diff --git a/src/refiners/fluxion/utils.py b/src/refiners/fluxion/utils.py
index 8c69de2..a3f8da8 100644
--- a/src/refiners/fluxion/utils.py
+++ b/src/refiners/fluxion/utils.py
@@ -91,7 +91,7 @@ def gaussian_blur(
         sx, sy = sigma
 
     channels = tensor.shape[-3]
-    kernel = get_gaussian_kernel2d(kx, ky, sx, sy, dtype=torch.float32, device=tensor.device)
+    kernel = get_gaussian_kernel2d(kx, ky, sx, sy, dtype=tensor.dtype, device=tensor.device)
     kernel = kernel.expand(channels, 1, kernel.shape[0], kernel.shape[1])
 
     # pad = (left, right, top, bottom)
diff --git a/tests/fluxion/test_utils.py b/tests/fluxion/test_utils.py
index ff92f2e..c34c169 100644
--- a/tests/fluxion/test_utils.py
+++ b/tests/fluxion/test_utils.py
@@ -1,6 +1,8 @@
 from dataclasses import dataclass
+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
 import pytest
 import torch
 
@@ -14,6 +16,7 @@ class BlurInput:
     image_height: int = 512
     image_width: int = 512
     batch_size: int | None = 1
+    dtype: DType = torch.float32
 
 
 BLUR_INPUTS: list[BlurInput] = [
@@ -22,6 +25,7 @@ BLUR_INPUTS: list[BlurInput] = [
     BlurInput(kernel_size=9, sigma=1.0),
     BlurInput(kernel_size=9, sigma=1.0, image_height=768),
     BlurInput(kernel_size=(9, 5), sigma=(1.0, 0.8)),
+    BlurInput(kernel_size=9, dtype=torch.float16),
 ]
 
 
@@ -30,9 +34,12 @@ def blur_input(request: pytest.FixtureRequest) -> BlurInput:
     return request.param
 
 
-def test_gaussian_blur(blur_input: BlurInput) -> None:
+def test_gaussian_blur(test_device: Device, blur_input: BlurInput) -> None:
+    if test_device.type == "cpu" and blur_input.dtype == torch.float16:
+        warn("half float is not supported on the CPU because of `torch.mm`, skipping")
+        pytest.skip()
     manual_seed(2)
-    tensor = torch.randn(3, blur_input.image_height, blur_input.image_width)
+    tensor = torch.randn(3, blur_input.image_height, blur_input.image_width, device=test_device, dtype=blur_input.dtype)
     if blur_input.batch_size is not None:
         tensor = tensor.expand(blur_input.batch_size, -1, -1, -1)