Merge branch 'AUTOMATIC1111:master' into master

modules/hypernetworks/hypernetwork.py

@@ -325,6 +325,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...
     optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
+    steps_without_grad = 0
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, entries in pbar:
         hypernetwork.step = i + ititial_step
@@ -347,8 +349,17 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
 
             optimizer.zero_grad()
+            weights[0].grad = None
             loss.backward()
+
+            if weights[0].grad is None:
+                steps_without_grad += 1
+            else:
+                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'
+
             optimizer.step()
+
         mean_loss = losses.mean()
         if torch.isnan(mean_loss):
             raise RuntimeError("Loss diverged.")