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60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
import torch
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from safetensors.torch import save_file
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from refiners.fluxion.utils import (
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create_state_dict_mapping,
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convert_state_dict,
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)
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from diffusers import DiffusionPipeline
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from diffusers.models.unet_2d_condition import UNet2DConditionModel
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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=4, clip_embedding_dim=768)
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x = torch.randn(1, 4, 32, 32)
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timestep = torch.tensor([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)
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dst_model.set_clip_text_embedding(clip_text_embeddings)
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dst_args = (x,)
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mapping = create_state_dict_mapping(src_model, dst_model, src_args, dst_args)
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state_dict = convert_state_dict(src_model.state_dict(), dst_model.state_dict(), mapping)
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return {k: v.half() for k, v in state_dict.items()}
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def main():
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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-v1-5",
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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_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 = DiffusionPipeline.from_pretrained(args.source).unet
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tensors = convert(src_model)
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save_file(tensors, args.output_file)
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if __name__ == "__main__":
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main()
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