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| import torch | |
| from .sd_unet import Timesteps, ResnetBlock, AttentionBlock, PushBlock, PopBlock, DownSampler, UpSampler | |
| class SDXLUNet(torch.nn.Module): | |
| def __init__(self, is_kolors=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.add_time_proj = Timesteps(256) | |
| self.add_time_embedding = torch.nn.Sequential( | |
| torch.nn.Linear(5632 if is_kolors else 2816, 1280), | |
| torch.nn.SiLU(), | |
| torch.nn.Linear(1280, 1280) | |
| ) | |
| self.conv_in = torch.nn.Conv2d(4, 320, kernel_size=3, padding=1) | |
| self.text_intermediate_proj = torch.nn.Linear(4096, 2048) if is_kolors else None | |
| self.blocks = torch.nn.ModuleList([ | |
| # DownBlock2D | |
| ResnetBlock(320, 320, 1280), | |
| PushBlock(), | |
| ResnetBlock(320, 320, 1280), | |
| PushBlock(), | |
| DownSampler(320), | |
| PushBlock(), | |
| # CrossAttnDownBlock2D | |
| ResnetBlock(320, 640, 1280), | |
| AttentionBlock(10, 64, 640, 2, 2048), | |
| PushBlock(), | |
| ResnetBlock(640, 640, 1280), | |
| AttentionBlock(10, 64, 640, 2, 2048), | |
| PushBlock(), | |
| DownSampler(640), | |
| PushBlock(), | |
| # CrossAttnDownBlock2D | |
| ResnetBlock(640, 1280, 1280), | |
| AttentionBlock(20, 64, 1280, 10, 2048), | |
| PushBlock(), | |
| ResnetBlock(1280, 1280, 1280), | |
| AttentionBlock(20, 64, 1280, 10, 2048), | |
| PushBlock(), | |
| # UNetMidBlock2DCrossAttn | |
| ResnetBlock(1280, 1280, 1280), | |
| AttentionBlock(20, 64, 1280, 10, 2048), | |
| ResnetBlock(1280, 1280, 1280), | |
| # CrossAttnUpBlock2D | |
| PopBlock(), | |
| ResnetBlock(2560, 1280, 1280), | |
| AttentionBlock(20, 64, 1280, 10, 2048), | |
| PopBlock(), | |
| ResnetBlock(2560, 1280, 1280), | |
| AttentionBlock(20, 64, 1280, 10, 2048), | |
| PopBlock(), | |
| ResnetBlock(1920, 1280, 1280), | |
| AttentionBlock(20, 64, 1280, 10, 2048), | |
| UpSampler(1280), | |
| # CrossAttnUpBlock2D | |
| PopBlock(), | |
| ResnetBlock(1920, 640, 1280), | |
| AttentionBlock(10, 64, 640, 2, 2048), | |
| PopBlock(), | |
| ResnetBlock(1280, 640, 1280), | |
| AttentionBlock(10, 64, 640, 2, 2048), | |
| PopBlock(), | |
| ResnetBlock(960, 640, 1280), | |
| AttentionBlock(10, 64, 640, 2, 2048), | |
| UpSampler(640), | |
| # UpBlock2D | |
| PopBlock(), | |
| ResnetBlock(960, 320, 1280), | |
| PopBlock(), | |
| ResnetBlock(640, 320, 1280), | |
| PopBlock(), | |
| ResnetBlock(640, 320, 1280) | |
| ]) | |
| self.conv_norm_out = torch.nn.GroupNorm(num_channels=320, num_groups=32, eps=1e-5) | |
| self.conv_act = torch.nn.SiLU() | |
| self.conv_out = torch.nn.Conv2d(320, 4, kernel_size=3, padding=1) | |
| def forward( | |
| self, | |
| sample, timestep, encoder_hidden_states, add_time_id, add_text_embeds, | |
| tiled=False, tile_size=64, tile_stride=8, | |
| use_gradient_checkpointing=False, | |
| **kwargs | |
| ): | |
| # 1. time | |
| t_emb = self.time_proj(timestep[None]).to(sample.dtype) | |
| t_emb = self.time_embedding(t_emb) | |
| time_embeds = self.add_time_proj(add_time_id) | |
| time_embeds = time_embeds.reshape((add_text_embeds.shape[0], -1)) | |
| add_embeds = torch.concat([add_text_embeds, time_embeds], dim=-1) | |
| add_embeds = add_embeds.to(sample.dtype) | |
| add_embeds = self.add_time_embedding(add_embeds) | |
| time_emb = t_emb + add_embeds | |
| # 2. pre-process | |
| height, width = sample.shape[2], sample.shape[3] | |
| hidden_states = self.conv_in(sample) | |
| text_emb = encoder_hidden_states if self.text_intermediate_proj is None else self.text_intermediate_proj(encoder_hidden_states) | |
| res_stack = [hidden_states] | |
| # 3. blocks | |
| def create_custom_forward(module): | |
| def custom_forward(*inputs): | |
| return module(*inputs) | |
| return custom_forward | |
| for i, block in enumerate(self.blocks): | |
| if self.training and use_gradient_checkpointing and not (isinstance(block, PushBlock) or isinstance(block, PopBlock)): | |
| hidden_states, time_emb, text_emb, res_stack = torch.utils.checkpoint.checkpoint( | |
| create_custom_forward(block), | |
| hidden_states, time_emb, text_emb, res_stack, | |
| use_reentrant=False, | |
| ) | |
| else: | |
| hidden_states, time_emb, text_emb, res_stack = block( | |
| hidden_states, time_emb, text_emb, res_stack, | |
| tiled=tiled, tile_size=tile_size, tile_stride=tile_stride | |
| ) | |
| # 4. output | |
| hidden_states = self.conv_norm_out(hidden_states) | |
| hidden_states = self.conv_act(hidden_states) | |
| hidden_states = self.conv_out(hidden_states) | |
| return hidden_states | |
| def state_dict_converter(self): | |
| return SDXLUNetStateDictConverter() | |
| class SDXLUNetStateDictConverter: | |
| def __init__(self): | |
| pass | |
| def from_diffusers(self, state_dict): | |
| # architecture | |
| block_types = [ | |
| 'ResnetBlock', 'PushBlock', 'ResnetBlock', 'PushBlock', 'DownSampler', 'PushBlock', | |
| 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'DownSampler', 'PushBlock', | |
| 'ResnetBlock', 'AttentionBlock', 'PushBlock', 'ResnetBlock', 'AttentionBlock', 'PushBlock', | |
| 'ResnetBlock', 'AttentionBlock', 'ResnetBlock', | |
| 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', | |
| 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'PopBlock', 'ResnetBlock', 'AttentionBlock', 'UpSampler', | |
| 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock', 'PopBlock', 'ResnetBlock' | |
| ] | |
| # 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 names[0] in ["encoder_hid_proj"]: | |
| names[0] = "text_intermediate_proj" | |
| 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() | |
| state_dict_[rename_dict[name]] = param | |
| return state_dict_ | |
| def from_civitai(self, state_dict): | |
| rename_dict = { | |
| "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias", | |
| "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight", | |
| "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "blocks.0.time_emb_proj.bias", | |
| "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "blocks.0.time_emb_proj.weight", | |
| "model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "blocks.0.norm1.bias", | |
| "model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "blocks.0.norm1.weight", | |
| "model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "blocks.0.conv1.bias", | |
| "model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "blocks.0.conv1.weight", | |
| "model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "blocks.0.norm2.bias", | |
| "model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "blocks.0.norm2.weight", | |
| "model.diffusion_model.input_blocks.1.0.out_layers.3.bias": "blocks.0.conv2.bias", | |
| "model.diffusion_model.input_blocks.1.0.out_layers.3.weight": "blocks.0.conv2.weight", | |
| "model.diffusion_model.input_blocks.2.0.emb_layers.1.bias": "blocks.2.time_emb_proj.bias", | |
| "model.diffusion_model.input_blocks.2.0.emb_layers.1.weight": "blocks.2.time_emb_proj.weight", | |
| "model.diffusion_model.input_blocks.2.0.in_layers.0.bias": "blocks.2.norm1.bias", | |
| "model.diffusion_model.input_blocks.2.0.in_layers.0.weight": "blocks.2.norm1.weight", | |
| "model.diffusion_model.input_blocks.2.0.in_layers.2.bias": "blocks.2.conv1.bias", | |
| "model.diffusion_model.input_blocks.2.0.in_layers.2.weight": "blocks.2.conv1.weight", | |
| "model.diffusion_model.input_blocks.2.0.out_layers.0.bias": "blocks.2.norm2.bias", | |
| "model.diffusion_model.input_blocks.2.0.out_layers.0.weight": "blocks.2.norm2.weight", | |
| "model.diffusion_model.input_blocks.2.0.out_layers.3.bias": "blocks.2.conv2.bias", | |
| "model.diffusion_model.input_blocks.2.0.out_layers.3.weight": "blocks.2.conv2.weight", | |
| "model.diffusion_model.input_blocks.3.0.op.bias": "blocks.4.conv.bias", | |
| "model.diffusion_model.input_blocks.3.0.op.weight": "blocks.4.conv.weight", | |
| "model.diffusion_model.input_blocks.4.0.emb_layers.1.bias": "blocks.6.time_emb_proj.bias", | |
| "model.diffusion_model.input_blocks.4.0.emb_layers.1.weight": "blocks.6.time_emb_proj.weight", | |
| "model.diffusion_model.input_blocks.4.0.in_layers.0.bias": "blocks.6.norm1.bias", | |
| "model.diffusion_model.input_blocks.4.0.in_layers.0.weight": "blocks.6.norm1.weight", | |
| "model.diffusion_model.input_blocks.4.0.in_layers.2.bias": "blocks.6.conv1.bias", | |
| "model.diffusion_model.input_blocks.4.0.in_layers.2.weight": "blocks.6.conv1.weight", | |
| "model.diffusion_model.input_blocks.4.0.out_layers.0.bias": "blocks.6.norm2.bias", | |
| "model.diffusion_model.input_blocks.4.0.out_layers.0.weight": "blocks.6.norm2.weight", | |
| "model.diffusion_model.input_blocks.4.0.out_layers.3.bias": "blocks.6.conv2.bias", | |
| "model.diffusion_model.input_blocks.4.0.out_layers.3.weight": "blocks.6.conv2.weight", | |
| "model.diffusion_model.input_blocks.4.0.skip_connection.bias": "blocks.6.conv_shortcut.bias", | |
| "model.diffusion_model.input_blocks.4.0.skip_connection.weight": "blocks.6.conv_shortcut.weight", | |
| "model.diffusion_model.input_blocks.4.1.norm.bias": "blocks.7.norm.bias", | |
| "model.diffusion_model.input_blocks.4.1.norm.weight": "blocks.7.norm.weight", | |
| "model.diffusion_model.input_blocks.4.1.proj_in.bias": "blocks.7.proj_in.bias", | |
| "model.diffusion_model.input_blocks.4.1.proj_in.weight": "blocks.7.proj_in.weight", | |
| "model.diffusion_model.input_blocks.4.1.proj_out.bias": "blocks.7.proj_out.bias", | |
| "model.diffusion_model.input_blocks.4.1.proj_out.weight": "blocks.7.proj_out.weight", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_k.weight": "blocks.7.transformer_blocks.0.attn1.to_k.weight", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.7.transformer_blocks.0.attn1.to_out.bias", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.7.transformer_blocks.0.attn1.to_out.weight", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_q.weight": "blocks.7.transformer_blocks.0.attn1.to_q.weight", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_v.weight": "blocks.7.transformer_blocks.0.attn1.to_v.weight", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight": "blocks.7.transformer_blocks.0.attn2.to_k.weight", | |
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| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_out.0.weight": "blocks.7.transformer_blocks.0.attn2.to_out.weight", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_q.weight": "blocks.7.transformer_blocks.0.attn2.to_q.weight", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_v.weight": "blocks.7.transformer_blocks.0.attn2.to_v.weight", | |
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| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.0.proj.weight": "blocks.7.transformer_blocks.0.act_fn.proj.weight", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.2.bias": "blocks.7.transformer_blocks.0.ff.bias", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.2.weight": "blocks.7.transformer_blocks.0.ff.weight", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm1.bias": "blocks.7.transformer_blocks.0.norm1.bias", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm1.weight": "blocks.7.transformer_blocks.0.norm1.weight", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm2.bias": "blocks.7.transformer_blocks.0.norm2.bias", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm2.weight": "blocks.7.transformer_blocks.0.norm2.weight", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm3.bias": "blocks.7.transformer_blocks.0.norm3.bias", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.norm3.weight": "blocks.7.transformer_blocks.0.norm3.weight", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.attn1.to_k.weight": "blocks.7.transformer_blocks.1.attn1.to_k.weight", | |
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| "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.ff.net.0.proj.weight": "blocks.7.transformer_blocks.1.act_fn.proj.weight", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.ff.net.2.bias": "blocks.7.transformer_blocks.1.ff.bias", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.ff.net.2.weight": "blocks.7.transformer_blocks.1.ff.weight", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm1.bias": "blocks.7.transformer_blocks.1.norm1.bias", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm1.weight": "blocks.7.transformer_blocks.1.norm1.weight", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm2.bias": "blocks.7.transformer_blocks.1.norm2.bias", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm2.weight": "blocks.7.transformer_blocks.1.norm2.weight", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm3.bias": "blocks.7.transformer_blocks.1.norm3.bias", | |
| "model.diffusion_model.input_blocks.4.1.transformer_blocks.1.norm3.weight": "blocks.7.transformer_blocks.1.norm3.weight", | |
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| "model.diffusion_model.input_blocks.5.0.emb_layers.1.weight": "blocks.9.time_emb_proj.weight", | |
| "model.diffusion_model.input_blocks.5.0.in_layers.0.bias": "blocks.9.norm1.bias", | |
| "model.diffusion_model.input_blocks.5.0.in_layers.0.weight": "blocks.9.norm1.weight", | |
| "model.diffusion_model.input_blocks.5.0.in_layers.2.bias": "blocks.9.conv1.bias", | |
| "model.diffusion_model.input_blocks.5.0.in_layers.2.weight": "blocks.9.conv1.weight", | |
| "model.diffusion_model.input_blocks.5.0.out_layers.0.bias": "blocks.9.norm2.bias", | |
| "model.diffusion_model.input_blocks.5.0.out_layers.0.weight": "blocks.9.norm2.weight", | |
| "model.diffusion_model.input_blocks.5.0.out_layers.3.bias": "blocks.9.conv2.bias", | |
| "model.diffusion_model.input_blocks.5.0.out_layers.3.weight": "blocks.9.conv2.weight", | |
| "model.diffusion_model.input_blocks.5.1.norm.bias": "blocks.10.norm.bias", | |
| "model.diffusion_model.input_blocks.5.1.norm.weight": "blocks.10.norm.weight", | |
| "model.diffusion_model.input_blocks.5.1.proj_in.bias": "blocks.10.proj_in.bias", | |
| "model.diffusion_model.input_blocks.5.1.proj_in.weight": "blocks.10.proj_in.weight", | |
| "model.diffusion_model.input_blocks.5.1.proj_out.bias": "blocks.10.proj_out.bias", | |
| "model.diffusion_model.input_blocks.5.1.proj_out.weight": "blocks.10.proj_out.weight", | |
| "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn1.to_k.weight": "blocks.10.transformer_blocks.0.attn1.to_k.weight", | |
| "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn1.to_out.0.bias": "blocks.10.transformer_blocks.0.attn1.to_out.bias", | |
| "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn1.to_out.0.weight": "blocks.10.transformer_blocks.0.attn1.to_out.weight", | |
| "model.diffusion_model.input_blocks.5.1.transformer_blocks.0.attn1.to_q.weight": "blocks.10.transformer_blocks.0.attn1.to_q.weight", | |
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| "model.diffusion_model.output_blocks.5.1.transformer_blocks.1.ff.net.2.weight": "blocks.41.transformer_blocks.1.ff.weight", | |
| "model.diffusion_model.output_blocks.5.1.transformer_blocks.1.norm1.bias": "blocks.41.transformer_blocks.1.norm1.bias", | |
| "model.diffusion_model.output_blocks.5.1.transformer_blocks.1.norm1.weight": "blocks.41.transformer_blocks.1.norm1.weight", | |
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| "model.diffusion_model.output_blocks.5.1.transformer_blocks.1.norm2.weight": "blocks.41.transformer_blocks.1.norm2.weight", | |
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| "model.diffusion_model.output_blocks.5.1.transformer_blocks.1.norm3.weight": "blocks.41.transformer_blocks.1.norm3.weight", | |
| "model.diffusion_model.output_blocks.5.2.conv.bias": "blocks.42.conv.bias", | |
| "model.diffusion_model.output_blocks.5.2.conv.weight": "blocks.42.conv.weight", | |
| "model.diffusion_model.output_blocks.6.0.emb_layers.1.bias": "blocks.44.time_emb_proj.bias", | |
| "model.diffusion_model.output_blocks.6.0.emb_layers.1.weight": "blocks.44.time_emb_proj.weight", | |
| "model.diffusion_model.output_blocks.6.0.in_layers.0.bias": "blocks.44.norm1.bias", | |
| "model.diffusion_model.output_blocks.6.0.in_layers.0.weight": "blocks.44.norm1.weight", | |
| "model.diffusion_model.output_blocks.6.0.in_layers.2.bias": "blocks.44.conv1.bias", | |
| "model.diffusion_model.output_blocks.6.0.in_layers.2.weight": "blocks.44.conv1.weight", | |
| "model.diffusion_model.output_blocks.6.0.out_layers.0.bias": "blocks.44.norm2.bias", | |
| "model.diffusion_model.output_blocks.6.0.out_layers.0.weight": "blocks.44.norm2.weight", | |
| "model.diffusion_model.output_blocks.6.0.out_layers.3.bias": "blocks.44.conv2.bias", | |
| "model.diffusion_model.output_blocks.6.0.out_layers.3.weight": "blocks.44.conv2.weight", | |
| "model.diffusion_model.output_blocks.6.0.skip_connection.bias": "blocks.44.conv_shortcut.bias", | |
| "model.diffusion_model.output_blocks.6.0.skip_connection.weight": "blocks.44.conv_shortcut.weight", | |
| "model.diffusion_model.output_blocks.7.0.emb_layers.1.bias": "blocks.46.time_emb_proj.bias", | |
| "model.diffusion_model.output_blocks.7.0.emb_layers.1.weight": "blocks.46.time_emb_proj.weight", | |
| "model.diffusion_model.output_blocks.7.0.in_layers.0.bias": "blocks.46.norm1.bias", | |
| "model.diffusion_model.output_blocks.7.0.in_layers.0.weight": "blocks.46.norm1.weight", | |
| "model.diffusion_model.output_blocks.7.0.in_layers.2.bias": "blocks.46.conv1.bias", | |
| "model.diffusion_model.output_blocks.7.0.in_layers.2.weight": "blocks.46.conv1.weight", | |
| "model.diffusion_model.output_blocks.7.0.out_layers.0.bias": "blocks.46.norm2.bias", | |
| "model.diffusion_model.output_blocks.7.0.out_layers.0.weight": "blocks.46.norm2.weight", | |
| "model.diffusion_model.output_blocks.7.0.out_layers.3.bias": "blocks.46.conv2.bias", | |
| "model.diffusion_model.output_blocks.7.0.out_layers.3.weight": "blocks.46.conv2.weight", | |
| "model.diffusion_model.output_blocks.7.0.skip_connection.bias": "blocks.46.conv_shortcut.bias", | |
| "model.diffusion_model.output_blocks.7.0.skip_connection.weight": "blocks.46.conv_shortcut.weight", | |
| "model.diffusion_model.output_blocks.8.0.emb_layers.1.bias": "blocks.48.time_emb_proj.bias", | |
| "model.diffusion_model.output_blocks.8.0.emb_layers.1.weight": "blocks.48.time_emb_proj.weight", | |
| "model.diffusion_model.output_blocks.8.0.in_layers.0.bias": "blocks.48.norm1.bias", | |
| "model.diffusion_model.output_blocks.8.0.in_layers.0.weight": "blocks.48.norm1.weight", | |
| "model.diffusion_model.output_blocks.8.0.in_layers.2.bias": "blocks.48.conv1.bias", | |
| "model.diffusion_model.output_blocks.8.0.in_layers.2.weight": "blocks.48.conv1.weight", | |
| "model.diffusion_model.output_blocks.8.0.out_layers.0.bias": "blocks.48.norm2.bias", | |
| "model.diffusion_model.output_blocks.8.0.out_layers.0.weight": "blocks.48.norm2.weight", | |
| "model.diffusion_model.output_blocks.8.0.out_layers.3.bias": "blocks.48.conv2.bias", | |
| "model.diffusion_model.output_blocks.8.0.out_layers.3.weight": "blocks.48.conv2.weight", | |
| "model.diffusion_model.output_blocks.8.0.skip_connection.bias": "blocks.48.conv_shortcut.bias", | |
| "model.diffusion_model.output_blocks.8.0.skip_connection.weight": "blocks.48.conv_shortcut.weight", | |
| "model.diffusion_model.time_embed.0.bias": "time_embedding.0.bias", | |
| "model.diffusion_model.time_embed.0.weight": "time_embedding.0.weight", | |
| "model.diffusion_model.time_embed.2.bias": "time_embedding.2.bias", | |
| "model.diffusion_model.time_embed.2.weight": "time_embedding.2.weight", | |
| } | |
| 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_ | |