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import argparse |
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import torch |
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from safetensors.torch import load_file |
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from diffusers import MotionAdapter |
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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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parser.add_argument("--use_motion_mid_block", action="store_true") |
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parser.add_argument("--motion_max_seq_length", type=int, default=32) |
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parser.add_argument("--block_out_channels", nargs="+", default=[320, 640, 1280, 1280], type=int) |
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parser.add_argument("--save_fp16", action="store_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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adapter = MotionAdapter( |
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block_out_channels=args.block_out_channels, |
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use_motion_mid_block=args.use_motion_mid_block, |
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motion_max_seq_length=args.motion_max_seq_length, |
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) |
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adapter.load_state_dict(conv_state_dict, strict=False) |
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adapter.save_pretrained(args.output_path) |
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if args.save_fp16: |
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adapter.to(dtype=torch.float16).save_pretrained(args.output_path, variant="fp16") |
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