diff --git a/examples/text_to_image/train_text_to_image_lora.py b/examples/text_to_image/train_text_to_image_lora.py
index ba733efe6003..480f4b36df61 100644
--- a/examples/text_to_image/train_text_to_image_lora.py
+++ b/examples/text_to_image/train_text_to_image_lora.py
@@ -418,6 +418,15 @@ def parse_args():
         default=4,
         help=("The dimension of the LoRA update matrices."),
     )
+    parser.add_argument(
+        "--image_interpolation_mode",
+        type=str,
+        default="lanczos",
+        choices=[
+            f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__")
+        ],
+        help="The image interpolation method to use for resizing images.",
+    )
 
     args = parser.parse_args()
     env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
@@ -649,10 +658,17 @@ def tokenize_captions(examples, is_train=True):
         )
         return inputs.input_ids
 
-    # Preprocessing the datasets.
+    # Get the specified interpolation method from the args
+    interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper(), None)
+
+    # Raise an error if the interpolation method is invalid
+    if interpolation is None:
+        raise ValueError(f"Unsupported interpolation mode {args.image_interpolation_mode}.")
+
+    # Data preprocessing transformations
     train_transforms = transforms.Compose(
         [
-            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
+            transforms.Resize(args.resolution, interpolation=interpolation),  # Use dynamic interpolation method
             transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution),
             transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda(lambda x: x),
             transforms.ToTensor(),