diff --git a/train_db.py b/train_db.py
index 1de504ed8..e9f8f9f8c 100644
--- a/train_db.py
+++ b/train_db.py
@@ -271,7 +271,7 @@ def train(args):
     accelerator.print(
         f"  total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}"
     )
-    accelerator.print(f"  gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
+    accelerator.print(f"  gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
     accelerator.print(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
 
     progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
diff --git a/train_textual_inversion.py b/train_textual_inversion.py
index 10fce2677..cbb6daaa2 100644
--- a/train_textual_inversion.py
+++ b/train_textual_inversion.py
@@ -490,7 +490,7 @@ def train(self, args):
         accelerator.print(
             f"  total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}"
         )
-        accelerator.print(f"  gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
+        accelerator.print(f"  gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
         accelerator.print(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
 
         progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
diff --git a/train_textual_inversion_XTI.py b/train_textual_inversion_XTI.py
index ddd03d532..55b670355 100644
--- a/train_textual_inversion_XTI.py
+++ b/train_textual_inversion_XTI.py
@@ -387,7 +387,7 @@ def train(args):
     logger.info(
         f"  total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ（並列学習、勾配合計含む）: {total_batch_size}"
     )
-    logger.info(f"  gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
+    logger.info(f"  gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
     logger.info(f"  total optimization steps / 学習ステップ数: {args.max_train_steps}")
 
     progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")