diff --git a/examples/unconditional_image_generation/train_unconditional.py b/examples/unconditional_image_generation/train_unconditional.py
index b77be033..b46b4edb 100644
--- a/examples/unconditional_image_generation/train_unconditional.py
+++ b/examples/unconditional_image_generation/train_unconditional.py
@@ -24,6 +24,7 @@ from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import EMAModel
 from diffusers.utils import check_min_version, is_accelerate_version, is_tensorboard_available, is_wandb_available
+from diffusers.utils.import_utils import is_xformers_available
 
 
 # Will error if the minimal version of diffusers is not installed. Remove at your own risks.
@@ -259,6 +260,9 @@ def parse_args():
             ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
         ),
     )
+    parser.add_argument(
+        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
+    )
 
     args = parser.parse_args()
     env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
@@ -410,6 +414,19 @@ def main(args):
             model_config=model.config,
         )
 
+    if args.enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            import xformers
+
+            xformers_version = version.parse(xformers.__version__)
+            if xformers_version == version.parse("0.0.16"):
+                logger.warn(
+                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
+                )
+            model.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError("xformers is not available. Make sure it is installed correctly")
+
     # Initialize the scheduler
     accepts_prediction_type = "prediction_type" in set(inspect.signature(DDPMScheduler.__init__).parameters.keys())
     if accepts_prediction_type: