tensor q_267_pad_type_0 = const()[name = tensor("q_267_pad_type_0"), val = tensor("custom")]; tensor q_267_pad_0 = const()[name = tensor("q_267_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_267 = conv(dilations = var_13148, groups = var_12525, pad = q_267_pad_0, pad_type = q_267_pad_type_0, strides = var_13146, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight, x = hidden_states_537)[name = tensor("q_267")]; - tensor var_13152 = const()[name = tensor("op_13152"), val = tensor([1, 1])]; - tensor var_13154 = const()[name = tensor("op_13154"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5004780096)))]; + tensor q_267_cast = conv(dilations = var_13030, groups = var_31, pad = q_267_pad_0, pad_type = q_267_pad_type_0, strides = var_13028, weight = unet_up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_537_cast)[name = tensor("q_267_cast")]; + tensor var_13034 = const()[name = tensor("op_13034"), val = tensor([1, 1])]; + tensor var_13036 = const()[name = tensor("op_13036"), val = tensor([1, 1])]; tensor k_267_pad_type_0 = const()[name = tensor("k_267_pad_type_0"), val = tensor("custom")]; tensor k_267_pad_0 = const()[name = tensor("k_267_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_267 = conv(dilations = var_13154, groups = var_12525, pad = k_267_pad_0, pad_type = k_267_pad_type_0, strides = var_13152, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_267")]; - tensor var_13158 = const()[name = tensor("op_13158"), val = tensor([1, 1])]; - tensor var_13160 = const()[name = tensor("op_13160"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5005599360)))]; + tensor k_267_cast = conv(dilations = var_13036, groups = var_31, pad = k_267_pad_0, pad_type = k_267_pad_type_0, strides = var_13034, weight = unet_up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_267_cast")]; + tensor var_13040 = const()[name = tensor("op_13040"), val = tensor([1, 1])]; + tensor var_13042 = const()[name = tensor("op_13042"), val = tensor([1, 1])]; tensor v_267_pad_type_0 = const()[name = tensor("v_267_pad_type_0"), val = tensor("custom")]; tensor v_267_pad_0 = const()[name = tensor("v_267_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_267 = conv(dilations = var_13160, groups = var_12525, pad = v_267_pad_0, pad_type = v_267_pad_type_0, strides = var_13158, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_267")]; - tensor var_13164 = const()[name = tensor("op_13164"), val = tensor([2, 10, 64, -1])]; - tensor var_13165 = reshape(shape = var_13164, x = q_267)[name = tensor("op_13165")]; - tensor var_13166 = const()[name = tensor("op_13166"), val = tensor([2, 10, 64, -1])]; - tensor var_13167 = reshape(shape = var_13166, x = k_267)[name = tensor("op_13167")]; - tensor var_13168 = const()[name = tensor("op_13168"), val = tensor([2, 10, 64, -1])]; - tensor var_13169 = reshape(shape = var_13168, x = v_267)[name = tensor("op_13169")]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5008220864)))]; + tensor v_267_cast = conv(dilations = var_13042, groups = var_31, pad = v_267_pad_0, pad_type = v_267_pad_type_0, strides = var_13040, weight = unet_up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_267_cast")]; + tensor var_13046 = const()[name = tensor("op_13046"), val = tensor([2, 10, 64, -1])]; + tensor var_13047_cast = reshape(shape = var_13046, x = q_267_cast)[name = tensor("op_13047_cast")]; + tensor var_13048 = const()[name = tensor("op_13048"), val = tensor([2, 10, 64, -1])]; + tensor var_13049_cast = reshape(shape = var_13048, x = k_267_cast)[name = tensor("op_13049_cast")]; + tensor var_13050 = const()[name = tensor("op_13050"), val = tensor([2, 10, 64, -1])]; + tensor var_13051_cast = reshape(shape = var_13050, x = v_267_cast)[name = tensor("op_13051_cast")]; tensor attn_weights_533_transpose_x_0 = const()[name = tensor("attn_weights_533_transpose_x_0"), val = tensor(true)]; tensor attn_weights_533_transpose_y_0 = const()[name = tensor("attn_weights_533_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_533 = matmul(transpose_x = attn_weights_533_transpose_x_0, transpose_y = attn_weights_533_transpose_y_0, x = var_13165, y = var_13167)[name = tensor("attn_weights_533")]; - tensor attn_weights_535 = mul(x = attn_weights_533, y = var_12516)[name = tensor("attn_weights_535")]; - tensor var_13173 = softmax(axis = var_12509, x = attn_weights_535)[name = tensor("op_13173")]; + tensor attn_weights_533_cast = matmul(transpose_x = attn_weights_533_transpose_x_0, transpose_y = attn_weights_533_transpose_y_0, x = var_13047_cast, y = var_13049_cast)[name = tensor("attn_weights_533_cast")]; + tensor attn_weights_535_cast = mul(x = attn_weights_533_cast, y = var_12_to_fp16)[name = tensor("attn_weights_535_cast")]; + tensor var_13055_cast = softmax(axis = var_18, x = attn_weights_535_cast)[name = tensor("op_13055_cast")]; tensor attn_267_transpose_x_0 = const()[name = tensor("attn_267_transpose_x_0"), val = tensor(false)]; tensor attn_267_transpose_y_0 = const()[name = tensor("attn_267_transpose_y_0"), val = tensor(true)]; - tensor attn_267 = matmul(transpose_x = attn_267_transpose_x_0, transpose_y = attn_267_transpose_y_0, x = var_13169, y = var_13173)[name = tensor("attn_267")]; - tensor var_13177 = const()[name = tensor("op_13177"), val = tensor([2, 640, 1, -1])]; - tensor input_773 = reshape(shape = var_13177, x = attn_267)[name = tensor("input_773")]; - tensor var_13182 = const()[name = tensor("op_13182"), val = tensor([1, 1])]; - tensor var_13184 = const()[name = tensor("op_13184"), val = tensor([1, 1])]; - tensor var_13186_pad_type_0 = const()[name = tensor("op_13186_pad_type_0"), val = tensor("custom")]; - tensor var_13186_pad_0 = const()[name = tensor("op_13186_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_13186 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias, dilations = var_13184, groups = var_12525, pad = var_13186_pad_0, pad_type = var_13186_pad_type_0, strides = var_13182, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight, x = input_773)[name = tensor("op_13186")]; - tensor inputs_401 = add(x = var_13186, y = inputs_399)[name = tensor("inputs_401")]; - tensor var_13190 = const()[name = tensor("op_13190"), val = tensor([1])]; - tensor channels_mean_401 = reduce_mean(axes = var_13190, keep_dims = var_12520, x = inputs_401)[name = tensor("channels_mean_401")]; - tensor zero_mean_401 = sub(x = inputs_401, y = channels_mean_401)[name = tensor("zero_mean_401")]; - tensor zero_mean_sq_401 = mul(x = zero_mean_401, y = zero_mean_401)[name = tensor("zero_mean_sq_401")]; - tensor var_13194 = const()[name = tensor("op_13194"), val = tensor([1])]; - tensor var_13195 = reduce_mean(axes = var_13194, keep_dims = var_12520, x = zero_mean_sq_401)[name = tensor("op_13195")]; - tensor var_13196 = const()[name = tensor("op_13196"), val = tensor(0x1.4f8b58p-17)]; - tensor var_13197 = add(x = var_13195, y = var_13196)[name = tensor("op_13197")]; - tensor denom_401_epsilon_0 = const()[name = tensor("denom_401_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_401 = rsqrt(epsilon = denom_401_epsilon_0, x = var_13197)[name = tensor("denom_401")]; - tensor out_401 = mul(x = zero_mean_401, y = denom_401)[name = tensor("out_401")]; - tensor var_13201 = const()[name = tensor("op_13201"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269918080)))]; - tensor var_13202 = add(x = out_401, y = var_13201)[name = tensor("op_13202")]; - tensor var_13204 = const()[name = tensor("op_13204"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269920704)))]; - tensor input_775 = mul(x = var_13202, y = var_13204)[name = tensor("input_775")]; - tensor var_13212 = const()[name = tensor("op_13212"), val = tensor([1, 1])]; - tensor var_13214 = const()[name = tensor("op_13214"), val = tensor([1, 1])]; - tensor var_13216_pad_type_0 = const()[name = tensor("op_13216_pad_type_0"), val = tensor("custom")]; - tensor var_13216_pad_0 = const()[name = tensor("op_13216_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_13216 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias, dilations = var_13214, groups = var_12525, pad = var_13216_pad_0, pad_type = var_13216_pad_type_0, strides = var_13212, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight, x = input_775)[name = tensor("op_13216")]; - tensor var_13217_split_sizes_0 = const()[name = tensor("op_13217_split_sizes_0"), val = tensor([2560, 2560])]; - tensor var_13217_axis_0 = const()[name = tensor("op_13217_axis_0"), val = tensor(1)]; - tensor var_13217_0, tensor var_13217_1 = split(axis = var_13217_axis_0, split_sizes = var_13217_split_sizes_0, x = var_13216)[name = tensor("op_13217")]; - tensor var_13219_mode_0 = const()[name = tensor("op_13219_mode_0"), val = tensor("EXACT")]; - tensor var_13219 = gelu(mode = var_13219_mode_0, x = var_13217_1)[name = tensor("op_13219")]; - tensor input_777 = mul(x = var_13217_0, y = var_13219)[name = tensor("input_777")]; - tensor var_13223 = const()[name = tensor("op_13223"), val = tensor([1, 1])]; - tensor var_13225 = const()[name = tensor("op_13225"), val = tensor([1, 1])]; - tensor var_13227_pad_type_0 = const()[name = tensor("op_13227_pad_type_0"), val = tensor("custom")]; - tensor var_13227_pad_0 = const()[name = tensor("op_13227_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_13227 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias, dilations = var_13225, groups = var_12525, pad = var_13227_pad_0, pad_type = var_13227_pad_type_0, strides = var_13223, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight, x = input_777)[name = tensor("op_13227")]; - tensor inputs_403 = add(x = var_13227, y = inputs_401)[name = tensor("inputs_403")]; - tensor var_13237 = const()[name = tensor("op_13237"), val = tensor([1])]; - tensor channels_mean_403 = reduce_mean(axes = var_13237, keep_dims = var_12520, x = inputs_403)[name = tensor("channels_mean_403")]; - tensor zero_mean_403 = sub(x = inputs_403, y = channels_mean_403)[name = tensor("zero_mean_403")]; - tensor zero_mean_sq_403 = mul(x = zero_mean_403, y = zero_mean_403)[name = tensor("zero_mean_sq_403")]; - tensor var_13241 = const()[name = tensor("op_13241"), val = tensor([1])]; - tensor var_13242 = reduce_mean(axes = var_13241, keep_dims = var_12520, x = zero_mean_sq_403)[name = tensor("op_13242")]; - tensor var_13243 = const()[name = tensor("op_13243"), val = tensor(0x1.4f8b58p-17)]; - tensor var_13244 = add(x = var_13242, y = var_13243)[name = tensor("op_13244")]; - tensor denom_403_epsilon_0 = const()[name = tensor("denom_403_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_403 = rsqrt(epsilon = denom_403_epsilon_0, x = var_13244)[name = tensor("denom_403")]; - tensor out_403 = mul(x = zero_mean_403, y = denom_403)[name = tensor("out_403")]; - tensor var_13248 = const()[name = tensor("op_13248"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269923328)))]; - tensor var_13249 = add(x = out_403, y = var_13248)[name = tensor("op_13249")]; - tensor var_13251 = const()[name = tensor("op_13251"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269925952)))]; - tensor hidden_states_541 = mul(x = var_13249, y = var_13251)[name = tensor("hidden_states_541")]; - tensor var_13258 = const()[name = tensor("op_13258"), val = tensor([1, 1])]; - tensor var_13260 = const()[name = tensor("op_13260"), val = tensor([1, 1])]; + tensor attn_267_cast = matmul(transpose_x = attn_267_transpose_x_0, transpose_y = attn_267_transpose_y_0, x = var_13051_cast, y = var_13055_cast)[name = tensor("attn_267_cast")]; + tensor var_13059 = const()[name = tensor("op_13059"), val = tensor([2, 640, 1, -1])]; + tensor input_773_cast = reshape(shape = var_13059, x = attn_267_cast)[name = tensor("input_773_cast")]; + tensor var_13064 = const()[name = tensor("op_13064"), val = tensor([1, 1])]; + tensor var_13066 = const()[name = tensor("op_13066"), val = tensor([1, 1])]; + tensor var_13068_pad_type_0 = const()[name = tensor("op_13068_pad_type_0"), val = tensor("custom")]; + tensor var_13068_pad_0 = const()[name = tensor("op_13068_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5010842368)))]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5011661632)))]; + tensor var_13068_cast = conv(bias = unet_up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_13066, groups = var_31, pad = var_13068_pad_0, pad_type = var_13068_pad_type_0, strides = var_13064, weight = unet_up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_773_cast)[name = tensor("op_13068_cast")]; + tensor inputs_401_cast = add(x = var_13068_cast, y = inputs_399_cast)[name = tensor("inputs_401_cast")]; + tensor var_13072 = const()[name = tensor("op_13072"), val = tensor([1])]; + tensor channels_mean_401_cast = reduce_mean(axes = var_13072, keep_dims = var_23, x = inputs_401_cast)[name = tensor("channels_mean_401_cast")]; + tensor zero_mean_401_cast = sub(x = inputs_401_cast, y = channels_mean_401_cast)[name = tensor("zero_mean_401_cast")]; + tensor zero_mean_sq_401_cast = mul(x = zero_mean_401_cast, y = zero_mean_401_cast)[name = tensor("zero_mean_sq_401_cast")]; + tensor var_13076 = const()[name = tensor("op_13076"), val = tensor([1])]; + tensor var_13077_cast = reduce_mean(axes = var_13076, keep_dims = var_23, x = zero_mean_sq_401_cast)[name = tensor("op_13077_cast")]; + tensor var_13078_to_fp16 = const()[name = tensor("op_13078_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13079_cast = add(x = var_13077_cast, y = var_13078_to_fp16)[name = tensor("op_13079_cast")]; + tensor denom_401_epsilon_0_to_fp16 = const()[name = tensor("denom_401_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_401_cast = rsqrt(epsilon = denom_401_epsilon_0_to_fp16, x = var_13079_cast)[name = tensor("denom_401_cast")]; + tensor out_401_cast = mul(x = zero_mean_401_cast, y = denom_401_cast)[name = tensor("out_401_cast")]; + tensor var_13083_to_fp16 = const()[name = tensor("op_13083_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5011662976)))]; + tensor var_13084_cast = add(x = out_401_cast, y = var_13083_to_fp16)[name = tensor("op_13084_cast")]; + tensor var_13086_to_fp16 = const()[name = tensor("op_13086_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5011664320)))]; + tensor input_775_cast = mul(x = var_13084_cast, y = var_13086_to_fp16)[name = tensor("input_775_cast")]; + tensor var_13094 = const()[name = tensor("op_13094"), val = tensor([1, 1])]; + tensor var_13096 = const()[name = tensor("op_13096"), val = tensor([1, 1])]; + tensor var_13098_pad_type_0 = const()[name = tensor("op_13098_pad_type_0"), val = tensor("custom")]; + tensor var_13098_pad_0 = const()[name = tensor("op_13098_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5011665664)))]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5018219328)))]; + tensor var_13098_cast = conv(bias = unet_up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_13096, groups = var_31, pad = var_13098_pad_0, pad_type = var_13098_pad_type_0, strides = var_13094, weight = unet_up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_775_cast)[name = tensor("op_13098_cast")]; + tensor var_13099_split_sizes_0 = const()[name = tensor("op_13099_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_13099_axis_0 = const()[name = tensor("op_13099_axis_0"), val = tensor(1)]; + tensor var_13099_cast_0, tensor var_13099_cast_1 = split(axis = var_13099_axis_0, split_sizes = var_13099_split_sizes_0, x = var_13098_cast)[name = tensor("op_13099_cast")]; + tensor var_13101_mode_0 = const()[name = tensor("op_13101_mode_0"), val = tensor("EXACT")]; + tensor var_13101_cast = gelu(mode = var_13101_mode_0, x = var_13099_cast_1)[name = tensor("op_13101_cast")]; + tensor input_777_cast = mul(x = var_13099_cast_0, y = var_13101_cast)[name = tensor("input_777_cast")]; + tensor var_13105 = const()[name = tensor("op_13105"), val = tensor([1, 1])]; + tensor var_13107 = const()[name = tensor("op_13107"), val = tensor([1, 1])]; + tensor var_13109_pad_type_0 = const()[name = tensor("op_13109_pad_type_0"), val = tensor("custom")]; + tensor var_13109_pad_0 = const()[name = tensor("op_13109_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5018229632)))]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5021506496)))]; + tensor var_13109_cast = conv(bias = unet_up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_13107, groups = var_31, pad = var_13109_pad_0, pad_type = var_13109_pad_type_0, strides = var_13105, weight = unet_up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_777_cast)[name = tensor("op_13109_cast")]; + tensor inputs_403_cast = add(x = var_13109_cast, y = inputs_401_cast)[name = tensor("inputs_403_cast")]; + tensor var_13119 = const()[name = tensor("op_13119"), val = tensor([1])]; + tensor channels_mean_403_cast = reduce_mean(axes = var_13119, keep_dims = var_23, x = inputs_403_cast)[name = tensor("channels_mean_403_cast")]; + tensor zero_mean_403_cast = sub(x = inputs_403_cast, y = channels_mean_403_cast)[name = tensor("zero_mean_403_cast")]; + tensor zero_mean_sq_403_cast = mul(x = zero_mean_403_cast, y = zero_mean_403_cast)[name = tensor("zero_mean_sq_403_cast")]; + tensor var_13123 = const()[name = tensor("op_13123"), val = tensor([1])]; + tensor var_13124_cast = reduce_mean(axes = var_13123, keep_dims = var_23, x = zero_mean_sq_403_cast)[name = tensor("op_13124_cast")]; + tensor var_13125_to_fp16 = const()[name = tensor("op_13125_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13126_cast = add(x = var_13124_cast, y = var_13125_to_fp16)[name = tensor("op_13126_cast")]; + tensor denom_403_epsilon_0_to_fp16 = const()[name = tensor("denom_403_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_403_cast = rsqrt(epsilon = denom_403_epsilon_0_to_fp16, x = var_13126_cast)[name = tensor("denom_403_cast")]; + tensor out_403_cast = mul(x = zero_mean_403_cast, y = denom_403_cast)[name = tensor("out_403_cast")]; + tensor var_13130_to_fp16 = const()[name = tensor("op_13130_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5021507840)))]; + tensor var_13131_cast = add(x = out_403_cast, y = var_13130_to_fp16)[name = tensor("op_13131_cast")]; + tensor var_13133_to_fp16 = const()[name = tensor("op_13133_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5021509184)))]; + tensor hidden_states_541_cast = mul(x = var_13131_cast, y = var_13133_to_fp16)[name = tensor("hidden_states_541_cast")]; + tensor var_13140 = const()[name = tensor("op_13140"), val = tensor([1, 1])]; + tensor var_13142 = const()[name = tensor("op_13142"), val = tensor([1, 1])]; tensor q_269_pad_type_0 = const()[name = tensor("q_269_pad_type_0"), val = tensor("custom")]; tensor q_269_pad_0 = const()[name = tensor("q_269_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_269 = conv(dilations = var_13260, groups = var_12525, pad = q_269_pad_0, pad_type = q_269_pad_type_0, strides = var_13258, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight, x = hidden_states_541)[name = tensor("q_269")]; - tensor var_13264 = const()[name = tensor("op_13264"), val = tensor([1, 1])]; - tensor var_13266 = const()[name = tensor("op_13266"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5021510528)))]; + tensor q_269_cast = conv(dilations = var_13142, groups = var_31, pad = q_269_pad_0, pad_type = q_269_pad_type_0, strides = var_13140, weight = unet_up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_541_cast)[name = tensor("q_269_cast")]; + tensor var_13146 = const()[name = tensor("op_13146"), val = tensor([1, 1])]; + tensor var_13148 = const()[name = tensor("op_13148"), val = tensor([1, 1])]; tensor k_269_pad_type_0 = const()[name = tensor("k_269_pad_type_0"), val = tensor("custom")]; tensor k_269_pad_0 = const()[name = tensor("k_269_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_269 = conv(dilations = var_13266, groups = var_12525, pad = k_269_pad_0, pad_type = k_269_pad_type_0, strides = var_13264, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight, x = hidden_states_541)[name = tensor("k_269")]; - tensor var_13270 = const()[name = tensor("op_13270"), val = tensor([1, 1])]; - tensor var_13272 = const()[name = tensor("op_13272"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5022329792)))]; + tensor k_269_cast = conv(dilations = var_13148, groups = var_31, pad = k_269_pad_0, pad_type = k_269_pad_type_0, strides = var_13146, weight = unet_up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_541_cast)[name = tensor("k_269_cast")]; + tensor var_13152 = const()[name = tensor("op_13152"), val = tensor([1, 1])]; + tensor var_13154 = const()[name = tensor("op_13154"), val = tensor([1, 1])]; tensor v_269_pad_type_0 = const()[name = tensor("v_269_pad_type_0"), val = tensor("custom")]; tensor v_269_pad_0 = const()[name = tensor("v_269_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_269 = conv(dilations = var_13272, groups = var_12525, pad = v_269_pad_0, pad_type = v_269_pad_type_0, strides = var_13270, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight, x = hidden_states_541)[name = tensor("v_269")]; - tensor var_13276 = const()[name = tensor("op_13276"), val = tensor([2, 10, 64, -1])]; - tensor var_13277 = reshape(shape = var_13276, x = q_269)[name = tensor("op_13277")]; - tensor var_13278 = const()[name = tensor("op_13278"), val = tensor([2, 10, 64, -1])]; - tensor var_13279 = reshape(shape = var_13278, x = k_269)[name = tensor("op_13279")]; - tensor var_13280 = const()[name = tensor("op_13280"), val = tensor([2, 10, 64, -1])]; - tensor var_13281 = reshape(shape = var_13280, x = v_269)[name = tensor("op_13281")]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5023149056)))]; + tensor v_269_cast = conv(dilations = var_13154, groups = var_31, pad = v_269_pad_0, pad_type = v_269_pad_type_0, strides = var_13152, weight = unet_up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_541_cast)[name = tensor("v_269_cast")]; + tensor var_13158 = const()[name = tensor("op_13158"), val = tensor([2, 10, 64, -1])]; + tensor var_13159_cast = reshape(shape = var_13158, x = q_269_cast)[name = tensor("op_13159_cast")]; + tensor var_13160 = const()[name = tensor("op_13160"), val = tensor([2, 10, 64, -1])]; + tensor var_13161_cast = reshape(shape = var_13160, x = k_269_cast)[name = tensor("op_13161_cast")]; + tensor var_13162 = const()[name = tensor("op_13162"), val = tensor([2, 10, 64, -1])]; + tensor var_13163_cast = reshape(shape = var_13162, x = v_269_cast)[name = tensor("op_13163_cast")]; tensor attn_weights_537_transpose_x_0 = const()[name = tensor("attn_weights_537_transpose_x_0"), val = tensor(true)]; tensor attn_weights_537_transpose_y_0 = const()[name = tensor("attn_weights_537_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_537 = matmul(transpose_x = attn_weights_537_transpose_x_0, transpose_y = attn_weights_537_transpose_y_0, x = var_13277, y = var_13279)[name = tensor("attn_weights_537")]; - tensor attn_weights_539 = mul(x = attn_weights_537, y = var_12516)[name = tensor("attn_weights_539")]; - tensor var_13285 = softmax(axis = var_12509, x = attn_weights_539)[name = tensor("op_13285")]; + tensor attn_weights_537_cast = matmul(transpose_x = attn_weights_537_transpose_x_0, transpose_y = attn_weights_537_transpose_y_0, x = var_13159_cast, y = var_13161_cast)[name = tensor("attn_weights_537_cast")]; + tensor attn_weights_539_cast = mul(x = attn_weights_537_cast, y = var_12_to_fp16)[name = tensor("attn_weights_539_cast")]; + tensor var_13167_cast = softmax(axis = var_18, x = attn_weights_539_cast)[name = tensor("op_13167_cast")]; tensor attn_269_transpose_x_0 = const()[name = tensor("attn_269_transpose_x_0"), val = tensor(false)]; tensor attn_269_transpose_y_0 = const()[name = tensor("attn_269_transpose_y_0"), val = tensor(true)]; - tensor attn_269 = matmul(transpose_x = attn_269_transpose_x_0, transpose_y = attn_269_transpose_y_0, x = var_13281, y = var_13285)[name = tensor("attn_269")]; - tensor var_13289 = const()[name = tensor("op_13289"), val = tensor([2, 640, 1, -1])]; - tensor input_779 = reshape(shape = var_13289, x = attn_269)[name = tensor("input_779")]; - tensor var_13294 = const()[name = tensor("op_13294"), val = tensor([1, 1])]; - tensor var_13296 = const()[name = tensor("op_13296"), val = tensor([1, 1])]; - tensor var_13298_pad_type_0 = const()[name = tensor("op_13298_pad_type_0"), val = tensor("custom")]; - tensor var_13298_pad_0 = const()[name = tensor("op_13298_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_13298 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias, dilations = var_13296, groups = var_12525, pad = var_13298_pad_0, pad_type = var_13298_pad_type_0, strides = var_13294, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight, x = input_779)[name = tensor("op_13298")]; - tensor inputs_405 = add(x = var_13298, y = inputs_403)[name = tensor("inputs_405")]; - tensor var_13302 = const()[name = tensor("op_13302"), val = tensor([1])]; - tensor channels_mean_405 = reduce_mean(axes = var_13302, keep_dims = var_12520, x = inputs_405)[name = tensor("channels_mean_405")]; - tensor zero_mean_405 = sub(x = inputs_405, y = channels_mean_405)[name = tensor("zero_mean_405")]; - tensor zero_mean_sq_405 = mul(x = zero_mean_405, y = zero_mean_405)[name = tensor("zero_mean_sq_405")]; - tensor var_13306 = const()[name = tensor("op_13306"), val = tensor([1])]; - tensor var_13307 = reduce_mean(axes = var_13306, keep_dims = var_12520, x = zero_mean_sq_405)[name = tensor("op_13307")]; - tensor var_13308 = const()[name = tensor("op_13308"), val = tensor(0x1.4f8b58p-17)]; - tensor var_13309 = add(x = var_13307, y = var_13308)[name = tensor("op_13309")]; - tensor denom_405_epsilon_0 = const()[name = tensor("denom_405_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_405 = rsqrt(epsilon = denom_405_epsilon_0, x = var_13309)[name = tensor("denom_405")]; - tensor out_405 = mul(x = zero_mean_405, y = denom_405)[name = tensor("out_405")]; - tensor var_13313 = const()[name = tensor("op_13313"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269928576)))]; - tensor var_13314 = add(x = out_405, y = var_13313)[name = tensor("op_13314")]; - tensor var_13316 = const()[name = tensor("op_13316"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269931200)))]; - tensor hidden_states_543 = mul(x = var_13314, y = var_13316)[name = tensor("hidden_states_543")]; - tensor var_13323 = const()[name = tensor("op_13323"), val = tensor([1, 1])]; - tensor var_13325 = const()[name = tensor("op_13325"), val = tensor([1, 1])]; + tensor attn_269_cast = matmul(transpose_x = attn_269_transpose_x_0, transpose_y = attn_269_transpose_y_0, x = var_13163_cast, y = var_13167_cast)[name = tensor("attn_269_cast")]; + tensor var_13171 = const()[name = tensor("op_13171"), val = tensor([2, 640, 1, -1])]; + tensor input_779_cast = reshape(shape = var_13171, x = attn_269_cast)[name = tensor("input_779_cast")]; + tensor var_13176 = const()[name = tensor("op_13176"), val = tensor([1, 1])]; + tensor var_13178 = const()[name = tensor("op_13178"), val = tensor([1, 1])]; + tensor var_13180_pad_type_0 = const()[name = tensor("op_13180_pad_type_0"), val = tensor("custom")]; + tensor var_13180_pad_0 = const()[name = tensor("op_13180_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5023968320)))]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5024787584)))]; + tensor var_13180_cast = conv(bias = unet_up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_13178, groups = var_31, pad = var_13180_pad_0, pad_type = var_13180_pad_type_0, strides = var_13176, weight = unet_up_blocks_1_attentions_1_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_779_cast)[name = tensor("op_13180_cast")]; + tensor inputs_405_cast = add(x = var_13180_cast, y = inputs_403_cast)[name = tensor("inputs_405_cast")]; + tensor var_13184 = const()[name = tensor("op_13184"), val = tensor([1])]; + tensor channels_mean_405_cast = reduce_mean(axes = var_13184, keep_dims = var_23, x = inputs_405_cast)[name = tensor("channels_mean_405_cast")]; + tensor zero_mean_405_cast = sub(x = inputs_405_cast, y = channels_mean_405_cast)[name = tensor("zero_mean_405_cast")]; + tensor zero_mean_sq_405_cast = mul(x = zero_mean_405_cast, y = zero_mean_405_cast)[name = tensor("zero_mean_sq_405_cast")]; + tensor var_13188 = const()[name = tensor("op_13188"), val = tensor([1])]; + tensor var_13189_cast = reduce_mean(axes = var_13188, keep_dims = var_23, x = zero_mean_sq_405_cast)[name = tensor("op_13189_cast")]; + tensor var_13190_to_fp16 = const()[name = tensor("op_13190_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13191_cast = add(x = var_13189_cast, y = var_13190_to_fp16)[name = tensor("op_13191_cast")]; + tensor denom_405_epsilon_0_to_fp16 = const()[name = tensor("denom_405_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_405_cast = rsqrt(epsilon = denom_405_epsilon_0_to_fp16, x = var_13191_cast)[name = tensor("denom_405_cast")]; + tensor out_405_cast = mul(x = zero_mean_405_cast, y = denom_405_cast)[name = tensor("out_405_cast")]; + tensor var_13195_to_fp16 = const()[name = tensor("op_13195_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5024788928)))]; + tensor var_13196_cast = add(x = out_405_cast, y = var_13195_to_fp16)[name = tensor("op_13196_cast")]; + tensor var_13198_to_fp16 = const()[name = tensor("op_13198_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5024790272)))]; + tensor hidden_states_543_cast = mul(x = var_13196_cast, y = var_13198_to_fp16)[name = tensor("hidden_states_543_cast")]; + tensor var_13205 = const()[name = tensor("op_13205"), val = tensor([1, 1])]; + tensor var_13207 = const()[name = tensor("op_13207"), val = tensor([1, 1])]; tensor q_271_pad_type_0 = const()[name = tensor("q_271_pad_type_0"), val = tensor("custom")]; tensor q_271_pad_0 = const()[name = tensor("q_271_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_271 = conv(dilations = var_13325, groups = var_12525, pad = q_271_pad_0, pad_type = q_271_pad_type_0, strides = var_13323, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight, x = hidden_states_543)[name = tensor("q_271")]; - tensor var_13329 = const()[name = tensor("op_13329"), val = tensor([1, 1])]; - tensor var_13331 = const()[name = tensor("op_13331"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5024791616)))]; + tensor q_271_cast = conv(dilations = var_13207, groups = var_31, pad = q_271_pad_0, pad_type = q_271_pad_type_0, strides = var_13205, weight = unet_up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_543_cast)[name = tensor("q_271_cast")]; + tensor var_13211 = const()[name = tensor("op_13211"), val = tensor([1, 1])]; + tensor var_13213 = const()[name = tensor("op_13213"), val = tensor([1, 1])]; tensor k_271_pad_type_0 = const()[name = tensor("k_271_pad_type_0"), val = tensor("custom")]; tensor k_271_pad_0 = const()[name = tensor("k_271_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_271 = conv(dilations = var_13331, groups = var_12525, pad = k_271_pad_0, pad_type = k_271_pad_type_0, strides = var_13329, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_271")]; - tensor var_13335 = const()[name = tensor("op_13335"), val = tensor([1, 1])]; - tensor var_13337 = const()[name = tensor("op_13337"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5025610880)))]; + tensor k_271_cast = conv(dilations = var_13213, groups = var_31, pad = k_271_pad_0, pad_type = k_271_pad_type_0, strides = var_13211, weight = unet_up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_271_cast")]; + tensor var_13217 = const()[name = tensor("op_13217"), val = tensor([1, 1])]; + tensor var_13219 = const()[name = tensor("op_13219"), val = tensor([1, 1])]; tensor v_271_pad_type_0 = const()[name = tensor("v_271_pad_type_0"), val = tensor("custom")]; tensor v_271_pad_0 = const()[name = tensor("v_271_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_271 = conv(dilations = var_13337, groups = var_12525, pad = v_271_pad_0, pad_type = v_271_pad_type_0, strides = var_13335, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_271")]; - tensor var_13341 = const()[name = tensor("op_13341"), val = tensor([2, 10, 64, -1])]; - tensor var_13342 = reshape(shape = var_13341, x = q_271)[name = tensor("op_13342")]; - tensor var_13343 = const()[name = tensor("op_13343"), val = tensor([2, 10, 64, -1])]; - tensor var_13344 = reshape(shape = var_13343, x = k_271)[name = tensor("op_13344")]; - tensor var_13345 = const()[name = tensor("op_13345"), val = tensor([2, 10, 64, -1])]; - tensor var_13346 = reshape(shape = var_13345, x = v_271)[name = tensor("op_13346")]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5028232384)))]; + tensor v_271_cast = conv(dilations = var_13219, groups = var_31, pad = v_271_pad_0, pad_type = v_271_pad_type_0, strides = var_13217, weight = unet_up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_271_cast")]; + tensor var_13223 = const()[name = tensor("op_13223"), val = tensor([2, 10, 64, -1])]; + tensor var_13224_cast = reshape(shape = var_13223, x = q_271_cast)[name = tensor("op_13224_cast")]; + tensor var_13225 = const()[name = tensor("op_13225"), val = tensor([2, 10, 64, -1])]; + tensor var_13226_cast = reshape(shape = var_13225, x = k_271_cast)[name = tensor("op_13226_cast")]; + tensor var_13227 = const()[name = tensor("op_13227"), val = tensor([2, 10, 64, -1])]; + tensor var_13228_cast = reshape(shape = var_13227, x = v_271_cast)[name = tensor("op_13228_cast")]; tensor attn_weights_541_transpose_x_0 = const()[name = tensor("attn_weights_541_transpose_x_0"), val = tensor(true)]; tensor attn_weights_541_transpose_y_0 = const()[name = tensor("attn_weights_541_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_541 = matmul(transpose_x = attn_weights_541_transpose_x_0, transpose_y = attn_weights_541_transpose_y_0, x = var_13342, y = var_13344)[name = tensor("attn_weights_541")]; - tensor attn_weights_543 = mul(x = attn_weights_541, y = var_12516)[name = tensor("attn_weights_543")]; - tensor var_13350 = softmax(axis = var_12509, x = attn_weights_543)[name = tensor("op_13350")]; + tensor attn_weights_541_cast = matmul(transpose_x = attn_weights_541_transpose_x_0, transpose_y = attn_weights_541_transpose_y_0, x = var_13224_cast, y = var_13226_cast)[name = tensor("attn_weights_541_cast")]; + tensor attn_weights_543_cast = mul(x = attn_weights_541_cast, y = var_12_to_fp16)[name = tensor("attn_weights_543_cast")]; + tensor var_13232_cast = softmax(axis = var_18, x = attn_weights_543_cast)[name = tensor("op_13232_cast")]; tensor attn_271_transpose_x_0 = const()[name = tensor("attn_271_transpose_x_0"), val = tensor(false)]; tensor attn_271_transpose_y_0 = const()[name = tensor("attn_271_transpose_y_0"), val = tensor(true)]; - tensor attn_271 = matmul(transpose_x = attn_271_transpose_x_0, transpose_y = attn_271_transpose_y_0, x = var_13346, y = var_13350)[name = tensor("attn_271")]; - tensor var_13354 = const()[name = tensor("op_13354"), val = tensor([2, 640, 1, -1])]; - tensor input_781 = reshape(shape = var_13354, x = attn_271)[name = tensor("input_781")]; - tensor var_13359 = const()[name = tensor("op_13359"), val = tensor([1, 1])]; - tensor var_13361 = const()[name = tensor("op_13361"), val = tensor([1, 1])]; - tensor var_13363_pad_type_0 = const()[name = tensor("op_13363_pad_type_0"), val = tensor("custom")]; - tensor var_13363_pad_0 = const()[name = tensor("op_13363_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_13363 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias, dilations = var_13361, groups = var_12525, pad = var_13363_pad_0, pad_type = var_13363_pad_type_0, strides = var_13359, weight = up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight, x = input_781)[name = tensor("op_13363")]; - tensor inputs_407 = add(x = var_13363, y = inputs_405)[name = tensor("inputs_407")]; - tensor var_13367 = const()[name = tensor("op_13367"), val = tensor([1])]; - tensor channels_mean_407 = reduce_mean(axes = var_13367, keep_dims = var_12520, x = inputs_407)[name = tensor("channels_mean_407")]; - tensor zero_mean_407 = sub(x = inputs_407, y = channels_mean_407)[name = tensor("zero_mean_407")]; - tensor zero_mean_sq_407 = mul(x = zero_mean_407, y = zero_mean_407)[name = tensor("zero_mean_sq_407")]; - tensor var_13371 = const()[name = tensor("op_13371"), val = tensor([1])]; - tensor var_13372 = reduce_mean(axes = var_13371, keep_dims = var_12520, x = zero_mean_sq_407)[name = tensor("op_13372")]; - tensor var_13373 = const()[name = tensor("op_13373"), val = tensor(0x1.4f8b58p-17)]; - tensor var_13374 = add(x = var_13372, y = var_13373)[name = tensor("op_13374")]; - tensor denom_407_epsilon_0 = const()[name = tensor("denom_407_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_407 = rsqrt(epsilon = denom_407_epsilon_0, x = var_13374)[name = tensor("denom_407")]; - tensor out_407 = mul(x = zero_mean_407, y = denom_407)[name = tensor("out_407")]; - tensor var_13378 = const()[name = tensor("op_13378"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269933824)))]; - tensor var_13379 = add(x = out_407, y = var_13378)[name = tensor("op_13379")]; - tensor var_13381 = const()[name = tensor("op_13381"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269936448)))]; - tensor input_783 = mul(x = var_13379, y = var_13381)[name = tensor("input_783")]; - tensor var_13389 = const()[name = tensor("op_13389"), val = tensor([1, 1])]; - tensor var_13391 = const()[name = tensor("op_13391"), val = tensor([1, 1])]; - tensor var_13393_pad_type_0 = const()[name = tensor("op_13393_pad_type_0"), val = tensor("custom")]; - tensor var_13393_pad_0 = const()[name = tensor("op_13393_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_13393 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias, dilations = var_13391, groups = var_12525, pad = var_13393_pad_0, pad_type = var_13393_pad_type_0, strides = var_13389, weight = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight, x = input_783)[name = tensor("op_13393")]; - tensor var_13394_split_sizes_0 = const()[name = tensor("op_13394_split_sizes_0"), val = tensor([2560, 2560])]; - tensor var_13394_axis_0 = const()[name = tensor("op_13394_axis_0"), val = tensor(1)]; - tensor var_13394_0, tensor var_13394_1 = split(axis = var_13394_axis_0, split_sizes = var_13394_split_sizes_0, x = var_13393)[name = tensor("op_13394")]; - tensor var_13396_mode_0 = const()[name = tensor("op_13396_mode_0"), val = tensor("EXACT")]; - tensor var_13396 = gelu(mode = var_13396_mode_0, x = var_13394_1)[name = tensor("op_13396")]; - tensor input_785 = mul(x = var_13394_0, y = var_13396)[name = tensor("input_785")]; - tensor var_13400 = const()[name = tensor("op_13400"), val = tensor([1, 1])]; - tensor var_13402 = const()[name = tensor("op_13402"), val = tensor([1, 1])]; - tensor var_13404_pad_type_0 = const()[name = tensor("op_13404_pad_type_0"), val = tensor("custom")]; - tensor var_13404_pad_0 = const()[name = tensor("op_13404_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_13404 = conv(bias = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias, dilations = var_13402, groups = var_12525, pad = var_13404_pad_0, pad_type = var_13404_pad_type_0, strides = var_13400, weight = up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight, x = input_785)[name = tensor("op_13404")]; - tensor hidden_states_547 = add(x = var_13404, y = inputs_407)[name = tensor("hidden_states_547")]; - tensor var_13406 = const()[name = tensor("op_13406"), val = tensor([2, 640, 64, 64])]; - tensor input_787 = reshape(shape = var_13406, x = hidden_states_547)[name = tensor("input_787")]; - tensor var_13410 = const()[name = tensor("op_13410"), val = tensor([1, 1])]; - tensor var_13412 = const()[name = tensor("op_13412"), val = tensor([1, 1])]; + tensor attn_271_cast = matmul(transpose_x = attn_271_transpose_x_0, transpose_y = attn_271_transpose_y_0, x = var_13228_cast, y = var_13232_cast)[name = tensor("attn_271_cast")]; + tensor var_13236 = const()[name = tensor("op_13236"), val = tensor([2, 640, 1, -1])]; + tensor input_781_cast = reshape(shape = var_13236, x = attn_271_cast)[name = tensor("input_781_cast")]; + tensor var_13241 = const()[name = tensor("op_13241"), val = tensor([1, 1])]; + tensor var_13243 = const()[name = tensor("op_13243"), val = tensor([1, 1])]; + tensor var_13245_pad_type_0 = const()[name = tensor("op_13245_pad_type_0"), val = tensor("custom")]; + tensor var_13245_pad_0 = const()[name = tensor("op_13245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5030853888)))]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5031673152)))]; + tensor var_13245_cast = conv(bias = unet_up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_13243, groups = var_31, pad = var_13245_pad_0, pad_type = var_13245_pad_type_0, strides = var_13241, weight = unet_up_blocks_1_attentions_1_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_781_cast)[name = tensor("op_13245_cast")]; + tensor inputs_407_cast = add(x = var_13245_cast, y = inputs_405_cast)[name = tensor("inputs_407_cast")]; + tensor var_13249 = const()[name = tensor("op_13249"), val = tensor([1])]; + tensor channels_mean_407_cast = reduce_mean(axes = var_13249, keep_dims = var_23, x = inputs_407_cast)[name = tensor("channels_mean_407_cast")]; + tensor zero_mean_407_cast = sub(x = inputs_407_cast, y = channels_mean_407_cast)[name = tensor("zero_mean_407_cast")]; + tensor zero_mean_sq_407_cast = mul(x = zero_mean_407_cast, y = zero_mean_407_cast)[name = tensor("zero_mean_sq_407_cast")]; + tensor var_13253 = const()[name = tensor("op_13253"), val = tensor([1])]; + tensor var_13254_cast = reduce_mean(axes = var_13253, keep_dims = var_23, x = zero_mean_sq_407_cast)[name = tensor("op_13254_cast")]; + tensor var_13255_to_fp16 = const()[name = tensor("op_13255_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13256_cast = add(x = var_13254_cast, y = var_13255_to_fp16)[name = tensor("op_13256_cast")]; + tensor denom_407_epsilon_0_to_fp16 = const()[name = tensor("denom_407_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_407_cast = rsqrt(epsilon = denom_407_epsilon_0_to_fp16, x = var_13256_cast)[name = tensor("denom_407_cast")]; + tensor out_407_cast = mul(x = zero_mean_407_cast, y = denom_407_cast)[name = tensor("out_407_cast")]; + tensor var_13260_to_fp16 = const()[name = tensor("op_13260_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5031674496)))]; + tensor var_13261_cast = add(x = out_407_cast, y = var_13260_to_fp16)[name = tensor("op_13261_cast")]; + tensor var_13263_to_fp16 = const()[name = tensor("op_13263_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5031675840)))]; + tensor input_783_cast = mul(x = var_13261_cast, y = var_13263_to_fp16)[name = tensor("input_783_cast")]; + tensor var_13271 = const()[name = tensor("op_13271"), val = tensor([1, 1])]; + tensor var_13273 = const()[name = tensor("op_13273"), val = tensor([1, 1])]; + tensor var_13275_pad_type_0 = const()[name = tensor("op_13275_pad_type_0"), val = tensor("custom")]; + tensor var_13275_pad_0 = const()[name = tensor("op_13275_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5031677184)))]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5038230848)))]; + tensor var_13275_cast = conv(bias = unet_up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_13273, groups = var_31, pad = var_13275_pad_0, pad_type = var_13275_pad_type_0, strides = var_13271, weight = unet_up_blocks_1_attentions_1_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_783_cast)[name = tensor("op_13275_cast")]; + tensor var_13276_split_sizes_0 = const()[name = tensor("op_13276_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_13276_axis_0 = const()[name = tensor("op_13276_axis_0"), val = tensor(1)]; + tensor var_13276_cast_0, tensor var_13276_cast_1 = split(axis = var_13276_axis_0, split_sizes = var_13276_split_sizes_0, x = var_13275_cast)[name = tensor("op_13276_cast")]; + tensor var_13278_mode_0 = const()[name = tensor("op_13278_mode_0"), val = tensor("EXACT")]; + tensor var_13278_cast = gelu(mode = var_13278_mode_0, x = var_13276_cast_1)[name = tensor("op_13278_cast")]; + tensor input_785_cast = mul(x = var_13276_cast_0, y = var_13278_cast)[name = tensor("input_785_cast")]; + tensor var_13282 = const()[name = tensor("op_13282"), val = tensor([1, 1])]; + tensor var_13284 = const()[name = tensor("op_13284"), val = tensor([1, 1])]; + tensor var_13286_pad_type_0 = const()[name = tensor("op_13286_pad_type_0"), val = tensor("custom")]; + tensor var_13286_pad_0 = const()[name = tensor("op_13286_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5038241152)))]; + tensor unet_up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5041518016)))]; + tensor var_13286_cast = conv(bias = unet_up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_13284, groups = var_31, pad = var_13286_pad_0, pad_type = var_13286_pad_type_0, strides = var_13282, weight = unet_up_blocks_1_attentions_1_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_785_cast)[name = tensor("op_13286_cast")]; + tensor hidden_states_547_cast = add(x = var_13286_cast, y = inputs_407_cast)[name = tensor("hidden_states_547_cast")]; + tensor var_13288 = const()[name = tensor("op_13288"), val = tensor([2, 640, 64, 64])]; + tensor input_787_cast = reshape(shape = var_13288, x = hidden_states_547_cast)[name = tensor("input_787_cast")]; + tensor var_13292 = const()[name = tensor("op_13292"), val = tensor([1, 1])]; + tensor var_13294 = const()[name = tensor("op_13294"), val = tensor([1, 1])]; tensor hidden_states_549_pad_type_0 = const()[name = tensor("hidden_states_549_pad_type_0"), val = tensor("custom")]; tensor hidden_states_549_pad_0 = const()[name = tensor("hidden_states_549_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_549 = conv(bias = up_blocks_1_attentions_1_proj_out_bias, dilations = var_13412, groups = var_12525, pad = hidden_states_549_pad_0, pad_type = hidden_states_549_pad_type_0, strides = var_13410, weight = up_blocks_1_attentions_1_proj_out_weight, x = input_787)[name = tensor("hidden_states_549")]; - tensor hidden_states_551 = add(x = hidden_states_549, y = hidden_states_531)[name = tensor("hidden_states_551")]; + tensor unet_up_blocks_1_attentions_1_proj_out_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5041519360)))]; + tensor unet_up_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5042338624)))]; + tensor hidden_states_549_cast = conv(bias = unet_up_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_13294, groups = var_31, pad = hidden_states_549_pad_0, pad_type = hidden_states_549_pad_type_0, strides = var_13292, weight = unet_up_blocks_1_attentions_1_proj_out_weight_to_fp16, x = input_787_cast)[name = tensor("hidden_states_549_cast")]; + tensor hidden_states_551_cast = add(x = hidden_states_549_cast, y = hidden_states_531_cast)[name = tensor("hidden_states_551_cast")]; tensor input_789_interleave_0 = const()[name = tensor("input_789_interleave_0"), val = tensor(false)]; - tensor input_789 = concat(axis = var_12525, interleave = input_789_interleave_0, values = (hidden_states_551, input_45))[name = tensor("input_789")]; + tensor input_789_cast = concat(axis = var_31, interleave = input_789_interleave_0, values = (hidden_states_551_cast, input_45_cast))[name = tensor("input_789_cast")]; tensor reshape_144_shape_0 = const()[name = tensor("reshape_144_shape_0"), val = tensor([2, 32, 30, 64, 64])]; - tensor reshape_144 = reshape(shape = reshape_144_shape_0, x = input_789)[name = tensor("reshape_144")]; + tensor reshape_144_cast = reshape(shape = reshape_144_shape_0, x = input_789_cast)[name = tensor("reshape_144_cast")]; tensor reduce_mean_108_axes_0 = const()[name = tensor("reduce_mean_108_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_108_keep_dims_0 = const()[name = tensor("reduce_mean_108_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_108 = reduce_mean(axes = reduce_mean_108_axes_0, keep_dims = reduce_mean_108_keep_dims_0, x = reshape_144)[name = tensor("reduce_mean_108")]; - tensor sub_72 = sub(x = reshape_144, y = reduce_mean_108)[name = tensor("sub_72")]; - tensor square_36 = square(x = sub_72)[name = tensor("square_36")]; + tensor reduce_mean_108_cast = reduce_mean(axes = reduce_mean_108_axes_0, keep_dims = reduce_mean_108_keep_dims_0, x = reshape_144_cast)[name = tensor("reduce_mean_108_cast")]; + tensor sub_72_cast = sub(x = reshape_144_cast, y = reduce_mean_108_cast)[name = tensor("sub_72_cast")]; + tensor square_36_cast = square(x = sub_72_cast)[name = tensor("square_36_cast")]; tensor reduce_mean_110_axes_0 = const()[name = tensor("reduce_mean_110_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_110_keep_dims_0 = const()[name = tensor("reduce_mean_110_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_110 = reduce_mean(axes = reduce_mean_110_axes_0, keep_dims = reduce_mean_110_keep_dims_0, x = square_36)[name = tensor("reduce_mean_110")]; - tensor add_72_y_0 = const()[name = tensor("add_72_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_72 = add(x = reduce_mean_110, y = add_72_y_0)[name = tensor("add_72")]; - tensor sqrt_36 = sqrt(x = add_72)[name = tensor("sqrt_36")]; - tensor real_div_36 = real_div(x = sub_72, y = sqrt_36)[name = tensor("real_div_36")]; + tensor reduce_mean_110_cast = reduce_mean(axes = reduce_mean_110_axes_0, keep_dims = reduce_mean_110_keep_dims_0, x = square_36_cast)[name = tensor("reduce_mean_110_cast")]; + tensor add_72_y_0_to_fp16 = const()[name = tensor("add_72_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_72_cast = add(x = reduce_mean_110_cast, y = add_72_y_0_to_fp16)[name = tensor("add_72_cast")]; + tensor sqrt_36_cast = sqrt(x = add_72_cast)[name = tensor("sqrt_36_cast")]; + tensor real_div_36_cast = real_div(x = sub_72_cast, y = sqrt_36_cast)[name = tensor("real_div_36_cast")]; tensor reshape_145_shape_0 = const()[name = tensor("reshape_145_shape_0"), val = tensor([2, 960, 64, 64])]; - tensor reshape_145 = reshape(shape = reshape_145_shape_0, x = real_div_36)[name = tensor("reshape_145")]; - tensor add_73_mean_0 = const()[name = tensor("add_73_mean_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269939072)))]; - tensor add_73_variance_0 = const()[name = tensor("add_73_variance_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269942976)))]; - tensor add_73_gamma_0 = const()[name = tensor("add_73_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269946880)))]; - tensor add_73_beta_0 = const()[name = tensor("add_73_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269950784)))]; - tensor add_73_epsilon_0 = const()[name = tensor("add_73_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_73 = batch_norm(beta = add_73_beta_0, epsilon = add_73_epsilon_0, gamma = add_73_gamma_0, mean = add_73_mean_0, variance = add_73_variance_0, x = reshape_145)[name = tensor("add_73")]; - tensor input_793 = silu(x = add_73)[name = tensor("input_793")]; - tensor var_13430 = const()[name = tensor("op_13430"), val = tensor([1, 1])]; - tensor var_13432 = const()[name = tensor("op_13432"), val = tensor([1, 1])]; + tensor reshape_145_cast = reshape(shape = reshape_145_shape_0, x = real_div_36_cast)[name = tensor("reshape_145_cast")]; + tensor add_73_mean_0_to_fp16 = const()[name = tensor("add_73_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5042339968)))]; + tensor add_73_variance_0_to_fp16 = const()[name = tensor("add_73_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5042341952)))]; + tensor add_73_gamma_0_to_fp16 = const()[name = tensor("add_73_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5042343936)))]; + tensor add_73_beta_0_to_fp16 = const()[name = tensor("add_73_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5042345920)))]; + tensor add_73_epsilon_0_to_fp16 = const()[name = tensor("add_73_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_73_cast = batch_norm(beta = add_73_beta_0_to_fp16, epsilon = add_73_epsilon_0_to_fp16, gamma = add_73_gamma_0_to_fp16, mean = add_73_mean_0_to_fp16, variance = add_73_variance_0_to_fp16, x = reshape_145_cast)[name = tensor("add_73_cast")]; + tensor input_793_cast = silu(x = add_73_cast)[name = tensor("input_793_cast")]; + tensor var_13312 = const()[name = tensor("op_13312"), val = tensor([1, 1])]; + tensor var_13314 = const()[name = tensor("op_13314"), val = tensor([1, 1])]; tensor hidden_states_553_pad_type_0 = const()[name = tensor("hidden_states_553_pad_type_0"), val = tensor("custom")]; tensor hidden_states_553_pad_0 = const()[name = tensor("hidden_states_553_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_553 = conv(bias = up_blocks_1_resnets_2_conv1_bias, dilations = var_13432, groups = var_12525, pad = hidden_states_553_pad_0, pad_type = hidden_states_553_pad_type_0, strides = var_13430, weight = up_blocks_1_resnets_2_conv1_weight, x = input_793)[name = tensor("hidden_states_553")]; - tensor var_13438 = const()[name = tensor("op_13438"), val = tensor([1, 1])]; - tensor var_13440 = const()[name = tensor("op_13440"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5042347904)))]; + tensor unet_up_blocks_1_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5053407168)))]; + tensor hidden_states_553_cast = conv(bias = unet_up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_13314, groups = var_31, pad = hidden_states_553_pad_0, pad_type = hidden_states_553_pad_type_0, strides = var_13312, weight = unet_up_blocks_1_resnets_2_conv1_weight_to_fp16, x = input_793_cast)[name = tensor("hidden_states_553_cast")]; + tensor var_13320 = const()[name = tensor("op_13320"), val = tensor([1, 1])]; + tensor var_13322 = const()[name = tensor("op_13322"), val = tensor([1, 1])]; tensor temb_27_pad_type_0 = const()[name = tensor("temb_27_pad_type_0"), val = tensor("custom")]; tensor temb_27_pad_0 = const()[name = tensor("temb_27_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb_27 = conv(bias = up_blocks_1_resnets_2_time_emb_proj_bias, dilations = var_13440, groups = var_12525, pad = temb_27_pad_0, pad_type = temb_27_pad_type_0, strides = var_13438, weight = up_blocks_1_resnets_2_time_emb_proj_weight, x = input_21)[name = tensor("temb_27")]; - tensor input_797 = add(x = hidden_states_553, y = temb_27)[name = tensor("input_797")]; + tensor unet_up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5053408512)))]; + tensor unet_up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5055046976)))]; + tensor temb_27_cast = conv(bias = unet_up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_13322, groups = var_31, pad = temb_27_pad_0, pad_type = temb_27_pad_type_0, strides = var_13320, weight = unet_up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_27_cast")]; + tensor input_797_cast = add(x = hidden_states_553_cast, y = temb_27_cast)[name = tensor("input_797_cast")]; tensor reshape_148_shape_0 = const()[name = tensor("reshape_148_shape_0"), val = tensor([2, 32, 20, 64, 64])]; - tensor reshape_148 = reshape(shape = reshape_148_shape_0, x = input_797)[name = tensor("reshape_148")]; + tensor reshape_148_cast = reshape(shape = reshape_148_shape_0, x = input_797_cast)[name = tensor("reshape_148_cast")]; tensor reduce_mean_111_axes_0 = const()[name = tensor("reduce_mean_111_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_111_keep_dims_0 = const()[name = tensor("reduce_mean_111_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_111 = reduce_mean(axes = reduce_mean_111_axes_0, keep_dims = reduce_mean_111_keep_dims_0, x = reshape_148)[name = tensor("reduce_mean_111")]; - tensor sub_74 = sub(x = reshape_148, y = reduce_mean_111)[name = tensor("sub_74")]; - tensor square_37 = square(x = sub_74)[name = tensor("square_37")]; + tensor reduce_mean_111_cast = reduce_mean(axes = reduce_mean_111_axes_0, keep_dims = reduce_mean_111_keep_dims_0, x = reshape_148_cast)[name = tensor("reduce_mean_111_cast")]; + tensor sub_74_cast = sub(x = reshape_148_cast, y = reduce_mean_111_cast)[name = tensor("sub_74_cast")]; + tensor square_37_cast = square(x = sub_74_cast)[name = tensor("square_37_cast")]; tensor reduce_mean_113_axes_0 = const()[name = tensor("reduce_mean_113_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_113_keep_dims_0 = const()[name = tensor("reduce_mean_113_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_113 = reduce_mean(axes = reduce_mean_113_axes_0, keep_dims = reduce_mean_113_keep_dims_0, x = square_37)[name = tensor("reduce_mean_113")]; - tensor add_74_y_0 = const()[name = tensor("add_74_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_74 = add(x = reduce_mean_113, y = add_74_y_0)[name = tensor("add_74")]; - tensor sqrt_37 = sqrt(x = add_74)[name = tensor("sqrt_37")]; - tensor real_div_37 = real_div(x = sub_74, y = sqrt_37)[name = tensor("real_div_37")]; + tensor reduce_mean_113_cast = reduce_mean(axes = reduce_mean_113_axes_0, keep_dims = reduce_mean_113_keep_dims_0, x = square_37_cast)[name = tensor("reduce_mean_113_cast")]; + tensor add_74_y_0_to_fp16 = const()[name = tensor("add_74_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_74_cast = add(x = reduce_mean_113_cast, y = add_74_y_0_to_fp16)[name = tensor("add_74_cast")]; + tensor sqrt_37_cast = sqrt(x = add_74_cast)[name = tensor("sqrt_37_cast")]; + tensor real_div_37_cast = real_div(x = sub_74_cast, y = sqrt_37_cast)[name = tensor("real_div_37_cast")]; tensor reshape_149_shape_0 = const()[name = tensor("reshape_149_shape_0"), val = tensor([2, 640, 64, 64])]; - tensor reshape_149 = reshape(shape = reshape_149_shape_0, x = real_div_37)[name = tensor("reshape_149")]; - tensor add_75_gamma_0 = const()[name = tensor("add_75_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269954688)))]; - tensor add_75_beta_0 = const()[name = tensor("add_75_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269957312)))]; - tensor add_75_epsilon_0 = const()[name = tensor("add_75_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_75 = batch_norm(beta = add_75_beta_0, epsilon = add_75_epsilon_0, gamma = add_75_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_149)[name = tensor("add_75")]; - tensor input_801 = silu(x = add_75)[name = tensor("input_801")]; - tensor var_13450 = const()[name = tensor("op_13450"), val = tensor([1, 1])]; - tensor var_13452 = const()[name = tensor("op_13452"), val = tensor([1, 1])]; + tensor reshape_149_cast = reshape(shape = reshape_149_shape_0, x = real_div_37_cast)[name = tensor("reshape_149_cast")]; + tensor add_75_gamma_0_to_fp16 = const()[name = tensor("add_75_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5055048320)))]; + tensor add_75_beta_0_to_fp16 = const()[name = tensor("add_75_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5055049664)))]; + tensor add_75_epsilon_0_to_fp16 = const()[name = tensor("add_75_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_75_cast = batch_norm(beta = add_75_beta_0_to_fp16, epsilon = add_75_epsilon_0_to_fp16, gamma = add_75_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_149_cast)[name = tensor("add_75_cast")]; + tensor input_801_cast = silu(x = add_75_cast)[name = tensor("input_801_cast")]; + tensor var_13332 = const()[name = tensor("op_13332"), val = tensor([1, 1])]; + tensor var_13334 = const()[name = tensor("op_13334"), val = tensor([1, 1])]; tensor hidden_states_555_pad_type_0 = const()[name = tensor("hidden_states_555_pad_type_0"), val = tensor("custom")]; tensor hidden_states_555_pad_0 = const()[name = tensor("hidden_states_555_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_555 = conv(bias = up_blocks_1_resnets_2_conv2_bias, dilations = var_13452, groups = var_12525, pad = hidden_states_555_pad_0, pad_type = hidden_states_555_pad_type_0, strides = var_13450, weight = up_blocks_1_resnets_2_conv2_weight, x = input_801)[name = tensor("hidden_states_555")]; - tensor var_13457 = const()[name = tensor("op_13457"), val = tensor([1, 1])]; - tensor var_13459 = const()[name = tensor("op_13459"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5055051008)))]; + tensor unet_up_blocks_1_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5062423872)))]; + tensor hidden_states_555_cast = conv(bias = unet_up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_13334, groups = var_31, pad = hidden_states_555_pad_0, pad_type = hidden_states_555_pad_type_0, strides = var_13332, weight = unet_up_blocks_1_resnets_2_conv2_weight_to_fp16, x = input_801_cast)[name = tensor("hidden_states_555_cast")]; + tensor var_13339 = const()[name = tensor("op_13339"), val = tensor([1, 1])]; + tensor var_13341 = const()[name = tensor("op_13341"), val = tensor([1, 1])]; tensor x_15_pad_type_0 = const()[name = tensor("x_15_pad_type_0"), val = tensor("custom")]; tensor x_15_pad_0 = const()[name = tensor("x_15_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor x_15 = conv(bias = up_blocks_1_resnets_2_conv_shortcut_bias, dilations = var_13459, groups = var_12525, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = var_13457, weight = up_blocks_1_resnets_2_conv_shortcut_weight, x = input_789)[name = tensor("x_15")]; - tensor hidden_states_557 = add(x = x_15, y = hidden_states_555)[name = tensor("hidden_states_557")]; + tensor unet_up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5062425216)))]; + tensor unet_up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5063654080)))]; + tensor x_15_cast = conv(bias = unet_up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_13341, groups = var_31, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = var_13339, weight = unet_up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16, x = input_789_cast)[name = tensor("x_15_cast")]; + tensor hidden_states_557_cast = add(x = x_15_cast, y = hidden_states_555_cast)[name = tensor("hidden_states_557_cast")]; tensor reshape_152_shape_0 = const()[name = tensor("reshape_152_shape_0"), val = tensor([2, 32, 20, 64, 64])]; - tensor reshape_152 = reshape(shape = reshape_152_shape_0, x = hidden_states_557)[name = tensor("reshape_152")]; + tensor reshape_152_cast = reshape(shape = reshape_152_shape_0, x = hidden_states_557_cast)[name = tensor("reshape_152_cast")]; tensor reduce_mean_114_axes_0 = const()[name = tensor("reduce_mean_114_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_114_keep_dims_0 = const()[name = tensor("reduce_mean_114_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_114 = reduce_mean(axes = reduce_mean_114_axes_0, keep_dims = reduce_mean_114_keep_dims_0, x = reshape_152)[name = tensor("reduce_mean_114")]; - tensor sub_76 = sub(x = reshape_152, y = reduce_mean_114)[name = tensor("sub_76")]; - tensor square_38 = square(x = sub_76)[name = tensor("square_38")]; + tensor reduce_mean_114_cast = reduce_mean(axes = reduce_mean_114_axes_0, keep_dims = reduce_mean_114_keep_dims_0, x = reshape_152_cast)[name = tensor("reduce_mean_114_cast")]; + tensor sub_76_cast = sub(x = reshape_152_cast, y = reduce_mean_114_cast)[name = tensor("sub_76_cast")]; + tensor square_38_cast = square(x = sub_76_cast)[name = tensor("square_38_cast")]; tensor reduce_mean_116_axes_0 = const()[name = tensor("reduce_mean_116_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_116_keep_dims_0 = const()[name = tensor("reduce_mean_116_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_116 = reduce_mean(axes = reduce_mean_116_axes_0, keep_dims = reduce_mean_116_keep_dims_0, x = square_38)[name = tensor("reduce_mean_116")]; - tensor add_76_y_0 = const()[name = tensor("add_76_y_0"), val = tensor(0x1.0c6f7ap-20)]; - tensor add_76 = add(x = reduce_mean_116, y = add_76_y_0)[name = tensor("add_76")]; - tensor sqrt_38 = sqrt(x = add_76)[name = tensor("sqrt_38")]; - tensor real_div_38 = real_div(x = sub_76, y = sqrt_38)[name = tensor("real_div_38")]; + tensor reduce_mean_116_cast = reduce_mean(axes = reduce_mean_116_axes_0, keep_dims = reduce_mean_116_keep_dims_0, x = square_38_cast)[name = tensor("reduce_mean_116_cast")]; + tensor add_76_y_0_to_fp16 = const()[name = tensor("add_76_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_76_cast = add(x = reduce_mean_116_cast, y = add_76_y_0_to_fp16)[name = tensor("add_76_cast")]; + tensor sqrt_38_cast = sqrt(x = add_76_cast)[name = tensor("sqrt_38_cast")]; + tensor real_div_38_cast = real_div(x = sub_76_cast, y = sqrt_38_cast)[name = tensor("real_div_38_cast")]; tensor reshape_153_shape_0 = const()[name = tensor("reshape_153_shape_0"), val = tensor([2, 640, 64, 64])]; - tensor reshape_153 = reshape(shape = reshape_153_shape_0, x = real_div_38)[name = tensor("reshape_153")]; - tensor add_77_gamma_0 = const()[name = tensor("add_77_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269959936)))]; - tensor add_77_beta_0 = const()[name = tensor("add_77_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269962560)))]; - tensor add_77_epsilon_0 = const()[name = tensor("add_77_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_77 = batch_norm(beta = add_77_beta_0, epsilon = add_77_epsilon_0, gamma = add_77_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_153)[name = tensor("add_77")]; - tensor var_13481 = const()[name = tensor("op_13481"), val = tensor([1, 1])]; - tensor var_13483 = const()[name = tensor("op_13483"), val = tensor([1, 1])]; + tensor reshape_153_cast = reshape(shape = reshape_153_shape_0, x = real_div_38_cast)[name = tensor("reshape_153_cast")]; + tensor add_77_gamma_0_to_fp16 = const()[name = tensor("add_77_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5063655424)))]; + tensor add_77_beta_0_to_fp16 = const()[name = tensor("add_77_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5063656768)))]; + tensor add_77_epsilon_0_to_fp16 = const()[name = tensor("add_77_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_77_cast = batch_norm(beta = add_77_beta_0_to_fp16, epsilon = add_77_epsilon_0_to_fp16, gamma = add_77_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_153_cast)[name = tensor("add_77_cast")]; + tensor var_13363 = const()[name = tensor("op_13363"), val = tensor([1, 1])]; + tensor var_13365 = const()[name = tensor("op_13365"), val = tensor([1, 1])]; tensor hidden_states_559_pad_type_0 = const()[name = tensor("hidden_states_559_pad_type_0"), val = tensor("custom")]; tensor hidden_states_559_pad_0 = const()[name = tensor("hidden_states_559_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_559 = conv(bias = up_blocks_1_attentions_2_proj_in_bias, dilations = var_13483, groups = var_12525, pad = hidden_states_559_pad_0, pad_type = hidden_states_559_pad_type_0, strides = var_13481, weight = up_blocks_1_attentions_2_proj_in_weight, x = add_77)[name = tensor("hidden_states_559")]; - tensor var_13488 = const()[name = tensor("op_13488"), val = tensor([2, 640, 1, 4096])]; - tensor inputs_409 = reshape(shape = var_13488, x = hidden_states_559)[name = tensor("inputs_409")]; - tensor var_13498 = const()[name = tensor("op_13498"), val = tensor([1])]; - tensor channels_mean_409 = reduce_mean(axes = var_13498, keep_dims = var_12520, x = inputs_409)[name = tensor("channels_mean_409")]; - tensor zero_mean_409 = sub(x = inputs_409, y = channels_mean_409)[name = tensor("zero_mean_409")]; - tensor zero_mean_sq_409 = mul(x = zero_mean_409, y = zero_mean_409)[name = tensor("zero_mean_sq_409")]; - tensor var_13502 = const()[name = tensor("op_13502"), val = tensor([1])]; - tensor var_13503 = reduce_mean(axes = var_13502, keep_dims = var_12520, x = zero_mean_sq_409)[name = tensor("op_13503")]; - tensor var_13504 = const()[name = tensor("op_13504"), val = tensor(0x1.4f8b58p-17)]; - tensor var_13505 = add(x = var_13503, y = var_13504)[name = tensor("op_13505")]; - tensor denom_409_epsilon_0 = const()[name = tensor("denom_409_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_409 = rsqrt(epsilon = denom_409_epsilon_0, x = var_13505)[name = tensor("denom_409")]; - tensor out_409 = mul(x = zero_mean_409, y = denom_409)[name = tensor("out_409")]; - tensor var_13509 = const()[name = tensor("op_13509"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269965184)))]; - tensor var_13510 = add(x = out_409, y = var_13509)[name = tensor("op_13510")]; - tensor var_13512 = const()[name = tensor("op_13512"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269967808)))]; - tensor hidden_states_561 = mul(x = var_13510, y = var_13512)[name = tensor("hidden_states_561")]; - tensor var_13519 = const()[name = tensor("op_13519"), val = tensor([1, 1])]; - tensor var_13521 = const()[name = tensor("op_13521"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_2_proj_in_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_proj_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5063658112)))]; + tensor unet_up_blocks_1_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5064477376)))]; + tensor hidden_states_559_cast = conv(bias = unet_up_blocks_1_attentions_2_proj_in_bias_to_fp16, dilations = var_13365, groups = var_31, pad = hidden_states_559_pad_0, pad_type = hidden_states_559_pad_type_0, strides = var_13363, weight = unet_up_blocks_1_attentions_2_proj_in_weight_to_fp16, x = add_77_cast)[name = tensor("hidden_states_559_cast")]; + tensor var_13370 = const()[name = tensor("op_13370"), val = tensor([2, 640, 1, 4096])]; + tensor inputs_409_cast = reshape(shape = var_13370, x = hidden_states_559_cast)[name = tensor("inputs_409_cast")]; + tensor var_13380 = const()[name = tensor("op_13380"), val = tensor([1])]; + tensor channels_mean_409_cast = reduce_mean(axes = var_13380, keep_dims = var_23, x = inputs_409_cast)[name = tensor("channels_mean_409_cast")]; + tensor zero_mean_409_cast = sub(x = inputs_409_cast, y = channels_mean_409_cast)[name = tensor("zero_mean_409_cast")]; + tensor zero_mean_sq_409_cast = mul(x = zero_mean_409_cast, y = zero_mean_409_cast)[name = tensor("zero_mean_sq_409_cast")]; + tensor var_13384 = const()[name = tensor("op_13384"), val = tensor([1])]; + tensor var_13385_cast = reduce_mean(axes = var_13384, keep_dims = var_23, x = zero_mean_sq_409_cast)[name = tensor("op_13385_cast")]; + tensor var_13386_to_fp16 = const()[name = tensor("op_13386_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13387_cast = add(x = var_13385_cast, y = var_13386_to_fp16)[name = tensor("op_13387_cast")]; + tensor denom_409_epsilon_0_to_fp16 = const()[name = tensor("denom_409_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_409_cast = rsqrt(epsilon = denom_409_epsilon_0_to_fp16, x = var_13387_cast)[name = tensor("denom_409_cast")]; + tensor out_409_cast = mul(x = zero_mean_409_cast, y = denom_409_cast)[name = tensor("out_409_cast")]; + tensor var_13391_to_fp16 = const()[name = tensor("op_13391_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5064478720)))]; + tensor var_13392_cast = add(x = out_409_cast, y = var_13391_to_fp16)[name = tensor("op_13392_cast")]; + tensor var_13394_to_fp16 = const()[name = tensor("op_13394_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5064480064)))]; + tensor hidden_states_561_cast = mul(x = var_13392_cast, y = var_13394_to_fp16)[name = tensor("hidden_states_561_cast")]; + tensor var_13401 = const()[name = tensor("op_13401"), val = tensor([1, 1])]; + tensor var_13403 = const()[name = tensor("op_13403"), val = tensor([1, 1])]; tensor q_273_pad_type_0 = const()[name = tensor("q_273_pad_type_0"), val = tensor("custom")]; tensor q_273_pad_0 = const()[name = tensor("q_273_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_273 = conv(dilations = var_13521, groups = var_12525, pad = q_273_pad_0, pad_type = q_273_pad_type_0, strides = var_13519, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight, x = hidden_states_561)[name = tensor("q_273")]; - tensor var_13525 = const()[name = tensor("op_13525"), val = tensor([1, 1])]; - tensor var_13527 = const()[name = tensor("op_13527"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5064481408)))]; + tensor q_273_cast = conv(dilations = var_13403, groups = var_31, pad = q_273_pad_0, pad_type = q_273_pad_type_0, strides = var_13401, weight = unet_up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16, x = hidden_states_561_cast)[name = tensor("q_273_cast")]; + tensor var_13407 = const()[name = tensor("op_13407"), val = tensor([1, 1])]; + tensor var_13409 = const()[name = tensor("op_13409"), val = tensor([1, 1])]; tensor k_273_pad_type_0 = const()[name = tensor("k_273_pad_type_0"), val = tensor("custom")]; tensor k_273_pad_0 = const()[name = tensor("k_273_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_273 = conv(dilations = var_13527, groups = var_12525, pad = k_273_pad_0, pad_type = k_273_pad_type_0, strides = var_13525, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight, x = hidden_states_561)[name = tensor("k_273")]; - tensor var_13531 = const()[name = tensor("op_13531"), val = tensor([1, 1])]; - tensor var_13533 = const()[name = tensor("op_13533"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5065300672)))]; + tensor k_273_cast = conv(dilations = var_13409, groups = var_31, pad = k_273_pad_0, pad_type = k_273_pad_type_0, strides = var_13407, weight = unet_up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16, x = hidden_states_561_cast)[name = tensor("k_273_cast")]; + tensor var_13413 = const()[name = tensor("op_13413"), val = tensor([1, 1])]; + tensor var_13415 = const()[name = tensor("op_13415"), val = tensor([1, 1])]; tensor v_273_pad_type_0 = const()[name = tensor("v_273_pad_type_0"), val = tensor("custom")]; tensor v_273_pad_0 = const()[name = tensor("v_273_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_273 = conv(dilations = var_13533, groups = var_12525, pad = v_273_pad_0, pad_type = v_273_pad_type_0, strides = var_13531, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight, x = hidden_states_561)[name = tensor("v_273")]; - tensor var_13537 = const()[name = tensor("op_13537"), val = tensor([2, 10, 64, -1])]; - tensor var_13538 = reshape(shape = var_13537, x = q_273)[name = tensor("op_13538")]; - tensor var_13539 = const()[name = tensor("op_13539"), val = tensor([2, 10, 64, -1])]; - tensor var_13540 = reshape(shape = var_13539, x = k_273)[name = tensor("op_13540")]; - tensor var_13541 = const()[name = tensor("op_13541"), val = tensor([2, 10, 64, -1])]; - tensor var_13542 = reshape(shape = var_13541, x = v_273)[name = tensor("op_13542")]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5066119936)))]; + tensor v_273_cast = conv(dilations = var_13415, groups = var_31, pad = v_273_pad_0, pad_type = v_273_pad_type_0, strides = var_13413, weight = unet_up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16, x = hidden_states_561_cast)[name = tensor("v_273_cast")]; + tensor var_13419 = const()[name = tensor("op_13419"), val = tensor([2, 10, 64, -1])]; + tensor var_13420_cast = reshape(shape = var_13419, x = q_273_cast)[name = tensor("op_13420_cast")]; + tensor var_13421 = const()[name = tensor("op_13421"), val = tensor([2, 10, 64, -1])]; + tensor var_13422_cast = reshape(shape = var_13421, x = k_273_cast)[name = tensor("op_13422_cast")]; + tensor var_13423 = const()[name = tensor("op_13423"), val = tensor([2, 10, 64, -1])]; + tensor var_13424_cast = reshape(shape = var_13423, x = v_273_cast)[name = tensor("op_13424_cast")]; tensor attn_weights_545_transpose_x_0 = const()[name = tensor("attn_weights_545_transpose_x_0"), val = tensor(true)]; tensor attn_weights_545_transpose_y_0 = const()[name = tensor("attn_weights_545_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_545 = matmul(transpose_x = attn_weights_545_transpose_x_0, transpose_y = attn_weights_545_transpose_y_0, x = var_13538, y = var_13540)[name = tensor("attn_weights_545")]; - tensor attn_weights_547 = mul(x = attn_weights_545, y = var_12516)[name = tensor("attn_weights_547")]; - tensor var_13546 = softmax(axis = var_12509, x = attn_weights_547)[name = tensor("op_13546")]; + tensor attn_weights_545_cast = matmul(transpose_x = attn_weights_545_transpose_x_0, transpose_y = attn_weights_545_transpose_y_0, x = var_13420_cast, y = var_13422_cast)[name = tensor("attn_weights_545_cast")]; + tensor attn_weights_547_cast = mul(x = attn_weights_545_cast, y = var_12_to_fp16)[name = tensor("attn_weights_547_cast")]; + tensor var_13428_cast = softmax(axis = var_18, x = attn_weights_547_cast)[name = tensor("op_13428_cast")]; tensor attn_273_transpose_x_0 = const()[name = tensor("attn_273_transpose_x_0"), val = tensor(false)]; tensor attn_273_transpose_y_0 = const()[name = tensor("attn_273_transpose_y_0"), val = tensor(true)]; - tensor attn_273 = matmul(transpose_x = attn_273_transpose_x_0, transpose_y = attn_273_transpose_y_0, x = var_13542, y = var_13546)[name = tensor("attn_273")]; - tensor var_13550 = const()[name = tensor("op_13550"), val = tensor([2, 640, 1, -1])]; - tensor input_805 = reshape(shape = var_13550, x = attn_273)[name = tensor("input_805")]; - tensor var_13555 = const()[name = tensor("op_13555"), val = tensor([1, 1])]; - tensor var_13557 = const()[name = tensor("op_13557"), val = tensor([1, 1])]; - tensor var_13559_pad_type_0 = const()[name = tensor("op_13559_pad_type_0"), val = tensor("custom")]; - tensor var_13559_pad_0 = const()[name = tensor("op_13559_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_13559 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias, dilations = var_13557, groups = var_12525, pad = var_13559_pad_0, pad_type = var_13559_pad_type_0, strides = var_13555, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight, x = input_805)[name = tensor("op_13559")]; - tensor inputs_411 = add(x = var_13559, y = inputs_409)[name = tensor("inputs_411")]; - tensor var_13563 = const()[name = tensor("op_13563"), val = tensor([1])]; - tensor channels_mean_411 = reduce_mean(axes = var_13563, keep_dims = var_12520, x = inputs_411)[name = tensor("channels_mean_411")]; - tensor zero_mean_411 = sub(x = inputs_411, y = channels_mean_411)[name = tensor("zero_mean_411")]; - tensor zero_mean_sq_411 = mul(x = zero_mean_411, y = zero_mean_411)[name = tensor("zero_mean_sq_411")]; - tensor var_13567 = const()[name = tensor("op_13567"), val = tensor([1])]; - tensor var_13568 = reduce_mean(axes = var_13567, keep_dims = var_12520, x = zero_mean_sq_411)[name = tensor("op_13568")]; - tensor var_13569 = const()[name = tensor("op_13569"), val = tensor(0x1.4f8b58p-17)]; - tensor var_13570 = add(x = var_13568, y = var_13569)[name = tensor("op_13570")]; - tensor denom_411_epsilon_0 = const()[name = tensor("denom_411_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_411 = rsqrt(epsilon = denom_411_epsilon_0, x = var_13570)[name = tensor("denom_411")]; - tensor out_411 = mul(x = zero_mean_411, y = denom_411)[name = tensor("out_411")]; - tensor var_13574 = const()[name = tensor("op_13574"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269970432)))]; - tensor var_13575 = add(x = out_411, y = var_13574)[name = tensor("op_13575")]; - tensor var_13577 = const()[name = tensor("op_13577"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269973056)))]; - tensor hidden_states_563 = mul(x = var_13575, y = var_13577)[name = tensor("hidden_states_563")]; - tensor var_13584 = const()[name = tensor("op_13584"), val = tensor([1, 1])]; - tensor var_13586 = const()[name = tensor("op_13586"), val = tensor([1, 1])]; + tensor attn_273_cast = matmul(transpose_x = attn_273_transpose_x_0, transpose_y = attn_273_transpose_y_0, x = var_13424_cast, y = var_13428_cast)[name = tensor("attn_273_cast")]; + tensor var_13432 = const()[name = tensor("op_13432"), val = tensor([2, 640, 1, -1])]; + tensor input_805_cast = reshape(shape = var_13432, x = attn_273_cast)[name = tensor("input_805_cast")]; + tensor var_13437 = const()[name = tensor("op_13437"), val = tensor([1, 1])]; + tensor var_13439 = const()[name = tensor("op_13439"), val = tensor([1, 1])]; + tensor var_13441_pad_type_0 = const()[name = tensor("op_13441_pad_type_0"), val = tensor("custom")]; + tensor var_13441_pad_0 = const()[name = tensor("op_13441_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5066939200)))]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5067758464)))]; + tensor var_13441_cast = conv(bias = unet_up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_13439, groups = var_31, pad = var_13441_pad_0, pad_type = var_13441_pad_type_0, strides = var_13437, weight = unet_up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16, x = input_805_cast)[name = tensor("op_13441_cast")]; + tensor inputs_411_cast = add(x = var_13441_cast, y = inputs_409_cast)[name = tensor("inputs_411_cast")]; + tensor var_13445 = const()[name = tensor("op_13445"), val = tensor([1])]; + tensor channels_mean_411_cast = reduce_mean(axes = var_13445, keep_dims = var_23, x = inputs_411_cast)[name = tensor("channels_mean_411_cast")]; + tensor zero_mean_411_cast = sub(x = inputs_411_cast, y = channels_mean_411_cast)[name = tensor("zero_mean_411_cast")]; + tensor zero_mean_sq_411_cast = mul(x = zero_mean_411_cast, y = zero_mean_411_cast)[name = tensor("zero_mean_sq_411_cast")]; + tensor var_13449 = const()[name = tensor("op_13449"), val = tensor([1])]; + tensor var_13450_cast = reduce_mean(axes = var_13449, keep_dims = var_23, x = zero_mean_sq_411_cast)[name = tensor("op_13450_cast")]; + tensor var_13451_to_fp16 = const()[name = tensor("op_13451_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13452_cast = add(x = var_13450_cast, y = var_13451_to_fp16)[name = tensor("op_13452_cast")]; + tensor denom_411_epsilon_0_to_fp16 = const()[name = tensor("denom_411_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_411_cast = rsqrt(epsilon = denom_411_epsilon_0_to_fp16, x = var_13452_cast)[name = tensor("denom_411_cast")]; + tensor out_411_cast = mul(x = zero_mean_411_cast, y = denom_411_cast)[name = tensor("out_411_cast")]; + tensor var_13456_to_fp16 = const()[name = tensor("op_13456_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5067759808)))]; + tensor var_13457_cast = add(x = out_411_cast, y = var_13456_to_fp16)[name = tensor("op_13457_cast")]; + tensor var_13459_to_fp16 = const()[name = tensor("op_13459_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5067761152)))]; + tensor hidden_states_563_cast = mul(x = var_13457_cast, y = var_13459_to_fp16)[name = tensor("hidden_states_563_cast")]; + tensor var_13466 = const()[name = tensor("op_13466"), val = tensor([1, 1])]; + tensor var_13468 = const()[name = tensor("op_13468"), val = tensor([1, 1])]; tensor q_275_pad_type_0 = const()[name = tensor("q_275_pad_type_0"), val = tensor("custom")]; tensor q_275_pad_0 = const()[name = tensor("q_275_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_275 = conv(dilations = var_13586, groups = var_12525, pad = q_275_pad_0, pad_type = q_275_pad_type_0, strides = var_13584, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight, x = hidden_states_563)[name = tensor("q_275")]; - tensor var_13590 = const()[name = tensor("op_13590"), val = tensor([1, 1])]; - tensor var_13592 = const()[name = tensor("op_13592"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5067762496)))]; + tensor q_275_cast = conv(dilations = var_13468, groups = var_31, pad = q_275_pad_0, pad_type = q_275_pad_type_0, strides = var_13466, weight = unet_up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16, x = hidden_states_563_cast)[name = tensor("q_275_cast")]; + tensor var_13472 = const()[name = tensor("op_13472"), val = tensor([1, 1])]; + tensor var_13474 = const()[name = tensor("op_13474"), val = tensor([1, 1])]; tensor k_275_pad_type_0 = const()[name = tensor("k_275_pad_type_0"), val = tensor("custom")]; tensor k_275_pad_0 = const()[name = tensor("k_275_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_275 = conv(dilations = var_13592, groups = var_12525, pad = k_275_pad_0, pad_type = k_275_pad_type_0, strides = var_13590, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k_275")]; - tensor var_13596 = const()[name = tensor("op_13596"), val = tensor([1, 1])]; - tensor var_13598 = const()[name = tensor("op_13598"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5068581760)))]; + tensor k_275_cast = conv(dilations = var_13474, groups = var_31, pad = k_275_pad_0, pad_type = k_275_pad_type_0, strides = var_13472, weight = unet_up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_275_cast")]; + tensor var_13478 = const()[name = tensor("op_13478"), val = tensor([1, 1])]; + tensor var_13480 = const()[name = tensor("op_13480"), val = tensor([1, 1])]; tensor v_275_pad_type_0 = const()[name = tensor("v_275_pad_type_0"), val = tensor("custom")]; tensor v_275_pad_0 = const()[name = tensor("v_275_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_275 = conv(dilations = var_13598, groups = var_12525, pad = v_275_pad_0, pad_type = v_275_pad_type_0, strides = var_13596, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v_275")]; - tensor var_13602 = const()[name = tensor("op_13602"), val = tensor([2, 10, 64, -1])]; - tensor var_13603 = reshape(shape = var_13602, x = q_275)[name = tensor("op_13603")]; - tensor var_13604 = const()[name = tensor("op_13604"), val = tensor([2, 10, 64, -1])]; - tensor var_13605 = reshape(shape = var_13604, x = k_275)[name = tensor("op_13605")]; - tensor var_13606 = const()[name = tensor("op_13606"), val = tensor([2, 10, 64, -1])]; - tensor var_13607 = reshape(shape = var_13606, x = v_275)[name = tensor("op_13607")]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5071203264)))]; + tensor v_275_cast = conv(dilations = var_13480, groups = var_31, pad = v_275_pad_0, pad_type = v_275_pad_type_0, strides = var_13478, weight = unet_up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_275_cast")]; + tensor var_13484 = const()[name = tensor("op_13484"), val = tensor([2, 10, 64, -1])]; + tensor var_13485_cast = reshape(shape = var_13484, x = q_275_cast)[name = tensor("op_13485_cast")]; + tensor var_13486 = const()[name = tensor("op_13486"), val = tensor([2, 10, 64, -1])]; + tensor var_13487_cast = reshape(shape = var_13486, x = k_275_cast)[name = tensor("op_13487_cast")]; + tensor var_13488 = const()[name = tensor("op_13488"), val = tensor([2, 10, 64, -1])]; + tensor var_13489_cast = reshape(shape = var_13488, x = v_275_cast)[name = tensor("op_13489_cast")]; tensor attn_weights_549_transpose_x_0 = const()[name = tensor("attn_weights_549_transpose_x_0"), val = tensor(true)]; tensor attn_weights_549_transpose_y_0 = const()[name = tensor("attn_weights_549_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_549 = matmul(transpose_x = attn_weights_549_transpose_x_0, transpose_y = attn_weights_549_transpose_y_0, x = var_13603, y = var_13605)[name = tensor("attn_weights_549")]; - tensor attn_weights_551 = mul(x = attn_weights_549, y = var_12516)[name = tensor("attn_weights_551")]; - tensor var_13611 = softmax(axis = var_12509, x = attn_weights_551)[name = tensor("op_13611")]; + tensor attn_weights_549_cast = matmul(transpose_x = attn_weights_549_transpose_x_0, transpose_y = attn_weights_549_transpose_y_0, x = var_13485_cast, y = var_13487_cast)[name = tensor("attn_weights_549_cast")]; + tensor attn_weights_551_cast = mul(x = attn_weights_549_cast, y = var_12_to_fp16)[name = tensor("attn_weights_551_cast")]; + tensor var_13493_cast = softmax(axis = var_18, x = attn_weights_551_cast)[name = tensor("op_13493_cast")]; tensor attn_275_transpose_x_0 = const()[name = tensor("attn_275_transpose_x_0"), val = tensor(false)]; tensor attn_275_transpose_y_0 = const()[name = tensor("attn_275_transpose_y_0"), val = tensor(true)]; - tensor attn_275 = matmul(transpose_x = attn_275_transpose_x_0, transpose_y = attn_275_transpose_y_0, x = var_13607, y = var_13611)[name = tensor("attn_275")]; - tensor var_13615 = const()[name = tensor("op_13615"), val = tensor([2, 640, 1, -1])]; - tensor input_807 = reshape(shape = var_13615, x = attn_275)[name = tensor("input_807")]; - tensor var_13620 = const()[name = tensor("op_13620"), val = tensor([1, 1])]; - tensor var_13622 = const()[name = tensor("op_13622"), val = tensor([1, 1])]; - tensor var_13624_pad_type_0 = const()[name = tensor("op_13624_pad_type_0"), val = tensor("custom")]; - tensor var_13624_pad_0 = const()[name = tensor("op_13624_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_13624 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias, dilations = var_13622, groups = var_12525, pad = var_13624_pad_0, pad_type = var_13624_pad_type_0, strides = var_13620, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight, x = input_807)[name = tensor("op_13624")]; - tensor inputs_413 = add(x = var_13624, y = inputs_411)[name = tensor("inputs_413")]; - tensor var_13628 = const()[name = tensor("op_13628"), val = tensor([1])]; - tensor channels_mean_413 = reduce_mean(axes = var_13628, keep_dims = var_12520, x = inputs_413)[name = tensor("channels_mean_413")]; - tensor zero_mean_413 = sub(x = inputs_413, y = channels_mean_413)[name = tensor("zero_mean_413")]; - tensor zero_mean_sq_413 = mul(x = zero_mean_413, y = zero_mean_413)[name = tensor("zero_mean_sq_413")]; - tensor var_13632 = const()[name = tensor("op_13632"), val = tensor([1])]; - tensor var_13633 = reduce_mean(axes = var_13632, keep_dims = var_12520, x = zero_mean_sq_413)[name = tensor("op_13633")]; - tensor var_13634 = const()[name = tensor("op_13634"), val = tensor(0x1.4f8b58p-17)]; - tensor var_13635 = add(x = var_13633, y = var_13634)[name = tensor("op_13635")]; - tensor denom_413_epsilon_0 = const()[name = tensor("denom_413_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_413 = rsqrt(epsilon = denom_413_epsilon_0, x = var_13635)[name = tensor("denom_413")]; - tensor out_413 = mul(x = zero_mean_413, y = denom_413)[name = tensor("out_413")]; - tensor var_13639 = const()[name = tensor("op_13639"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269975680)))]; - tensor var_13640 = add(x = out_413, y = var_13639)[name = tensor("op_13640")]; - tensor var_13642 = const()[name = tensor("op_13642"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269978304)))]; - tensor input_809 = mul(x = var_13640, y = var_13642)[name = tensor("input_809")]; - tensor var_13650 = const()[name = tensor("op_13650"), val = tensor([1, 1])]; - tensor var_13652 = const()[name = tensor("op_13652"), val = tensor([1, 1])]; - tensor var_13654_pad_type_0 = const()[name = tensor("op_13654_pad_type_0"), val = tensor("custom")]; - tensor var_13654_pad_0 = const()[name = tensor("op_13654_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_13654 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias, dilations = var_13652, groups = var_12525, pad = var_13654_pad_0, pad_type = var_13654_pad_type_0, strides = var_13650, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight, x = input_809)[name = tensor("op_13654")]; - tensor var_13655_split_sizes_0 = const()[name = tensor("op_13655_split_sizes_0"), val = tensor([2560, 2560])]; - tensor var_13655_axis_0 = const()[name = tensor("op_13655_axis_0"), val = tensor(1)]; - tensor var_13655_0, tensor var_13655_1 = split(axis = var_13655_axis_0, split_sizes = var_13655_split_sizes_0, x = var_13654)[name = tensor("op_13655")]; - tensor var_13657_mode_0 = const()[name = tensor("op_13657_mode_0"), val = tensor("EXACT")]; - tensor var_13657 = gelu(mode = var_13657_mode_0, x = var_13655_1)[name = tensor("op_13657")]; - tensor input_811 = mul(x = var_13655_0, y = var_13657)[name = tensor("input_811")]; - tensor var_13661 = const()[name = tensor("op_13661"), val = tensor([1, 1])]; - tensor var_13663 = const()[name = tensor("op_13663"), val = tensor([1, 1])]; - tensor var_13665_pad_type_0 = const()[name = tensor("op_13665_pad_type_0"), val = tensor("custom")]; - tensor var_13665_pad_0 = const()[name = tensor("op_13665_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_13665 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias, dilations = var_13663, groups = var_12525, pad = var_13665_pad_0, pad_type = var_13665_pad_type_0, strides = var_13661, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight, x = input_811)[name = tensor("op_13665")]; - tensor inputs_415 = add(x = var_13665, y = inputs_413)[name = tensor("inputs_415")]; - tensor var_13675 = const()[name = tensor("op_13675"), val = tensor([1])]; - tensor channels_mean_415 = reduce_mean(axes = var_13675, keep_dims = var_12520, x = inputs_415)[name = tensor("channels_mean_415")]; - tensor zero_mean_415 = sub(x = inputs_415, y = channels_mean_415)[name = tensor("zero_mean_415")]; - tensor zero_mean_sq_415 = mul(x = zero_mean_415, y = zero_mean_415)[name = tensor("zero_mean_sq_415")]; - tensor var_13679 = const()[name = tensor("op_13679"), val = tensor([1])]; - tensor var_13680 = reduce_mean(axes = var_13679, keep_dims = var_12520, x = zero_mean_sq_415)[name = tensor("op_13680")]; - tensor var_13681 = const()[name = tensor("op_13681"), val = tensor(0x1.4f8b58p-17)]; - tensor var_13682 = add(x = var_13680, y = var_13681)[name = tensor("op_13682")]; - tensor denom_415_epsilon_0 = const()[name = tensor("denom_415_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_415 = rsqrt(epsilon = denom_415_epsilon_0, x = var_13682)[name = tensor("denom_415")]; - tensor out_415 = mul(x = zero_mean_415, y = denom_415)[name = tensor("out_415")]; - tensor var_13686 = const()[name = tensor("op_13686"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269980928)))]; - tensor var_13687 = add(x = out_415, y = var_13686)[name = tensor("op_13687")]; - tensor var_13689 = const()[name = tensor("op_13689"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269983552)))]; - tensor hidden_states_567 = mul(x = var_13687, y = var_13689)[name = tensor("hidden_states_567")]; - tensor var_13696 = const()[name = tensor("op_13696"), val = tensor([1, 1])]; - tensor var_13698 = const()[name = tensor("op_13698"), val = tensor([1, 1])]; + tensor attn_275_cast = matmul(transpose_x = attn_275_transpose_x_0, transpose_y = attn_275_transpose_y_0, x = var_13489_cast, y = var_13493_cast)[name = tensor("attn_275_cast")]; + tensor var_13497 = const()[name = tensor("op_13497"), val = tensor([2, 640, 1, -1])]; + tensor input_807_cast = reshape(shape = var_13497, x = attn_275_cast)[name = tensor("input_807_cast")]; + tensor var_13502 = const()[name = tensor("op_13502"), val = tensor([1, 1])]; + tensor var_13504 = const()[name = tensor("op_13504"), val = tensor([1, 1])]; + tensor var_13506_pad_type_0 = const()[name = tensor("op_13506_pad_type_0"), val = tensor("custom")]; + tensor var_13506_pad_0 = const()[name = tensor("op_13506_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5073824768)))]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5074644032)))]; + tensor var_13506_cast = conv(bias = unet_up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_13504, groups = var_31, pad = var_13506_pad_0, pad_type = var_13506_pad_type_0, strides = var_13502, weight = unet_up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16, x = input_807_cast)[name = tensor("op_13506_cast")]; + tensor inputs_413_cast = add(x = var_13506_cast, y = inputs_411_cast)[name = tensor("inputs_413_cast")]; + tensor var_13510 = const()[name = tensor("op_13510"), val = tensor([1])]; + tensor channels_mean_413_cast = reduce_mean(axes = var_13510, keep_dims = var_23, x = inputs_413_cast)[name = tensor("channels_mean_413_cast")]; + tensor zero_mean_413_cast = sub(x = inputs_413_cast, y = channels_mean_413_cast)[name = tensor("zero_mean_413_cast")]; + tensor zero_mean_sq_413_cast = mul(x = zero_mean_413_cast, y = zero_mean_413_cast)[name = tensor("zero_mean_sq_413_cast")]; + tensor var_13514 = const()[name = tensor("op_13514"), val = tensor([1])]; + tensor var_13515_cast = reduce_mean(axes = var_13514, keep_dims = var_23, x = zero_mean_sq_413_cast)[name = tensor("op_13515_cast")]; + tensor var_13516_to_fp16 = const()[name = tensor("op_13516_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13517_cast = add(x = var_13515_cast, y = var_13516_to_fp16)[name = tensor("op_13517_cast")]; + tensor denom_413_epsilon_0_to_fp16 = const()[name = tensor("denom_413_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_413_cast = rsqrt(epsilon = denom_413_epsilon_0_to_fp16, x = var_13517_cast)[name = tensor("denom_413_cast")]; + tensor out_413_cast = mul(x = zero_mean_413_cast, y = denom_413_cast)[name = tensor("out_413_cast")]; + tensor var_13521_to_fp16 = const()[name = tensor("op_13521_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5074645376)))]; + tensor var_13522_cast = add(x = out_413_cast, y = var_13521_to_fp16)[name = tensor("op_13522_cast")]; + tensor var_13524_to_fp16 = const()[name = tensor("op_13524_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5074646720)))]; + tensor input_809_cast = mul(x = var_13522_cast, y = var_13524_to_fp16)[name = tensor("input_809_cast")]; + tensor var_13532 = const()[name = tensor("op_13532"), val = tensor([1, 1])]; + tensor var_13534 = const()[name = tensor("op_13534"), val = tensor([1, 1])]; + tensor var_13536_pad_type_0 = const()[name = tensor("op_13536_pad_type_0"), val = tensor("custom")]; + tensor var_13536_pad_0 = const()[name = tensor("op_13536_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5074648064)))]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5081201728)))]; + tensor var_13536_cast = conv(bias = unet_up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16, dilations = var_13534, groups = var_31, pad = var_13536_pad_0, pad_type = var_13536_pad_type_0, strides = var_13532, weight = unet_up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16, x = input_809_cast)[name = tensor("op_13536_cast")]; + tensor var_13537_split_sizes_0 = const()[name = tensor("op_13537_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_13537_axis_0 = const()[name = tensor("op_13537_axis_0"), val = tensor(1)]; + tensor var_13537_cast_0, tensor var_13537_cast_1 = split(axis = var_13537_axis_0, split_sizes = var_13537_split_sizes_0, x = var_13536_cast)[name = tensor("op_13537_cast")]; + tensor var_13539_mode_0 = const()[name = tensor("op_13539_mode_0"), val = tensor("EXACT")]; + tensor var_13539_cast = gelu(mode = var_13539_mode_0, x = var_13537_cast_1)[name = tensor("op_13539_cast")]; + tensor input_811_cast = mul(x = var_13537_cast_0, y = var_13539_cast)[name = tensor("input_811_cast")]; + tensor var_13543 = const()[name = tensor("op_13543"), val = tensor([1, 1])]; + tensor var_13545 = const()[name = tensor("op_13545"), val = tensor([1, 1])]; + tensor var_13547_pad_type_0 = const()[name = tensor("op_13547_pad_type_0"), val = tensor("custom")]; + tensor var_13547_pad_0 = const()[name = tensor("op_13547_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5081212032)))]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5084488896)))]; + tensor var_13547_cast = conv(bias = unet_up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_13545, groups = var_31, pad = var_13547_pad_0, pad_type = var_13547_pad_type_0, strides = var_13543, weight = unet_up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_811_cast)[name = tensor("op_13547_cast")]; + tensor inputs_415_cast = add(x = var_13547_cast, y = inputs_413_cast)[name = tensor("inputs_415_cast")]; + tensor var_13557 = const()[name = tensor("op_13557"), val = tensor([1])]; + tensor channels_mean_415_cast = reduce_mean(axes = var_13557, keep_dims = var_23, x = inputs_415_cast)[name = tensor("channels_mean_415_cast")]; + tensor zero_mean_415_cast = sub(x = inputs_415_cast, y = channels_mean_415_cast)[name = tensor("zero_mean_415_cast")]; + tensor zero_mean_sq_415_cast = mul(x = zero_mean_415_cast, y = zero_mean_415_cast)[name = tensor("zero_mean_sq_415_cast")]; + tensor var_13561 = const()[name = tensor("op_13561"), val = tensor([1])]; + tensor var_13562_cast = reduce_mean(axes = var_13561, keep_dims = var_23, x = zero_mean_sq_415_cast)[name = tensor("op_13562_cast")]; + tensor var_13563_to_fp16 = const()[name = tensor("op_13563_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13564_cast = add(x = var_13562_cast, y = var_13563_to_fp16)[name = tensor("op_13564_cast")]; + tensor denom_415_epsilon_0_to_fp16 = const()[name = tensor("denom_415_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_415_cast = rsqrt(epsilon = denom_415_epsilon_0_to_fp16, x = var_13564_cast)[name = tensor("denom_415_cast")]; + tensor out_415_cast = mul(x = zero_mean_415_cast, y = denom_415_cast)[name = tensor("out_415_cast")]; + tensor var_13568_to_fp16 = const()[name = tensor("op_13568_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5084490240)))]; + tensor var_13569_cast = add(x = out_415_cast, y = var_13568_to_fp16)[name = tensor("op_13569_cast")]; + tensor var_13571_to_fp16 = const()[name = tensor("op_13571_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5084491584)))]; + tensor hidden_states_567_cast = mul(x = var_13569_cast, y = var_13571_to_fp16)[name = tensor("hidden_states_567_cast")]; + tensor var_13578 = const()[name = tensor("op_13578"), val = tensor([1, 1])]; + tensor var_13580 = const()[name = tensor("op_13580"), val = tensor([1, 1])]; tensor q_277_pad_type_0 = const()[name = tensor("q_277_pad_type_0"), val = tensor("custom")]; tensor q_277_pad_0 = const()[name = tensor("q_277_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q_277 = conv(dilations = var_13698, groups = var_12525, pad = q_277_pad_0, pad_type = q_277_pad_type_0, strides = var_13696, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight, x = hidden_states_567)[name = tensor("q_277")]; - tensor var_13702 = const()[name = tensor("op_13702"), val = tensor([1, 1])]; - tensor var_13704 = const()[name = tensor("op_13704"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5084492928)))]; + tensor q_277_cast = conv(dilations = var_13580, groups = var_31, pad = q_277_pad_0, pad_type = q_277_pad_type_0, strides = var_13578, weight = unet_up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_q_weight_to_fp16, x = hidden_states_567_cast)[name = tensor("q_277_cast")]; + tensor var_13584 = const()[name = tensor("op_13584"), val = tensor([1, 1])]; + tensor var_13586 = const()[name = tensor("op_13586"), val = tensor([1, 1])]; tensor k_277_pad_type_0 = const()[name = tensor("k_277_pad_type_0"), val = tensor("custom")]; tensor k_277_pad_0 = const()[name = tensor("k_277_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k_277 = conv(dilations = var_13704, groups = var_12525, pad = k_277_pad_0, pad_type = k_277_pad_type_0, strides = var_13702, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight, x = hidden_states_567)[name = tensor("k_277")]; - tensor var_13708 = const()[name = tensor("op_13708"), val = tensor([1, 1])]; - tensor var_13710 = const()[name = tensor("op_13710"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5085312192)))]; + tensor k_277_cast = conv(dilations = var_13586, groups = var_31, pad = k_277_pad_0, pad_type = k_277_pad_type_0, strides = var_13584, weight = unet_up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_k_weight_to_fp16, x = hidden_states_567_cast)[name = tensor("k_277_cast")]; + tensor var_13590 = const()[name = tensor("op_13590"), val = tensor([1, 1])]; + tensor var_13592 = const()[name = tensor("op_13592"), val = tensor([1, 1])]; tensor v_277_pad_type_0 = const()[name = tensor("v_277_pad_type_0"), val = tensor("custom")]; tensor v_277_pad_0 = const()[name = tensor("v_277_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v_277 = conv(dilations = var_13710, groups = var_12525, pad = v_277_pad_0, pad_type = v_277_pad_type_0, strides = var_13708, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight, x = hidden_states_567)[name = tensor("v_277")]; - tensor var_13714 = const()[name = tensor("op_13714"), val = tensor([2, 10, 64, -1])]; - tensor var_13715 = reshape(shape = var_13714, x = q_277)[name = tensor("op_13715")]; - tensor var_13716 = const()[name = tensor("op_13716"), val = tensor([2, 10, 64, -1])]; - tensor var_13717 = reshape(shape = var_13716, x = k_277)[name = tensor("op_13717")]; - tensor var_13718 = const()[name = tensor("op_13718"), val = tensor([2, 10, 64, -1])]; - tensor var_13719 = reshape(shape = var_13718, x = v_277)[name = tensor("op_13719")]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5086131456)))]; + tensor v_277_cast = conv(dilations = var_13592, groups = var_31, pad = v_277_pad_0, pad_type = v_277_pad_type_0, strides = var_13590, weight = unet_up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_v_weight_to_fp16, x = hidden_states_567_cast)[name = tensor("v_277_cast")]; + tensor var_13596 = const()[name = tensor("op_13596"), val = tensor([2, 10, 64, -1])]; + tensor var_13597_cast = reshape(shape = var_13596, x = q_277_cast)[name = tensor("op_13597_cast")]; + tensor var_13598 = const()[name = tensor("op_13598"), val = tensor([2, 10, 64, -1])]; + tensor var_13599_cast = reshape(shape = var_13598, x = k_277_cast)[name = tensor("op_13599_cast")]; + tensor var_13600 = const()[name = tensor("op_13600"), val = tensor([2, 10, 64, -1])]; + tensor var_13601_cast = reshape(shape = var_13600, x = v_277_cast)[name = tensor("op_13601_cast")]; tensor attn_weights_553_transpose_x_0 = const()[name = tensor("attn_weights_553_transpose_x_0"), val = tensor(true)]; tensor attn_weights_553_transpose_y_0 = const()[name = tensor("attn_weights_553_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_553 = matmul(transpose_x = attn_weights_553_transpose_x_0, transpose_y = attn_weights_553_transpose_y_0, x = var_13715, y = var_13717)[name = tensor("attn_weights_553")]; - tensor attn_weights_555 = mul(x = attn_weights_553, y = var_12516)[name = tensor("attn_weights_555")]; - tensor var_13723 = softmax(axis = var_12509, x = attn_weights_555)[name = tensor("op_13723")]; + tensor attn_weights_553_cast = matmul(transpose_x = attn_weights_553_transpose_x_0, transpose_y = attn_weights_553_transpose_y_0, x = var_13597_cast, y = var_13599_cast)[name = tensor("attn_weights_553_cast")]; + tensor attn_weights_555_cast = mul(x = attn_weights_553_cast, y = var_12_to_fp16)[name = tensor("attn_weights_555_cast")]; + tensor var_13605_cast = softmax(axis = var_18, x = attn_weights_555_cast)[name = tensor("op_13605_cast")]; tensor attn_277_transpose_x_0 = const()[name = tensor("attn_277_transpose_x_0"), val = tensor(false)]; tensor attn_277_transpose_y_0 = const()[name = tensor("attn_277_transpose_y_0"), val = tensor(true)]; - tensor attn_277 = matmul(transpose_x = attn_277_transpose_x_0, transpose_y = attn_277_transpose_y_0, x = var_13719, y = var_13723)[name = tensor("attn_277")]; - tensor var_13727 = const()[name = tensor("op_13727"), val = tensor([2, 640, 1, -1])]; - tensor input_813 = reshape(shape = var_13727, x = attn_277)[name = tensor("input_813")]; - tensor var_13732 = const()[name = tensor("op_13732"), val = tensor([1, 1])]; - tensor var_13734 = const()[name = tensor("op_13734"), val = tensor([1, 1])]; - tensor var_13736_pad_type_0 = const()[name = tensor("op_13736_pad_type_0"), val = tensor("custom")]; - tensor var_13736_pad_0 = const()[name = tensor("op_13736_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_13736 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias, dilations = var_13734, groups = var_12525, pad = var_13736_pad_0, pad_type = var_13736_pad_type_0, strides = var_13732, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight, x = input_813)[name = tensor("op_13736")]; - tensor inputs_417 = add(x = var_13736, y = inputs_415)[name = tensor("inputs_417")]; - tensor var_13740 = const()[name = tensor("op_13740"), val = tensor([1])]; - tensor channels_mean_417 = reduce_mean(axes = var_13740, keep_dims = var_12520, x = inputs_417)[name = tensor("channels_mean_417")]; - tensor zero_mean_417 = sub(x = inputs_417, y = channels_mean_417)[name = tensor("zero_mean_417")]; - tensor zero_mean_sq_417 = mul(x = zero_mean_417, y = zero_mean_417)[name = tensor("zero_mean_sq_417")]; - tensor var_13744 = const()[name = tensor("op_13744"), val = tensor([1])]; - tensor var_13745 = reduce_mean(axes = var_13744, keep_dims = var_12520, x = zero_mean_sq_417)[name = tensor("op_13745")]; - tensor var_13746 = const()[name = tensor("op_13746"), val = tensor(0x1.4f8b58p-17)]; - tensor var_13747 = add(x = var_13745, y = var_13746)[name = tensor("op_13747")]; - tensor denom_417_epsilon_0 = const()[name = tensor("denom_417_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom_417 = rsqrt(epsilon = denom_417_epsilon_0, x = var_13747)[name = tensor("denom_417")]; - tensor out_417 = mul(x = zero_mean_417, y = denom_417)[name = tensor("out_417")]; - tensor var_13751 = const()[name = tensor("op_13751"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269986176)))]; - tensor var_13752 = add(x = out_417, y = var_13751)[name = tensor("op_13752")]; - tensor var_13754 = const()[name = tensor("op_13754"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269988800)))]; - tensor hidden_states_569 = mul(x = var_13752, y = var_13754)[name = tensor("hidden_states_569")]; - tensor var_13761 = const()[name = tensor("op_13761"), val = tensor([1, 1])]; - tensor var_13763 = const()[name = tensor("op_13763"), val = tensor([1, 1])]; + tensor attn_277_cast = matmul(transpose_x = attn_277_transpose_x_0, transpose_y = attn_277_transpose_y_0, x = var_13601_cast, y = var_13605_cast)[name = tensor("attn_277_cast")]; + tensor var_13609 = const()[name = tensor("op_13609"), val = tensor([2, 640, 1, -1])]; + tensor input_813_cast = reshape(shape = var_13609, x = attn_277_cast)[name = tensor("input_813_cast")]; + tensor var_13614 = const()[name = tensor("op_13614"), val = tensor([1, 1])]; + tensor var_13616 = const()[name = tensor("op_13616"), val = tensor([1, 1])]; + tensor var_13618_pad_type_0 = const()[name = tensor("op_13618_pad_type_0"), val = tensor("custom")]; + tensor var_13618_pad_0 = const()[name = tensor("op_13618_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5086950720)))]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5087769984)))]; + tensor var_13618_cast = conv(bias = unet_up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_bias_to_fp16, dilations = var_13616, groups = var_31, pad = var_13618_pad_0, pad_type = var_13618_pad_type_0, strides = var_13614, weight = unet_up_blocks_1_attentions_2_transformer_blocks_1_attn1_to_out_0_weight_to_fp16, x = input_813_cast)[name = tensor("op_13618_cast")]; + tensor inputs_417_cast = add(x = var_13618_cast, y = inputs_415_cast)[name = tensor("inputs_417_cast")]; + tensor var_13622 = const()[name = tensor("op_13622"), val = tensor([1])]; + tensor channels_mean_417_cast = reduce_mean(axes = var_13622, keep_dims = var_23, x = inputs_417_cast)[name = tensor("channels_mean_417_cast")]; + tensor zero_mean_417_cast = sub(x = inputs_417_cast, y = channels_mean_417_cast)[name = tensor("zero_mean_417_cast")]; + tensor zero_mean_sq_417_cast = mul(x = zero_mean_417_cast, y = zero_mean_417_cast)[name = tensor("zero_mean_sq_417_cast")]; + tensor var_13626 = const()[name = tensor("op_13626"), val = tensor([1])]; + tensor var_13627_cast = reduce_mean(axes = var_13626, keep_dims = var_23, x = zero_mean_sq_417_cast)[name = tensor("op_13627_cast")]; + tensor var_13628_to_fp16 = const()[name = tensor("op_13628_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13629_cast = add(x = var_13627_cast, y = var_13628_to_fp16)[name = tensor("op_13629_cast")]; + tensor denom_417_epsilon_0_to_fp16 = const()[name = tensor("denom_417_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_417_cast = rsqrt(epsilon = denom_417_epsilon_0_to_fp16, x = var_13629_cast)[name = tensor("denom_417_cast")]; + tensor out_417_cast = mul(x = zero_mean_417_cast, y = denom_417_cast)[name = tensor("out_417_cast")]; + tensor var_13633_to_fp16 = const()[name = tensor("op_13633_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5087771328)))]; + tensor var_13634_cast = add(x = out_417_cast, y = var_13633_to_fp16)[name = tensor("op_13634_cast")]; + tensor var_13636_to_fp16 = const()[name = tensor("op_13636_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5087772672)))]; + tensor hidden_states_569_cast = mul(x = var_13634_cast, y = var_13636_to_fp16)[name = tensor("hidden_states_569_cast")]; + tensor var_13643 = const()[name = tensor("op_13643"), val = tensor([1, 1])]; + tensor var_13645 = const()[name = tensor("op_13645"), val = tensor([1, 1])]; tensor q_pad_type_0 = const()[name = tensor("q_pad_type_0"), val = tensor("custom")]; tensor q_pad_0 = const()[name = tensor("q_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor q = conv(dilations = var_13763, groups = var_12525, pad = q_pad_0, pad_type = q_pad_type_0, strides = var_13761, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight, x = hidden_states_569)[name = tensor("q")]; - tensor var_13767 = const()[name = tensor("op_13767"), val = tensor([1, 1])]; - tensor var_13769 = const()[name = tensor("op_13769"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5087774016)))]; + tensor q_cast = conv(dilations = var_13645, groups = var_31, pad = q_pad_0, pad_type = q_pad_type_0, strides = var_13643, weight = unet_up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_q_weight_to_fp16, x = hidden_states_569_cast)[name = tensor("q_cast")]; + tensor var_13649 = const()[name = tensor("op_13649"), val = tensor([1, 1])]; + tensor var_13651 = const()[name = tensor("op_13651"), val = tensor([1, 1])]; tensor k_pad_type_0 = const()[name = tensor("k_pad_type_0"), val = tensor("custom")]; tensor k_pad_0 = const()[name = tensor("k_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor k = conv(dilations = var_13769, groups = var_12525, pad = k_pad_0, pad_type = k_pad_type_0, strides = var_13767, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight, x = encoder_hidden_states)[name = tensor("k")]; - tensor var_13773 = const()[name = tensor("op_13773"), val = tensor([1, 1])]; - tensor var_13775 = const()[name = tensor("op_13775"), val = tensor([1, 1])]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5088593280)))]; + tensor k_cast = conv(dilations = var_13651, groups = var_31, pad = k_pad_0, pad_type = k_pad_type_0, strides = var_13649, weight = unet_up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_k_weight_to_fp16, x = encoder_hidden_states)[name = tensor("k_cast")]; + tensor var_13655 = const()[name = tensor("op_13655"), val = tensor([1, 1])]; + tensor var_13657 = const()[name = tensor("op_13657"), val = tensor([1, 1])]; tensor v_pad_type_0 = const()[name = tensor("v_pad_type_0"), val = tensor("custom")]; tensor v_pad_0 = const()[name = tensor("v_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor v = conv(dilations = var_13775, groups = var_12525, pad = v_pad_0, pad_type = v_pad_type_0, strides = var_13773, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight, x = encoder_hidden_states)[name = tensor("v")]; - tensor var_13779 = const()[name = tensor("op_13779"), val = tensor([2, 10, 64, -1])]; - tensor var_13780 = reshape(shape = var_13779, x = q)[name = tensor("op_13780")]; - tensor var_13781 = const()[name = tensor("op_13781"), val = tensor([2, 10, 64, -1])]; - tensor var_13782 = reshape(shape = var_13781, x = k)[name = tensor("op_13782")]; - tensor var_13783 = const()[name = tensor("op_13783"), val = tensor([2, 10, 64, -1])]; - tensor var_13784 = reshape(shape = var_13783, x = v)[name = tensor("op_13784")]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5091214784)))]; + tensor v_cast = conv(dilations = var_13657, groups = var_31, pad = v_pad_0, pad_type = v_pad_type_0, strides = var_13655, weight = unet_up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_v_weight_to_fp16, x = encoder_hidden_states)[name = tensor("v_cast")]; + tensor var_13661 = const()[name = tensor("op_13661"), val = tensor([2, 10, 64, -1])]; + tensor var_13662_cast = reshape(shape = var_13661, x = q_cast)[name = tensor("op_13662_cast")]; + tensor var_13663 = const()[name = tensor("op_13663"), val = tensor([2, 10, 64, -1])]; + tensor var_13664_cast = reshape(shape = var_13663, x = k_cast)[name = tensor("op_13664_cast")]; + tensor var_13665 = const()[name = tensor("op_13665"), val = tensor([2, 10, 64, -1])]; + tensor var_13666_cast = reshape(shape = var_13665, x = v_cast)[name = tensor("op_13666_cast")]; tensor attn_weights_557_transpose_x_0 = const()[name = tensor("attn_weights_557_transpose_x_0"), val = tensor(true)]; tensor attn_weights_557_transpose_y_0 = const()[name = tensor("attn_weights_557_transpose_y_0"), val = tensor(false)]; - tensor attn_weights_557 = matmul(transpose_x = attn_weights_557_transpose_x_0, transpose_y = attn_weights_557_transpose_y_0, x = var_13780, y = var_13782)[name = tensor("attn_weights_557")]; - tensor attn_weights = mul(x = attn_weights_557, y = var_12516)[name = tensor("attn_weights")]; - tensor var_13788 = softmax(axis = var_12509, x = attn_weights)[name = tensor("op_13788")]; + tensor attn_weights_557_cast = matmul(transpose_x = attn_weights_557_transpose_x_0, transpose_y = attn_weights_557_transpose_y_0, x = var_13662_cast, y = var_13664_cast)[name = tensor("attn_weights_557_cast")]; + tensor attn_weights_cast = mul(x = attn_weights_557_cast, y = var_12_to_fp16)[name = tensor("attn_weights_cast")]; + tensor var_13670_cast = softmax(axis = var_18, x = attn_weights_cast)[name = tensor("op_13670_cast")]; tensor attn_transpose_x_0 = const()[name = tensor("attn_transpose_x_0"), val = tensor(false)]; tensor attn_transpose_y_0 = const()[name = tensor("attn_transpose_y_0"), val = tensor(true)]; - tensor attn = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_13784, y = var_13788)[name = tensor("attn")]; - tensor var_13792 = const()[name = tensor("op_13792"), val = tensor([2, 640, 1, -1])]; - tensor input_815 = reshape(shape = var_13792, x = attn)[name = tensor("input_815")]; - tensor var_13797 = const()[name = tensor("op_13797"), val = tensor([1, 1])]; - tensor var_13799 = const()[name = tensor("op_13799"), val = tensor([1, 1])]; - tensor var_13801_pad_type_0 = const()[name = tensor("op_13801_pad_type_0"), val = tensor("custom")]; - tensor var_13801_pad_0 = const()[name = tensor("op_13801_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_13801 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias, dilations = var_13799, groups = var_12525, pad = var_13801_pad_0, pad_type = var_13801_pad_type_0, strides = var_13797, weight = up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight, x = input_815)[name = tensor("op_13801")]; - tensor inputs = add(x = var_13801, y = inputs_417)[name = tensor("inputs")]; - tensor var_13805 = const()[name = tensor("op_13805"), val = tensor([1])]; - tensor channels_mean = reduce_mean(axes = var_13805, keep_dims = var_12520, x = inputs)[name = tensor("channels_mean")]; - tensor zero_mean = sub(x = inputs, y = channels_mean)[name = tensor("zero_mean")]; - tensor zero_mean_sq = mul(x = zero_mean, y = zero_mean)[name = tensor("zero_mean_sq")]; - tensor var_13809 = const()[name = tensor("op_13809"), val = tensor([1])]; - tensor var_13810 = reduce_mean(axes = var_13809, keep_dims = var_12520, x = zero_mean_sq)[name = tensor("op_13810")]; - tensor var_13811 = const()[name = tensor("op_13811"), val = tensor(0x1.4f8b58p-17)]; - tensor var_13812 = add(x = var_13810, y = var_13811)[name = tensor("op_13812")]; - tensor denom_epsilon_0 = const()[name = tensor("denom_epsilon_0"), val = tensor(0x1.197998p-40)]; - tensor denom = rsqrt(epsilon = denom_epsilon_0, x = var_13812)[name = tensor("denom")]; - tensor out = mul(x = zero_mean, y = denom)[name = tensor("out")]; - tensor var_13816 = const()[name = tensor("op_13816"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269991424)))]; - tensor var_13817 = add(x = out, y = var_13816)[name = tensor("op_13817")]; - tensor var_13819 = const()[name = tensor("op_13819"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269994048)))]; - tensor input_817 = mul(x = var_13817, y = var_13819)[name = tensor("input_817")]; - tensor var_13827 = const()[name = tensor("op_13827"), val = tensor([1, 1])]; - tensor var_13829 = const()[name = tensor("op_13829"), val = tensor([1, 1])]; - tensor var_13831_pad_type_0 = const()[name = tensor("op_13831_pad_type_0"), val = tensor("custom")]; - tensor var_13831_pad_0 = const()[name = tensor("op_13831_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_13831 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias, dilations = var_13829, groups = var_12525, pad = var_13831_pad_0, pad_type = var_13831_pad_type_0, strides = var_13827, weight = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight, x = input_817)[name = tensor("op_13831")]; - tensor var_13832_split_sizes_0 = const()[name = tensor("op_13832_split_sizes_0"), val = tensor([2560, 2560])]; - tensor var_13832_axis_0 = const()[name = tensor("op_13832_axis_0"), val = tensor(1)]; - tensor var_13832_0, tensor var_13832_1 = split(axis = var_13832_axis_0, split_sizes = var_13832_split_sizes_0, x = var_13831)[name = tensor("op_13832")]; - tensor var_13834_mode_0 = const()[name = tensor("op_13834_mode_0"), val = tensor("EXACT")]; - tensor var_13834 = gelu(mode = var_13834_mode_0, x = var_13832_1)[name = tensor("op_13834")]; - tensor input_819 = mul(x = var_13832_0, y = var_13834)[name = tensor("input_819")]; - tensor var_13838 = const()[name = tensor("op_13838"), val = tensor([1, 1])]; - tensor var_13840 = const()[name = tensor("op_13840"), val = tensor([1, 1])]; - tensor var_13842_pad_type_0 = const()[name = tensor("op_13842_pad_type_0"), val = tensor("custom")]; - tensor var_13842_pad_0 = const()[name = tensor("op_13842_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor var_13842 = conv(bias = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias, dilations = var_13840, groups = var_12525, pad = var_13842_pad_0, pad_type = var_13842_pad_type_0, strides = var_13838, weight = up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight, x = input_819)[name = tensor("op_13842")]; - tensor hidden_states_573 = add(x = var_13842, y = inputs)[name = tensor("hidden_states_573")]; - tensor var_13844 = const()[name = tensor("op_13844"), val = tensor([2, 640, 64, 64])]; - tensor input_821 = reshape(shape = var_13844, x = hidden_states_573)[name = tensor("input_821")]; - tensor var_13848 = const()[name = tensor("op_13848"), val = tensor([1, 1])]; - tensor var_13850 = const()[name = tensor("op_13850"), val = tensor([1, 1])]; + tensor attn_cast = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_13666_cast, y = var_13670_cast)[name = tensor("attn_cast")]; + tensor var_13674 = const()[name = tensor("op_13674"), val = tensor([2, 640, 1, -1])]; + tensor input_815_cast = reshape(shape = var_13674, x = attn_cast)[name = tensor("input_815_cast")]; + tensor var_13679 = const()[name = tensor("op_13679"), val = tensor([1, 1])]; + tensor var_13681 = const()[name = tensor("op_13681"), val = tensor([1, 1])]; + tensor var_13683_pad_type_0 = const()[name = tensor("op_13683_pad_type_0"), val = tensor("custom")]; + tensor var_13683_pad_0 = const()[name = tensor("op_13683_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5093836288)))]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5094655552)))]; + tensor var_13683_cast = conv(bias = unet_up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_bias_to_fp16, dilations = var_13681, groups = var_31, pad = var_13683_pad_0, pad_type = var_13683_pad_type_0, strides = var_13679, weight = unet_up_blocks_1_attentions_2_transformer_blocks_1_attn2_to_out_0_weight_to_fp16, x = input_815_cast)[name = tensor("op_13683_cast")]; + tensor inputs_cast = add(x = var_13683_cast, y = inputs_417_cast)[name = tensor("inputs_cast")]; + tensor var_13687 = const()[name = tensor("op_13687"), val = tensor([1])]; + tensor channels_mean_cast = reduce_mean(axes = var_13687, keep_dims = var_23, x = inputs_cast)[name = tensor("channels_mean_cast")]; + tensor zero_mean_cast = sub(x = inputs_cast, y = channels_mean_cast)[name = tensor("zero_mean_cast")]; + tensor zero_mean_sq_cast = mul(x = zero_mean_cast, y = zero_mean_cast)[name = tensor("zero_mean_sq_cast")]; + tensor var_13691 = const()[name = tensor("op_13691"), val = tensor([1])]; + tensor var_13692_cast = reduce_mean(axes = var_13691, keep_dims = var_23, x = zero_mean_sq_cast)[name = tensor("op_13692_cast")]; + tensor var_13693_to_fp16 = const()[name = tensor("op_13693_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_13694_cast = add(x = var_13692_cast, y = var_13693_to_fp16)[name = tensor("op_13694_cast")]; + tensor denom_epsilon_0_to_fp16 = const()[name = tensor("denom_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_cast = rsqrt(epsilon = denom_epsilon_0_to_fp16, x = var_13694_cast)[name = tensor("denom_cast")]; + tensor out_cast = mul(x = zero_mean_cast, y = denom_cast)[name = tensor("out_cast")]; + tensor var_13698_to_fp16 = const()[name = tensor("op_13698_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5094656896)))]; + tensor var_13699_cast = add(x = out_cast, y = var_13698_to_fp16)[name = tensor("op_13699_cast")]; + tensor var_13701_to_fp16 = const()[name = tensor("op_13701_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5094658240)))]; + tensor input_817_cast = mul(x = var_13699_cast, y = var_13701_to_fp16)[name = tensor("input_817_cast")]; + tensor var_13709 = const()[name = tensor("op_13709"), val = tensor([1, 1])]; + tensor var_13711 = const()[name = tensor("op_13711"), val = tensor([1, 1])]; + tensor var_13713_pad_type_0 = const()[name = tensor("op_13713_pad_type_0"), val = tensor("custom")]; + tensor var_13713_pad_0 = const()[name = tensor("op_13713_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5094659584)))]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5101213248)))]; + tensor var_13713_cast = conv(bias = unet_up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_bias_to_fp16, dilations = var_13711, groups = var_31, pad = var_13713_pad_0, pad_type = var_13713_pad_type_0, strides = var_13709, weight = unet_up_blocks_1_attentions_2_transformer_blocks_1_ff_net_0_proj_weight_to_fp16, x = input_817_cast)[name = tensor("op_13713_cast")]; + tensor var_13714_split_sizes_0 = const()[name = tensor("op_13714_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_13714_axis_0 = const()[name = tensor("op_13714_axis_0"), val = tensor(1)]; + tensor var_13714_cast_0, tensor var_13714_cast_1 = split(axis = var_13714_axis_0, split_sizes = var_13714_split_sizes_0, x = var_13713_cast)[name = tensor("op_13714_cast")]; + tensor var_13716_mode_0 = const()[name = tensor("op_13716_mode_0"), val = tensor("EXACT")]; + tensor var_13716_cast = gelu(mode = var_13716_mode_0, x = var_13714_cast_1)[name = tensor("op_13716_cast")]; + tensor input_819_cast = mul(x = var_13714_cast_0, y = var_13716_cast)[name = tensor("input_819_cast")]; + tensor var_13720 = const()[name = tensor("op_13720"), val = tensor([1, 1])]; + tensor var_13722 = const()[name = tensor("op_13722"), val = tensor([1, 1])]; + tensor var_13724_pad_type_0 = const()[name = tensor("op_13724_pad_type_0"), val = tensor("custom")]; + tensor var_13724_pad_0 = const()[name = tensor("op_13724_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5101223552)))]; + tensor unet_up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5104500416)))]; + tensor var_13724_cast = conv(bias = unet_up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_bias_to_fp16, dilations = var_13722, groups = var_31, pad = var_13724_pad_0, pad_type = var_13724_pad_type_0, strides = var_13720, weight = unet_up_blocks_1_attentions_2_transformer_blocks_1_ff_net_2_weight_to_fp16, x = input_819_cast)[name = tensor("op_13724_cast")]; + tensor hidden_states_573_cast = add(x = var_13724_cast, y = inputs_cast)[name = tensor("hidden_states_573_cast")]; + tensor var_13726 = const()[name = tensor("op_13726"), val = tensor([2, 640, 64, 64])]; + tensor input_821_cast = reshape(shape = var_13726, x = hidden_states_573_cast)[name = tensor("input_821_cast")]; + tensor var_13730 = const()[name = tensor("op_13730"), val = tensor([1, 1])]; + tensor var_13732 = const()[name = tensor("op_13732"), val = tensor([1, 1])]; tensor hidden_states_575_pad_type_0 = const()[name = tensor("hidden_states_575_pad_type_0"), val = tensor("custom")]; tensor hidden_states_575_pad_0 = const()[name = tensor("hidden_states_575_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor hidden_states_575 = conv(bias = up_blocks_1_attentions_2_proj_out_bias, dilations = var_13850, groups = var_12525, pad = hidden_states_575_pad_0, pad_type = hidden_states_575_pad_type_0, strides = var_13848, weight = up_blocks_1_attentions_2_proj_out_weight, x = input_821)[name = tensor("hidden_states_575")]; - tensor input_823 = add(x = hidden_states_575, y = hidden_states_557)[name = tensor("input_823")]; + tensor unet_up_blocks_1_attentions_2_proj_out_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_proj_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5104501760)))]; + tensor unet_up_blocks_1_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5105321024)))]; + tensor hidden_states_575_cast = conv(bias = unet_up_blocks_1_attentions_2_proj_out_bias_to_fp16, dilations = var_13732, groups = var_31, pad = hidden_states_575_pad_0, pad_type = hidden_states_575_pad_type_0, strides = var_13730, weight = unet_up_blocks_1_attentions_2_proj_out_weight_to_fp16, x = input_821_cast)[name = tensor("hidden_states_575_cast")]; + tensor input_823_cast = add(x = hidden_states_575_cast, y = hidden_states_557_cast)[name = tensor("input_823_cast")]; tensor input_825_scale_factor_height_0 = const()[name = tensor("input_825_scale_factor_height_0"), val = tensor(0x1p+1)]; tensor input_825_scale_factor_width_0 = const()[name = tensor("input_825_scale_factor_width_0"), val = tensor(0x1p+1)]; - tensor input_825 = upsample_nearest_neighbor(scale_factor_height = input_825_scale_factor_height_0, scale_factor_width = input_825_scale_factor_width_0, x = input_823)[name = tensor("input_825")]; - tensor var_13859 = const()[name = tensor("op_13859"), val = tensor([1, 1])]; - tensor var_13861 = const()[name = tensor("op_13861"), val = tensor([1, 1])]; + tensor input_825_cast = upsample_nearest_neighbor(scale_factor_height = input_825_scale_factor_height_0, scale_factor_width = input_825_scale_factor_width_0, x = input_823_cast)[name = tensor("input_825_cast")]; + tensor var_13741 = const()[name = tensor("op_13741"), val = tensor([1, 1])]; + tensor var_13743 = const()[name = tensor("op_13743"), val = tensor([1, 1])]; tensor hidden_states_577_pad_type_0 = const()[name = tensor("hidden_states_577_pad_type_0"), val = tensor("custom")]; tensor hidden_states_577_pad_0 = const()[name = tensor("hidden_states_577_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_577 = conv(bias = up_blocks_1_upsamplers_0_conv_bias, dilations = var_13861, groups = var_12525, pad = hidden_states_577_pad_0, pad_type = hidden_states_577_pad_type_0, strides = var_13859, weight = up_blocks_1_upsamplers_0_conv_weight, x = input_825)[name = tensor("hidden_states_577")]; - tensor var_13869 = const()[name = tensor("op_13869"), val = tensor(1)]; + tensor unet_up_blocks_1_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("unet_up_blocks_1_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5105322368)))]; + tensor unet_up_blocks_1_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("unet_up_blocks_1_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5112695232)))]; + tensor hidden_states_577_cast = conv(bias = unet_up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_13743, groups = var_31, pad = hidden_states_577_pad_0, pad_type = hidden_states_577_pad_type_0, strides = var_13741, weight = unet_up_blocks_1_upsamplers_0_conv_weight_to_fp16, x = input_825_cast)[name = tensor("hidden_states_577_cast")]; tensor input_827_interleave_0 = const()[name = tensor("input_827_interleave_0"), val = tensor(false)]; - tensor input_827 = concat(axis = var_13869, interleave = input_827_interleave_0, values = (hidden_states_577, input_43))[name = tensor("input_827")]; + tensor input_827_cast = concat(axis = var_31, interleave = input_827_interleave_0, values = (hidden_states_577_cast, input_43_cast))[name = tensor("input_827_cast")]; tensor reshape_156_shape_0 = const()[name = tensor("reshape_156_shape_0"), val = tensor([2, 32, 30, 128, 128])]; - tensor reshape_156 = reshape(shape = reshape_156_shape_0, x = input_827)[name = tensor("reshape_156")]; + tensor reshape_156_cast = reshape(shape = reshape_156_shape_0, x = input_827_cast)[name = tensor("reshape_156_cast")]; tensor reduce_mean_117_axes_0 = const()[name = tensor("reduce_mean_117_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_117_keep_dims_0 = const()[name = tensor("reduce_mean_117_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_117 = reduce_mean(axes = reduce_mean_117_axes_0, keep_dims = reduce_mean_117_keep_dims_0, x = reshape_156)[name = tensor("reduce_mean_117")]; - tensor sub_78 = sub(x = reshape_156, y = reduce_mean_117)[name = tensor("sub_78")]; - tensor square_39 = square(x = sub_78)[name = tensor("square_39")]; + tensor reduce_mean_117_cast = reduce_mean(axes = reduce_mean_117_axes_0, keep_dims = reduce_mean_117_keep_dims_0, x = reshape_156_cast)[name = tensor("reduce_mean_117_cast")]; + tensor sub_78_cast = sub(x = reshape_156_cast, y = reduce_mean_117_cast)[name = tensor("sub_78_cast")]; + tensor square_39_cast = square(x = sub_78_cast)[name = tensor("square_39_cast")]; tensor reduce_mean_119_axes_0 = const()[name = tensor("reduce_mean_119_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_119_keep_dims_0 = const()[name = tensor("reduce_mean_119_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_119 = reduce_mean(axes = reduce_mean_119_axes_0, keep_dims = reduce_mean_119_keep_dims_0, x = square_39)[name = tensor("reduce_mean_119")]; - tensor add_78_y_0 = const()[name = tensor("add_78_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_78 = add(x = reduce_mean_119, y = add_78_y_0)[name = tensor("add_78")]; - tensor sqrt_39 = sqrt(x = add_78)[name = tensor("sqrt_39")]; - tensor real_div_39 = real_div(x = sub_78, y = sqrt_39)[name = tensor("real_div_39")]; + tensor reduce_mean_119_cast = reduce_mean(axes = reduce_mean_119_axes_0, keep_dims = reduce_mean_119_keep_dims_0, x = square_39_cast)[name = tensor("reduce_mean_119_cast")]; + tensor add_78_y_0_to_fp16 = const()[name = tensor("add_78_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_78_cast = add(x = reduce_mean_119_cast, y = add_78_y_0_to_fp16)[name = tensor("add_78_cast")]; + tensor sqrt_39_cast = sqrt(x = add_78_cast)[name = tensor("sqrt_39_cast")]; + tensor real_div_39_cast = real_div(x = sub_78_cast, y = sqrt_39_cast)[name = tensor("real_div_39_cast")]; tensor reshape_157_shape_0 = const()[name = tensor("reshape_157_shape_0"), val = tensor([2, 960, 128, 128])]; - tensor reshape_157 = reshape(shape = reshape_157_shape_0, x = real_div_39)[name = tensor("reshape_157")]; - tensor add_79_gamma_0 = const()[name = tensor("add_79_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10269996672)))]; - tensor add_79_beta_0 = const()[name = tensor("add_79_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10270000576)))]; - tensor add_79_epsilon_0 = const()[name = tensor("add_79_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_79 = batch_norm(beta = add_79_beta_0, epsilon = add_79_epsilon_0, gamma = add_79_gamma_0, mean = add_73_mean_0, variance = add_73_variance_0, x = reshape_157)[name = tensor("add_79")]; - tensor input_831 = silu(x = add_79)[name = tensor("input_831")]; - tensor var_13890 = const()[name = tensor("op_13890"), val = tensor([1, 1])]; - tensor var_13892 = const()[name = tensor("op_13892"), val = tensor([1, 1])]; + tensor reshape_157_cast = reshape(shape = reshape_157_shape_0, x = real_div_39_cast)[name = tensor("reshape_157_cast")]; + tensor add_79_gamma_0_to_fp16 = const()[name = tensor("add_79_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5112696576)))]; + tensor add_79_beta_0_to_fp16 = const()[name = tensor("add_79_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5112698560)))]; + tensor add_79_epsilon_0_to_fp16 = const()[name = tensor("add_79_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_79_cast = batch_norm(beta = add_79_beta_0_to_fp16, epsilon = add_79_epsilon_0_to_fp16, gamma = add_79_gamma_0_to_fp16, mean = add_73_mean_0_to_fp16, variance = add_73_variance_0_to_fp16, x = reshape_157_cast)[name = tensor("add_79_cast")]; + tensor input_831_cast = silu(x = add_79_cast)[name = tensor("input_831_cast")]; + tensor var_13766 = const()[name = tensor("op_13766"), val = tensor([1, 1])]; + tensor var_13768 = const()[name = tensor("op_13768"), val = tensor([1, 1])]; tensor hidden_states_579_pad_type_0 = const()[name = tensor("hidden_states_579_pad_type_0"), val = tensor("custom")]; tensor hidden_states_579_pad_0 = const()[name = tensor("hidden_states_579_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_579 = conv(bias = up_blocks_2_resnets_0_conv1_bias, dilations = var_13892, groups = var_13869, pad = hidden_states_579_pad_0, pad_type = hidden_states_579_pad_type_0, strides = var_13890, weight = up_blocks_2_resnets_0_conv1_weight, x = input_831)[name = tensor("hidden_states_579")]; - tensor var_13898 = const()[name = tensor("op_13898"), val = tensor([1, 1])]; - tensor var_13900 = const()[name = tensor("op_13900"), val = tensor([1, 1])]; + tensor unet_up_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5112700544)))]; + tensor unet_up_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5118230208)))]; + tensor hidden_states_579_cast = conv(bias = unet_up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_13768, groups = var_31, pad = hidden_states_579_pad_0, pad_type = hidden_states_579_pad_type_0, strides = var_13766, weight = unet_up_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_831_cast)[name = tensor("hidden_states_579_cast")]; + tensor var_13774 = const()[name = tensor("op_13774"), val = tensor([1, 1])]; + tensor var_13776 = const()[name = tensor("op_13776"), val = tensor([1, 1])]; tensor temb_29_pad_type_0 = const()[name = tensor("temb_29_pad_type_0"), val = tensor("custom")]; tensor temb_29_pad_0 = const()[name = tensor("temb_29_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb_29 = conv(bias = up_blocks_2_resnets_0_time_emb_proj_bias, dilations = var_13900, groups = var_13869, pad = temb_29_pad_0, pad_type = temb_29_pad_type_0, strides = var_13898, weight = up_blocks_2_resnets_0_time_emb_proj_weight, x = input_21)[name = tensor("temb_29")]; - tensor input_835 = add(x = hidden_states_579, y = temb_29)[name = tensor("input_835")]; + tensor unet_up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5118230912)))]; + tensor unet_up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5119050176)))]; + tensor temb_29_cast = conv(bias = unet_up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_13776, groups = var_31, pad = temb_29_pad_0, pad_type = temb_29_pad_type_0, strides = var_13774, weight = unet_up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_29_cast")]; + tensor input_835_cast = add(x = hidden_states_579_cast, y = temb_29_cast)[name = tensor("input_835_cast")]; tensor reshape_160_shape_0 = const()[name = tensor("reshape_160_shape_0"), val = tensor([2, 32, 10, 128, 128])]; - tensor reshape_160 = reshape(shape = reshape_160_shape_0, x = input_835)[name = tensor("reshape_160")]; + tensor reshape_160_cast = reshape(shape = reshape_160_shape_0, x = input_835_cast)[name = tensor("reshape_160_cast")]; tensor reduce_mean_120_axes_0 = const()[name = tensor("reduce_mean_120_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_120_keep_dims_0 = const()[name = tensor("reduce_mean_120_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_120 = reduce_mean(axes = reduce_mean_120_axes_0, keep_dims = reduce_mean_120_keep_dims_0, x = reshape_160)[name = tensor("reduce_mean_120")]; - tensor sub_80 = sub(x = reshape_160, y = reduce_mean_120)[name = tensor("sub_80")]; - tensor square_40 = square(x = sub_80)[name = tensor("square_40")]; + tensor reduce_mean_120_cast = reduce_mean(axes = reduce_mean_120_axes_0, keep_dims = reduce_mean_120_keep_dims_0, x = reshape_160_cast)[name = tensor("reduce_mean_120_cast")]; + tensor sub_80_cast = sub(x = reshape_160_cast, y = reduce_mean_120_cast)[name = tensor("sub_80_cast")]; + tensor square_40_cast = square(x = sub_80_cast)[name = tensor("square_40_cast")]; tensor reduce_mean_122_axes_0 = const()[name = tensor("reduce_mean_122_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_122_keep_dims_0 = const()[name = tensor("reduce_mean_122_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_122 = reduce_mean(axes = reduce_mean_122_axes_0, keep_dims = reduce_mean_122_keep_dims_0, x = square_40)[name = tensor("reduce_mean_122")]; - tensor add_80_y_0 = const()[name = tensor("add_80_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_80 = add(x = reduce_mean_122, y = add_80_y_0)[name = tensor("add_80")]; - tensor sqrt_40 = sqrt(x = add_80)[name = tensor("sqrt_40")]; - tensor real_div_40 = real_div(x = sub_80, y = sqrt_40)[name = tensor("real_div_40")]; + tensor reduce_mean_122_cast = reduce_mean(axes = reduce_mean_122_axes_0, keep_dims = reduce_mean_122_keep_dims_0, x = square_40_cast)[name = tensor("reduce_mean_122_cast")]; + tensor add_80_y_0_to_fp16 = const()[name = tensor("add_80_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_80_cast = add(x = reduce_mean_122_cast, y = add_80_y_0_to_fp16)[name = tensor("add_80_cast")]; + tensor sqrt_40_cast = sqrt(x = add_80_cast)[name = tensor("sqrt_40_cast")]; + tensor real_div_40_cast = real_div(x = sub_80_cast, y = sqrt_40_cast)[name = tensor("real_div_40_cast")]; tensor reshape_161_shape_0 = const()[name = tensor("reshape_161_shape_0"), val = tensor([2, 320, 128, 128])]; - tensor reshape_161 = reshape(shape = reshape_161_shape_0, x = real_div_40)[name = tensor("reshape_161")]; - tensor add_81_gamma_0 = const()[name = tensor("add_81_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10270004480)))]; - tensor add_81_beta_0 = const()[name = tensor("add_81_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10270005824)))]; - tensor add_81_epsilon_0 = const()[name = tensor("add_81_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_81 = batch_norm(beta = add_81_beta_0, epsilon = add_81_epsilon_0, gamma = add_81_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_161)[name = tensor("add_81")]; - tensor input_839 = silu(x = add_81)[name = tensor("input_839")]; - tensor var_13910 = const()[name = tensor("op_13910"), val = tensor([1, 1])]; - tensor var_13912 = const()[name = tensor("op_13912"), val = tensor([1, 1])]; + tensor reshape_161_cast = reshape(shape = reshape_161_shape_0, x = real_div_40_cast)[name = tensor("reshape_161_cast")]; + tensor add_81_gamma_0_to_fp16 = const()[name = tensor("add_81_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5119050880)))]; + tensor add_81_beta_0_to_fp16 = const()[name = tensor("add_81_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5119051584)))]; + tensor add_81_epsilon_0_to_fp16 = const()[name = tensor("add_81_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_81_cast = batch_norm(beta = add_81_beta_0_to_fp16, epsilon = add_81_epsilon_0_to_fp16, gamma = add_81_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_161_cast)[name = tensor("add_81_cast")]; + tensor input_839_cast = silu(x = add_81_cast)[name = tensor("input_839_cast")]; + tensor var_13786 = const()[name = tensor("op_13786"), val = tensor([1, 1])]; + tensor var_13788 = const()[name = tensor("op_13788"), val = tensor([1, 1])]; tensor hidden_states_581_pad_type_0 = const()[name = tensor("hidden_states_581_pad_type_0"), val = tensor("custom")]; tensor hidden_states_581_pad_0 = const()[name = tensor("hidden_states_581_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_581 = conv(bias = up_blocks_2_resnets_0_conv2_bias, dilations = var_13912, groups = var_13869, pad = hidden_states_581_pad_0, pad_type = hidden_states_581_pad_type_0, strides = var_13910, weight = up_blocks_2_resnets_0_conv2_weight, x = input_839)[name = tensor("hidden_states_581")]; - tensor var_13917 = const()[name = tensor("op_13917"), val = tensor([1, 1])]; - tensor var_13919 = const()[name = tensor("op_13919"), val = tensor([1, 1])]; + tensor unet_up_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5119052288)))]; + tensor unet_up_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5120895552)))]; + tensor hidden_states_581_cast = conv(bias = unet_up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_13788, groups = var_31, pad = hidden_states_581_pad_0, pad_type = hidden_states_581_pad_type_0, strides = var_13786, weight = unet_up_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_839_cast)[name = tensor("hidden_states_581_cast")]; + tensor var_13793 = const()[name = tensor("op_13793"), val = tensor([1, 1])]; + tensor var_13795 = const()[name = tensor("op_13795"), val = tensor([1, 1])]; tensor x_17_pad_type_0 = const()[name = tensor("x_17_pad_type_0"), val = tensor("custom")]; tensor x_17_pad_0 = const()[name = tensor("x_17_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor x_17 = conv(bias = up_blocks_2_resnets_0_conv_shortcut_bias, dilations = var_13919, groups = var_13869, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = var_13917, weight = up_blocks_2_resnets_0_conv_shortcut_weight, x = input_827)[name = tensor("x_17")]; - tensor hidden_states_583 = add(x = x_17, y = hidden_states_581)[name = tensor("hidden_states_583")]; + tensor unet_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5120896256)))]; + tensor unet_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5121510720)))]; + tensor x_17_cast = conv(bias = unet_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_13795, groups = var_31, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = var_13793, weight = unet_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_827_cast)[name = tensor("x_17_cast")]; + tensor hidden_states_583_cast = add(x = x_17_cast, y = hidden_states_581_cast)[name = tensor("hidden_states_583_cast")]; tensor input_841_interleave_0 = const()[name = tensor("input_841_interleave_0"), val = tensor(false)]; - tensor input_841 = concat(axis = var_13869, interleave = input_841_interleave_0, values = (hidden_states_583, input_29))[name = tensor("input_841")]; + tensor input_841_cast = concat(axis = var_31, interleave = input_841_interleave_0, values = (hidden_states_583_cast, input_29_cast))[name = tensor("input_841_cast")]; tensor reshape_164_shape_0 = const()[name = tensor("reshape_164_shape_0"), val = tensor([2, 32, 20, 128, 128])]; - tensor reshape_164 = reshape(shape = reshape_164_shape_0, x = input_841)[name = tensor("reshape_164")]; + tensor reshape_164_cast = reshape(shape = reshape_164_shape_0, x = input_841_cast)[name = tensor("reshape_164_cast")]; tensor reduce_mean_123_axes_0 = const()[name = tensor("reduce_mean_123_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_123_keep_dims_0 = const()[name = tensor("reduce_mean_123_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_123 = reduce_mean(axes = reduce_mean_123_axes_0, keep_dims = reduce_mean_123_keep_dims_0, x = reshape_164)[name = tensor("reduce_mean_123")]; - tensor sub_82 = sub(x = reshape_164, y = reduce_mean_123)[name = tensor("sub_82")]; - tensor square_41 = square(x = sub_82)[name = tensor("square_41")]; + tensor reduce_mean_123_cast = reduce_mean(axes = reduce_mean_123_axes_0, keep_dims = reduce_mean_123_keep_dims_0, x = reshape_164_cast)[name = tensor("reduce_mean_123_cast")]; + tensor sub_82_cast = sub(x = reshape_164_cast, y = reduce_mean_123_cast)[name = tensor("sub_82_cast")]; + tensor square_41_cast = square(x = sub_82_cast)[name = tensor("square_41_cast")]; tensor reduce_mean_125_axes_0 = const()[name = tensor("reduce_mean_125_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_125_keep_dims_0 = const()[name = tensor("reduce_mean_125_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_125 = reduce_mean(axes = reduce_mean_125_axes_0, keep_dims = reduce_mean_125_keep_dims_0, x = square_41)[name = tensor("reduce_mean_125")]; - tensor add_82_y_0 = const()[name = tensor("add_82_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_82 = add(x = reduce_mean_125, y = add_82_y_0)[name = tensor("add_82")]; - tensor sqrt_41 = sqrt(x = add_82)[name = tensor("sqrt_41")]; - tensor real_div_41 = real_div(x = sub_82, y = sqrt_41)[name = tensor("real_div_41")]; + tensor reduce_mean_125_cast = reduce_mean(axes = reduce_mean_125_axes_0, keep_dims = reduce_mean_125_keep_dims_0, x = square_41_cast)[name = tensor("reduce_mean_125_cast")]; + tensor add_82_y_0_to_fp16 = const()[name = tensor("add_82_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_82_cast = add(x = reduce_mean_125_cast, y = add_82_y_0_to_fp16)[name = tensor("add_82_cast")]; + tensor sqrt_41_cast = sqrt(x = add_82_cast)[name = tensor("sqrt_41_cast")]; + tensor real_div_41_cast = real_div(x = sub_82_cast, y = sqrt_41_cast)[name = tensor("real_div_41_cast")]; tensor reshape_165_shape_0 = const()[name = tensor("reshape_165_shape_0"), val = tensor([2, 640, 128, 128])]; - tensor reshape_165 = reshape(shape = reshape_165_shape_0, x = real_div_41)[name = tensor("reshape_165")]; - tensor add_83_gamma_0 = const()[name = tensor("add_83_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10270007168)))]; - tensor add_83_beta_0 = const()[name = tensor("add_83_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10270009792)))]; - tensor add_83_epsilon_0 = const()[name = tensor("add_83_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_83 = batch_norm(beta = add_83_beta_0, epsilon = add_83_epsilon_0, gamma = add_83_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_165)[name = tensor("add_83")]; - tensor input_845 = silu(x = add_83)[name = tensor("input_845")]; - tensor var_13937 = const()[name = tensor("op_13937"), val = tensor([1, 1])]; - tensor var_13939 = const()[name = tensor("op_13939"), val = tensor([1, 1])]; + tensor reshape_165_cast = reshape(shape = reshape_165_shape_0, x = real_div_41_cast)[name = tensor("reshape_165_cast")]; + tensor add_83_gamma_0_to_fp16 = const()[name = tensor("add_83_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5121511424)))]; + tensor add_83_beta_0_to_fp16 = const()[name = tensor("add_83_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5121512768)))]; + tensor add_83_epsilon_0_to_fp16 = const()[name = tensor("add_83_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_83_cast = batch_norm(beta = add_83_beta_0_to_fp16, epsilon = add_83_epsilon_0_to_fp16, gamma = add_83_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_165_cast)[name = tensor("add_83_cast")]; + tensor input_845_cast = silu(x = add_83_cast)[name = tensor("input_845_cast")]; + tensor var_13813 = const()[name = tensor("op_13813"), val = tensor([1, 1])]; + tensor var_13815 = const()[name = tensor("op_13815"), val = tensor([1, 1])]; tensor hidden_states_585_pad_type_0 = const()[name = tensor("hidden_states_585_pad_type_0"), val = tensor("custom")]; tensor hidden_states_585_pad_0 = const()[name = tensor("hidden_states_585_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_585 = conv(bias = up_blocks_2_resnets_1_conv1_bias, dilations = var_13939, groups = var_13869, pad = hidden_states_585_pad_0, pad_type = hidden_states_585_pad_type_0, strides = var_13937, weight = up_blocks_2_resnets_1_conv1_weight, x = input_845)[name = tensor("hidden_states_585")]; - tensor var_13945 = const()[name = tensor("op_13945"), val = tensor([1, 1])]; - tensor var_13947 = const()[name = tensor("op_13947"), val = tensor([1, 1])]; + tensor unet_up_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5121514112)))]; + tensor unet_up_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5125200576)))]; + tensor hidden_states_585_cast = conv(bias = unet_up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_13815, groups = var_31, pad = hidden_states_585_pad_0, pad_type = hidden_states_585_pad_type_0, strides = var_13813, weight = unet_up_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_845_cast)[name = tensor("hidden_states_585_cast")]; + tensor var_13821 = const()[name = tensor("op_13821"), val = tensor([1, 1])]; + tensor var_13823 = const()[name = tensor("op_13823"), val = tensor([1, 1])]; tensor temb_31_pad_type_0 = const()[name = tensor("temb_31_pad_type_0"), val = tensor("custom")]; tensor temb_31_pad_0 = const()[name = tensor("temb_31_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb_31 = conv(bias = up_blocks_2_resnets_1_time_emb_proj_bias, dilations = var_13947, groups = var_13869, pad = temb_31_pad_0, pad_type = temb_31_pad_type_0, strides = var_13945, weight = up_blocks_2_resnets_1_time_emb_proj_weight, x = input_21)[name = tensor("temb_31")]; - tensor input_849 = add(x = hidden_states_585, y = temb_31)[name = tensor("input_849")]; + tensor unet_up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5125201280)))]; + tensor unet_up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5126020544)))]; + tensor temb_31_cast = conv(bias = unet_up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_13823, groups = var_31, pad = temb_31_pad_0, pad_type = temb_31_pad_type_0, strides = var_13821, weight = unet_up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_31_cast")]; + tensor input_849_cast = add(x = hidden_states_585_cast, y = temb_31_cast)[name = tensor("input_849_cast")]; tensor reshape_168_shape_0 = const()[name = tensor("reshape_168_shape_0"), val = tensor([2, 32, 10, 128, 128])]; - tensor reshape_168 = reshape(shape = reshape_168_shape_0, x = input_849)[name = tensor("reshape_168")]; + tensor reshape_168_cast = reshape(shape = reshape_168_shape_0, x = input_849_cast)[name = tensor("reshape_168_cast")]; tensor reduce_mean_126_axes_0 = const()[name = tensor("reduce_mean_126_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_126_keep_dims_0 = const()[name = tensor("reduce_mean_126_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_126 = reduce_mean(axes = reduce_mean_126_axes_0, keep_dims = reduce_mean_126_keep_dims_0, x = reshape_168)[name = tensor("reduce_mean_126")]; - tensor sub_84 = sub(x = reshape_168, y = reduce_mean_126)[name = tensor("sub_84")]; - tensor square_42 = square(x = sub_84)[name = tensor("square_42")]; + tensor reduce_mean_126_cast = reduce_mean(axes = reduce_mean_126_axes_0, keep_dims = reduce_mean_126_keep_dims_0, x = reshape_168_cast)[name = tensor("reduce_mean_126_cast")]; + tensor sub_84_cast = sub(x = reshape_168_cast, y = reduce_mean_126_cast)[name = tensor("sub_84_cast")]; + tensor square_42_cast = square(x = sub_84_cast)[name = tensor("square_42_cast")]; tensor reduce_mean_128_axes_0 = const()[name = tensor("reduce_mean_128_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_128_keep_dims_0 = const()[name = tensor("reduce_mean_128_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_128 = reduce_mean(axes = reduce_mean_128_axes_0, keep_dims = reduce_mean_128_keep_dims_0, x = square_42)[name = tensor("reduce_mean_128")]; - tensor add_84_y_0 = const()[name = tensor("add_84_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_84 = add(x = reduce_mean_128, y = add_84_y_0)[name = tensor("add_84")]; - tensor sqrt_42 = sqrt(x = add_84)[name = tensor("sqrt_42")]; - tensor real_div_42 = real_div(x = sub_84, y = sqrt_42)[name = tensor("real_div_42")]; + tensor reduce_mean_128_cast = reduce_mean(axes = reduce_mean_128_axes_0, keep_dims = reduce_mean_128_keep_dims_0, x = square_42_cast)[name = tensor("reduce_mean_128_cast")]; + tensor add_84_y_0_to_fp16 = const()[name = tensor("add_84_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_84_cast = add(x = reduce_mean_128_cast, y = add_84_y_0_to_fp16)[name = tensor("add_84_cast")]; + tensor sqrt_42_cast = sqrt(x = add_84_cast)[name = tensor("sqrt_42_cast")]; + tensor real_div_42_cast = real_div(x = sub_84_cast, y = sqrt_42_cast)[name = tensor("real_div_42_cast")]; tensor reshape_169_shape_0 = const()[name = tensor("reshape_169_shape_0"), val = tensor([2, 320, 128, 128])]; - tensor reshape_169 = reshape(shape = reshape_169_shape_0, x = real_div_42)[name = tensor("reshape_169")]; - tensor add_85_gamma_0 = const()[name = tensor("add_85_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10270012416)))]; - tensor add_85_beta_0 = const()[name = tensor("add_85_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10270013760)))]; - tensor add_85_epsilon_0 = const()[name = tensor("add_85_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_85 = batch_norm(beta = add_85_beta_0, epsilon = add_85_epsilon_0, gamma = add_85_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_169)[name = tensor("add_85")]; - tensor input_853 = silu(x = add_85)[name = tensor("input_853")]; - tensor var_13957 = const()[name = tensor("op_13957"), val = tensor([1, 1])]; - tensor var_13959 = const()[name = tensor("op_13959"), val = tensor([1, 1])]; + tensor reshape_169_cast = reshape(shape = reshape_169_shape_0, x = real_div_42_cast)[name = tensor("reshape_169_cast")]; + tensor add_85_gamma_0_to_fp16 = const()[name = tensor("add_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5126021248)))]; + tensor add_85_beta_0_to_fp16 = const()[name = tensor("add_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5126021952)))]; + tensor add_85_epsilon_0_to_fp16 = const()[name = tensor("add_85_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_85_cast = batch_norm(beta = add_85_beta_0_to_fp16, epsilon = add_85_epsilon_0_to_fp16, gamma = add_85_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_169_cast)[name = tensor("add_85_cast")]; + tensor input_853_cast = silu(x = add_85_cast)[name = tensor("input_853_cast")]; + tensor var_13833 = const()[name = tensor("op_13833"), val = tensor([1, 1])]; + tensor var_13835 = const()[name = tensor("op_13835"), val = tensor([1, 1])]; tensor hidden_states_587_pad_type_0 = const()[name = tensor("hidden_states_587_pad_type_0"), val = tensor("custom")]; tensor hidden_states_587_pad_0 = const()[name = tensor("hidden_states_587_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_587 = conv(bias = up_blocks_2_resnets_1_conv2_bias, dilations = var_13959, groups = var_13869, pad = hidden_states_587_pad_0, pad_type = hidden_states_587_pad_type_0, strides = var_13957, weight = up_blocks_2_resnets_1_conv2_weight, x = input_853)[name = tensor("hidden_states_587")]; - tensor var_13964 = const()[name = tensor("op_13964"), val = tensor([1, 1])]; - tensor var_13966 = const()[name = tensor("op_13966"), val = tensor([1, 1])]; + tensor unet_up_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5126022656)))]; + tensor unet_up_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5127865920)))]; + tensor hidden_states_587_cast = conv(bias = unet_up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_13835, groups = var_31, pad = hidden_states_587_pad_0, pad_type = hidden_states_587_pad_type_0, strides = var_13833, weight = unet_up_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_853_cast)[name = tensor("hidden_states_587_cast")]; + tensor var_13840 = const()[name = tensor("op_13840"), val = tensor([1, 1])]; + tensor var_13842 = const()[name = tensor("op_13842"), val = tensor([1, 1])]; tensor x_19_pad_type_0 = const()[name = tensor("x_19_pad_type_0"), val = tensor("custom")]; tensor x_19_pad_0 = const()[name = tensor("x_19_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor x_19 = conv(bias = up_blocks_2_resnets_1_conv_shortcut_bias, dilations = var_13966, groups = var_13869, pad = x_19_pad_0, pad_type = x_19_pad_type_0, strides = var_13964, weight = up_blocks_2_resnets_1_conv_shortcut_weight, x = input_841)[name = tensor("x_19")]; - tensor hidden_states_589 = add(x = x_19, y = hidden_states_587)[name = tensor("hidden_states_589")]; + tensor unet_up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5127866624)))]; + tensor unet_up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5128276288)))]; + tensor x_19_cast = conv(bias = unet_up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_13842, groups = var_31, pad = x_19_pad_0, pad_type = x_19_pad_type_0, strides = var_13840, weight = unet_up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16, x = input_841_cast)[name = tensor("x_19_cast")]; + tensor hidden_states_589_cast = add(x = x_19_cast, y = hidden_states_587_cast)[name = tensor("hidden_states_589_cast")]; tensor input_855_interleave_0 = const()[name = tensor("input_855_interleave_0"), val = tensor(false)]; - tensor input_855 = concat(axis = var_13869, interleave = input_855_interleave_0, values = (hidden_states_589, input_13))[name = tensor("input_855")]; + tensor input_855_cast = concat(axis = var_31, interleave = input_855_interleave_0, values = (hidden_states_589_cast, input_13_cast))[name = tensor("input_855_cast")]; tensor reshape_172_shape_0 = const()[name = tensor("reshape_172_shape_0"), val = tensor([2, 32, 20, 128, 128])]; - tensor reshape_172 = reshape(shape = reshape_172_shape_0, x = input_855)[name = tensor("reshape_172")]; + tensor reshape_172_cast = reshape(shape = reshape_172_shape_0, x = input_855_cast)[name = tensor("reshape_172_cast")]; tensor reduce_mean_129_axes_0 = const()[name = tensor("reduce_mean_129_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_129_keep_dims_0 = const()[name = tensor("reduce_mean_129_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_129 = reduce_mean(axes = reduce_mean_129_axes_0, keep_dims = reduce_mean_129_keep_dims_0, x = reshape_172)[name = tensor("reduce_mean_129")]; - tensor sub_86 = sub(x = reshape_172, y = reduce_mean_129)[name = tensor("sub_86")]; - tensor square_43 = square(x = sub_86)[name = tensor("square_43")]; + tensor reduce_mean_129_cast = reduce_mean(axes = reduce_mean_129_axes_0, keep_dims = reduce_mean_129_keep_dims_0, x = reshape_172_cast)[name = tensor("reduce_mean_129_cast")]; + tensor sub_86_cast = sub(x = reshape_172_cast, y = reduce_mean_129_cast)[name = tensor("sub_86_cast")]; + tensor square_43_cast = square(x = sub_86_cast)[name = tensor("square_43_cast")]; tensor reduce_mean_131_axes_0 = const()[name = tensor("reduce_mean_131_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_131_keep_dims_0 = const()[name = tensor("reduce_mean_131_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_131 = reduce_mean(axes = reduce_mean_131_axes_0, keep_dims = reduce_mean_131_keep_dims_0, x = square_43)[name = tensor("reduce_mean_131")]; - tensor add_86_y_0 = const()[name = tensor("add_86_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_86 = add(x = reduce_mean_131, y = add_86_y_0)[name = tensor("add_86")]; - tensor sqrt_43 = sqrt(x = add_86)[name = tensor("sqrt_43")]; - tensor real_div_43 = real_div(x = sub_86, y = sqrt_43)[name = tensor("real_div_43")]; + tensor reduce_mean_131_cast = reduce_mean(axes = reduce_mean_131_axes_0, keep_dims = reduce_mean_131_keep_dims_0, x = square_43_cast)[name = tensor("reduce_mean_131_cast")]; + tensor add_86_y_0_to_fp16 = const()[name = tensor("add_86_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_86_cast = add(x = reduce_mean_131_cast, y = add_86_y_0_to_fp16)[name = tensor("add_86_cast")]; + tensor sqrt_43_cast = sqrt(x = add_86_cast)[name = tensor("sqrt_43_cast")]; + tensor real_div_43_cast = real_div(x = sub_86_cast, y = sqrt_43_cast)[name = tensor("real_div_43_cast")]; tensor reshape_173_shape_0 = const()[name = tensor("reshape_173_shape_0"), val = tensor([2, 640, 128, 128])]; - tensor reshape_173 = reshape(shape = reshape_173_shape_0, x = real_div_43)[name = tensor("reshape_173")]; - tensor add_87_gamma_0 = const()[name = tensor("add_87_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10270015104)))]; - tensor add_87_beta_0 = const()[name = tensor("add_87_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10270017728)))]; - tensor add_87_epsilon_0 = const()[name = tensor("add_87_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_87 = batch_norm(beta = add_87_beta_0, epsilon = add_87_epsilon_0, gamma = add_87_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_173)[name = tensor("add_87")]; - tensor input_859 = silu(x = add_87)[name = tensor("input_859")]; - tensor var_13984 = const()[name = tensor("op_13984"), val = tensor([1, 1])]; - tensor var_13986 = const()[name = tensor("op_13986"), val = tensor([1, 1])]; + tensor reshape_173_cast = reshape(shape = reshape_173_shape_0, x = real_div_43_cast)[name = tensor("reshape_173_cast")]; + tensor add_87_gamma_0_to_fp16 = const()[name = tensor("add_87_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5128276992)))]; + tensor add_87_beta_0_to_fp16 = const()[name = tensor("add_87_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5128278336)))]; + tensor add_87_epsilon_0_to_fp16 = const()[name = tensor("add_87_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_87_cast = batch_norm(beta = add_87_beta_0_to_fp16, epsilon = add_87_epsilon_0_to_fp16, gamma = add_87_gamma_0_to_fp16, mean = add_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_173_cast)[name = tensor("add_87_cast")]; + tensor input_859_cast = silu(x = add_87_cast)[name = tensor("input_859_cast")]; + tensor var_13860 = const()[name = tensor("op_13860"), val = tensor([1, 1])]; + tensor var_13862 = const()[name = tensor("op_13862"), val = tensor([1, 1])]; tensor hidden_states_591_pad_type_0 = const()[name = tensor("hidden_states_591_pad_type_0"), val = tensor("custom")]; tensor hidden_states_591_pad_0 = const()[name = tensor("hidden_states_591_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_591 = conv(bias = up_blocks_2_resnets_2_conv1_bias, dilations = var_13986, groups = var_13869, pad = hidden_states_591_pad_0, pad_type = hidden_states_591_pad_type_0, strides = var_13984, weight = up_blocks_2_resnets_2_conv1_weight, x = input_859)[name = tensor("hidden_states_591")]; - tensor var_13992 = const()[name = tensor("op_13992"), val = tensor([1, 1])]; - tensor var_13994 = const()[name = tensor("op_13994"), val = tensor([1, 1])]; + tensor unet_up_blocks_2_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5128279680)))]; + tensor unet_up_blocks_2_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5131966144)))]; + tensor hidden_states_591_cast = conv(bias = unet_up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_13862, groups = var_31, pad = hidden_states_591_pad_0, pad_type = hidden_states_591_pad_type_0, strides = var_13860, weight = unet_up_blocks_2_resnets_2_conv1_weight_to_fp16, x = input_859_cast)[name = tensor("hidden_states_591_cast")]; + tensor var_13868 = const()[name = tensor("op_13868"), val = tensor([1, 1])]; + tensor var_13870 = const()[name = tensor("op_13870"), val = tensor([1, 1])]; tensor temb_pad_type_0 = const()[name = tensor("temb_pad_type_0"), val = tensor("custom")]; tensor temb_pad_0 = const()[name = tensor("temb_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor temb = conv(bias = up_blocks_2_resnets_2_time_emb_proj_bias, dilations = var_13994, groups = var_13869, pad = temb_pad_0, pad_type = temb_pad_type_0, strides = var_13992, weight = up_blocks_2_resnets_2_time_emb_proj_weight, x = input_21)[name = tensor("temb")]; - tensor input_863 = add(x = hidden_states_591, y = temb)[name = tensor("input_863")]; + tensor unet_up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5131966848)))]; + tensor unet_up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5132786112)))]; + tensor temb_cast = conv(bias = unet_up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_13870, groups = var_31, pad = temb_pad_0, pad_type = temb_pad_type_0, strides = var_13868, weight = unet_up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16, x = input_21_cast)[name = tensor("temb_cast")]; + tensor input_863_cast = add(x = hidden_states_591_cast, y = temb_cast)[name = tensor("input_863_cast")]; tensor reshape_176_shape_0 = const()[name = tensor("reshape_176_shape_0"), val = tensor([2, 32, 10, 128, 128])]; - tensor reshape_176 = reshape(shape = reshape_176_shape_0, x = input_863)[name = tensor("reshape_176")]; + tensor reshape_176_cast = reshape(shape = reshape_176_shape_0, x = input_863_cast)[name = tensor("reshape_176_cast")]; tensor reduce_mean_132_axes_0 = const()[name = tensor("reduce_mean_132_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_132_keep_dims_0 = const()[name = tensor("reduce_mean_132_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_132 = reduce_mean(axes = reduce_mean_132_axes_0, keep_dims = reduce_mean_132_keep_dims_0, x = reshape_176)[name = tensor("reduce_mean_132")]; - tensor sub_88 = sub(x = reshape_176, y = reduce_mean_132)[name = tensor("sub_88")]; - tensor square_44 = square(x = sub_88)[name = tensor("square_44")]; + tensor reduce_mean_132_cast = reduce_mean(axes = reduce_mean_132_axes_0, keep_dims = reduce_mean_132_keep_dims_0, x = reshape_176_cast)[name = tensor("reduce_mean_132_cast")]; + tensor sub_88_cast = sub(x = reshape_176_cast, y = reduce_mean_132_cast)[name = tensor("sub_88_cast")]; + tensor square_44_cast = square(x = sub_88_cast)[name = tensor("square_44_cast")]; tensor reduce_mean_134_axes_0 = const()[name = tensor("reduce_mean_134_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_134_keep_dims_0 = const()[name = tensor("reduce_mean_134_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_134 = reduce_mean(axes = reduce_mean_134_axes_0, keep_dims = reduce_mean_134_keep_dims_0, x = square_44)[name = tensor("reduce_mean_134")]; - tensor add_88_y_0 = const()[name = tensor("add_88_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_88 = add(x = reduce_mean_134, y = add_88_y_0)[name = tensor("add_88")]; - tensor sqrt_44 = sqrt(x = add_88)[name = tensor("sqrt_44")]; - tensor real_div_44 = real_div(x = sub_88, y = sqrt_44)[name = tensor("real_div_44")]; + tensor reduce_mean_134_cast = reduce_mean(axes = reduce_mean_134_axes_0, keep_dims = reduce_mean_134_keep_dims_0, x = square_44_cast)[name = tensor("reduce_mean_134_cast")]; + tensor add_88_y_0_to_fp16 = const()[name = tensor("add_88_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_88_cast = add(x = reduce_mean_134_cast, y = add_88_y_0_to_fp16)[name = tensor("add_88_cast")]; + tensor sqrt_44_cast = sqrt(x = add_88_cast)[name = tensor("sqrt_44_cast")]; + tensor real_div_44_cast = real_div(x = sub_88_cast, y = sqrt_44_cast)[name = tensor("real_div_44_cast")]; tensor reshape_177_shape_0 = const()[name = tensor("reshape_177_shape_0"), val = tensor([2, 320, 128, 128])]; - tensor reshape_177 = reshape(shape = reshape_177_shape_0, x = real_div_44)[name = tensor("reshape_177")]; - tensor add_89_gamma_0 = const()[name = tensor("add_89_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10270020352)))]; - tensor add_89_beta_0 = const()[name = tensor("add_89_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10270021696)))]; - tensor add_89_epsilon_0 = const()[name = tensor("add_89_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_89 = batch_norm(beta = add_89_beta_0, epsilon = add_89_epsilon_0, gamma = add_89_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_177)[name = tensor("add_89")]; - tensor input_867 = silu(x = add_89)[name = tensor("input_867")]; - tensor var_14004 = const()[name = tensor("op_14004"), val = tensor([1, 1])]; - tensor var_14006 = const()[name = tensor("op_14006"), val = tensor([1, 1])]; + tensor reshape_177_cast = reshape(shape = reshape_177_shape_0, x = real_div_44_cast)[name = tensor("reshape_177_cast")]; + tensor add_89_gamma_0_to_fp16 = const()[name = tensor("add_89_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5132786816)))]; + tensor add_89_beta_0_to_fp16 = const()[name = tensor("add_89_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5132787520)))]; + tensor add_89_epsilon_0_to_fp16 = const()[name = tensor("add_89_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_89_cast = batch_norm(beta = add_89_beta_0_to_fp16, epsilon = add_89_epsilon_0_to_fp16, gamma = add_89_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_177_cast)[name = tensor("add_89_cast")]; + tensor input_867_cast = silu(x = add_89_cast)[name = tensor("input_867_cast")]; + tensor var_13880 = const()[name = tensor("op_13880"), val = tensor([1, 1])]; + tensor var_13882 = const()[name = tensor("op_13882"), val = tensor([1, 1])]; tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("custom")]; tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states = conv(bias = up_blocks_2_resnets_2_conv2_bias, dilations = var_14006, groups = var_13869, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_14004, weight = up_blocks_2_resnets_2_conv2_weight, x = input_867)[name = tensor("hidden_states")]; - tensor var_14011 = const()[name = tensor("op_14011"), val = tensor([1, 1])]; - tensor var_14013 = const()[name = tensor("op_14013"), val = tensor([1, 1])]; + tensor unet_up_blocks_2_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5132788224)))]; + tensor unet_up_blocks_2_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5134631488)))]; + tensor hidden_states_cast = conv(bias = unet_up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_13882, groups = var_31, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_13880, weight = unet_up_blocks_2_resnets_2_conv2_weight_to_fp16, x = input_867_cast)[name = tensor("hidden_states_cast")]; + tensor var_13887 = const()[name = tensor("op_13887"), val = tensor([1, 1])]; + tensor var_13889 = const()[name = tensor("op_13889"), val = tensor([1, 1])]; tensor x_pad_type_0 = const()[name = tensor("x_pad_type_0"), val = tensor("custom")]; tensor x_pad_0 = const()[name = tensor("x_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor x = conv(bias = up_blocks_2_resnets_2_conv_shortcut_bias, dilations = var_14013, groups = var_13869, pad = x_pad_0, pad_type = x_pad_type_0, strides = var_14011, weight = up_blocks_2_resnets_2_conv_shortcut_weight, x = input_855)[name = tensor("x")]; - tensor input_869 = add(x = x, y = hidden_states)[name = tensor("input_869")]; + tensor unet_up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5134632192)))]; + tensor unet_up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("unet_up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5135041856)))]; + tensor x_cast = conv(bias = unet_up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_13889, groups = var_31, pad = x_pad_0, pad_type = x_pad_type_0, strides = var_13887, weight = unet_up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16, x = input_855_cast)[name = tensor("x_cast")]; + tensor input_869_cast = add(x = x_cast, y = hidden_states_cast)[name = tensor("input_869_cast")]; tensor reshape_180_shape_0 = const()[name = tensor("reshape_180_shape_0"), val = tensor([2, 32, 10, 128, 128])]; - tensor reshape_180 = reshape(shape = reshape_180_shape_0, x = input_869)[name = tensor("reshape_180")]; + tensor reshape_180_cast = reshape(shape = reshape_180_shape_0, x = input_869_cast)[name = tensor("reshape_180_cast")]; tensor reduce_mean_135_axes_0 = const()[name = tensor("reduce_mean_135_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_135_keep_dims_0 = const()[name = tensor("reduce_mean_135_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_135 = reduce_mean(axes = reduce_mean_135_axes_0, keep_dims = reduce_mean_135_keep_dims_0, x = reshape_180)[name = tensor("reduce_mean_135")]; - tensor sub_90 = sub(x = reshape_180, y = reduce_mean_135)[name = tensor("sub_90")]; - tensor square_45 = square(x = sub_90)[name = tensor("square_45")]; + tensor reduce_mean_135_cast = reduce_mean(axes = reduce_mean_135_axes_0, keep_dims = reduce_mean_135_keep_dims_0, x = reshape_180_cast)[name = tensor("reduce_mean_135_cast")]; + tensor sub_90_cast = sub(x = reshape_180_cast, y = reduce_mean_135_cast)[name = tensor("sub_90_cast")]; + tensor square_45_cast = square(x = sub_90_cast)[name = tensor("square_45_cast")]; tensor reduce_mean_137_axes_0 = const()[name = tensor("reduce_mean_137_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_137_keep_dims_0 = const()[name = tensor("reduce_mean_137_keep_dims_0"), val = tensor(true)]; - tensor reduce_mean_137 = reduce_mean(axes = reduce_mean_137_axes_0, keep_dims = reduce_mean_137_keep_dims_0, x = square_45)[name = tensor("reduce_mean_137")]; - tensor add_90_y_0 = const()[name = tensor("add_90_y_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_90 = add(x = reduce_mean_137, y = add_90_y_0)[name = tensor("add_90")]; - tensor sqrt_45 = sqrt(x = add_90)[name = tensor("sqrt_45")]; - tensor real_div_45 = real_div(x = sub_90, y = sqrt_45)[name = tensor("real_div_45")]; + tensor reduce_mean_137_cast = reduce_mean(axes = reduce_mean_137_axes_0, keep_dims = reduce_mean_137_keep_dims_0, x = square_45_cast)[name = tensor("reduce_mean_137_cast")]; + tensor add_90_y_0_to_fp16 = const()[name = tensor("add_90_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_90_cast = add(x = reduce_mean_137_cast, y = add_90_y_0_to_fp16)[name = tensor("add_90_cast")]; + tensor sqrt_45_cast = sqrt(x = add_90_cast)[name = tensor("sqrt_45_cast")]; + tensor real_div_45_cast = real_div(x = sub_90_cast, y = sqrt_45_cast)[name = tensor("real_div_45_cast")]; tensor reshape_181_shape_0 = const()[name = tensor("reshape_181_shape_0"), val = tensor([2, 320, 128, 128])]; - tensor reshape_181 = reshape(shape = reshape_181_shape_0, x = real_div_45)[name = tensor("reshape_181")]; - tensor add_91_gamma_0 = const()[name = tensor("add_91_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10270023040)))]; - tensor add_91_beta_0 = const()[name = tensor("add_91_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10270024384)))]; - tensor add_91_epsilon_0 = const()[name = tensor("add_91_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; - tensor add_91 = batch_norm(beta = add_91_beta_0, epsilon = add_91_epsilon_0, gamma = add_91_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_181)[name = tensor("add_91")]; - tensor input = silu(x = add_91)[name = tensor("input")]; - tensor var_14027 = const()[name = tensor("op_14027"), val = tensor(1)]; - tensor var_14030 = const()[name = tensor("op_14030"), val = tensor([1, 1])]; - tensor var_14032 = const()[name = tensor("op_14032"), val = tensor([1, 1])]; - tensor var_14034_pad_type_0 = const()[name = tensor("op_14034_pad_type_0"), val = tensor("custom")]; - tensor var_14034_pad_0 = const()[name = tensor("op_14034_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor noise_pred = conv(bias = conv_out_bias, dilations = var_14032, groups = var_14027, pad = var_14034_pad_0, pad_type = var_14034_pad_type_0, strides = var_14030, weight = conv_out_weight, x = input)[name = tensor("op_14034")]; + tensor reshape_181_cast = reshape(shape = reshape_181_shape_0, x = real_div_45_cast)[name = tensor("reshape_181_cast")]; + tensor add_91_gamma_0_to_fp16 = const()[name = tensor("add_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5135042560)))]; + tensor add_91_beta_0_to_fp16 = const()[name = tensor("add_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5135043264)))]; + tensor add_91_epsilon_0_to_fp16 = const()[name = tensor("add_91_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_91_cast = batch_norm(beta = add_91_beta_0_to_fp16, epsilon = add_91_epsilon_0_to_fp16, gamma = add_91_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_181_cast)[name = tensor("add_91_cast")]; + tensor input_cast = silu(x = add_91_cast)[name = tensor("input_cast")]; + tensor var_13899 = const()[name = tensor("op_13899"), val = tensor([1, 1])]; + tensor var_13901 = const()[name = tensor("op_13901"), val = tensor([1, 1])]; + tensor var_13903_pad_type_0 = const()[name = tensor("op_13903_pad_type_0"), val = tensor("custom")]; + tensor var_13903_pad_0 = const()[name = tensor("op_13903_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor unet_conv_out_weight_to_fp16 = const()[name = tensor("unet_conv_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5135043968)))]; + tensor unet_conv_out_bias_to_fp16 = const()[name = tensor("unet_conv_out_bias_to_fp16"), val = tensor([0x1.6e8p-9, -0x1.6ccp-10, 0x1.ff8p-10, -0x1.9dp-9])]; + tensor var_13903_cast = conv(bias = unet_conv_out_bias_to_fp16, dilations = var_13901, groups = var_31, pad = var_13903_pad_0, pad_type = var_13903_pad_type_0, strides = var_13899, weight = unet_conv_out_weight_to_fp16, x = input_cast)[name = tensor("op_13903_cast")]; + tensor var_13903_cast_to_fp32_dtype_0 = const()[name = tensor("op_13903_cast_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor noise_pred = cast(dtype = var_13903_cast_to_fp32_dtype_0, x = var_13903_cast)[name = tensor("cast_1013")]; } -> (noise_pred); } \ No newline at end of file