diff --git a/src/refiners/fluxion/utils.py b/src/refiners/fluxion/utils.py
index 78ad45f..31bc41f 100644
--- a/src/refiners/fluxion/utils.py
+++ b/src/refiners/fluxion/utils.py
@@ -141,7 +141,7 @@ def image_to_tensor(image: Image.Image, device: Device | str | None = None, dtyp
         If the image is in mode `RGB` the tensor will have shape `[3, H, W]`,
         otherwise `[1, H, W]` for mode `L` (grayscale) or `[4, H, W]` for mode `RGBA`.
 
-        Values are clamped to the range `[0, 1]`.
+        Values are normalized to the range `[0, 1]`.
     """
     image_tensor = torch.tensor(array(image).astype(float32) / 255.0, device=device, dtype=dtype)
 
diff --git a/src/refiners/foundationals/segment_anything/utils.py b/src/refiners/foundationals/segment_anything/utils.py
index 026e159..7058287 100644
--- a/src/refiners/foundationals/segment_anything/utils.py
+++ b/src/refiners/foundationals/segment_anything/utils.py
@@ -1,8 +1,7 @@
-import numpy as np
 from PIL import Image
-from torch import Size, Tensor, device as Device, dtype as DType, tensor
+from torch import Size, Tensor, device as Device, dtype as DType
 
-from refiners.fluxion.utils import interpolate, normalize, pad
+from refiners.fluxion.utils import image_to_tensor, interpolate, normalize, pad
 
 
 def compute_scaled_size(size: tuple[int, int], image_encoder_resolution: int) -> tuple[int, int]:
@@ -40,11 +39,8 @@ def image_to_scaled_tensor(
     """
     h, w = scaled_size
     resized = image.resize((w, h), resample=Image.Resampling.BILINEAR)  # type: ignore
-    return tensor(
-        np.array(resized).astype(np.float32).transpose(2, 0, 1),
-        device=device,
-        dtype=dtype,
-    ).unsqueeze(0)
+
+    return image_to_tensor(resized, device=device, dtype=dtype) * 255.0
 
 
 def preprocess_image(
@@ -61,9 +57,10 @@ def preprocess_image(
     Returns:
         The preprocessed image.
     """
+
     scaled_size = compute_scaled_size((image.height, image.width), image_encoder_resolution)
 
-    image_tensor = image_to_scaled_tensor(image, scaled_size, device, dtype)
+    image_tensor = image_to_scaled_tensor(image, scaled_size, device=device, dtype=dtype)
 
     return pad_image_tensor(
         normalize(image_tensor, mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]),
diff --git a/tests/foundationals/segment_anything/test_utils.py b/tests/foundationals/segment_anything/test_utils.py
index 6966cf9..b907804 100644
--- a/tests/foundationals/segment_anything/test_utils.py
+++ b/tests/foundationals/segment_anything/test_utils.py
@@ -22,13 +22,18 @@ def test_compute_scaled_size(image_encoder_resolution: int) -> None:
     assert scaled_size == (512, 1024)
 
 
-def test_image_to_scaled_tensor() -> None:
+def test_rgb_image_to_scaled_tensor() -> None:
     image = Image.new("RGB", (1536, 768))
     tensor = image_to_scaled_tensor(image, (512, 1024))
-
     assert tensor.shape == (1, 3, 512, 1024)
 
 
+def test_grayscale_image_to_scaled_tensor() -> None:
+    image = Image.new("L", (1536, 768))
+    tensor = image_to_scaled_tensor(image, (512, 1024))
+    assert tensor.shape == (1, 1, 512, 1024)
+
+
 def test_preprocess_image(image_encoder_resolution: int) -> None:
     image = Image.new("RGB", (1536, 768))
     preprocessed = preprocess_image(image, image_encoder_resolution)