diff --git a/train.py b/train.py
index e11ab99..48a1aaf 100644
--- a/train.py
+++ b/train.py
@@ -21,6 +21,7 @@ import math
 import signal
 import argparse
 import logging
+import threading
 import time
 import gc
 import random
@@ -348,6 +349,29 @@ def read_sample_prompts(sample_prompts_file_path: str):
     return sample_prompts
 
 
+
+def collate_fn(batch):
+    """
+    Collates batches
+    """
+    images = [example["image"] for example in batch]
+    captions = [example["caption"] for example in batch]
+    tokens = [example["tokens"] for example in batch]
+    runt_size = batch[0]["runt_size"]
+
+    images = torch.stack(images)
+    images = images.to(memory_format=torch.contiguous_format).float()
+
+    ret = {
+        "tokens": torch.stack(tuple(tokens)),
+        "image": images,
+        "captions": captions,
+        "runt_size": runt_size,
+    }
+    del batch
+    return ret
+
+
 def main(args):
     """
     Main entry point
@@ -676,19 +700,24 @@ def main(args):
         """
         handles sigterm
         """
-        global interrupted
-        if not interrupted:
-            interrupted=True            
-            global global_step
-            #TODO: save model on ctrl-c
-            interrupted_checkpoint_path = os.path.join(f"{log_folder}/ckpts/interrupted-gs{global_step}")
-            print()
-            logging.error(f"{Fore.LIGHTRED_EX} ************************************************************************{Style.RESET_ALL}")
-            logging.error(f"{Fore.LIGHTRED_EX} CTRL-C received, attempting to save model to {interrupted_checkpoint_path}{Style.RESET_ALL}")
-            logging.error(f"{Fore.LIGHTRED_EX} ************************************************************************{Style.RESET_ALL}")
-            time.sleep(2) # give opportunity to ctrl-C again to cancel save
-            __save_model(interrupted_checkpoint_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, args.save_full_precision)
-        exit(_SIGTERM_EXIT_CODE)
+        is_main_thread = (torch.utils.data.get_worker_info() == None)
+        if is_main_thread:
+            global interrupted
+            if not interrupted:
+                interrupted=True
+                global global_step
+                #TODO: save model on ctrl-c
+                interrupted_checkpoint_path = os.path.join(f"{log_folder}/ckpts/interrupted-gs{global_step}")
+                print()
+                logging.error(f"{Fore.LIGHTRED_EX} ************************************************************************{Style.RESET_ALL}")
+                logging.error(f"{Fore.LIGHTRED_EX} CTRL-C received, attempting to save model to {interrupted_checkpoint_path}{Style.RESET_ALL}")
+                logging.error(f"{Fore.LIGHTRED_EX} ************************************************************************{Style.RESET_ALL}")
+                time.sleep(2) # give opportunity to ctrl-C again to cancel save
+                __save_model(interrupted_checkpoint_path, unet, text_encoder, tokenizer, noise_scheduler, vae, args.save_ckpt_dir, args.save_full_precision)
+            exit(_SIGTERM_EXIT_CODE)
+        else:
+            # non-main threads (i.e. dataloader workers) should exit cleanly
+            exit(0)
 
     signal.signal(signal.SIGINT, sigterm_handler)
     
@@ -701,33 +730,13 @@ def main(args):
     logging.info(f" saving ckpts every {args.save_every_n_epochs } epochs")
 
 
-    def collate_fn(batch):
-        """
-        Collates batches
-        """
-        images = [example["image"] for example in batch]
-        captions = [example["caption"] for example in batch]
-        tokens = [example["tokens"] for example in batch]
-        runt_size = batch[0]["runt_size"]
-
-        images = torch.stack(images)
-        images = images.to(memory_format=torch.contiguous_format).float()
-
-        ret = {
-            "tokens": torch.stack(tuple(tokens)),
-            "image": images,
-            "captions": captions,
-            "runt_size": runt_size,
-        }
-        del batch
-        return ret
-
     train_dataloader = torch.utils.data.DataLoader(
         train_batch,
         batch_size=args.batch_size,
         shuffle=False,
-        num_workers=0,
-        collate_fn=collate_fn
+        num_workers=4,
+        collate_fn=collate_fn,
+        pin_memory=True
     )
 
     unet.train() if not args.disable_unet_training else unet.eval()