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     proj_outputs = [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)