import torch from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, DownSampler from .tiler import TileWorker class ControlNetConditioningLayer(torch.nn.Module): def __init__(self, channels = (3, 16, 32, 96, 256, 320)): super().__init__() self.blocks = torch.nn.ModuleList([]) self.blocks.append(torch.nn.Conv2d(channels[0], channels[1], kernel_size=3, padding=1)) self.blocks.append(torch.nn.SiLU()) for i in range(1, len(channels) - 2): self.blocks.append(torch.nn.Conv2d(channels[i], channels[i], kernel_size=3, padding=1)) self.blocks.append(torch.nn.SiLU()) self.blocks.append(torch.nn.Conv2d(channels[i], channels[i+1], kernel_size=3, padding=1, stride=2)) self.blocks.append(torch.nn.SiLU()) self.blocks.append(torch.nn.Conv2d(channels[-2], channels[-1], kernel_size=3, padding=1)) def forward(self, conditioning): for block in self.blocks: conditioning = block(conditioning) return conditioning class SDControlNet(torch.nn.Module): def __init__(self, global_pool=False): super().__init__() self.time_proj = Timesteps(320) self.time_embedding = torch.nn.Sequential( torch.nn.Linear(320, 1280), torch.nn.SiLU(), torch.nn.Linear(1280, 1280) ) self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) self.controlnet_conv_in = ControlNetConditioningLayer(channels=(3, 16, 32, 96, 256, 320)) self.blocks = torch.nn.ModuleList([ # CrossAttnDownBlock2D ResnetBlock(320, 320, 1280), AttentionBlock(8, 40, 320, 1, 768), PushBlock(), ResnetBlock(320, 320, 1280), AttentionBlock(8, 40, 320, 1, 768), PushBlock(), DownSampler(320), PushBlock(), # CrossAttnDownBlock2D ResnetBlock(320, 640, 1280), AttentionBlock(8, 80, 640, 1, 768), PushBlock(), ResnetBlock(640, 640, 1280), AttentionBlock(8, 80, 640, 1, 768), PushBlock(), DownSampler(640), PushBlock(), # CrossAttnDownBlock2D ResnetBlock(640, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768), PushBlock(), ResnetBlock(1280, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768), PushBlock(), DownSampler(1280), PushBlock(), # DownBlock2D ResnetBlock(1280, 1280, 1280), PushBlock(), ResnetBlock(1280, 1280, 1280), PushBlock(), # UNetMidBlock2DCrossAttn ResnetBlock(1280, 1280, 1280), AttentionBlock(8, 160, 1280, 1, 768), ResnetBlock(1280, 1280, 1280), PushBlock() ]) self.controlnet_blocks = torch.nn.ModuleList([ torch.nn.Conv2d(320, 320, kernel_size=(1, 1)), torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(320, 320, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(640, 640, kernel_size=(1, 1)), torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(640, 640, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1)), torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), torch.nn.Conv2d(1280, 1280, kernel_size=(1, 1), bias=False), ]) self.global_pool = global_pool def forward( self, sample, timestep, encoder_hidden_states, conditioning, tiled=False, tile_size=64, tile_stride=32, **kwargs ): # 1. time time_emb = self.time_proj(timestep).to(sample.dtype) time_emb = self.time_embedding(time_emb) time_emb = time_emb.repeat(sample.shape[0], 1) # 2. pre-process height, width = sample.shape[2], sample.shape[3] hidden_states = self.conv_in(sample) + self.controlnet_conv_in(conditioning) text_emb = encoder_hidden_states res_stack = [hidden_states] # 3. blocks for i, block in enumerate(self.blocks): if tiled and not isinstance(block, PushBlock): _, _, inter_height, _ = hidden_states.shape resize_scale = inter_height / height hidden_states = TileWorker().tiled_forward( lambda x: block(x, time_emb, text_emb, res_stack)[0], hidden_states, int(tile_size * resize_scale), int(tile_stride * resize_scale), tile_device=hidden_states.device, tile_dtype=hidden_states.dtype ) else: hidden_states, _, _, _ = block(hidden_states, time_emb, text_emb, res_stack) # 4. ControlNet blocks controlnet_res_stack = [block(res) for block, res in zip(self.controlnet_blocks, res_stack)] # pool if self.global_pool: controlnet_res_stack = [res.mean(dim=(2, 3), keepdim=True) for res in controlnet_res_stack] return controlnet_res_stack @staticmethod def state_dict_converter(): return SDControlNetStateDictConverter() class SDControlNetStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): # architecture block_types = [ 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'UpSampler', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock' ] # controlnet_rename_dict controlnet_rename_dict = { "controlnet_cond_embedding.conv_in.weight": "controlnet_conv_in.blocks.0.weight", "controlnet_cond_embedding.conv_in.bias": "controlnet_conv_in.blocks.0.bias", "controlnet_cond_embedding.blocks.0.weight": "controlnet_conv_in.blocks.2.weight", "controlnet_cond_embedding.blocks.0.bias": "controlnet_conv_in.blocks.2.bias", "controlnet_cond_embedding.blocks.1.weight": "controlnet_conv_in.blocks.4.weight", "controlnet_cond_embedding.blocks.1.bias": "controlnet_conv_in.blocks.4.bias", "controlnet_cond_embedding.blocks.2.weight": "controlnet_conv_in.blocks.6.weight", "controlnet_cond_embedding.blocks.2.bias": "controlnet_conv_in.blocks.6.bias", "controlnet_cond_embedding.blocks.3.weight": "controlnet_conv_in.blocks.8.weight", "controlnet_cond_embedding.blocks.3.bias": "controlnet_conv_in.blocks.8.bias", "controlnet_cond_embedding.blocks.4.weight": "controlnet_conv_in.blocks.10.weight", "controlnet_cond_embedding.blocks.4.bias": "controlnet_conv_in.blocks.10.bias", "controlnet_cond_embedding.blocks.5.weight": "controlnet_conv_in.blocks.12.weight", "controlnet_cond_embedding.blocks.5.bias": "controlnet_conv_in.blocks.12.bias", "controlnet_cond_embedding.conv_out.weight": "controlnet_conv_in.blocks.14.weight", "controlnet_cond_embedding.conv_out.bias": "controlnet_conv_in.blocks.14.bias", } # Rename each parameter name_list = sorted([name for name in state_dict]) rename_dict = {} block_id = {"ResnetBlock": -1, "AttentionBlock": -1, "DownSampler": -1, "UpSampler": -1} last_block_type_with_id = {"ResnetBlock": "", "AttentionBlock": "", "DownSampler": "", "UpSampler": ""} for name in name_list: names = name.split(".") if names[0] in ["conv_in", "conv_norm_out", "conv_out"]: pass elif name in controlnet_rename_dict: names = controlnet_rename_dict[name].split(".") elif names[0] == "controlnet_down_blocks": names[0] = "controlnet_blocks" elif names[0] == "controlnet_mid_block": names = ["controlnet_blocks", "12", names[-1]] elif names[0] in ["time_embedding", "add_embedding"]: if names[0] == "add_embedding": names[0] = "add_time_embedding" names[1] = {"linear_1": "0", "linear_2": "2"}[names[1]] elif names[0] in ["down_blocks", "mid_block", "up_blocks"]: if names[0] == "mid_block": names.insert(1, "0") block_type = {"resnets": "ResnetBlock", "attentions": "AttentionBlock", "downsamplers": "DownSampler", "upsamplers": "UpSampler"}[names[2]] block_type_with_id = ".".join(names[:4]) if block_type_with_id != last_block_type_with_id[block_type]: block_id[block_type] += 1 last_block_type_with_id[block_type] = block_type_with_id while block_id[block_type] < len(block_types) and block_types[block_id[block_type]] != block_type: block_id[block_type] += 1 block_type_with_id = ".".join(names[:4]) names = ["blocks", str(block_id[block_type])] + names[4:] if "ff" in names: ff_index = names.index("ff") component = ".".join(names[ff_index:ff_index+3]) component = {"ff.net.0": "act_fn", "ff.net.2": "ff"}[component] names = names[:ff_index] + [component] + names[ff_index+3:] if "to_out" in names: names.pop(names.index("to_out") + 1) else: raise ValueError(f"Unknown parameters: {name}") rename_dict[name] = ".".join(names) # Convert state_dict state_dict_ = {} for name, param in state_dict.items(): if ".proj_in." in name or ".proj_out." in name: param = param.squeeze() if rename_dict[name] in [ "controlnet_blocks.1.bias", "controlnet_blocks.2.bias", "controlnet_blocks.3.bias", "controlnet_blocks.5.bias", "controlnet_blocks.6.bias", "controlnet_blocks.8.bias", "controlnet_blocks.9.bias", "controlnet_blocks.10.bias", "controlnet_blocks.11.bias", "controlnet_blocks.12.bias" ]: continue state_dict_[rename_dict[name]] = param return state_dict_ def from_civitai(self, state_dict): if "mid_block.resnets.1.time_emb_proj.weight" in state_dict: # For controlnets in diffusers format return self.from_diffusers(state_dict) rename_dict = { "control_model.time_embed.0.weight": "time_embedding.0.weight", "control_model.time_embed.0.bias": "time_embedding.0.bias", "control_model.time_embed.2.weight": "time_embedding.2.weight", "control_model.time_embed.2.bias": "time_embedding.2.bias", "control_model.input_blocks.0.0.weight": "conv_in.weight", "control_model.input_blocks.0.0.bias": "conv_in.bias", "control_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight", "control_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias", "control_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight", "control_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias", "control_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight", "control_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias", "control_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight", "control_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias", "control_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight", "control_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias", "control_model.input_blocks.1.1.norm.weight": "blocks.1.norm.weight", "control_model.input_blocks.1.1.norm.bias": "blocks.1.norm.bias", "control_model.input_blocks.1.1.proj_in.weight": "blocks.1.proj_in.weight", "control_model.input_blocks.1.1.proj_in.bias": "blocks.1.proj_in.bias", "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "blocks.1.transformer_blocks.0.attn1.to_q.weight", "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "blocks.1.transformer_blocks.0.attn1.to_k.weight", "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": "blocks.1.transformer_blocks.0.attn1.to_v.weight", "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.1.transformer_blocks.0.attn1.to_out.weight", "control_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.1.transformer_blocks.0.attn1.to_out.bias", "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.1.transformer_blocks.0.act_fn.proj.weight", "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.1.transformer_blocks.0.act_fn.proj.bias", "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.weight": "blocks.1.transformer_blocks.0.ff.weight", "control_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias": "blocks.1.transformer_blocks.0.ff.bias", "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_q.weight": "blocks.1.transformer_blocks.0.attn2.to_q.weight", "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight": "blocks.1.transformer_blocks.0.attn2.to_k.weight", "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_v.weight": "blocks.1.transformer_blocks.0.attn2.to_v.weight", "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.1.transformer_blocks.0.attn2.to_out.weight", "control_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.1.transformer_blocks.0.attn2.to_out.bias", "control_model.input_blocks.1.1.transformer_blocks.0.norm1.weight": "blocks.1.transformer_blocks.0.norm1.weight", "control_model.input_blocks.1.1.transformer_blocks.0.norm1.bias": "blocks.1.transformer_blocks.0.norm1.bias", "control_model.input_blocks.1.1.transformer_blocks.0.norm2.weight": "blocks.1.transformer_blocks.0.norm2.weight", "control_model.input_blocks.1.1.transformer_blocks.0.norm2.bias": "blocks.1.transformer_blocks.0.norm2.bias", "control_model.input_blocks.1.1.transformer_blocks.0.norm3.weight": "blocks.1.transformer_blocks.0.norm3.weight", "control_model.input_blocks.1.1.transformer_blocks.0.norm3.bias": "blocks.1.transformer_blocks.0.norm3.bias", "control_model.input_blocks.1.1.proj_out.weight": "blocks.1.proj_out.weight", "control_model.input_blocks.1.1.proj_out.bias": "blocks.1.proj_out.bias", "control_model.input_blocks.2.0.in_layers.0.weight": "blocks.3.norm1.weight", "control_model.input_blocks.2.0.in_layers.0.bias": "blocks.3.norm1.bias", "control_model.input_blocks.2.0.in_layers.2.weight": "blocks.3.conv1.weight", "control_model.input_blocks.2.0.in_layers.2.bias": "blocks.3.conv1.bias", "control_model.input_blocks.2.0.emb_layers.1.weight": "blocks.3.time_emb_proj.weight", "control_model.input_blocks.2.0.emb_layers.1.bias": "blocks.3.time_emb_proj.bias", "control_model.input_blocks.2.0.out_layers.0.weight": "blocks.3.norm2.weight", "control_model.input_blocks.2.0.out_layers.0.bias": "blocks.3.norm2.bias", "control_model.input_blocks.2.0.out_layers.3.weight": "blocks.3.conv2.weight", "control_model.input_blocks.2.0.out_layers.3.bias": "blocks.3.conv2.bias", "control_model.input_blocks.2.1.norm.weight": "blocks.4.norm.weight", "control_model.input_blocks.2.1.norm.bias": "blocks.4.norm.bias", "control_model.input_blocks.2.1.proj_in.weight": "blocks.4.proj_in.weight", "control_model.input_blocks.2.1.proj_in.bias": "blocks.4.proj_in.bias", "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_q.weight": "blocks.4.transformer_blocks.0.attn1.to_q.weight", "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_k.weight": "blocks.4.transformer_blocks.0.attn1.to_k.weight", "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_v.weight": "blocks.4.transformer_blocks.0.attn1.to_v.weight", "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.4.transformer_blocks.0.attn1.to_out.weight", "control_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.4.transformer_blocks.0.attn1.to_out.bias", "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.4.transformer_blocks.0.act_fn.proj.weight", "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj.bias": "blocks.4.transformer_blocks.0.act_fn.proj.bias", "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.2.weight": "blocks.4.transformer_blocks.0.ff.weight", "control_model.input_blocks.2.1.transformer_blocks.0.ff.net.2.bias": "blocks.4.transformer_blocks.0.ff.bias", "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_q.weight": "blocks.4.transformer_blocks.0.attn2.to_q.weight", "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight": "blocks.4.transformer_blocks.0.attn2.to_k.weight", "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_v.weight": "blocks.4.transformer_blocks.0.attn2.to_v.weight", "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.4.transformer_blocks.0.attn2.to_out.weight", "control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_out.0.bias": "blocks.4.transformer_blocks.0.attn2.to_out.bias", "control_model.input_blocks.2.1.transformer_blocks.0.norm1.weight": "blocks.4.transformer_blocks.0.norm1.weight", "control_model.input_blocks.2.1.transformer_blocks.0.norm1.bias": "blocks.4.transformer_blocks.0.norm1.bias", "control_model.input_blocks.2.1.transformer_blocks.0.norm2.weight": "blocks.4.transformer_blocks.0.norm2.weight", "control_model.input_blocks.2.1.transformer_blocks.0.norm2.bias": "blocks.4.transformer_blocks.0.norm2.bias", "control_model.input_blocks.2.1.transformer_blocks.0.norm3.weight": "blocks.4.transformer_blocks.0.norm3.weight", "control_model.input_blocks.2.1.transformer_blocks.0.norm3.bias": "blocks.4.transformer_blocks.0.norm3.bias", "control_model.input_blocks.2.1.proj_out.weight": "blocks.4.proj_out.weight", "control_model.input_blocks.2.1.proj_out.bias": "blocks.4.proj_out.bias", "control_model.input_blocks.3.0.op.weight": "blocks.6.conv.weight", "control_model.input_blocks.3.0.op.bias": "blocks.6.conv.bias", "control_model.input_blocks.4.0.in_layers.0.weight": "blocks.8.norm1.weight", "control_model.input_blocks.4.0.in_layers.0.bias": "blocks.8.norm1.bias", "control_model.input_blocks.4.0.in_layers.2.weight": "blocks.8.conv1.weight", "control_model.input_blocks.4.0.in_layers.2.bias": "blocks.8.conv1.bias", "control_model.input_blocks.4.0.emb_layers.1.weight": "blocks.8.time_emb_proj.weight", "control_model.input_blocks.4.0.emb_layers.1.bias": "blocks.8.time_emb_proj.bias", "control_model.input_blocks.4.0.out_layers.0.weight": "blocks.8.norm2.weight", "control_model.input_blocks.4.0.out_layers.0.bias": "blocks.8.norm2.bias", "control_model.input_blocks.4.0.out_layers.3.weight": "blocks.8.conv2.weight", "control_model.input_blocks.4.0.out_layers.3.bias": "blocks.8.conv2.bias", "control_model.input_blocks.4.0.skip_connection.weight": "blocks.8.conv_shortcut.weight", "control_model.input_blocks.4.0.skip_connection.bias": "blocks.8.conv_shortcut.bias", "control_model.input_blocks.4.1.norm.weight": "blocks.9.norm.weight", "control_model.input_blocks.4.1.norm.bias": "blocks.9.norm.bias", "control_model.input_blocks.4.1.proj_in.weight": "blocks.9.proj_in.weight", "control_model.input_blocks.4.1.proj_in.bias": "blocks.9.proj_in.bias", "control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_q.weight": "blocks.9.transformer_blocks.0.attn1.to_q.weight", "control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_k.weight": "blocks.9.transformer_blocks.0.attn1.to_k.weight", "control_model.input_blocks.4.1.transformer_blocks.0.attn1.to_v.weight": 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"control_model.middle_block_out.0.weight": "controlnet_blocks.12.weight", "control_model.middle_block_out.0.bias": "controlnet_blocks.7.bias", } state_dict_ = {} for name in state_dict: if name in rename_dict: param = state_dict[name] if ".proj_in." in name or ".proj_out." in name: param = param.squeeze() state_dict_[rename_dict[name]] = param return state_dict_