diff --git a/examples/controlnet/train_controlnet_sdxl.py b/examples/controlnet/train_controlnet_sdxl.py
index 8b87db958d58..3368db1ec096 100644
--- a/examples/controlnet/train_controlnet_sdxl.py
+++ b/examples/controlnet/train_controlnet_sdxl.py
@@ -134,7 +134,25 @@ def log_validation(vae, unet, controlnet, args, accelerator, weight_dtype, step,
 
     for validation_prompt, validation_image in zip(validation_prompts, validation_images):
         validation_image = Image.open(validation_image).convert("RGB")
-        validation_image = validation_image.resize((args.resolution, args.resolution))
+
+        try:
+            interpolation = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper())
+        except (AttributeError, KeyError):
+            supported_interpolation_modes = [
+                f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__")
+            ]
+            raise ValueError(
+                f"Interpolation mode {args.image_interpolation_mode} is not supported. "
+                f"Please select one of the following: {', '.join(supported_interpolation_modes)}"
+            )
+
+        transform = transforms.Compose(
+            [
+                transforms.Resize(args.resolution, interpolation=interpolation),
+                transforms.CenterCrop(args.resolution),
+            ]
+        )
+        validation_image = transform(validation_image)
 
         images = []
 
@@ -587,6 +605,15 @@ def parse_args(input_args=None):
             " more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
         ),
     )
+    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)
@@ -732,9 +759,20 @@ def encode_prompt(prompt_batch, text_encoders, tokenizers, proportion_empty_prom
 
 
 def prepare_train_dataset(dataset, accelerator):
+    try:
+        interpolation_mode = getattr(transforms.InterpolationMode, args.image_interpolation_mode.upper())
+    except (AttributeError, KeyError):
+        supported_interpolation_modes = [
+            f.lower() for f in dir(transforms.InterpolationMode) if not f.startswith("__") and not f.endswith("__")
+        ]
+        raise ValueError(
+            f"Interpolation mode {args.image_interpolation_mode} is not supported. "
+            f"Please select one of the following: {', '.join(supported_interpolation_modes)}"
+        )
+
     image_transforms = transforms.Compose(
         [
-            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
+            transforms.Resize(args.resolution, interpolation=interpolation_mode),
             transforms.CenterCrop(args.resolution),
             transforms.ToTensor(),
             transforms.Normalize([0.5], [0.5]),
@@ -743,7 +781,7 @@ def prepare_train_dataset(dataset, accelerator):
 
     conditioning_image_transforms = transforms.Compose(
         [
-            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR),
+            transforms.Resize(args.resolution, interpolation=interpolation_mode),
             transforms.CenterCrop(args.resolution),
             transforms.ToTensor(),
         ]