refiners/scripts/convert-sdxl-unet-weights.py

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import torch
from safetensors.torch import save_file # type: ignore
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from refiners.fluxion.utils import create_state_dict_mapping, convert_state_dict
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from diffusers import DiffusionPipeline # type: ignore
from diffusers.models.unet_2d_condition import UNet2DConditionModel # type: ignore
from refiners.foundationals.latent_diffusion.sdxl_unet import SDXLUNet
@torch.no_grad()
def convert(src_model: UNet2DConditionModel) -> dict[str, torch.Tensor]:
dst_model = SDXLUNet(in_channels=4)
x = torch.randn(1, 4, 32, 32)
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timestep = torch.tensor(data=[0])
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clip_text_embeddings = torch.randn(1, 77, 2048)
added_cond_kwargs = {"text_embeds": torch.randn(1, 1280), "time_ids": torch.randn(1, 6)}
src_args = (x, timestep, clip_text_embeddings, None, None, None, None, added_cond_kwargs)
dst_model.set_timestep(timestep=timestep)
dst_model.set_clip_text_embedding(clip_text_embedding=clip_text_embeddings)
dst_model.set_time_ids(time_ids=added_cond_kwargs["time_ids"])
dst_model.set_pooled_text_embedding(pooled_text_embedding=added_cond_kwargs["text_embeds"])
dst_args = (x,)
mapping = create_state_dict_mapping(
source_model=src_model, target_model=dst_model, source_args=src_args, target_args=dst_args # type: ignore
)
if mapping is None:
raise RuntimeError("Could not create state dict mapping")
state_dict = convert_state_dict(
source_state_dict=src_model.state_dict(), target_state_dict=dst_model.state_dict(), state_dict_mapping=mapping
)
return {k: v for k, v in state_dict.items()}
def main() -> None:
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"--from",
type=str,
dest="source",
required=False,
default="stabilityai/stable-diffusion-xl-base-0.9",
help="Source model",
)
parser.add_argument(
"--output-file",
type=str,
required=False,
default="stable_diffusion_xl_unet.safetensors",
help="Path for the output file",
)
args = parser.parse_args()
src_model = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path=args.source).unet # type: ignore
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tensors = convert(src_model=src_model) # type: ignore
save_file(tensors=tensors, filename=args.output_file)
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if __name__ == "__main__":
main()