diff --git a/train_network.py b/train_network.py
index 5a80d825..f3c8d8c9 100644
--- a/train_network.py
+++ b/train_network.py
@@ -1199,7 +1199,8 @@ class NetworkTrainer:
             )
 
         loss_recorder = train_util.LossRecorder()
-        val_loss_recorder = train_util.LossRecorder()
+        val_step_loss_recorder = train_util.LossRecorder()
+        val_epoch_loss_recorder = train_util.LossRecorder()
 
         del train_dataset_group
         if val_dataset_group is not None:
@@ -1299,7 +1300,8 @@ class NetworkTrainer:
                     # temporary, for batch processing
                     self.on_step_start(args, accelerator, network, text_encoders, unet, batch, weight_dtype)
 
-                    loss = self.process_batch(batch, 
+                    loss = self.process_batch(
+                        batch, 
                         text_encoders, 
                         unet, 
                         network, 
@@ -1373,15 +1375,25 @@ class NetworkTrainer:
 
                 if is_tracking:
                     logs = self.generate_step_logs(
-                        args, current_loss, avr_loss, lr_scheduler, lr_descriptions, optimizer, keys_scaled, mean_norm, maximum_norm
+                        args, 
+                        current_loss, 
+                        avr_loss, 
+                        lr_scheduler, 
+                        lr_descriptions, 
+                        optimizer, 
+                        keys_scaled, 
+                        mean_norm, 
+                        maximum_norm
                     )
                     # accelerator.log(logs, step=global_step)
                     accelerator.log(logs)
 
                 # VALIDATION PER STEP
-                should_validate = (args.validation_every_n_step is not None 
-                                   and global_step % args.validation_every_n_step == 0)
-                if validation_steps > 0 and should_validate:
+                should_validate_epoch = (
+                    args.validate_every_n_steps is not None 
+                    and global_step % args.validate_every_n_steps == 0
+                )
+                if validation_steps > 0 and should_validate_epoch:
                     accelerator.print("Validating バリデーション処理...")
 
                     val_progress_bar = tqdm(
@@ -1409,16 +1421,17 @@ class NetworkTrainer:
                             is_train=False
                         )
 
-                        val_loss_recorder.add(epoch=epoch, step=val_step, loss=loss.detach().item())
+                        current_loss = loss.detach().item()
+                        val_step_loss_recorder.add(epoch=epoch, step=val_step, loss=current_loss)
                         val_progress_bar.update(1)
-                        val_progress_bar.set_postfix({ "val_avg_loss": val_loss_recorder.moving_average })
+                        val_progress_bar.set_postfix({ "val_avg_loss": val_step_loss_recorder.moving_average })
 
                         if is_tracking:
-                            logs = {"loss/current_val_loss": loss.detach().item()}
+                            logs = {"loss/step_validation_current": current_loss}
                             # accelerator.log(logs, step=(len(val_dataloader) * epoch) + 1 + val_step)
                             accelerator.log(logs)
 
-                            logs = {"loss/average_val_loss": val_loss_recorder.moving_average}
+                            logs = {"loss/step_validation_average": val_step_loss_recorder.moving_average}
                             # accelerator.log(logs, step=global_step)
                             accelerator.log(logs)
                                         
@@ -1426,12 +1439,18 @@ class NetworkTrainer:
                     break
 
             # VALIDATION EPOCH
-            if len(val_dataloader) > 0:
+            should_validate_epoch = (
+                (epoch + 1) % args.validate_every_n_epochs == 0 
+                if args.validate_every_n_epochs is not None 
+                else False
+            )
+
+            if should_validate_epoch and len(val_dataloader) > 0:
                 accelerator.print("Validating バリデーション処理...")
                 val_progress_bar = tqdm(
                     range(validation_steps), smoothing=0, 
                     disable=not accelerator.is_local_main_process, 
-                    desc="validation steps"
+                    desc="epoch validation steps"
                 )
 
                 for val_step, batch in enumerate(val_dataloader):
@@ -1455,18 +1474,18 @@ class NetworkTrainer:
                     )
 
                     current_loss = loss.detach().item()
-                    val_loss_recorder.add(epoch=epoch, step=val_step, loss=current_loss)
+                    val_epoch_loss_recorder.add(epoch=epoch, step=val_step, loss=current_loss)
                     val_progress_bar.update(1)
-                    val_progress_bar.set_postfix({ "val_avg_loss": val_loss_recorder.moving_average })
+                    val_progress_bar.set_postfix({ "val_epoch_avg_loss": val_epoch_loss_recorder.moving_average })
 
                     if is_tracking:
-                        logs = {"loss/validation_current": current_loss}
+                        logs = {"loss/epoch_validation_current": current_loss}
                         # accelerator.log(logs, step=(len(val_dataloader) * epoch) + 1 + val_step)
                         accelerator.log(logs)
 
                 if is_tracking:
-                    avr_loss: float = val_loss_recorder.moving_average
-                    logs = {"loss/validation_average": avr_loss}
+                    avr_loss: float = val_epoch_loss_recorder.moving_average
+                    logs = {"loss/epoch_validation_average": avr_loss}
                     # accelerator.log(logs, step=epoch + 1)
                     accelerator.log(logs)
 
@@ -1475,12 +1494,6 @@ class NetworkTrainer:
                 logs = {"loss/epoch_average": loss_recorder.moving_average}
                 # accelerator.log(logs, step=epoch + 1)
                 accelerator.log(logs)
-
-            if len(val_dataloader) > 0 and is_tracking:
-                avr_loss: float = val_loss_recorder.moving_average
-                logs = {"loss/validation_epoch_average": avr_loss}
-                # accelerator.log(logs, step=epoch + 1)
-                accelerator.log(logs)
                     
             accelerator.wait_for_everyone()
 
@@ -1676,10 +1689,16 @@ def setup_parser() -> argparse.ArgumentParser:
         help="Split for validation images out of the training dataset / 学習画像から検証画像に分割する割合"
     )
     parser.add_argument(
-        "--validation_every_n_step",
+        "--validate_every_n_steps",
         type=int,
         default=None,
-        help="Number of train steps for counting validation loss. By default, validation per train epoch is performed / 学習エポックごとに検証を行う場合はNoneを指定する"
+        help="Run validation dataset every N steps"
+    )
+    parser.add_argument(
+        "--validate_every_n_epochs",
+        type=int,
+        default=None,
+        help="Run validation dataset every N epochs. By default, validation will run every epoch if a validation dataset is available"
     )
     parser.add_argument(
         "--max_validation_steps",