diff --git a/examples/custom_diffusion/train_custom_diffusion.py b/examples/custom_diffusion/train_custom_diffusion.py
index 3288fe3258ac..4773446a615b 100644
--- a/examples/custom_diffusion/train_custom_diffusion.py
+++ b/examples/custom_diffusion/train_custom_diffusion.py
@@ -207,7 +207,7 @@ def __init__(
                     with open(concept["class_prompt"], "r") as f:
                         class_prompt = f.read().splitlines()
 
-                class_img_path = [(x, y) for (x, y) in zip(class_images_path, class_prompt)]
+                class_img_path = list(zip(class_images_path, class_prompt))
                 self.class_images_path.extend(class_img_path[:num_class_images])
 
         random.shuffle(self.instance_images_path)
@@ -1214,50 +1214,52 @@ def main(args):
             if global_step >= args.max_train_steps:
                 break
 
-        if accelerator.is_main_process:
-            images = []
+            if accelerator.is_main_process:
+                images = []
 
-            if args.validation_prompt is not None and global_step % args.validation_steps == 0:
-                logger.info(
-                    f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
-                    f" {args.validation_prompt}."
-                )
-                # create pipeline
-                pipeline = DiffusionPipeline.from_pretrained(
-                    args.pretrained_model_name_or_path,
-                    unet=accelerator.unwrap_model(unet),
-                    text_encoder=accelerator.unwrap_model(text_encoder),
-                    tokenizer=tokenizer,
-                    revision=args.revision,
-                    torch_dtype=weight_dtype,
-                )
-                pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
-                pipeline = pipeline.to(accelerator.device)
-                pipeline.set_progress_bar_config(disable=True)
-
-                # run inference
-                generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
-                images = [
-                    pipeline(args.validation_prompt, num_inference_steps=25, generator=generator, eta=1.0).images[0]
-                    for _ in range(args.num_validation_images)
-                ]
-
-                for tracker in accelerator.trackers:
-                    if tracker.name == "tensorboard":
-                        np_images = np.stack([np.asarray(img) for img in images])
-                        tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
-                    if tracker.name == "wandb":
-                        tracker.log(
-                            {
-                                "validation": [
-                                    wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
-                                    for i, image in enumerate(images)
-                                ]
-                            }
-                        )
+                if args.validation_prompt is not None and global_step % args.validation_steps == 0:
+                    logger.info(
+                        f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
+                        f" {args.validation_prompt}."
+                    )
+                    # create pipeline
+                    pipeline = DiffusionPipeline.from_pretrained(
+                        args.pretrained_model_name_or_path,
+                        unet=accelerator.unwrap_model(unet),
+                        text_encoder=accelerator.unwrap_model(text_encoder),
+                        tokenizer=tokenizer,
+                        revision=args.revision,
+                        torch_dtype=weight_dtype,
+                    )
+                    pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
+                    pipeline = pipeline.to(accelerator.device)
+                    pipeline.set_progress_bar_config(disable=True)
 
-                del pipeline
-                torch.cuda.empty_cache()
+                    # run inference
+                    generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
+                    images = [
+                        pipeline(args.validation_prompt, num_inference_steps=25, generator=generator, eta=1.0).images[
+                            0
+                        ]
+                        for _ in range(args.num_validation_images)
+                    ]
+
+                    for tracker in accelerator.trackers:
+                        if tracker.name == "tensorboard":
+                            np_images = np.stack([np.asarray(img) for img in images])
+                            tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
+                        if tracker.name == "wandb":
+                            tracker.log(
+                                {
+                                    "validation": [
+                                        wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
+                                        for i, image in enumerate(images)
+                                    ]
+                                }
+                            )
+
+                    del pipeline
+                    torch.cuda.empty_cache()
 
     # Save the custom diffusion layers
     accelerator.wait_for_everyone()