Simplify grad clip

modules/hypernetworks/hypernetwork.py

@@ -385,10 +385,10 @@ 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 = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else \
+        torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \
+        None
+    if clip_grad:
         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
@@ -433,7 +433,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         if shared.state.interrupted:
             break
 
-        if clip_grad_enabled:
+        if clip_grad:
             clip_grad_sched.step(hypernetwork.step)
 
         with torch.autocast("cuda"):
@@ -458,10 +458,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
                 steps_without_grad = 0
             assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue'
 
-            if clip_grad_mode_value:
-                torch.nn.utils.clip_grad_value_(weights, clip_value=clip_grad_sched.learn_rate)
-            elif clip_grad_mode_norm:
-                torch.nn.utils.clip_grad_norm_(weights, max_norm=clip_grad_sched.learn_rate)
+            if clip_grad:
+                clip_grad(weights, clip_grad_sched.learn_rate)
 
             optimizer.step()
 

modules/textual_inversion/textual_inversion.py

@@ -269,10 +269,10 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 
     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 = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else \
+        torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \
+        None
+    if clip_grad:
         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)}..."
@@ -302,7 +302,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
         if shared.state.interrupted:
             break
 
-        if clip_grad_enabled:
+        if clip_grad:
             clip_grad_sched.step(embedding.step)
 
         with torch.autocast("cuda"):
@@ -316,10 +316,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             optimizer.zero_grad()
             loss.backward()
 
-            if clip_grad_mode_value:
-                torch.nn.utils.clip_grad_value_(embedding.vec, clip_value=clip_grad_sched.learn_rate)
-            elif clip_grad_mode_norm:
-                torch.nn.utils.clip_grad_norm_(embedding.vec, max_norm=clip_grad_sched.learn_rate)
+            if clip_grad:
+                clip_grad(embedding.vec, clip_grad_sched.learn_rate)
 
             optimizer.step()