Merge branch 'kohya-ss:main' into min-SNR

train_network.py

@@ -139,7 +139,7 @@ def train(args):
         vae.requires_grad_(False)
         vae.eval()
         with torch.no_grad():
-            train_dataset_group.cache_latents(vae)
+            train_dataset_group.cache_latents(vae, args.vae_batch_size)
         vae.to("cpu")
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
@@ -196,7 +196,7 @@ 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 * math.ceil(len(train_dataloader) / accelerator.num_processes)
+        args.max_train_steps = args.max_train_epochs * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps)
         if is_main_process:
             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}")
 
@@ -654,7 +654,7 @@ def train(args):
         print("model saved.")
 
 
-if __name__ == "__main__":
+def setup_parser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser()
 
     train_util.add_sd_models_arguments(parser)
@@ -697,6 +697,12 @@ if __name__ == "__main__":
         "--training_comment", type=str, default=None, help="arbitrary comment string stored in metadata / メタデータに記録する任意のコメント文字列"
     )
 
+    return parser
+
+
+if __name__ == "__main__":
+    parser = setup_parser()
+
     args = parser.parse_args()
     args = train_util.read_config_from_file(args, parser)