diff --git a/fine_tune.py b/fine_tune.py
index 0f369182..1acf478f 100644
--- a/fine_tune.py
+++ b/fine_tune.py
@@ -194,7 +194,7 @@ def train(args):
 
     # 学習ステップ数を計算する
     if args.max_train_epochs is not None:
-        args.max_train_steps = args.max_train_epochs * len(train_dataloader)
+        args.max_train_steps = args.max_train_epochs * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps)
         print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
     # lr schedulerを用意する
@@ -240,7 +240,7 @@ def train(args):
     print(f"  num epochs / epoch数: {num_train_epochs}")
     print(f"  batch size per device / バッチサイズ: {args.train_batch_size}")
     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}")
-    print(f"  gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
+    print(f"  gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
     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_db.py b/train_db.py
index b97a34ba..527f8e9b 100644
--- a/train_db.py
+++ b/train_db.py
@@ -159,7 +159,7 @@ def train(args):
 
     # 学習ステップ数を計算する
     if args.max_train_epochs is not None:
-        args.max_train_steps = args.max_train_epochs * len(train_dataloader)
+        args.max_train_steps = args.max_train_epochs * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps)
         print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
     if args.stop_text_encoder_training is None:
diff --git a/train_textual_inversion.py b/train_textual_inversion.py
index 11a27048..85f0d57c 100644
--- a/train_textual_inversion.py
+++ b/train_textual_inversion.py
@@ -257,7 +257,7 @@ def train(args):
 
     # 学習ステップ数を計算する
     if args.max_train_epochs is not None:
-        args.max_train_steps = args.max_train_epochs * len(train_dataloader)
+        args.max_train_steps = args.max_train_epochs * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps)
         print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
 
     # lr schedulerを用意する