Function init_weights_on_device

diffsynth/models/utils.py:7–53

(device = torch.device("meta"), include_buffers :bool = False)

@contextmanager
def init_weights_on_device(device = torch.device("meta"), include_buffers :bool = False):
    
    old_register_parameter = torch.nn.Module.register_parameter
    if include_buffers:
        old_register_buffer = torch.nn.Module.register_buffer
    
    def register_empty_parameter(module, name, param):
        old_register_parameter(module, name, param)
        if param is not None:
            param_cls = type(module._parameters[name])
            kwargs = module._parameters[name].__dict__
            kwargs["requires_grad"] = param.requires_grad
            module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs)

    def register_empty_buffer(module, name, buffer, persistent=True):
        old_register_buffer(module, name, buffer, persistent=persistent)
        if buffer is not None:
            module._buffers[name] = module._buffers[name].to(device)
            
    def patch_tensor_constructor(fn):
        def wrapper(*args, **kwargs):
            kwargs["device"] = device
            return fn(*args, **kwargs)

        return wrapper
    
    if include_buffers:
        tensor_constructors_to_patch = {
            torch_function_name: getattr(torch, torch_function_name)
            for torch_function_name in ["empty", "zeros", "ones", "full"]
        }
    else:
        tensor_constructors_to_patch = {}
    
    try:
        torch.nn.Module.register_parameter = register_empty_parameter
        if include_buffers:
            torch.nn.Module.register_buffer = register_empty_buffer
        for torch_function_name in tensor_constructors_to_patch.keys():
            setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name)))
        yield
    finally:
        torch.nn.Module.register_parameter = old_register_parameter
        if include_buffers:
            torch.nn.Module.register_buffer = old_register_buffer
        for torch_function_name, old_torch_function in tensor_constructors_to_patch.items():
            setattr(torch, torch_function_name, old_torch_function)

def load_state_dict_from_folder(file_path, torch_dtype=None):
    state_dict = {}