diff --git a/examples/unconditional_image_generation/train_unconditional.py b/examples/unconditional_image_generation/train_unconditional.py
index 53b0d532..af3d0ddc 100644
--- a/examples/unconditional_image_generation/train_unconditional.py
+++ b/examples/unconditional_image_generation/train_unconditional.py
@@ -173,6 +173,16 @@ def parse_args():
     parser.add_argument(
         "--hub_private_repo", action="store_true", help="Whether or not to create a private repository."
     )
+    parser.add_argument(
+        "--logger",
+        type=str,
+        default="tensorboard",
+        choices=["tensorboard", "wandb"],
+        help=(
+            "Whether to use [tensorboard](https://www.tensorflow.org/tensorboard) or [wandb](https://www.wandb.ai)"
+            " for experiment tracking and logging of model metrics and model checkpoints"
+        ),
+    )
     parser.add_argument(
         "--logging_dir",
         type=str,
@@ -248,7 +258,7 @@ def main(args):
     accelerator = Accelerator(
         gradient_accumulation_steps=args.gradient_accumulation_steps,
         mixed_precision=args.mixed_precision,
-        log_with="tensorboard",
+        log_with=args.logger,
         logging_dir=logging_dir,
     )
 
@@ -477,9 +487,11 @@ def main(args):
 
                 # denormalize the images and save to tensorboard
                 images_processed = (images * 255).round().astype("uint8")
-                accelerator.trackers[0].writer.add_images(
-                    "test_samples", images_processed.transpose(0, 3, 1, 2), epoch
-                )
+
+                if args.logger == "tensorboard":
+                    accelerator.get_tracker("tensorboard").add_images(
+                        "test_samples", images_processed.transpose(0, 3, 1, 2), epoch
+                    )
 
             if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
                 # save the model