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| import argparse | |
| import torch | |
| from safetensors.torch import load_file | |
| from diffusers import MotionAdapter | |
| def convert_motion_module(original_state_dict): | |
| converted_state_dict = {} | |
| for k, v in original_state_dict.items(): | |
| if "pos_encoder" in k: | |
| continue | |
| else: | |
| converted_state_dict[ | |
| k.replace(".norms.0", ".norm1") | |
| .replace(".norms.1", ".norm2") | |
| .replace(".ff_norm", ".norm3") | |
| .replace(".attention_blocks.0", ".attn1") | |
| .replace(".attention_blocks.1", ".attn2") | |
| .replace(".temporal_transformer", "") | |
| ] = v | |
| return converted_state_dict | |
| def get_args(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--ckpt_path", type=str, required=True) | |
| parser.add_argument("--output_path", type=str, required=True) | |
| parser.add_argument("--use_motion_mid_block", action="store_true") | |
| parser.add_argument("--motion_max_seq_length", type=int, default=32) | |
| parser.add_argument("--block_out_channels", nargs="+", default=[320, 640, 1280, 1280], type=int) | |
| parser.add_argument("--save_fp16", action="store_true") | |
| return parser.parse_args() | |
| if __name__ == "__main__": | |
| args = get_args() | |
| if args.ckpt_path.endswith(".safetensors"): | |
| state_dict = load_file(args.ckpt_path) | |
| else: | |
| state_dict = torch.load(args.ckpt_path, map_location="cpu") | |
| if "state_dict" in state_dict.keys(): | |
| state_dict = state_dict["state_dict"] | |
| conv_state_dict = convert_motion_module(state_dict) | |
| adapter = MotionAdapter( | |
| block_out_channels=args.block_out_channels, | |
| use_motion_mid_block=args.use_motion_mid_block, | |
| motion_max_seq_length=args.motion_max_seq_length, | |
| ) | |
| # skip loading position embeddings | |
| adapter.load_state_dict(conv_state_dict, strict=False) | |
| adapter.save_pretrained(args.output_path) | |
| if args.save_fp16: | |
| adapter.to(dtype=torch.float16).save_pretrained(args.output_path, variant="fp16") | |