From aa850aa531b0e396b6f2fbd68cd1e6f1319d1d0b Mon Sep 17 00:00:00 2001
From: gesen2egee <79357052+gesen2egee@users.noreply.github.com>
Date: Sun, 4 Aug 2024 17:34:20 +0800
Subject: [PATCH] Update train_network.py

---
 train_network.py | 38 +++++++++++++++++++-------------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/train_network.py b/train_network.py
index fa6407eef..938e41938 100644
--- a/train_network.py
+++ b/train_network.py
@@ -1034,25 +1034,25 @@ def remove_model(old_ckpt_name):
                     logs = self.generate_step_logs(args, current_loss, avr_loss, lr_scheduler, keys_scaled, mean_norm, maximum_norm)
                     accelerator.log(logs, step=global_step)
                 
-			if len(val_dataloader) > 0:
-				if  (args.validation_every_n_step is not None and global_step % args.validation_every_n_step == 0) or (args.validation_every_n_step is None and step == len(train_dataloader) - 1) or global_step >= args.max_train_steps:
-					accelerator.print("Validating バリデーション処理...")
-					total_loss = 0.0
-					with torch.no_grad():
-						validation_steps = min(args.max_validation_steps, len(val_dataloader)) if args.max_validation_steps is not None else len(val_dataloader)
-						for val_step in tqdm(range(validation_steps), desc='Validation Steps'):
-							batch = next(cyclic_val_dataloader)
-							loss = self.process_val_batch(batch, tokenizers, text_encoders, unet, vae, noise_scheduler, vae_dtype, weight_dtype, accelerator, args)
-							total_loss += loss.detach().item()
-						current_loss = total_loss / validation_steps
-						val_loss_recorder.add(epoch=0, step=global_step, loss=current_loss)   
-	
-					if args.logging_dir is not None:
-						logs = {"loss/current_val_loss": current_loss}
-						accelerator.log(logs, step=global_step)                            
-						avr_loss: float = val_loss_recorder.moving_average
-						logs = {"loss/average_val_loss": avr_loss}
-						accelerator.log(logs, step=global_step)
+				if len(val_dataloader) > 0:
+					if  (args.validation_every_n_step is not None and global_step % args.validation_every_n_step == 0) or (args.validation_every_n_step is None and step == len(train_dataloader) - 1) or global_step >= args.max_train_steps:
+						accelerator.print("Validating バリデーション処理...")
+						total_loss = 0.0
+						with torch.no_grad():
+							validation_steps = min(args.max_validation_steps, len(val_dataloader)) if args.max_validation_steps is not None else len(val_dataloader)
+							for val_step in tqdm(range(validation_steps), desc='Validation Steps'):
+								batch = next(cyclic_val_dataloader)
+								loss = self.process_val_batch(batch, tokenizers, text_encoders, unet, vae, noise_scheduler, vae_dtype, weight_dtype, accelerator, args)
+								total_loss += loss.detach().item()
+							current_loss = total_loss / validation_steps
+							val_loss_recorder.add(epoch=0, step=global_step, loss=current_loss)   
+		
+						if args.logging_dir is not None:
+							logs = {"loss/current_val_loss": current_loss}
+							accelerator.log(logs, step=global_step)                            
+							avr_loss: float = val_loss_recorder.moving_average
+							logs = {"loss/average_val_loss": avr_loss}
+							accelerator.log(logs, step=global_step)
 		                                
                 if global_step >= args.max_train_steps:
                     break