refiners/scripts/conversion/convert_transformers_clip_text_model.py

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import argparse
from pathlib import Path
from typing import cast
from torch import nn
from refiners.fluxion.model_converter import ModelConverter
from transformers import CLIPTextModelWithProjection # type: ignore
from refiners.foundationals.clip.text_encoder import CLIPTextEncoder, CLIPTextEncoderL, CLIPTextEncoderG
from refiners.foundationals.clip.tokenizer import CLIPTokenizer
from refiners.foundationals.latent_diffusion.stable_diffusion_xl.text_encoder import DoubleTextEncoder
from refiners.fluxion.utils import save_to_safetensors
import refiners.fluxion.layers as fl
class Args(argparse.Namespace):
source_path: str
subfolder: str
output_path: str | None
half: bool
verbose: bool
def setup_converter(args: Args) -> ModelConverter:
source: nn.Module = CLIPTextModelWithProjection.from_pretrained( # type: ignore
pretrained_model_name_or_path=args.source_path, subfolder=args.subfolder
)
assert isinstance(source, nn.Module), "Source model is not a nn.Module"
architecture: str = source.config.architectures[0] # type: ignore
embedding_dim: int = source.config.hidden_size # type: ignore
projection_dim: int = source.config.projection_dim # type: ignore
num_layers: int = source.config.num_hidden_layers # type: ignore
num_attention_heads: int = source.config.num_attention_heads # type: ignore
feed_forward_dim: int = source.config.intermediate_size # type: ignore
use_quick_gelu: bool = source.config.hidden_act == "quick_gelu" # type: ignore
target = CLIPTextEncoder(
embedding_dim=embedding_dim,
num_layers=num_layers,
num_attention_heads=num_attention_heads,
feedforward_dim=feed_forward_dim,
use_quick_gelu=use_quick_gelu,
)
match architecture:
case "CLIPTextModel":
source.text_projection = fl.Identity()
case "CLIPTextModelWithProjection":
target.append(module=fl.Linear(in_features=embedding_dim, out_features=projection_dim, bias=False))
case _:
raise RuntimeError(f"Unsupported architecture: {architecture}")
text = "What a nice cat you have there!"
tokenizer = target.ensure_find(CLIPTokenizer)
tokens = tokenizer(text)
converter = ModelConverter(source_model=source, target_model=target, skip_output_check=True, verbose=args.verbose)
if not converter.run(source_args=(tokens,), target_args=(text,)):
raise RuntimeError("Model conversion failed")
return converter
def main() -> None:
parser = argparse.ArgumentParser(
description="Converts a CLIPTextEncoder from the library transformers from the HuggingFace Hub to refiners."
)
parser.add_argument(
"--from",
type=str,
dest="source_path",
default="runwayml/stable-diffusion-v1-5",
help=(
"Can be a path to a .bin file, a .safetensors file or a model name from the HuggingFace Hub. Default:"
" runwayml/stable-diffusion-v1-5"
),
)
parser.add_argument(
"--subfolder",
type=str,
dest="subfolder",
default="text_encoder",
help=(
"Subfolder in the source path where the model is located inside the Hub. Default: text_encoder (for"
" CLIPTextModel)"
),
)
parser.add_argument(
"--subfolder2",
type=str,
dest="subfolder2",
default=None,
help="Additional subfolder for the 2nd text encoder (useful for SDXL). Default: None",
)
parser.add_argument(
"--to",
type=str,
dest="output_path",
default=None,
help=(
"Output path (.safetensors) for converted model. If not provided, the output path will be the same as the"
" source path."
),
)
parser.add_argument("--half", action="store_true", help="Convert to half precision. Default: True")
parser.add_argument(
"--verbose",
action="store_true",
default=False,
help="Prints additional information during conversion. Default: False",
)
args = parser.parse_args(namespace=Args())
if args.output_path is None:
args.output_path = f"{Path(args.source_path).stem}-{args.subfolder}.safetensors"
converter = setup_converter(args=args)
if args.subfolder2 is not None:
# Assume this is the second text encoder of Stable Diffusion XL
args.subfolder = args.subfolder2
converter2 = setup_converter(args=args)
text_encoder_l = CLIPTextEncoderL()
text_encoder_l.load_state_dict(state_dict=converter.get_state_dict())
projection = cast(CLIPTextEncoder, converter2.target_model)[-1]
assert isinstance(projection, fl.Linear)
text_encoder_g_with_projection = CLIPTextEncoderG()
text_encoder_g_with_projection.append(module=projection)
text_encoder_g_with_projection.load_state_dict(state_dict=converter2.get_state_dict())
projection = text_encoder_g_with_projection.pop(index=-1)
assert isinstance(projection, fl.Linear)
double_text_encoder = DoubleTextEncoder(
text_encoder_l=text_encoder_l, text_encoder_g=text_encoder_g_with_projection, projection=projection
)
state_dict = double_text_encoder.state_dict()
if args.half:
state_dict = {key: value.half() for key, value in state_dict.items()}
save_to_safetensors(path=args.output_path, tensors=state_dict)
else:
converter.save_to_safetensors(path=args.output_path, half=args.half)
if __name__ == "__main__":
main()