diff --git a/src/refiners/fluxion/utils.py b/src/refiners/fluxion/utils.py
index c55b0a6..3f5e710 100644
--- a/src/refiners/fluxion/utils.py
+++ b/src/refiners/fluxion/utils.py
@@ -102,13 +102,49 @@ def gaussian_blur(
 
 
 def image_to_tensor(image: Image.Image, device: Device | str | None = None, dtype: DType | None = None) -> Tensor:
-    return torch.tensor(array(image).astype(float32).transpose(2, 0, 1) / 255.0, device=device, dtype=dtype).unsqueeze(
-        0
-    )
+    """
+    Convert a PIL Image to a Tensor.
+
+    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]`.
+    """
+    image_tensor = torch.tensor(array(image).astype(float32) / 255.0, device=device, dtype=dtype)
+
+    match image.mode:
+        case "L":
+            image_tensor = image_tensor.unsqueeze(0)
+        case "RGBA" | "RGB":
+            image_tensor = image_tensor.permute(2, 0, 1)
+        case _:
+            raise ValueError(f"Unsupported image mode: {image.mode}")
+
+    return image_tensor.unsqueeze(0)
 
 
 def tensor_to_image(tensor: Tensor) -> Image.Image:
-    return Image.fromarray((tensor.clamp(0, 1).squeeze(0).permute(1, 2, 0).cpu().numpy() * 255).astype("uint8"))  # type: ignore
+    """
+    Convert a Tensor to a PIL Image.
+
+    The tensor must have shape `[1, channels, height, width]` where the number of
+    channels is either 1 (grayscale) or 3 (RGB) or 4 (RGBA).
+
+    Expected values are in the range `[0, 1]` and are clamped to this range.
+    """
+    assert tensor.ndim == 4 and tensor.shape[0] == 1, f"Unsupported tensor shape: {tensor.shape}"
+    num_channels = tensor.shape[1]
+    tensor = tensor.clamp(0, 1).squeeze(0)
+
+    match num_channels:
+        case 1:
+            tensor = tensor.squeeze(0)
+        case 3 | 4:
+            tensor = tensor.permute(1, 2, 0)
+        case _:
+            raise ValueError(f"Unsupported number of channels: {num_channels}")
+
+    return Image.fromarray((tensor.cpu().numpy() * 255).astype("uint8"))  # type: ignore[reportUnknownType]
 
 
 def safe_open(
diff --git a/tests/fluxion/test_utils.py b/tests/fluxion/test_utils.py
index c34c169..e83b789 100644
--- a/tests/fluxion/test_utils.py
+++ b/tests/fluxion/test_utils.py
@@ -3,10 +3,11 @@ from warnings import warn
 
 from torchvision.transforms.functional import gaussian_blur as torch_gaussian_blur  # type: ignore
 from torch import device as Device, dtype as DType
+from PIL import Image
 import pytest
 import torch
 
-from refiners.fluxion.utils import gaussian_blur, manual_seed
+from refiners.fluxion.utils import gaussian_blur, image_to_tensor, manual_seed, tensor_to_image
 
 
 @dataclass
@@ -47,3 +48,17 @@ def test_gaussian_blur(test_device: Device, blur_input: BlurInput) -> None:
     our_blur = gaussian_blur(tensor, blur_input.kernel_size, blur_input.sigma)
 
     assert torch.equal(our_blur, ref_blur)
+
+
+def test_image_to_tensor() -> None:
+    image = Image.new("RGB", (512, 512))
+
+    assert image_to_tensor(image).shape == (1, 3, 512, 512)
+    assert image_to_tensor(image.convert("L")).shape == (1, 1, 512, 512)
+    assert image_to_tensor(image.convert("RGBA")).shape == (1, 4, 512, 512)
+
+
+def test_tensor_to_image() -> None:
+    assert tensor_to_image(torch.zeros(1, 3, 512, 512)).mode == "RGB"
+    assert tensor_to_image(torch.zeros(1, 1, 512, 512)).mode == "L"
+    assert tensor_to_image(torch.zeros(1, 4, 512, 512)).mode == "RGBA"