Save state on train end

fine_tune.py

@@ -457,7 +457,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
 
     accelerator.end_training()
 
-    if args.save_state and is_main_process:
+    if is_main_process and (args.save_state or args.save_state_on_train_end):        
         train_util.save_state_on_train_end(args, accelerator)
 
     del accelerator  # この後メモリを使うのでこれは消す

library/train_util.py

@@ -2890,6 +2890,11 @@ def add_training_arguments(parser: argparse.ArgumentParser, support_dreambooth:
         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(
+        "--save_state_on_train_end",
+        action="store_true",
+        help="save training state additionally (including optimizer states etc.) on train end / 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")
 
     parser.add_argument("--train_batch_size", type=int, default=1, help="batch size for training / 学習時のバッチサイズ")

sdxl_train.py

@@ -712,7 +712,7 @@ def fn_recursive_set_mem_eff(module: torch.nn.Module):
 
     accelerator.end_training()
 
-    if args.save_state:  # and is_main_process:
+    if args.save_state or args.save_state_on_train_end:        
         train_util.save_state_on_train_end(args, accelerator)
 
     del accelerator  # この後メモリを使うのでこれは消す

sdxl_train_control_net_lllite.py

@@ -549,7 +549,7 @@ def remove_model(old_ckpt_name):
 
     accelerator.end_training()
 
-    if is_main_process and args.save_state:
+    if is_main_process and (args.save_state or args.save_state_on_train_end):
         train_util.save_state_on_train_end(args, accelerator)
 
     if is_main_process:

train_controlnet.py

@@ -565,7 +565,7 @@ def remove_model(old_ckpt_name):
 
     accelerator.end_training()
 
-    if is_main_process and args.save_state:
+    if is_main_process and (args.save_state or args.save_state_on_train_end):
         train_util.save_state_on_train_end(args, accelerator)
 
     # del accelerator  # この後メモリを使うのでこれは消す→printで使うので消さずにおく

train_db.py

@@ -444,7 +444,7 @@ def train(args):
 
     accelerator.end_training()
 
-    if args.save_state and is_main_process:
+    if is_main_process and (args.save_state or args.save_state_on_train_end):
         train_util.save_state_on_train_end(args, accelerator)
 
     del accelerator  # この後メモリを使うのでこれは消す

train_network.py

@@ -935,7 +935,7 @@ def remove_model(old_ckpt_name):
 
         accelerator.end_training()
 
-        if is_main_process and args.save_state:
+        if is_main_process and (args.save_state or args.save_state_on_train_end):
             train_util.save_state_on_train_end(args, accelerator)
 
         if is_main_process:

train_textual_inversion.py

@@ -732,7 +732,7 @@ def remove_model(old_ckpt_name):
 
         accelerator.end_training()
 
-        if args.save_state and is_main_process:
+        if is_main_process and (args.save_state or args.save_state_on_train_end):
             train_util.save_state_on_train_end(args, accelerator)
 
         if is_main_process:

train_textual_inversion_XTI.py

@@ -586,7 +586,7 @@ def remove_model(old_ckpt_name):
 
     accelerator.end_training()
 
-    if args.save_state and is_main_process:
+    if is_main_process and (args.save_state or args.save_state_on_train_end):
         train_util.save_state_on_train_end(args, accelerator)
 
     updated_embs = text_encoder.get_input_embeddings().weight[token_ids_XTI].data.detach().clone()