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

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2023-08-04 13:28:41 +00:00
import torch
from safetensors.torch import save_file
from refiners.fluxion.utils import (
create_state_dict_mapping,
convert_state_dict,
)
from diffusers import DiffusionPipeline
from diffusers.models.unet_2d_condition import UNet2DConditionModel
from refiners.foundationals.latent_diffusion.unet import UNet
@torch.no_grad()
def convert(src_model: UNet2DConditionModel) -> dict[str, torch.Tensor]:
dst_model = UNet(in_channels=4, clip_embedding_dim=768)
x = torch.randn(1, 4, 32, 32)
timestep = torch.tensor([0])
clip_text_embeddings = torch.randn(1, 77, 768)
src_args = (x, timestep, clip_text_embeddings)
dst_model.set_timestep(timestep)
dst_model.set_clip_text_embedding(clip_text_embeddings)
dst_args = (x,)
mapping = create_state_dict_mapping(src_model, dst_model, src_args, dst_args)
state_dict = convert_state_dict(src_model.state_dict(), dst_model.state_dict(), mapping)
return {k: v.half() for k, v in state_dict.items()}
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"--from",
type=str,
dest="source",
required=False,
default="runwayml/stable-diffusion-v1-5",
help="Source model",
)
parser.add_argument(
"--output-file",
type=str,
required=False,
default="stable_diffusion_1_5_unet.safetensors",
help="Path for the output file",
)
args = parser.parse_args()
src_model = DiffusionPipeline.from_pretrained(args.source).unet
tensors = convert(src_model)
save_file(tensors, args.output_file)
if __name__ == "__main__":
main()