diff --git a/library/config_util.py b/library/config_util.py
index 9c8c90c2..6817d9a3 100644
--- a/library/config_util.py
+++ b/library/config_util.py
@@ -499,11 +499,12 @@ def load_user_config(file: str) -> dict:
 
 def blueprint_args_conflict(args,blueprint:Blueprint):
   # train_dataset_group.set_current_epoch()とtrain_dataset_group.set_current_step()がWorkerを生成するタイミングで適用される影響で、persistent_workers有効時はずっと一定になってしまうため無効にする
-  for b in blueprint.dataset_group.datasets:
-      for t in b.subsets:
-          if args.persistent_data_loader_workers and (t.params.caption_dropout_every_n_epochs > 0 or t.params.token_warmup_step>0):
-              print("Warning: %s: --persistent_data_loader_workers option is disabled because it conflicts with caption_dropout_every_n_epochs and token_wormup_step. / caption_dropout_every_n_epochs及びtoken_warmup_stepと競合するため、--persistent_data_loader_workersオプションは無効になります。"%(t.params.image_dir))
-              args.persistent_data_loader_workers = False
+  # for b in blueprint.dataset_group.datasets:
+  #     for t in b.subsets:
+  #         if args.persistent_data_loader_workers and (t.params.caption_dropout_every_n_epochs > 0 or t.params.token_warmup_step>0):
+  #             print("Warning: %s: --persistent_data_loader_workers option is disabled because it conflicts with caption_dropout_every_n_epochs and token_wormup_step. / caption_dropout_every_n_epochs及びtoken_warmup_stepと競合するため、--persistent_data_loader_workersオプションは無効になります。"%(t.params.image_dir))
+  #             # args.persistent_data_loader_workers = False
+  return
 
 # for config test
 if __name__ == "__main__":
diff --git a/library/train_util.py b/library/train_util.py
index d1df9c58..223e403b 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -517,6 +517,7 @@ class BaseDataset(torch.utils.data.Dataset):
                 else:
                     caption = caption.replace(str_from, str_to)
 
+        print(self.current_step, self.max_train_steps, caption)
         return caption
 
     def get_input_ids(self, caption):
diff --git a/train_network.py b/train_network.py
index 02a2d925..e148a92a 100644
--- a/train_network.py
+++ b/train_network.py
@@ -8,6 +8,7 @@ import random
 import time
 import json
 import toml
+from multiprocessing import Value
 
 from tqdm import tqdm
 import torch
@@ -24,9 +25,15 @@ from library.config_util import (
     BlueprintGenerator,
 )
 
-
-def collate_fn(examples):
-    return examples[0]
+class collater_class:
+    def __init__(self,epoch,step):
+        self.current_epoch=epoch
+        self.current_step=step
+    def __call__(self, examples):
+        dataset = torch.utils.data.get_worker_info().dataset
+        dataset.set_current_epoch(self.current_epoch.value)
+        dataset.set_current_step(self.current_step.value)
+        return examples[0]
 
 
 # TODO 他のスクリプトと共通化する
@@ -101,6 +108,10 @@ def train(args):
     config_util.blueprint_args_conflict(args,blueprint)
     train_dataset_group = config_util.generate_dataset_group_by_blueprint(blueprint.dataset_group)
 
+    current_epoch = Value('i',0)
+    current_step = Value('i',0)
+    collater = collater_class(current_epoch,current_step)
+
     if args.debug_dataset:
         train_util.debug_dataset(train_dataset_group)
         return
@@ -190,7 +201,7 @@ def train(args):
         train_dataset_group,
         batch_size=1,
         shuffle=True,
-        collate_fn=collate_fn,
+        collate_fn=collater,
         num_workers=n_workers,
         persistent_workers=args.persistent_data_loader_workers,
     )
@@ -501,14 +512,14 @@ def train(args):
     for epoch in range(num_train_epochs):
         if is_main_process:
             print(f"epoch {epoch+1}/{num_train_epochs}")
-        train_dataset_group.set_current_epoch(epoch + 1)
-        train_dataset_group.set_current_step(global_step)
+        current_epoch.value = epoch+1
 
         metadata["ss_epoch"] = str(epoch + 1)
 
         network.on_epoch_start(text_encoder, unet)
 
         for step, batch in enumerate(train_dataloader):
+            current_step.value = global_step
             with accelerator.accumulate(network):
                 with torch.no_grad():
                     if "latents" in batch and batch["latents"] is not None: