import argparse from pathlib import Path 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 from refiners.foundationals.clip.tokenizer import CLIPTokenizer 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.find(layer_type=CLIPTokenizer) assert tokenizer is not None, "Could not find tokenizer" 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( "--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", default=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) converter.save_to_safetensors(path=args.output_path, half=args.half) if __name__ == "__main__": main()