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convert autoencoder: add an option for subfolder
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@ -16,7 +16,7 @@ class Args(argparse.Namespace):
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def setup_converter(args: Args) -> ModelConverter:
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def setup_converter(args: Args) -> ModelConverter:
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target = LatentDiffusionAutoencoder()
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target = LatentDiffusionAutoencoder()
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source: nn.Module = AutoencoderKL.from_pretrained(pretrained_model_name_or_path=args.source_path, subfolder="vae") # type: ignore
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source: nn.Module = AutoencoderKL.from_pretrained(pretrained_model_name_or_path=args.source_path, subfolder=args.subfolder) # type: ignore
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x = torch.randn(1, 3, 512, 512)
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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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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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if not converter.run(source_args=(x,)):
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@ -35,6 +35,13 @@ if __name__ == "__main__":
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default="runwayml/stable-diffusion-v1-5",
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default="runwayml/stable-diffusion-v1-5",
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help="Path to the source pretrained model (default: 'runwayml/stable-diffusion-v1-5').",
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help="Path to the source pretrained model (default: 'runwayml/stable-diffusion-v1-5').",
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)
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)
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parser.add_argument(
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"--subfolder",
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type=str,
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dest="subfolder",
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default="vae",
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help="Subfolder in the source path where the model is located inside the Hub (default: 'vae')",
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)
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parser.add_argument(
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parser.add_argument(
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"--to",
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"--to",
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type=str,
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type=str,
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