diff --git a/fine_tune.py b/fine_tune.py
index 19a35229..c79f97d2 100644
--- a/fine_tune.py
+++ b/fine_tune.py
@@ -383,7 +383,7 @@ def train(args):
 
                     if args.min_snr_gamma:
                         loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
-                    if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
+                    if args.scale_v_pred_loss_like_noise_pred:
                         loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                     if args.debiased_estimation_loss:
                         loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
diff --git a/library/train_util.py b/library/train_util.py
index adb983d2..f479dcc6 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -3727,15 +3727,16 @@ def verify_training_args(args: argparse.Namespace):
     if args.adaptive_noise_scale is not None and args.noise_offset is None:
         raise ValueError("adaptive_noise_scale requires noise_offset / adaptive_noise_scaleを使用するにはnoise_offsetが必要です")
 
+    if args.scale_v_pred_loss_like_noise_pred:
+        logger.warning(
+            f"scale_v_pred_loss_like_noise_pred is deprecated. it is suggested to use min_snr_gamma or debiased_estimation_loss"
+            + " / scale_v_pred_loss_like_noise_pred は非推奨です。min_snr_gammaまたはdebiased_estimation_lossを使用することをお勧めします"
+        )
+
     if args.scale_v_pred_loss_like_noise_pred and not args.v_parameterization:
         raise ValueError(
             "scale_v_pred_loss_like_noise_pred can be enabled only with v_parameterization / scale_v_pred_loss_like_noise_predはv_parameterizationが有効なときのみ有効にできます"
         )
-    
-    if args.scale_v_pred_loss_like_noise_pred and args.zero_terminal_snr:
-        raise ValueError(
-            "zero_terminal_snr enabled. scale_v_pred_loss_like_noise_pred will not be used / zero_terminal_snrが有効です。scale_v_pred_loss_like_noise_predは使用されません"
-        )
 
     if args.v_pred_like_loss and args.v_parameterization:
         raise ValueError(
diff --git a/sdxl_train.py b/sdxl_train.py
index 44ee9233..b533b274 100644
--- a/sdxl_train.py
+++ b/sdxl_train.py
@@ -725,7 +725,7 @@ def train(args):
 
                     if args.min_snr_gamma:
                         loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
-                    if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
+                    if args.scale_v_pred_loss_like_noise_pred:
                         loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                     if args.v_pred_like_loss:
                         loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
diff --git a/sdxl_train_control_net_lllite.py b/sdxl_train_control_net_lllite.py
index 436f0e19..0e67cde5 100644
--- a/sdxl_train_control_net_lllite.py
+++ b/sdxl_train_control_net_lllite.py
@@ -474,7 +474,7 @@ def train(args):
 
                 if args.min_snr_gamma:
                     loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
-                if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
+                if args.scale_v_pred_loss_like_noise_pred:
                     loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                 if args.v_pred_like_loss:
                     loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
diff --git a/sdxl_train_control_net_lllite_old.py b/sdxl_train_control_net_lllite_old.py
index 8fba9eba..4a01f9e2 100644
--- a/sdxl_train_control_net_lllite_old.py
+++ b/sdxl_train_control_net_lllite_old.py
@@ -434,7 +434,7 @@ def train(args):
 
                 if args.min_snr_gamma:
                     loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
-                if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
+                if args.scale_v_pred_loss_like_noise_pred:
                     loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                 if args.v_pred_like_loss:
                     loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
diff --git a/train_db.py b/train_db.py
index d5a94a56..e7cf3cde 100644
--- a/train_db.py
+++ b/train_db.py
@@ -370,7 +370,7 @@ def train(args):
 
                 if args.min_snr_gamma:
                     loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
-                if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
+                if args.scale_v_pred_loss_like_noise_pred:
                     loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                 if args.debiased_estimation_loss:
                     loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)
diff --git a/train_network.py b/train_network.py
index 790fbfc9..7bf125dc 100644
--- a/train_network.py
+++ b/train_network.py
@@ -993,7 +993,7 @@ class NetworkTrainer:
 
                     if args.min_snr_gamma:
                         loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
-                    if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
+                    if args.scale_v_pred_loss_like_noise_pred:
                         loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                     if args.v_pred_like_loss:
                         loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
diff --git a/train_textual_inversion.py b/train_textual_inversion.py
index 10b34db5..37349da7 100644
--- a/train_textual_inversion.py
+++ b/train_textual_inversion.py
@@ -598,7 +598,7 @@ class TextualInversionTrainer:
 
                     if args.min_snr_gamma:
                         loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
-                    if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
+                    if args.scale_v_pred_loss_like_noise_pred:
                         loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                     if args.v_pred_like_loss:
                         loss = add_v_prediction_like_loss(loss, timesteps, noise_scheduler, args.v_pred_like_loss)
diff --git a/train_textual_inversion_XTI.py b/train_textual_inversion_XTI.py
index 084b90c6..fac0787b 100644
--- a/train_textual_inversion_XTI.py
+++ b/train_textual_inversion_XTI.py
@@ -483,7 +483,7 @@ def train(args):
                 loss = loss * loss_weights
                 if args.min_snr_gamma:
                     loss = apply_snr_weight(loss, timesteps, noise_scheduler, args.min_snr_gamma, args.v_parameterization)
-                if args.scale_v_pred_loss_like_noise_pred and not args.zero_terminal_snr:
+                if args.scale_v_pred_loss_like_noise_pred:
                     loss = scale_v_prediction_loss_like_noise_prediction(loss, timesteps, noise_scheduler)
                 if args.debiased_estimation_loss:
                     loss = apply_debiased_estimation(loss, timesteps, noise_scheduler, args.v_parameterization)