diff --git a/train_network.py b/train_network.py
index 02a2d925..ef10921f 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 +25,18 @@ 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)
+        # print("self.current_step:%d"%self.current_step)
+        # print("dataset_lengh:%d"%len(dataset))
+        print("id(self)(collate):%d"%id(self))
+        return examples[0]
 
 
 # TODO 他のスクリプトと共通化する
@@ -101,6 +111,10 @@ 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
@@ -186,11 +200,12 @@ def train(args):
     # dataloaderを準備する
     # DataLoaderのプロセス数：0はメインプロセスになる
     n_workers = min(args.max_data_loader_n_workers, os.cpu_count() - 1)  # cpu_count-1 ただし最大で指定された数まで
+    
     train_dataloader = torch.utils.data.DataLoader(
         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,
     )
@@ -498,17 +513,18 @@ def train(args):
 
     loss_list = []
     loss_total = 0.0
+    del train_dataset_group
     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: