diff --git a/train_db.py b/train_db.py
index 35ae1212..50ed5a64 100644
--- a/train_db.py
+++ b/train_db.py
@@ -9,6 +9,7 @@ import itertools
 import math
 import os
 import random
+import shutil
 
 from tqdm import tqdm
 import torch
@@ -816,16 +817,33 @@ def train(args):
           ckpt_file = os.path.join(args.output_dir, model_util.get_epoch_ckpt_name(use_safetensors, epoch + 1))
           model_util.save_stable_diffusion_checkpoint(args.v2, ckpt_file, unwrap_model(text_encoder), unwrap_model(unet),
                                                       src_stable_diffusion_ckpt, epoch + 1, global_step, save_dtype, vae)
+          if args.save_last_n_epochs is not None:
+            old_ckpt_file = os.path.join(args.output_dir, model_util.get_epoch_ckpt_name(use_safetensors, epoch + 1 - args.save_every_n_epochs * args.save_last_n_epochs))
+            if os.path.exists(old_ckpt_file):
+              os.remove(old_ckpt_file)
         else:
           out_dir = os.path.join(args.output_dir, train_util.EPOCH_DIFFUSERS_DIR_NAME.format(epoch + 1))
           os.makedirs(out_dir, exist_ok=True)
           model_util.save_diffusers_checkpoint(args.v2, out_dir, unwrap_model(text_encoder),
                                                unwrap_model(unet), src_diffusers_model_path,
                                                use_safetensors=use_safetensors)
+          if args.save_last_n_epochs is not None:
+            out_dir_old = os.path.join(args.output_dir, train_util.EPOCH_DIFFUSERS_DIR_NAME.format(epoch + 1 - args.save_every_n_epochs * args.save_last_n_epochs))
+            if os.path.exists(out_dir_old):
+              shutil.rmtree(out_dir_old)
+
+
+
+
+            
 
         if args.save_state:
           print("saving state.")
           accelerator.save_state(os.path.join(args.output_dir, train_util.EPOCH_STATE_NAME.format(epoch + 1)))
+          if args.save_last_n_epochs is not None:
+            state_dir_old = os.path.join(args.output_dir, train_util.EPOCH_STATE_NAME.format(epoch + 1 - args.save_every_n_epochs * args.save_last_n_epochs))
+            if os.path.exists(state_dir_old):
+              shutil.rmtree(state_dir_old)
 
   is_main_process = accelerator.is_main_process
   if is_main_process:
@@ -888,6 +906,8 @@ if __name__ == '__main__':
                       help="use safetensors format to save (if save_model_as is not specified) / checkpoint、モデルをsafetensors形式で保存する（save_model_as未指定時）")
   parser.add_argument("--save_every_n_epochs", type=int, default=None,
                       help="save checkpoint every N epochs / 学習中のモデルを指定エポックごとに保存する")
+  parser.add_argument("--save_last_n_epochs", type=int, default=None,
+                      help="save last N checkpoints / 最大Nエポック保存する")
   parser.add_argument("--save_state", action="store_true",
                       help="save training state additionally (including optimizer states etc.) / optimizerなど学習状態も含めたstateを追加で保存する")
   parser.add_argument("--resume", type=str, default=None, help="saved state to resume training / 学習再開するモデルのstate")
@@ -942,3 +962,4 @@ if __name__ == '__main__':
 
   args = parser.parse_args()
   train(args)
+