diff --git a/examples/unconditional_image_generation/train_unconditional.py b/examples/unconditional_image_generation/train_unconditional.py
index e9c849b22b6b..3b784eda6a34 100644
--- a/examples/unconditional_image_generation/train_unconditional.py
+++ b/examples/unconditional_image_generation/train_unconditional.py
@@ -625,8 +625,11 @@ def transform_images(examples):
         if accelerator.is_main_process:
             if epoch % args.save_images_epochs == 0 or epoch == args.num_epochs - 1:
                 unet = accelerator.unwrap_model(model)
+
                 if args.use_ema:
+                    ema_model.store(unet.parameters())
                     ema_model.copy_to(unet.parameters())
+
                 pipeline = DDPMPipeline(
                     unet=unet,
                     scheduler=noise_scheduler,
@@ -641,6 +644,9 @@ def transform_images(examples):
                     output_type="numpy",
                 ).images
 
+                if args.use_ema:
+                    ema_model.restore(unet.parameters())
+
                 # denormalize the images and save to tensorboard
                 images_processed = (images * 255).round().astype("uint8")
 
@@ -659,7 +665,22 @@ def transform_images(examples):
 
             if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
                 # save the model
+                unet = accelerator.unwrap_model(model)
+
+                if args.use_ema:
+                    ema_model.store(unet.parameters())
+                    ema_model.copy_to(unet.parameters())
+
+                pipeline = DDPMPipeline(
+                    unet=unet,
+                    scheduler=noise_scheduler,
+                )
+
                 pipeline.save_pretrained(args.output_dir)
+
+                if args.use_ema:
+                    ema_model.restore(unet.parameters())
+
                 if args.push_to_hub:
                     repo.push_to_hub(commit_message=f"Epoch {epoch}", blocking=False)