diff --git a/train_network.py b/train_network.py
index ac0747c0..f2a6241b 100644
--- a/train_network.py
+++ b/train_network.py
@@ -913,12 +913,12 @@ class NetworkTrainer:
                     if "latents" in batch and batch["latents"] is not None:
                         latents = batch["latents"].to(accelerator.device).to(dtype=weight_dtype)
                     else:
-                        if args.train_vae_batch is None or len(batch["images"]) <= args.train_vae_batch:
+                        if args.vae_batch_size is None or len(batch["images"]) <= args.vae_batch_size:
                             with torch.no_grad():
                                 # latentに変換
                                 latents = vae.encode(batch["images"].to(dtype=vae_dtype)).latent_dist.sample().to(dtype=weight_dtype)
                         else:
-                            chunks = [batch["images"][i:i + args.train_vae_batch] for i in range(0, len(batch["images"]), args.train_vae_batch)]
+                            chunks = [batch["images"][i:i + args.vae_batch_size] for i in range(0, len(batch["images"]), args.vae_batch_size)]
                             list_latents = []
                             for chunk in chunks:
                                 with torch.no_grad():
@@ -1240,12 +1240,7 @@ def setup_parser() -> argparse.ArgumentParser:
         help="initial step number including all epochs, 0 means first step (same as not specifying). overwrites initial_epoch."
         + " / 初期ステップ数、全エポックを含むステップ数、0で最初のステップ（未指定時と同じ）。initial_epochを上書きする",
     )
-    parser.add_argument(
-    "--train_vae_batch",
-    type=int,
-    default=None,
-    help="Sets batch size of VAE when not using cached latents",
-    )
+
     # parser.add_argument("--loraplus_lr_ratio", default=None, type=float, help="LoRA+ learning rate ratio")
     # parser.add_argument("--loraplus_unet_lr_ratio", default=None, type=float, help="LoRA+ UNet learning rate ratio")
     # parser.add_argument("--loraplus_text_encoder_lr_ratio", default=None, type=float, help="LoRA+ text encoder learning rate ratio")