Add a check and explanation for tensor with all NaNs.

modules/devices.py

@@ -106,6 +106,33 @@ def autocast(disable=False):
     return torch.autocast("cuda")
 
 
+class NansException(Exception):
+    pass
+
+
+def test_for_nans(x, where):
+    from modules import shared
+
+    if not torch.all(torch.isnan(x)).item():
+        return
+
+    if where == "unet":
+        message = "A tensor with all NaNs was produced in Unet."
+
+        if not shared.cmd_opts.no_half:
+            message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try using --no-half commandline argument to fix this."
+
+    elif where == "vae":
+        message = "A tensor with all NaNs was produced in VAE."
+
+        if not shared.cmd_opts.no_half and not shared.cmd_opts.no_half_vae:
+            message += " This could be because there's not enough precision to represent the picture. Try adding --no-half-vae commandline argument to fix this."
+    else:
+        message = "A tensor with all NaNs was produced."
+
+    raise NansException(message)
+
+
 # MPS workaround for https://github.com/pytorch/pytorch/issues/79383
 orig_tensor_to = torch.Tensor.to
 def tensor_to_fix(self, *args, **kwargs):
@@ -156,3 +183,4 @@ if has_mps():
             torch.Tensor.cumsum = lambda self, *args, **kwargs: ( cumsum_fix(self, orig_Tensor_cumsum, *args, **kwargs) )
         orig_narrow = torch.narrow
         torch.narrow = lambda *args, **kwargs: ( orig_narrow(*args, **kwargs).clone() )
+