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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)
# convert to new format
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")
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