Fix merge conflicts

modules/hypernetworks/hypernetwork.py

@@ -373,6 +373,12 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
     scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
     
+    clip_grad_mode_value = clip_grad_mode == "value"
+    clip_grad_mode_norm = clip_grad_mode == "norm"
+    clip_grad_enabled = clip_grad_mode_value or clip_grad_mode_norm
+    if clip_grad_enabled:
+        clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False)
+    
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
@@ -389,21 +395,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     previous_mean_loss = 0
     print("Mean loss of {} elements".format(size))
 
-    last_saved_file = "<none>"
-    last_saved_image = "<none>"
-    forced_filename = "<none>"
-
     ititial_step = hypernetwork.step or 0
     if ititial_step > steps:
         return hypernetwork, filename
     
-    clip_grad_mode_value = clip_grad_mode == "value"
-    clip_grad_mode_norm = clip_grad_mode == "norm"
-    clip_grad_enabled = clip_grad_mode_value or clip_grad_mode_norm
-    if clip_grad_enabled:
-        clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False)
-
-    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
 
     weights = hypernetwork.weights()
     for weight in weights: