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60 lines
2.2 KiB
Python
60 lines
2.2 KiB
Python
import argparse
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from pathlib import Path
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import torch
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from diffusers import T2IAdapter # type: ignore
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from torch import nn
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from refiners.fluxion.model_converter import ModelConverter
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from refiners.foundationals.latent_diffusion.t2i_adapter import ConditionEncoder, ConditionEncoderXL
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Convert a pretrained diffusers T2I-Adapter model to refiners")
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parser.add_argument(
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"--from",
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type=str,
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dest="source_path",
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required=True,
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help="Path or repository name of the source model. (e.g.: 'ip-adapter_sd15.bin').",
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)
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parser.add_argument(
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"--to",
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type=str,
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dest="output_path",
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default=None,
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help=(
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"Path to save the converted model (extension will be .safetensors). If not specified, the output path will"
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" be the source path with the extension changed to .safetensors."
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),
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)
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parser.add_argument(
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"--half",
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action="store_true",
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dest="use_half",
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default=False,
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help="Use this flag to save the output file as half precision (default: full precision).",
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)
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parser.add_argument(
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"--verbose",
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action="store_true",
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dest="verbose",
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default=False,
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help="Use this flag to print verbose output during conversion.",
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)
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args = parser.parse_args()
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if args.output_path is None:
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args.output_path = f"{Path(args.source_path).name}.safetensors"
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assert args.output_path is not None
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sdxl = "xl" in args.source_path
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target = ConditionEncoderXL() if sdxl else ConditionEncoder()
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source: nn.Module = T2IAdapter.from_pretrained(pretrained_model_name_or_path=args.source_path) # type: ignore
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assert isinstance(source, nn.Module), "Source model is not a nn.Module"
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x = torch.randn(1, 3, 1024, 1024) if sdxl else torch.randn(1, 3, 512, 512)
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converter = ModelConverter(source_model=source, target_model=target, verbose=args.verbose)
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if not converter.run(source_args=(x,)):
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raise RuntimeError("Model conversion failed")
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converter.save_to_safetensors(path=args.output_path, half=args.use_half)
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