diff --git a/library/train_util.py b/library/train_util.py
index 3a7c2c8a..aa65dc3c 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -1299,7 +1299,6 @@ def get_epoch_ckpt_name(args: argparse.Namespace, use_safetensors, epoch):
 
 def save_on_epoch_end(args: argparse.Namespace, save_func, remove_old_func, epoch_no: int, num_train_epochs: int):
   saving = epoch_no % args.save_every_n_epochs == 0 and epoch_no < num_train_epochs
-  remove_epoch_no = None
   if saving:
     os.makedirs(args.output_dir, exist_ok=True)
     save_func()
@@ -1356,12 +1355,9 @@ def save_state_on_epoch_end(args: argparse.Namespace, accelerator, model_name, e
   print("saving state.")
   accelerator.save_state(os.path.join(args.output_dir, EPOCH_STATE_NAME.format(model_name, epoch_no)))
 
-  remove_epoch_no = None
-  last_n_epoch = args.save_last_n_epochs_state if args.save_last_n_epochs_state else args.save_last_n_epochs
-  if last_n_epoch is not None:
-    remove_epoch_no = epoch_no - args.save_every_n_epochs * last_n_epoch
-
-  if remove_epoch_no is not None:
+  last_n_epochs = args.save_last_n_epochs_state if args.save_last_n_epochs_state else args.save_last_n_epochs
+  if last_n_epochs is not None:
+    remove_epoch_no = epoch_no - args.save_every_n_epochs * last_n_epochs
     state_dir_old = os.path.join(args.output_dir, EPOCH_STATE_NAME.format(model_name, remove_epoch_no))
     if os.path.exists(state_dir_old):
       print(f"removing old state: {state_dir_old}")
diff --git a/train_network.py b/train_network.py
index 03fd01e7..b2c7b579 100644
--- a/train_network.py
+++ b/train_network.py
@@ -367,9 +367,9 @@ def train(args):
           print(f"removing old checkpoint: {old_ckpt_file}")
           os.remove(old_ckpt_file)
 
-      saving, remove_epoch_no = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs)
+      saving = train_util.save_on_epoch_end(args, save_func, remove_old_func, epoch + 1, num_train_epochs)
       if saving and args.save_state:
-        train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1, remove_epoch_no)
+        train_util.save_state_on_epoch_end(args, accelerator, model_name, epoch + 1)
 
     # end of epoch