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| # https://gist.github.com/takuma104/4adfb3d968d80bea1d18a30c06439242 | |
| # 2nd editing by lllyasviel | |
| import torch | |
| # =================# | |
| # UNet Conversion # | |
| # =================# | |
| unet_conversion_map = [ | |
| # (stable-diffusion, HF Diffusers) | |
| ("time_embed.0.weight", "time_embedding.linear_1.weight"), | |
| ("time_embed.0.bias", "time_embedding.linear_1.bias"), | |
| ("time_embed.2.weight", "time_embedding.linear_2.weight"), | |
| ("time_embed.2.bias", "time_embedding.linear_2.bias"), | |
| ("label_emb.0.0.weight", "add_embedding.linear_1.weight"), | |
| ("label_emb.0.0.bias", "add_embedding.linear_1.bias"), | |
| ("label_emb.0.2.weight", "add_embedding.linear_2.weight"), | |
| ("label_emb.0.2.bias", "add_embedding.linear_2.bias"), | |
| ("input_blocks.0.0.weight", "conv_in.weight"), | |
| ("input_blocks.0.0.bias", "conv_in.bias"), | |
| ("middle_block_out.0.weight", "controlnet_mid_block.weight"), | |
| ("middle_block_out.0.bias", "controlnet_mid_block.bias"), | |
| ] | |
| unet_conversion_map_resnet = [ | |
| # (stable-diffusion, HF Diffusers) | |
| ("in_layers.0", "norm1"), | |
| ("in_layers.2", "conv1"), | |
| ("out_layers.0", "norm2"), | |
| ("out_layers.3", "conv2"), | |
| ("emb_layers.1", "time_emb_proj"), | |
| ("skip_connection", "conv_shortcut"), | |
| ] | |
| unet_conversion_map_layer = [] | |
| # hardcoded number of downblocks and resnets/attentions... | |
| # would need smarter logic for other networks. | |
| for i in range(4): | |
| # loop over downblocks/upblocks | |
| for j in range(10): | |
| # loop over resnets/attentions for downblocks | |
| hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." | |
| sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0." | |
| unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) | |
| hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." | |
| sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1." | |
| unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) | |
| hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." | |
| sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op." | |
| unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) | |
| hf_mid_atn_prefix = "mid_block.attentions.0." | |
| sd_mid_atn_prefix = "middle_block.1." | |
| unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) | |
| for j in range(2): | |
| hf_mid_res_prefix = f"mid_block.resnets.{j}." | |
| sd_mid_res_prefix = f"middle_block.{2*j}." | |
| unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) | |
| # controlnet specific | |
| controlnet_cond_embedding_names = ['conv_in'] + [f'blocks.{i}' for i in range(6)] + ['conv_out'] | |
| for i, hf_prefix in enumerate(controlnet_cond_embedding_names): | |
| hf_prefix = f"controlnet_cond_embedding.{hf_prefix}." | |
| sd_prefix = f"input_hint_block.{i*2}." | |
| unet_conversion_map_layer.append((sd_prefix, hf_prefix)) | |
| for i in range(12): | |
| hf_prefix = f"controlnet_down_blocks.{i}." | |
| sd_prefix = f"zero_convs.{i}.0." | |
| unet_conversion_map_layer.append((sd_prefix, hf_prefix)) | |
| def convert_from_diffuser_state_dict(unet_state_dict): | |
| mapping = {k: k for k in unet_state_dict.keys()} | |
| for sd_name, hf_name in unet_conversion_map: | |
| mapping[hf_name] = sd_name | |
| for k, v in mapping.items(): | |
| if "resnets" in k: | |
| for sd_part, hf_part in unet_conversion_map_resnet: | |
| v = v.replace(hf_part, sd_part) | |
| mapping[k] = v | |
| for k, v in mapping.items(): | |
| for sd_part, hf_part in unet_conversion_map_layer: | |
| v = v.replace(hf_part, sd_part) | |
| mapping[k] = v | |
| new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items() if k in unet_state_dict} | |
| return new_state_dict | |