mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-22 06:08:46 +00:00
58 lines
2.1 KiB
Python
58 lines
2.1 KiB
Python
import torch
|
|
|
|
from safetensors.torch import save_file # type: ignore
|
|
from refiners.fluxion.utils import create_state_dict_mapping, convert_state_dict
|
|
|
|
from diffusers import DiffusionPipeline # type: ignore
|
|
from transformers.models.clip.modeling_clip import CLIPTextModel # type: ignore
|
|
|
|
from refiners.foundationals.clip.tokenizer import CLIPTokenizer
|
|
from refiners.foundationals.clip.text_encoder import CLIPTextEncoderG
|
|
import refiners.fluxion.layers as fl
|
|
|
|
|
|
@torch.no_grad()
|
|
def convert(src_model: CLIPTextModel) -> dict[str, torch.Tensor]:
|
|
dst_model = CLIPTextEncoderG()
|
|
# Extra projection layer (see CLIPTextModelWithProjection in transformers)
|
|
dst_model.append(module=fl.Linear(in_features=1280, out_features=1280, bias=False))
|
|
tokenizer = dst_model.find(layer_type=CLIPTokenizer)
|
|
assert tokenizer is not None, "Could not find tokenizer"
|
|
tokens = tokenizer("Nice cat")
|
|
mapping = create_state_dict_mapping(source_model=src_model, target_model=dst_model, source_args=[tokens], target_args=["Nice cat"]) # 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.half() 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="CLIPTextEncoderG.safetensors",
|
|
help="Path for the output file",
|
|
)
|
|
args = parser.parse_args()
|
|
src_model = DiffusionPipeline.from_pretrained(pretrained_model_name_or_path=args.source).text_encoder_2 # type: ignore
|
|
tensors = convert(src_model=src_model) # type: ignore
|
|
save_file(tensors=tensors, filename=args.output_file)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|