Move TE/UN loss calc to train script

Fannovel16 · Jan 21, 2023 · 22ee0ac · 22ee0ac
1 parent 17089b1
commit 22ee0ac
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Showing 2 changed files with 15 additions and 14 deletions.
diff --git a/library/train_util.py b/library/train_util.py
@@ -1423,17  1423,5 @@ def save_state_on_train_end(args: argparse.Namespace, accelerator):
   model_name = DEFAULT_LAST_OUTPUT_NAME if args.output_name is None else args.output_name
   accelerator.save_state(os.path.join(args.output_dir, LAST_STATE_NAME.format(model_name)))
 
-def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
-    logs = {"loss/current": current_loss, "loss/average": avr_loss}
-
-    if args.network_train_unet_only:
-      logs["lr/unet"] = lr_scheduler.get_last_lr()[0]
-    elif args.network_train_text_encoder_only:
-      logs["lr/textencoder"] = lr_scheduler.get_last_lr()[0]
-    else:
-      logs["lr/textencoder"] = lr_scheduler.get_last_lr()[0]
-      logs["lr/unet"] = lr_scheduler.get_last_lr()[-1]
-
-    return logs
 
 # endregion
diff --git a/train_network.py b/train_network.py
@@ -21,6  21,20 @@ def collate_fn(examples):
   return examples[0]
 
 
 def generate_step_logs(args: argparse.Namespace, current_loss, avr_loss, lr_scheduler):
   logs = {"loss/current": current_loss, "loss/average": avr_loss}
 
   if args.network_train_unet_only:
     logs["lr/unet"] = lr_scheduler.get_last_lr()[0]
   elif args.network_train_text_encoder_only:
     logs["lr/textencoder"] = lr_scheduler.get_last_lr()[0]
   else:
     logs["lr/textencoder"] = lr_scheduler.get_last_lr()[0]
     logs["lr/unet"] = lr_scheduler.get_last_lr()[-1]          # may be same to textencoder
 
   return logs
 
 
 def train(args):
   session_id = random.randint(0, 2**32)
   training_started_at = time.time()
@@ -353,8  367,7 @@ def train(args):
       progress_bar.set_postfix(**logs)
 
       if args.logging_dir is not None:
-        logs = train_util.generate_step_logs(args, current_loss, avr_loss, lr_scheduler)
-
         logs = generate_step_logs(args, current_loss, avr_loss, lr_scheduler)
         accelerator.log(logs, step=global_step)
 
       if global_step >= args.max_train_steps: