diff --git a/examples/text_to_image/train_text_to_image_lora_sdxl.py b/examples/text_to_image/train_text_to_image_lora_sdxl.py
index 0a93a0d1c4c2..a5c72c1e9716 100644
--- a/examples/text_to_image/train_text_to_image_lora_sdxl.py
+++ b/examples/text_to_image/train_text_to_image_lora_sdxl.py
@@ -480,6 +480,15 @@ def parse_args(input_args=None):
         action="store_true",
         help="debug loss for each image, if filenames are available in the dataset",
     )
+    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.",
+    )
 
     if input_args is not None:
         args = parser.parse_args(input_args)
@@ -913,8 +922,14 @@ def tokenize_captions(examples, is_train=True):
         tokens_two = tokenize_prompt(tokenizer_two, captions)
         return tokens_one, tokens_two
 
+    # 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}.")
     # Preprocessing the datasets.
-    train_resize = transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR)
+    train_resize = transforms.Resize(args.resolution, interpolation=interpolation)  # Use dynamic interpolation method
     train_crop = transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop(args.resolution)
     train_flip = transforms.RandomHorizontalFlip(p=1.0)
     train_transforms = transforms.Compose(