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# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint. |
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# *Only* converts the UNet, VAE, and Text Encoder. |
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# Does not convert optimizer state or any other thing. |
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# Written by jachiam |
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import argparse |
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import os.path as osp |
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import torch |
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# =================# |
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# UNet Conversion # |
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# =================# |
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unet_conversion_map = [ |
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# (stable-diffusion, HF Diffusers) |
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("time_embed.0.weight", "time_embedding.linear_1.weight"), |
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("time_embed.0.bias", "time_embedding.linear_1.bias"), |
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("time_embed.2.weight", "time_embedding.linear_2.weight"), |
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("time_embed.2.bias", "time_embedding.linear_2.bias"), |
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("input_blocks.0.0.weight", "conv_in.weight"), |
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("input_blocks.0.0.bias", "conv_in.bias"), |
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("out.0.weight", "conv_norm_out.weight"), |
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("out.0.bias", "conv_norm_out.bias"), |
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("out.2.weight", "conv_out.weight"), |
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("out.2.bias", "conv_out.bias"), |
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] |
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unet_conversion_map_resnet = [ |
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# (stable-diffusion, HF Diffusers) |
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("in_layers.0", "norm1"), |
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("in_layers.2", "conv1"), |
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("out_layers.0", "norm2"), |
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("out_layers.3", "conv2"), |
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("emb_layers.1", "time_emb_proj"), |
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("skip_connection", "conv_shortcut"), |
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] |
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unet_conversion_map_layer = [] |
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# hardcoded number of downblocks and resnets/attentions... |
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# would need smarter logic for other networks. |
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for i in range(4): |
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# loop over downblocks/upblocks |
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for j in range(2): |
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# loop over resnets/attentions for downblocks |
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hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." |
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sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0." |
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unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) |
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if i < 3: |
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# no attention layers in down_blocks.3 |
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hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." |
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sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1." |
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unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) |
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for j in range(3): |
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# loop over resnets/attentions for upblocks |
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hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." |
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sd_up_res_prefix = f"output_blocks.{3*i + j}.0." |
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unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) |
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if i > 0: |
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# no attention layers in up_blocks.0 |
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hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." |
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sd_up_atn_prefix = f"output_blocks.{3*i + j}.1." |
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unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) |
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if i < 3: |
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# no downsample in down_blocks.3 |
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hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." |
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sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op." |
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unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) |
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# no upsample in up_blocks.3 |
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hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." |
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sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}." |
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unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) |
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hf_mid_atn_prefix = "mid_block.attentions.0." |
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sd_mid_atn_prefix = "middle_block.1." |
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unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) |
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for j in range(2): |
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hf_mid_res_prefix = f"mid_block.resnets.{j}." |
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sd_mid_res_prefix = f"middle_block.{2*j}." |
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unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) |
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def convert_unet_state_dict(unet_state_dict): |
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# buyer beware: this is a *brittle* function, |
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# and correct output requires that all of these pieces interact in |
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# the exact order in which I have arranged them. |
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mapping = {k: k for k in unet_state_dict.keys()} |
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for sd_name, hf_name in unet_conversion_map: |
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mapping[hf_name] = sd_name |
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for k, v in mapping.items(): |
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if "resnets" in k: |
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for sd_part, hf_part in unet_conversion_map_resnet: |
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v = v.replace(hf_part, sd_part) |
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mapping[k] = v |
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for k, v in mapping.items(): |
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for sd_part, hf_part in unet_conversion_map_layer: |
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v = v.replace(hf_part, sd_part) |
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mapping[k] = v |
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new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()} |
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return new_state_dict |
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# ================# |
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# VAE Conversion # |
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# ================# |
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vae_conversion_map = [ |
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# (stable-diffusion, HF Diffusers) |
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("nin_shortcut", "conv_shortcut"), |
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("norm_out", "conv_norm_out"), |
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("mid.attn_1.", "mid_block.attentions.0."), |
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] |
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for i in range(4): |
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# down_blocks have two resnets |
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for j in range(2): |
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hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." |
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sd_down_prefix = f"encoder.down.{i}.block.{j}." |
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vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) |
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if i < 3: |
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hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." |
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sd_downsample_prefix = f"down.{i}.downsample." |
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vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) |
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hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." |
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sd_upsample_prefix = f"up.{3-i}.upsample." |
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vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) |
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# up_blocks have three resnets |
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# also, up blocks in hf are numbered in reverse from sd |
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for j in range(3): |
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hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." |
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sd_up_prefix = f"decoder.up.{3-i}.block.{j}." |
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vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) |
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# this part accounts for mid blocks in both the encoder and the decoder |
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for i in range(2): |
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hf_mid_res_prefix = f"mid_block.resnets.{i}." |
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sd_mid_res_prefix = f"mid.block_{i+1}." |
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vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) |
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vae_conversion_map_attn = [ |
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# (stable-diffusion, HF Diffusers) |
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("norm.", "group_norm."), |
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("q.", "query."), |
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("k.", "key."), |
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("v.", "value."), |
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("proj_out.", "proj_attn."), |
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] |
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def reshape_weight_for_sd(w): |
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# convert HF linear weights to SD conv2d weights |
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return w.reshape(*w.shape, 1, 1) |
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def convert_vae_state_dict(vae_state_dict): |
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mapping = {k: k for k in vae_state_dict.keys()} |
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for k, v in mapping.items(): |
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for sd_part, hf_part in vae_conversion_map: |
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v = v.replace(hf_part, sd_part) |
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mapping[k] = v |
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for k, v in mapping.items(): |
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if "attentions" in k: |
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for sd_part, hf_part in vae_conversion_map_attn: |
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v = v.replace(hf_part, sd_part) |
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mapping[k] = v |
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new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} |
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weights_to_convert = ["q", "k", "v", "proj_out"] |
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for k, v in new_state_dict.items(): |
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for weight_name in weights_to_convert: |
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if f"mid.attn_1.{weight_name}.weight" in k: |
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print(f"Reshaping {k} for SD format") |
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new_state_dict[k] = reshape_weight_for_sd(v) |
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return new_state_dict |
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# =========================# |
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# Text Encoder Conversion # |
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# =========================# |
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# pretty much a no-op |
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def convert_text_enc_state_dict(text_enc_dict): |
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return text_enc_dict |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.") |
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parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.") |
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parser.add_argument("--half", action="store_true", help="Save weights in half precision.") |
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args = parser.parse_args() |
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assert args.model_path is not None, "Must provide a model path!" |
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assert args.checkpoint_path is not None, "Must provide a checkpoint path!" |
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unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin") |
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vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin") |
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text_enc_path = osp.join(args.model_path, "text_encoder", "pytorch_model.bin") |
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# Convert the UNet model |
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unet_state_dict = torch.load(unet_path, map_location='cpu') |
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unet_state_dict = convert_unet_state_dict(unet_state_dict) |
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unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()} |
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# Convert the VAE model |
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vae_state_dict = torch.load(vae_path, map_location='cpu') |
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vae_state_dict = convert_vae_state_dict(vae_state_dict) |
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vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()} |
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# Convert the text encoder model |
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text_enc_dict = torch.load(text_enc_path, map_location='cpu') |
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text_enc_dict = convert_text_enc_state_dict(text_enc_dict) |
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text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()} |
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# Put together new checkpoint |
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state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict} |
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if args.half: |
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state_dict = {k:v.half() for k,v in state_dict.items()} |
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state_dict = {"state_dict": state_dict} |
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torch.save(state_dict, args.checkpoint_path) |
works! thank you