mirror of
https://github.com/finegrain-ai/refiners.git
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65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
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import argparse
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from typing import TYPE_CHECKING, cast
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import torch
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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.preprocessors.informative_drawings import InformativeDrawings
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try:
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from model import Generator # type: ignore
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except ImportError:
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raise ImportError(
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"Please download the model.py file from https://github.com/carolineec/informative-drawings and add it to your"
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" PYTHONPATH"
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)
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if TYPE_CHECKING:
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Generator = cast(nn.Module, Generator)
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class Args(argparse.Namespace):
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source_path: str
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output_path: str
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verbose: bool
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half: bool
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def setup_converter(args: Args) -> ModelConverter:
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source = Generator(3, 1, 3)
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source.load_state_dict(state_dict=torch.load(f=args.source_path, map_location="cpu")) # type: ignore
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source.eval()
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target = InformativeDrawings()
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x = torch.randn(1, 3, 512, 512)
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converter = ModelConverter(source_model=source, target_model=target, skip_output_check=True, 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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return converter
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def main() -> None:
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parser = argparse.ArgumentParser(
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description="Converts a pretrained Informative Drawings model to a refiners Informative Drawings model"
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)
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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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default="model2.pth",
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help="Path to the source model. (default: 'model2.pth').",
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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="informative-drawings.safetensors",
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help="Path to save the converted model. (default: 'informative-drawings.safetensors').",
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)
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parser.add_argument("--verbose", action="store_true", dest="verbose")
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parser.add_argument("--half", action="store_true", dest="half")
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args = parser.parse_args(namespace=Args())
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converter = setup_converter(args=args)
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converter.save_to_safetensors(path=args.output_path, half=args.half)
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if __name__ == "__main__":
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main()
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