Inpaint example runtime error

[xv5kVu4FN]

Hi! I'm trying to execute the controlnet.ipynb notebook for the inpaintint example and the execution raises a RuntimeError.

Input type (torch.FloatTensor) and weight type (CPUBFloat16Type) should be the same or input should be a MKLDNN tensor and weight is a dense tensor

Is there any way to make it work?

Thanks for your help!

• OS: Windows 10 > Windows Subsystem for Linux (Ubuntu 20.04.6 LTS)
• Models: Lite
• DType: bfloat16
• Traceback:

  
  RuntimeError                              Traceback (most recent call last)
  Cell In[17], line 15
   12 threshold = 0.2
   14 with torch.no_grad(), torch.cuda.amp.autocast(dtype=torch.bfloat16):
  ---> 15     cnet, cnet_input = core.get_cnet(
   16         batch, models, extras, mask=mask, outpaint=outpaint, threshold=threshold
   17     )
   18     cnet_uncond = cnet
   20 show_images(batch[\"images\"])

File c:\StableCascade\train\train_c_controlnet.py:149, in WurstCore.get_cnet(self, batch, models, extras, cnet_input, **kwargs) 147 cnet_input_preview = cnet_input 148 cnet_input, cnet_input_preview = cnet_input.to(self.device), cnet_input_preview.to(self.device) --> 149 cnet = models.controlnet(cnet_input) 150 return cnet, cnet_input_preview

File c:\Users\Me\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\ n\modules\module.py:1518, in Module._wrapped_call_impl(self, *args, kwargs) 1516 return self._compiled_call_impl(*args, *kwargs) # type: ignore[misc] 1517 else: -> 1518 return self._call_impl(args, kwargs)

File c:\Users\Me\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\ n\modules\module.py:1527, in Module._call_impl(self, *args, *kwargs) 1522 # If we don't have any hooks, we want to skip the rest of the logic in 1523 # this function, and just call forward. 1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 1525 or _global_backward_pre_hooks or _global_backward_hooks 1526 or _global_forward_hooks or _global_forward_pre_hooks): -> 1527 return forward_call(args, **kwargs) 1529 try: 1530 result = None

File c:\StableCascade\modules\controlnet.py:77, in ControlNet.forward(self, x) 76 def forward(self, x): ---> 77 x = self.backbone(x) 78 projoutputs = [None for in range(max(self.proj_blocks) + 1)] 79 for i, idx in enumerate(self.proj_blocks):

File c:\Users\Me\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\ n\modules\module.py:1518, in Module._wrapped_call_impl(self, *args, kwargs) 1516 return self._compiled_call_impl(*args, *kwargs) # type: ignore[misc] 1517 else: -> 1518 return self._call_impl(args, kwargs)

File c:\Users\Me\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\ n\modules\module.py:1527, in Module._call_impl(self, *args, *kwargs) 1522 # If we don't have any hooks, we want to skip the rest of the logic in 1523 # this function, and just call forward. 1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 1525 or _global_backward_pre_hooks or _global_backward_hooks 1526 or _global_forward_hooks or _global_forward_pre_hooks): -> 1527 return forward_call(args, **kwargs) 1529 try: 1530 result = None

File c:\Users\Me\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\ n\modules\container.py:215, in Sequential.forward(self, input) 213 def forward(self, input): 214 for module in self: --> 215 input = module(input) 216 return input

File c:\Users\Me\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\ n\modules\module.py:1518, in Module._wrapped_call_impl(self, *args, kwargs) 1516 return self._compiled_call_impl(*args, *kwargs) # type: ignore[misc] 1517 else: -> 1518 return self._call_impl(args, kwargs)

File c:\Users\Me\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\ n\modules\module.py:1527, in Module._call_impl(self, *args, *kwargs) 1522 # If we don't have any hooks, we want to skip the rest of the logic in 1523 # this function, and just call forward. 1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 1525 or _global_backward_pre_hooks or _global_backward_hooks 1526 or _global_forward_hooks or _global_forward_pre_hooks): -> 1527 return forward_call(args, **kwargs) 1529 try: 1530 result = None

File c:\Users\Me\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\ n\modules\container.py:215, in Sequential.forward(self, input) 213 def forward(self, input): 214 for module in self: --> 215 input = module(input) 216 return input

File c:\Users\Me\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\ n\modules\module.py:1518, in Module._wrapped_call_impl(self, *args, kwargs) 1516 return self._compiled_call_impl(*args, *kwargs) # type: ignore[misc] 1517 else: -> 1518 return self._call_impl(args, kwargs)

File c:\Users\Me\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\ n\modules\module.py:1527, in Module._call_impl(self, *args, *kwargs) 1522 # If we don't have any hooks, we want to skip the rest of the logic in 1523 # this function, and just call forward. 1524 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks 1525 or _global_backward_pre_hooks or _global_backward_hooks 1526 or _global_forward_hooks or _global_forward_pre_hooks): -> 1527 return forward_call(args, **kwargs) 1529 try: 1530 result = None

File c:\Users\Me\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\ n\modules\conv.py:460, in Conv2d.forward(self, input) 459 def forward(self, input: Tensor) -> Tensor: --> 460 return self._conv_forward(input, self.weight, self.bias)

File c:\Users\Me\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\ n\modules\conv.py:456, in Conv2d._conv_forward(self, input, weight, bias) 452 if self.padding_mode != 'zeros': 453 return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode), 454 weight, bias, self.stride, 455 _pair(0), self.dilation, self.groups) --> 456 return F.conv2d(input, weight, bias, self.stride, 457 self.padding, self.dilation, self.groups)