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import argparse |
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import torch |
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from safetensors.torch import load_file, save_file |
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def convert_motion_module(original_state_dict): |
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converted_state_dict = {} |
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for k, v in original_state_dict.items(): |
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if "pos_encoder" in k: |
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continue |
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else: |
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converted_state_dict[ |
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k.replace(".norms.0", ".norm1") |
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.replace(".norms.1", ".norm2") |
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.replace(".ff_norm", ".norm3") |
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.replace(".attention_blocks.0", ".attn1") |
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.replace(".attention_blocks.1", ".attn2") |
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.replace(".temporal_transformer", "") |
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] = v |
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return converted_state_dict |
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def get_args(): |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--ckpt_path", type=str, required=True) |
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parser.add_argument("--output_path", type=str, required=True) |
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return parser.parse_args() |
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if __name__ == "__main__": |
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args = get_args() |
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if args.ckpt_path.endswith(".safetensors"): |
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state_dict = load_file(args.ckpt_path) |
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else: |
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state_dict = torch.load(args.ckpt_path, map_location="cpu") |
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if "state_dict" in state_dict.keys(): |
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state_dict = state_dict["state_dict"] |
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conv_state_dict = convert_motion_module(state_dict) |
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output_dict = {} |
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for module_name, params in conv_state_dict.items(): |
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if type(params) is not torch.Tensor: |
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continue |
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output_dict.update({f"unet.{module_name}": params}) |
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save_file(output_dict, f"{args.output_path}/diffusion_pytorch_model.safetensors") |
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