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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")
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