diff --git a/networks/lora_flux.py b/networks/lora_flux.py
index 92b3979a..0b30f1b8 100644
--- a/networks/lora_flux.py
+++ b/networks/lora_flux.py
@@ -955,26 +955,26 @@ class LoRANetwork(torch.nn.Module):
         for lora in self.text_encoder_loras + self.unet_loras:
             lora.update_grad_norms()
 
-    def grad_norms(self) -> Tensor:
+    def grad_norms(self) -> Tensor | None:
         grad_norms = []
         for lora in self.text_encoder_loras + self.unet_loras:
             if hasattr(lora, "grad_norms") and lora.grad_norms is not None:
                 grad_norms.append(lora.grad_norms.mean(dim=0))
-        return torch.stack(grad_norms) if len(grad_norms) > 0 else torch.tensor([])
+        return torch.stack(grad_norms) if len(grad_norms) > 0 else None
 
-    def weight_norms(self) -> Tensor:
+    def weight_norms(self) -> Tensor | None:
         weight_norms = []
         for lora in self.text_encoder_loras + self.unet_loras:
             if hasattr(lora, "weight_norms") and lora.weight_norms is not None:
                 weight_norms.append(lora.weight_norms.mean(dim=0))
-        return torch.stack(weight_norms) if len(weight_norms) > 0 else torch.tensor([])
+        return torch.stack(weight_norms) if len(weight_norms) > 0 else None
 
-    def combined_weight_norms(self) -> Tensor:
+    def combined_weight_norms(self) -> Tensor | None:
         combined_weight_norms = []
         for lora in self.text_encoder_loras + self.unet_loras:
             if hasattr(lora, "combined_weight_norms") and lora.combined_weight_norms is not None:
                 combined_weight_norms.append(lora.combined_weight_norms.mean(dim=0))
-        return torch.stack(combined_weight_norms) if len(combined_weight_norms) > 0 else torch.tensor([])
+        return torch.stack(combined_weight_norms) if len(combined_weight_norms) > 0 else None
 
 
     def load_weights(self, file):
diff --git a/train_network.py b/train_network.py
index d6bc66ed..2b4e6d3f 100644
--- a/train_network.py
+++ b/train_network.py
@@ -1444,8 +1444,10 @@ class NetworkTrainer:
                 else:
                     if hasattr(network, "weight_norms"):
                         mean_norm = network.weight_norms().mean().item()
-                        mean_grad_norm = network.grad_norms().mean().item()
-                        mean_combined_norm = network.combined_weight_norms().mean().item()
+                        grad_norms = network.grad_norms()
+                        mean_grad_norm = grad_norms.mean().item() if grad_norms is not None else None
+                        combined_weight_norms = network.combined_weight_norms()
+                        mean_combined_norm = combined_weight_norms.mean().item() if combined_weight_norms is not None else None
                         weight_norms = network.weight_norms()
                         maximum_norm = weight_norms.max().item() if weight_norms.numel() > 0 else None
                         keys_scaled = None