2022-09-23 08:20:41 +00:00
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import torch
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import argparse
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if __name__ == "__main__":
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2022-09-23 08:21:53 +00:00
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# Make a version of the checkpoint with only ema weights (around 4GB)
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2022-09-23 08:20:41 +00:00
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parser = argparse.ArgumentParser()
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parser.add_argument("--original_ckpt", help="full size checkpoint file")
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parser.add_argument("--output_path", help="filename for ema only checkpoint")
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args = parser.parse_args()
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print(f"loading from {args.original_ckpt}")
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d = torch.load(args.original_ckpt, map_location="cpu")
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new_d = {"state_dict": {}}
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ema_state = {k: v for k, v in d["state_dict"].items() if not k.startswith("model.diffusion_model")}
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new_d["state_dict"] = ema_state
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print(f"saving to {args.output_path}")
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2022-09-23 08:21:53 +00:00
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torch.save(new_d, args.output_path)
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