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convert_diffusers_to_sd.py

ZeroCool22
Dec 11th, 2022
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  1. #!/usr/bin/env python3
  2.  
  3. # by ratwithashotgun, freely usable
  4. # converts from huggingface diffusers StableDiffusionPipeline models to original stablediffusion ckpt files.
  5. # this requires you to have the original model that was used to create the diff mode, for most dreambooth cases currently that's SD 1.4.
  6.  
  7. # requirements: pip install torch diffusers transformers pytorch-lightning
  8. # usage: ./convert_diffusers_to_sd.py \unet\diffusion_pytorch_model.bin \Modelos para Stable Diffusion\sd-v1-4.ckpt \Dreambooth\convertido
  9.  
  10. import argparse
  11. import os
  12.  
  13. import torch
  14. from diffusers import StableDiffusionPipeline, DiffusionPipeline
  15.  
  16. # yes this is horrid
  17. KeyMap = {
  18. "model.diffusion_model.time_embed.0.weight": "time_embedding.linear_1.weight",
  19. "model.diffusion_model.time_embed.0.bias": "time_embedding.linear_1.bias",
  20. "model.diffusion_model.time_embed.2.weight": "time_embedding.linear_2.weight",
  21. "model.diffusion_model.time_embed.2.bias": "time_embedding.linear_2.bias",
  22. "model.diffusion_model.input_blocks.0.0.weight": "conv_in.weight",
  23. "model.diffusion_model.input_blocks.0.0.bias": "conv_in.bias",
  24. "model.diffusion_model.out.0.weight": "conv_norm_out.weight",
  25. "model.diffusion_model.out.0.bias": "conv_norm_out.bias",
  26. "model.diffusion_model.out.2.weight": "conv_out.weight",
  27. "model.diffusion_model.out.2.bias": "conv_out.bias",
  28. "model.diffusion_model.input_blocks.1.0.in_layers.0.weight": "down_blocks.0.resnets.0.norm1.weight",
  29. "model.diffusion_model.input_blocks.1.0.in_layers.0.bias": "down_blocks.0.resnets.0.norm1.bias",
  30. "model.diffusion_model.input_blocks.1.0.in_layers.2.weight": "down_blocks.0.resnets.0.conv1.weight",
  31. "model.diffusion_model.input_blocks.1.0.in_layers.2.bias": "down_blocks.0.resnets.0.conv1.bias",
  32. "model.diffusion_model.input_blocks.1.0.emb_layers.1.weight": "down_blocks.0.resnets.0.time_emb_proj.weight",
  33. "model.diffusion_model.input_blocks.1.0.emb_layers.1.bias": "down_blocks.0.resnets.0.time_emb_proj.bias",
  34. "model.diffusion_model.input_blocks.1.0.out_layers.0.weight": "down_blocks.0.resnets.0.norm2.weight",
  35. "model.diffusion_model.input_blocks.1.0.out_layers.0.bias": "down_blocks.0.resnets.0.norm2.bias",
  36. "model.diffusion_model.input_blocks.1.0.out_layers.3.weight": "down_blocks.0.resnets.0.conv2.weight",
  37. "model.diffusion_model.input_blocks.1.0.out_layers.3.bias": "down_blocks.0.resnets.0.conv2.bias",
  38. "model.diffusion_model.input_blocks.1.1.norm.weight": "down_blocks.0.attentions.0.norm.weight",
  39. "model.diffusion_model.input_blocks.1.1.norm.bias": "down_blocks.0.attentions.0.norm.bias",
  40. "model.diffusion_model.input_blocks.1.1.proj_in.weight": "down_blocks.0.attentions.0.proj_in.weight",
  41. "model.diffusion_model.input_blocks.1.1.proj_in.bias": "down_blocks.0.attentions.0.proj_in.bias",
  42. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q.weight": "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_q.weight",
  43. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k.weight": "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_k.weight",
  44. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v.weight": "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_v.weight",
  45. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.weight": "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_out.0.weight",
  46. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0.bias": "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_out.0.bias",
  47. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.weight": "down_blocks.0.attentions.0.transformer_blocks.0.ff.net.0.proj.weight",
  48. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj.bias": "down_blocks.0.attentions.0.transformer_blocks.0.ff.net.0.proj.bias",
  49. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.weight": "down_blocks.0.attentions.0.transformer_blocks.0.ff.net.2.weight",
  50. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2.bias": "down_blocks.0.attentions.0.transformer_blocks.0.ff.net.2.bias",
  51. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_q.weight": "down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_q.weight",
  52. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k.weight": "down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_k.weight",
  53. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_v.weight": "down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_v.weight",
  54. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0.weight": "down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_out.0.weight",
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  56. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.weight": "down_blocks.0.attentions.0.transformer_blocks.0.norm1.weight",
  57. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1.bias": "down_blocks.0.attentions.0.transformer_blocks.0.norm1.bias",
  58. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.weight": "down_blocks.0.attentions.0.transformer_blocks.0.norm2.weight",
  59. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2.bias": "down_blocks.0.attentions.0.transformer_blocks.0.norm2.bias",
  60. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.weight": "down_blocks.0.attentions.0.transformer_blocks.0.norm3.weight",
  61. "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3.bias": "down_blocks.0.attentions.0.transformer_blocks.0.norm3.bias",
  62. "model.diffusion_model.input_blocks.1.1.proj_out.weight": "down_blocks.0.attentions.0.proj_out.weight",
  63. "model.diffusion_model.input_blocks.1.1.proj_out.bias": "down_blocks.0.attentions.0.proj_out.bias",
  64. "model.diffusion_model.input_blocks.2.0.in_layers.0.weight": "down_blocks.0.resnets.1.norm1.weight",
  65. "model.diffusion_model.input_blocks.2.0.in_layers.0.bias": "down_blocks.0.resnets.1.norm1.bias",
  66. "model.diffusion_model.input_blocks.2.0.in_layers.2.weight": "down_blocks.0.resnets.1.conv1.weight",
  67. "model.diffusion_model.input_blocks.2.0.in_layers.2.bias": "down_blocks.0.resnets.1.conv1.bias",
  68. "model.diffusion_model.input_blocks.2.0.emb_layers.1.weight": "down_blocks.0.resnets.1.time_emb_proj.weight",
  69. "model.diffusion_model.input_blocks.2.0.emb_layers.1.bias": "down_blocks.0.resnets.1.time_emb_proj.bias",
  70. "model.diffusion_model.input_blocks.2.0.out_layers.0.weight": "down_blocks.0.resnets.1.norm2.weight",
  71. "model.diffusion_model.input_blocks.2.0.out_layers.0.bias": "down_blocks.0.resnets.1.norm2.bias",
  72. "model.diffusion_model.input_blocks.2.0.out_layers.3.weight": "down_blocks.0.resnets.1.conv2.weight",
  73. "model.diffusion_model.input_blocks.2.0.out_layers.3.bias": "down_blocks.0.resnets.1.conv2.bias",
  74. "model.diffusion_model.input_blocks.2.1.norm.weight": "down_blocks.0.attentions.1.norm.weight",
  75. "model.diffusion_model.input_blocks.2.1.norm.bias": "down_blocks.0.attentions.1.norm.bias",
  76. "model.diffusion_model.input_blocks.2.1.proj_in.weight": "down_blocks.0.attentions.1.proj_in.weight",
  77. "model.diffusion_model.input_blocks.2.1.proj_in.bias": "down_blocks.0.attentions.1.proj_in.bias",
  78. "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn1.to_q.weight": "down_blocks.0.attentions.1.transformer_blocks.0.attn1.to_q.weight",
  79. "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn1.to_k.weight": "down_blocks.0.attentions.1.transformer_blocks.0.attn1.to_k.weight",
  80. "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn1.to_v.weight": "down_blocks.0.attentions.1.transformer_blocks.0.attn1.to_v.weight",
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  96. "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.norm3.weight": "down_blocks.0.attentions.1.transformer_blocks.0.norm3.weight",
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  101. "model.diffusion_model.input_blocks.3.0.op.bias": "down_blocks.0.downsamplers.0.conv.bias",
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  697. "model.diffusion_model.output_blocks.11.1.transformer_blocks.0.norm1.bias": "up_blocks.3.attentions.2.transformer_blocks.0.norm1.bias",
  698. "model.diffusion_model.output_blocks.11.1.transformer_blocks.0.norm2.weight": "up_blocks.3.attentions.2.transformer_blocks.0.norm2.weight",
  699. "model.diffusion_model.output_blocks.11.1.transformer_blocks.0.norm2.bias": "up_blocks.3.attentions.2.transformer_blocks.0.norm2.bias",
  700. "model.diffusion_model.output_blocks.11.1.transformer_blocks.0.norm3.weight": "up_blocks.3.attentions.2.transformer_blocks.0.norm3.weight",
  701. "model.diffusion_model.output_blocks.11.1.transformer_blocks.0.norm3.bias": "up_blocks.3.attentions.2.transformer_blocks.0.norm3.bias",
  702. "model.diffusion_model.output_blocks.11.1.proj_out.weight": "up_blocks.3.attentions.2.proj_out.weight",
  703. "model.diffusion_model.output_blocks.11.1.proj_out.bias": "up_blocks.3.attentions.2.proj_out.bias"
  704. }
  705.  
  706.  
  707. class StableDiffusionPipelineStripped(DiffusionPipeline):
  708. def __init__(self, unet=None, *args, **kwargs):
  709. print("got unet", len(args), kwargs.keys())
  710. self.unet = unet
  711.  
  712.  
  713. def convert_diff_to_sd(diffusers_model_path: str, base_ckpt_path: str, output_ckpt_path: str,
  714. overwrite=False, huggingface_use_auth_token=None):
  715. if not overwrite and os.path.exists(output_ckpt_path):
  716. raise ValueError("output_ckpt_path exists already", output_ckpt_path)
  717.  
  718. print(f"loading diff model from {diffusers_model_path!r}")
  719. try:
  720. diff_pipe = StableDiffusionPipeline.from_pretrained(diffusers_model_path,
  721. use_auth_token=huggingface_use_auth_token)
  722. except Exception as ex:
  723. if "required positional arguments" in str(ex):
  724. print("load error, trying loading stripped version", ex)
  725. diff_pipe = StableDiffusionPipelineStripped.from_pretrained(diffusers_model_path,
  726. use_auth_token=huggingface_use_auth_token)
  727. else:
  728. raise
  729.  
  730. print("loading diff model done!")
  731.  
  732. diff_pipe_unet_sd = diff_pipe.unet.state_dict()
  733. print("diff_pipe_unet_sd done")
  734.  
  735. print(f"loading sd ckpt from {base_ckpt_path!r}")
  736. org_model = torch.load(base_ckpt_path)
  737. org_sd = org_model["state_dict"]
  738. print(f"loading sd ckpt done!")
  739.  
  740. for ckpt_key, diff_key in KeyMap.items():
  741. org_sd[ckpt_key] = diff_pipe_unet_sd[diff_key]
  742.  
  743. print(f"saving converted unet to {output_ckpt_path!r}")
  744. torch.save(org_model, output_ckpt_path)
  745. print("done")
  746.  
  747.  
  748. if __name__ == "__main__":
  749. def setup():
  750. parser = argparse.ArgumentParser()
  751. parser.add_argument("diffusers_model")
  752. # parser.add_argument("config_path")
  753. parser.add_argument("base_ckpt_path")
  754. parser.add_argument("output_ckpt_path")
  755. parser.add_argument("-u", "--use_huggingface_auth_token")
  756. parser.add_argument("-o", "--overwrite", action="store_true")
  757. args = parser.parse_args()
  758. convert_diff_to_sd(args.diffusers_model, args.base_ckpt_path, args.output_ckpt_path,
  759. overwrite=args.overwrite, huggingface_use_auth_token=args.use_huggingface_auth_token)
  760.  
  761.  
  762. setup()
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