using safetensor as default
Traceback (most recent call last):
File ".\inference.py", line 144, in <module>
main(args)
File ".\inference.py", line 36, in main
preprocess_model = CropAndExtract(sadtalker_paths, device)
File "E:\a\SadTalker\SadTalker\SadTalkerv2\src\utils\preprocess.py", line 49, in __init__
self.propress = Preprocesser(device)
File "E:\a\SadTalker\SadTalker\SadTalkerv2\src\utils\croper.py", line 21, in __init__
self.predictor = KeypointExtractor(device)
File "E:\a\SadTalker\SadTalker\SadTalkerv2\src\face3d\extract_kp_videos_safe.py", line 27, in __init__
self.detector = init_alignment_model('awing_fan',device=device, model_rootpath=root_path)
File "E:\a\SadTalker\SadTalker\SadTalkerv2\python38\lib\site-packages\facexlib\alignment\__init__.py", line 19, in init_alignment_model
model.load_state_dict(torch.load(model_path)['state_dict'], strict=True)
File "E:\a\SadTalker\SadTalker\SadTalkerv2\python38\lib\site-packages\torch\serialization.py", line 795, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "E:\a\SadTalker\SadTalker\SadTalkerv2\python38\lib\site-packages\torch\serialization.py", line 1012, in _legacy_load
result = unpickler.load()
File "E:\a\SadTalker\SadTalker\SadTalkerv2\python38\lib\site-packages\torch\serialization.py", line 958, in persistent_load
wrap_storage=restore_location(obj, location),
File "E:\a\SadTalker\SadTalker\SadTalkerv2\python38\lib\site-packages\torch\serialization.py", line 215, in default_restore_location
result = fn(storage, location)
File "E:\a\SadTalker\SadTalker\SadTalkerv2\python38\lib\site-packages\torch\serialization.py", line 182, in _cuda_deserialize
device = validate_cuda_device(location)
File "E:\a\SadTalker\SadTalker\SadTalkerv2\python38\lib\site-packages\torch\serialization.py", line 166, in validate_cuda_device
raise RuntimeError('Attempting to deserialize object on a CUDA '
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.
