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| import torch | |
| from contextlib import contextmanager | |
| class Linear(torch.nn.Linear): | |
| def reset_parameters(self): | |
| return None | |
| class Conv2d(torch.nn.Conv2d): | |
| def reset_parameters(self): | |
| return None | |
| class Conv3d(torch.nn.Conv3d): | |
| def reset_parameters(self): | |
| return None | |
| def conv_nd(dims, *args, **kwargs): | |
| if dims == 2: | |
| return Conv2d(*args, **kwargs) | |
| elif dims == 3: | |
| return Conv3d(*args, **kwargs) | |
| else: | |
| raise ValueError(f"unsupported dimensions: {dims}") | |
| def use_comfy_ops(device=None, dtype=None): # Kind of an ugly hack but I can't think of a better way | |
| old_torch_nn_linear = torch.nn.Linear | |
| force_device = device | |
| force_dtype = dtype | |
| def linear_with_dtype(in_features: int, out_features: int, bias: bool = True, device=None, dtype=None): | |
| if force_device is not None: | |
| device = force_device | |
| if force_dtype is not None: | |
| dtype = force_dtype | |
| return Linear(in_features, out_features, bias=bias, device=device, dtype=dtype) | |
| torch.nn.Linear = linear_with_dtype | |
| try: | |
| yield | |
| finally: | |
| torch.nn.Linear = old_torch_nn_linear | |