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59 lines
2 KiB
Python
59 lines
2 KiB
Python
import torch
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from refiners.fluxion.utils import create_state_dict_mapping, convert_state_dict, save_to_safetensors
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from diffusers import StableDiffusionInpaintPipeline # type: ignore
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from diffusers.models.unet_2d_condition import UNet2DConditionModel # type: ignore
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from refiners.foundationals.latent_diffusion.unet import UNet
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@torch.no_grad()
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def convert(src_model: UNet2DConditionModel) -> dict[str, torch.Tensor]:
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dst_model = UNet(in_channels=9, clip_embedding_dim=768)
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x = torch.randn(1, 9, 32, 32)
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timestep = torch.tensor(data=[0])
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clip_text_embeddings = torch.randn(1, 77, 768)
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src_args = (x, timestep, clip_text_embeddings)
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dst_model.set_timestep(timestep=timestep)
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dst_model.set_clip_text_embedding(clip_text_embedding=clip_text_embeddings)
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dst_args = (x,)
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mapping = create_state_dict_mapping(source_model=src_model, target_model=dst_model, source_args=src_args, target_args=dst_args) # type: ignore
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assert mapping is not None, "Model conversion failed"
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state_dict = convert_state_dict(
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source_state_dict=src_model.state_dict(), target_state_dict=dst_model.state_dict(), state_dict_mapping=mapping
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)
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return {k: v.half() for k, v in state_dict.items()}
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def main() -> None:
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--from",
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type=str,
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dest="source",
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required=False,
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default="runwayml/stable-diffusion-inpainting",
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help="Source model",
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)
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parser.add_argument(
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"--output-file",
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type=str,
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required=False,
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default="stable_diffusion_1_5_inpainting_unet.safetensors",
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help="Path for the output file",
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)
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args = parser.parse_args()
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src_model = StableDiffusionInpaintPipeline.from_pretrained(pretrained_model_name_or_path=args.source).unet # type: ignore
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tensors = convert(src_model=src_model) # type: ignore
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save_to_safetensors(path=args.output_file, tensors=tensors)
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if __name__ == "__main__":
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main()
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