| | import argparse |
| |
|
| | import torch |
| | from safetensors.torch import load_file, save_file |
| |
|
| |
|
| | 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) |
| |
|
| | 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) |
| |
|
| | |
| | output_dict = {} |
| | for module_name, params in conv_state_dict.items(): |
| | if type(params) is not torch.Tensor: |
| | continue |
| | output_dict.update({f"unet.{module_name}": params}) |
| |
|
| | save_file(output_dict, f"{args.output_path}/diffusion_pytorch_model.safetensors") |
| |
|
