Print errors and continue when lora weights are not compatible.

comfy/sd.py

@@ -376,7 +376,10 @@ class ModelPatcher:
                     mat3 = v[3].float().to(weight.device)
                     final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]]
                     mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)
-                weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype)
+                try:
+                    weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype)
+                except Exception as e:
+                    print("ERROR", key, e)
             elif len(v) == 8: #lokr
                 w1 = v[0]
                 w2 = v[1]
@@ -407,7 +410,10 @@ class ModelPatcher:
                 if v[2] is not None and dim is not None:
                     alpha *= v[2] / dim
 
-                weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)
+                try:
+                    weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)
+                except Exception as e:
+                    print("ERROR", key, e)
             else: #loha
                 w1a = v[0]
                 w1b = v[1]
@@ -424,7 +430,11 @@ class ModelPatcher:
                     m1 = torch.mm(w1a.float().to(weight.device), w1b.float().to(weight.device))
                     m2 = torch.mm(w2a.float().to(weight.device), w2b.float().to(weight.device))
 
-                weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
+                try:
+                    weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
+                except Exception as e:
+                    print("ERROR", key, e)
+
         return weight
 
     def unpatch_model(self):