import torch from .sd3_dit import TimestepEmbeddings, AdaLayerNorm from einops import rearrange from .tiler import TileWorker class RoPEEmbedding(torch.nn.Module): def __init__(self, dim, theta, axes_dim): super().__init__() self.dim = dim self.theta = theta self.axes_dim = axes_dim def rope(self, pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor: assert dim % 2 == 0, "The dimension must be even." scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim omega = 1.0 / (theta**scale) batch_size, seq_length = pos.shape out = torch.einsum("...n,d->...nd", pos, omega) cos_out = torch.cos(out) sin_out = torch.sin(out) stacked_out = torch.stack([cos_out, -sin_out, sin_out, cos_out], dim=-1) out = stacked_out.view(batch_size, -1, dim // 2, 2, 2) return out.float() def forward(self, ids): n_axes = ids.shape[-1] emb = torch.cat([self.rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)], dim=-3) return emb.unsqueeze(1) class RMSNorm(torch.nn.Module): def __init__(self, dim, eps): super().__init__() self.weight = torch.nn.Parameter(torch.ones((dim,))) self.eps = eps def forward(self, hidden_states): input_dtype = hidden_states.dtype variance = hidden_states.to(torch.float32).square().mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.eps) hidden_states = hidden_states.to(input_dtype) * self.weight return hidden_states class FluxJointAttention(torch.nn.Module): def __init__(self, dim_a, dim_b, num_heads, head_dim, only_out_a=False): super().__init__() self.num_heads = num_heads self.head_dim = head_dim self.only_out_a = only_out_a self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3) self.b_to_qkv = torch.nn.Linear(dim_b, dim_b * 3) self.norm_q_a = RMSNorm(head_dim, eps=1e-6) self.norm_k_a = RMSNorm(head_dim, eps=1e-6) self.norm_q_b = RMSNorm(head_dim, eps=1e-6) self.norm_k_b = RMSNorm(head_dim, eps=1e-6) self.a_to_out = torch.nn.Linear(dim_a, dim_a) if not only_out_a: self.b_to_out = torch.nn.Linear(dim_b, dim_b) def apply_rope(self, xq, xk, freqs_cis): xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) def forward(self, hidden_states_a, hidden_states_b, image_rotary_emb): batch_size = hidden_states_a.shape[0] # Part A qkv_a = self.a_to_qkv(hidden_states_a) qkv_a = qkv_a.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) q_a, k_a, v_a = qkv_a.chunk(3, dim=1) q_a, k_a = self.norm_q_a(q_a), self.norm_k_a(k_a) # Part B qkv_b = self.b_to_qkv(hidden_states_b) qkv_b = qkv_b.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) q_b, k_b, v_b = qkv_b.chunk(3, dim=1) q_b, k_b = self.norm_q_b(q_b), self.norm_k_b(k_b) q = torch.concat([q_b, q_a], dim=2) k = torch.concat([k_b, k_a], dim=2) v = torch.concat([v_b, v_a], dim=2) q, k = self.apply_rope(q, k, image_rotary_emb) hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v) hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) hidden_states = hidden_states.to(q.dtype) hidden_states_b, hidden_states_a = hidden_states[:, :hidden_states_b.shape[1]], hidden_states[:, hidden_states_b.shape[1]:] hidden_states_a = self.a_to_out(hidden_states_a) if self.only_out_a: return hidden_states_a else: hidden_states_b = self.b_to_out(hidden_states_b) return hidden_states_a, hidden_states_b class FluxJointTransformerBlock(torch.nn.Module): def __init__(self, dim, num_attention_heads): super().__init__() self.norm1_a = AdaLayerNorm(dim) self.norm1_b = AdaLayerNorm(dim) self.attn = FluxJointAttention(dim, dim, num_attention_heads, dim // num_attention_heads) self.norm2_a = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) self.ff_a = torch.nn.Sequential( torch.nn.Linear(dim, dim*4), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(dim*4, dim) ) self.norm2_b = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) self.ff_b = torch.nn.Sequential( torch.nn.Linear(dim, dim*4), torch.nn.GELU(approximate="tanh"), torch.nn.Linear(dim*4, dim) ) def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb): norm_hidden_states_a, gate_msa_a, shift_mlp_a, scale_mlp_a, gate_mlp_a = self.norm1_a(hidden_states_a, emb=temb) norm_hidden_states_b, gate_msa_b, shift_mlp_b, scale_mlp_b, gate_mlp_b = self.norm1_b(hidden_states_b, emb=temb) # Attention attn_output_a, attn_output_b = self.attn(norm_hidden_states_a, norm_hidden_states_b, image_rotary_emb) # Part A hidden_states_a = hidden_states_a + gate_msa_a * attn_output_a norm_hidden_states_a = self.norm2_a(hidden_states_a) * (1 + scale_mlp_a) + shift_mlp_a hidden_states_a = hidden_states_a + gate_mlp_a * self.ff_a(norm_hidden_states_a) # Part B hidden_states_b = hidden_states_b + gate_msa_b * attn_output_b norm_hidden_states_b = self.norm2_b(hidden_states_b) * (1 + scale_mlp_b) + shift_mlp_b hidden_states_b = hidden_states_b + gate_mlp_b * self.ff_b(norm_hidden_states_b) return hidden_states_a, hidden_states_b class FluxSingleAttention(torch.nn.Module): def __init__(self, dim_a, dim_b, num_heads, head_dim): super().__init__() self.num_heads = num_heads self.head_dim = head_dim self.a_to_qkv = torch.nn.Linear(dim_a, dim_a * 3) self.norm_q_a = RMSNorm(head_dim, eps=1e-6) self.norm_k_a = RMSNorm(head_dim, eps=1e-6) def apply_rope(self, xq, xk, freqs_cis): xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) def forward(self, hidden_states, image_rotary_emb): batch_size = hidden_states.shape[0] qkv_a = self.a_to_qkv(hidden_states) qkv_a = qkv_a.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) q_a, k_a, v = qkv_a.chunk(3, dim=1) q_a, k_a = self.norm_q_a(q_a), self.norm_k_a(k_a) q, k = self.apply_rope(q_a, k_a, image_rotary_emb) hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v) hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) hidden_states = hidden_states.to(q.dtype) return hidden_states class AdaLayerNormSingle(torch.nn.Module): def __init__(self, dim): super().__init__() self.silu = torch.nn.SiLU() self.linear = torch.nn.Linear(dim, 3 * dim, bias=True) self.norm = torch.nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) def forward(self, x, emb): emb = self.linear(self.silu(emb)) shift_msa, scale_msa, gate_msa = emb.chunk(3, dim=1) x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None] return x, gate_msa class FluxSingleTransformerBlock(torch.nn.Module): def __init__(self, dim, num_attention_heads): super().__init__() self.num_heads = num_attention_heads self.head_dim = dim // num_attention_heads self.dim = dim self.norm = AdaLayerNormSingle(dim) # self.proj_in = torch.nn.Sequential(torch.nn.Linear(dim, dim * 4), torch.nn.GELU(approximate="tanh")) # self.attn = FluxSingleAttention(dim, dim, num_attention_heads, dim // num_attention_heads) self.linear = torch.nn.Linear(dim, dim * (3 + 4)) self.norm_q_a = RMSNorm(self.head_dim, eps=1e-6) self.norm_k_a = RMSNorm(self.head_dim, eps=1e-6) self.proj_out = torch.nn.Linear(dim * 5, dim) def apply_rope(self, xq, xk, freqs_cis): xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2) xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2) xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1] xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1] return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk) def process_attention(self, hidden_states, image_rotary_emb): batch_size = hidden_states.shape[0] qkv = hidden_states.view(batch_size, -1, 3 * self.num_heads, self.head_dim).transpose(1, 2) q, k, v = qkv.chunk(3, dim=1) q, k = self.norm_q_a(q), self.norm_k_a(k) q, k = self.apply_rope(q, k, image_rotary_emb) hidden_states = torch.nn.functional.scaled_dot_product_attention(q, k, v) hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_dim) hidden_states = hidden_states.to(q.dtype) return hidden_states def forward(self, hidden_states_a, hidden_states_b, temb, image_rotary_emb): residual = hidden_states_a norm_hidden_states, gate = self.norm(hidden_states_a, emb=temb) hidden_states_a = self.linear(norm_hidden_states) attn_output, mlp_hidden_states = hidden_states_a[:, :, :self.dim * 3], hidden_states_a[:, :, self.dim * 3:] attn_output = self.process_attention(attn_output, image_rotary_emb) mlp_hidden_states = torch.nn.functional.gelu(mlp_hidden_states, approximate="tanh") hidden_states_a = torch.cat([attn_output, mlp_hidden_states], dim=2) hidden_states_a = gate.unsqueeze(1) * self.proj_out(hidden_states_a) hidden_states_a = residual + hidden_states_a return hidden_states_a, hidden_states_b class AdaLayerNormContinuous(torch.nn.Module): def __init__(self, dim): super().__init__() self.silu = torch.nn.SiLU() self.linear = torch.nn.Linear(dim, dim * 2, bias=True) self.norm = torch.nn.LayerNorm(dim, eps=1e-6, elementwise_affine=False) def forward(self, x, conditioning): emb = self.linear(self.silu(conditioning)) scale, shift = torch.chunk(emb, 2, dim=1) x = self.norm(x) * (1 + scale)[:, None] + shift[:, None] return x class FluxDiT(torch.nn.Module): def __init__(self): super().__init__() self.pos_embedder = RoPEEmbedding(3072, 10000, [16, 56, 56]) self.time_embedder = TimestepEmbeddings(256, 3072) self.guidance_embedder = TimestepEmbeddings(256, 3072) self.pooled_text_embedder = torch.nn.Sequential(torch.nn.Linear(768, 3072), torch.nn.SiLU(), torch.nn.Linear(3072, 3072)) self.context_embedder = torch.nn.Linear(4096, 3072) self.x_embedder = torch.nn.Linear(64, 3072) self.blocks = torch.nn.ModuleList([FluxJointTransformerBlock(3072, 24) for _ in range(19)]) self.single_blocks = torch.nn.ModuleList([FluxSingleTransformerBlock(3072, 24) for _ in range(38)]) self.norm_out = AdaLayerNormContinuous(3072) self.proj_out = torch.nn.Linear(3072, 64) def patchify(self, hidden_states): hidden_states = rearrange(hidden_states, "B C (H P) (W Q) -> B (H W) (C P Q)", P=2, Q=2) return hidden_states def unpatchify(self, hidden_states, height, width): hidden_states = rearrange(hidden_states, "B (H W) (C P Q) -> B C (H P) (W Q)", P=2, Q=2, H=height//2, W=width//2) return hidden_states def prepare_image_ids(self, latents): batch_size, _, height, width = latents.shape latent_image_ids = torch.zeros(height // 2, width // 2, 3) latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None] latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :] latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1) latent_image_ids = latent_image_ids.reshape( batch_size, latent_image_id_height * latent_image_id_width, latent_image_id_channels ) latent_image_ids = latent_image_ids.to(device=latents.device, dtype=latents.dtype) return latent_image_ids def tiled_forward( self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, tile_size=128, tile_stride=64, **kwargs ): # Due to the global positional embedding, we cannot implement layer-wise tiled forward. hidden_states = TileWorker().tiled_forward( lambda x: self.forward(x, timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None), hidden_states, tile_size, tile_stride, tile_device=hidden_states.device, tile_dtype=hidden_states.dtype ) return hidden_states def forward( self, hidden_states, timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, image_ids=None, tiled=False, tile_size=128, tile_stride=64, **kwargs ): if tiled: return self.tiled_forward( hidden_states, timestep, prompt_emb, pooled_prompt_emb, guidance, text_ids, tile_size=tile_size, tile_stride=tile_stride, **kwargs ) if image_ids is None: image_ids = self.prepare_image_ids(hidden_states) conditioning = self.time_embedder(timestep, hidden_states.dtype)\ + self.guidance_embedder(guidance, hidden_states.dtype)\ + self.pooled_text_embedder(pooled_prompt_emb) prompt_emb = self.context_embedder(prompt_emb) image_rotary_emb = self.pos_embedder(torch.cat((text_ids, image_ids), dim=1)) height, width = hidden_states.shape[-2:] hidden_states = self.patchify(hidden_states) hidden_states = self.x_embedder(hidden_states) for block in self.blocks: hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb) hidden_states = torch.cat([prompt_emb, hidden_states], dim=1) for block in self.single_blocks: hidden_states, prompt_emb = block(hidden_states, prompt_emb, conditioning, image_rotary_emb) hidden_states = hidden_states[:, prompt_emb.shape[1]:] hidden_states = self.norm_out(hidden_states, conditioning) hidden_states = self.proj_out(hidden_states) hidden_states = self.unpatchify(hidden_states, height, width) return hidden_states @staticmethod def state_dict_converter(): return FluxDiTStateDictConverter() class FluxDiTStateDictConverter: def __init__(self): pass def from_diffusers(self, state_dict): rename_dict = { "context_embedder": "context_embedder", "x_embedder": "x_embedder", "time_text_embed.timestep_embedder.linear_1": "time_embedder.timestep_embedder.0", "time_text_embed.timestep_embedder.linear_2": "time_embedder.timestep_embedder.2", "time_text_embed.guidance_embedder.linear_1": "guidance_embedder.timestep_embedder.0", "time_text_embed.guidance_embedder.linear_2": "guidance_embedder.timestep_embedder.2", "time_text_embed.text_embedder.linear_1": "pooled_text_embedder.0", "time_text_embed.text_embedder.linear_2": "pooled_text_embedder.2", "norm_out.linear": "norm_out.linear", "proj_out": "proj_out", "norm1.linear": "norm1_a.linear", "norm1_context.linear": "norm1_b.linear", "attn.to_q": "attn.a_to_q", "attn.to_k": "attn.a_to_k", "attn.to_v": "attn.a_to_v", "attn.to_out.0": "attn.a_to_out", "attn.add_q_proj": "attn.b_to_q", "attn.add_k_proj": "attn.b_to_k", "attn.add_v_proj": "attn.b_to_v", "attn.to_add_out": "attn.b_to_out", "ff.net.0.proj": "ff_a.0", "ff.net.2": "ff_a.2", "ff_context.net.0.proj": "ff_b.0", "ff_context.net.2": "ff_b.2", "attn.norm_q": "attn.norm_q_a", "attn.norm_k": "attn.norm_k_a", "attn.norm_added_q": "attn.norm_q_b", "attn.norm_added_k": "attn.norm_k_b", } rename_dict_single = { "attn.to_q": "a_to_q", "attn.to_k": "a_to_k", "attn.to_v": "a_to_v", "attn.norm_q": "norm_q_a", "attn.norm_k": "norm_k_a", "norm.linear": "norm.linear", "proj_mlp": "proj_in_besides_attn", "proj_out": "proj_out", } state_dict_ = {} for name, param in state_dict.items(): if name in rename_dict: state_dict_[rename_dict[name]] = param elif name.endswith(".weight") or name.endswith(".bias"): suffix = ".weight" if name.endswith(".weight") else ".bias" prefix = name[:-len(suffix)] if prefix in rename_dict: state_dict_[rename_dict[prefix] + suffix] = param elif prefix.startswith("transformer_blocks."): names = prefix.split(".") names[0] = "blocks" middle = ".".join(names[2:]) if middle in rename_dict: name_ = ".".join(names[:2] + [rename_dict[middle]] + [suffix[1:]]) state_dict_[name_] = param elif prefix.startswith("single_transformer_blocks."): names = prefix.split(".") names[0] = "single_blocks" middle = ".".join(names[2:]) if middle in rename_dict_single: name_ = ".".join(names[:2] + [rename_dict_single[middle]] + [suffix[1:]]) state_dict_[name_] = param else: print(name) else: print(name) for name in list(state_dict_.keys()): if ".proj_in_besides_attn." in name: name_ = name.replace(".proj_in_besides_attn.", ".linear.") param = torch.concat([ state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_q.")], state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_k.")], state_dict_[name.replace(".proj_in_besides_attn.", f".a_to_v.")], state_dict_[name], ], dim=0) state_dict_[name_] = param state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_q.")) state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_k.")) state_dict_.pop(name.replace(".proj_in_besides_attn.", f".a_to_v.")) state_dict_.pop(name) for name in list(state_dict_.keys()): for component in ["a", "b"]: if f".{component}_to_q." in name: name_ = name.replace(f".{component}_to_q.", f".{component}_to_qkv.") param = torch.concat([ state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_q.")], state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_k.")], state_dict_[name.replace(f".{component}_to_q.", f".{component}_to_v.")], ], dim=0) state_dict_[name_] = param state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_q.")) state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_k.")) state_dict_.pop(name.replace(f".{component}_to_q.", f".{component}_to_v.")) return state_dict_ def from_civitai(self, state_dict): rename_dict = { "time_in.in_layer.bias": "time_embedder.timestep_embedder.0.bias", "time_in.in_layer.weight": "time_embedder.timestep_embedder.0.weight", "time_in.out_layer.bias": "time_embedder.timestep_embedder.2.bias", "time_in.out_layer.weight": "time_embedder.timestep_embedder.2.weight", "txt_in.bias": "context_embedder.bias", "txt_in.weight": "context_embedder.weight", "vector_in.in_layer.bias": "pooled_text_embedder.0.bias", "vector_in.in_layer.weight": "pooled_text_embedder.0.weight", "vector_in.out_layer.bias": "pooled_text_embedder.2.bias", "vector_in.out_layer.weight": "pooled_text_embedder.2.weight", "final_layer.linear.bias": "proj_out.bias", "final_layer.linear.weight": "proj_out.weight", "guidance_in.in_layer.bias": "guidance_embedder.timestep_embedder.0.bias", "guidance_in.in_layer.weight": "guidance_embedder.timestep_embedder.0.weight", "guidance_in.out_layer.bias": "guidance_embedder.timestep_embedder.2.bias", "guidance_in.out_layer.weight": "guidance_embedder.timestep_embedder.2.weight", "img_in.bias": "x_embedder.bias", "img_in.weight": "x_embedder.weight", "final_layer.adaLN_modulation.1.weight": "norm_out.linear.weight", "final_layer.adaLN_modulation.1.bias": "norm_out.linear.bias", } suffix_rename_dict = { "img_attn.norm.key_norm.scale": "attn.norm_k_a.weight", "img_attn.norm.query_norm.scale": "attn.norm_q_a.weight", "img_attn.proj.bias": "attn.a_to_out.bias", "img_attn.proj.weight": "attn.a_to_out.weight", "img_attn.qkv.bias": "attn.a_to_qkv.bias", "img_attn.qkv.weight": "attn.a_to_qkv.weight", "img_mlp.0.bias": "ff_a.0.bias", "img_mlp.0.weight": "ff_a.0.weight", "img_mlp.2.bias": "ff_a.2.bias", "img_mlp.2.weight": "ff_a.2.weight", "img_mod.lin.bias": "norm1_a.linear.bias", "img_mod.lin.weight": "norm1_a.linear.weight", "txt_attn.norm.key_norm.scale": "attn.norm_k_b.weight", "txt_attn.norm.query_norm.scale": "attn.norm_q_b.weight", "txt_attn.proj.bias": "attn.b_to_out.bias", "txt_attn.proj.weight": "attn.b_to_out.weight", "txt_attn.qkv.bias": "attn.b_to_qkv.bias", "txt_attn.qkv.weight": "attn.b_to_qkv.weight", "txt_mlp.0.bias": "ff_b.0.bias", "txt_mlp.0.weight": "ff_b.0.weight", "txt_mlp.2.bias": "ff_b.2.bias", "txt_mlp.2.weight": "ff_b.2.weight", "txt_mod.lin.bias": "norm1_b.linear.bias", "txt_mod.lin.weight": "norm1_b.linear.weight", "linear1.bias": "linear.bias", "linear1.weight": "linear.weight", "linear2.bias": "proj_out.bias", "linear2.weight": "proj_out.weight", "modulation.lin.bias": "norm.linear.bias", "modulation.lin.weight": "norm.linear.weight", "norm.key_norm.scale": "norm_k_a.weight", "norm.query_norm.scale": "norm_q_a.weight", } state_dict_ = {} for name, param in state_dict.items(): names = name.split(".") if name in rename_dict: rename = rename_dict[name] if name.startswith("final_layer.adaLN_modulation.1."): param = torch.concat([param[3072:], param[:3072]], dim=0) state_dict_[rename] = param elif names[0] == "double_blocks": rename = f"blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])] state_dict_[rename] = param elif names[0] == "single_blocks": if ".".join(names[2:]) in suffix_rename_dict: rename = f"single_blocks.{names[1]}." + suffix_rename_dict[".".join(names[2:])] state_dict_[rename] = param else: print(name) return state_dict_