| import argparse |
|
|
| import torch |
|
|
| 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("--save_fp16", action="store_true") |
|
|
| return parser.parse_args() |
|
|
|
|
| if __name__ == "__main__": |
| args = get_args() |
|
|
| 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( |
| use_motion_mid_block=args.use_motion_mid_block, motion_max_seq_length=args.motion_max_seq_length |
| ) |
| |
| adapter.load_state_dict(conv_state_dict, strict=False) |
| adapter.save_pretrained(args.output_path) |
|
|
| if args.save_fp16: |
| adapter.to(torch.float16).save_pretrained(args.output_path, variant="fp16") |
|
|
