diff --git a/src/refiners/fluxion/utils.py b/src/refiners/fluxion/utils.py
index 824cae8..d3dc0d4 100644
--- a/src/refiners/fluxion/utils.py
+++ b/src/refiners/fluxion/utils.py
@@ -70,9 +70,11 @@ def gaussian_blur(
 ) -> Float[Tensor, "*batch channels height width"]:
     assert torch.is_floating_point(tensor)
 
-    def get_gaussian_kernel1d(kernel_size: int, sigma: float) -> Float[Tensor, "kernel_size"]:
+    def get_gaussian_kernel1d(
+        kernel_size: int, sigma: float, dtype: torch.dtype, device: torch.device
+    ) -> Float[Tensor, "kernel_size"]:
         ksize_half = (kernel_size - 1) * 0.5
-        x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)
+        x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size, device=device, dtype=dtype)
         pdf = torch.exp(-0.5 * (x / sigma).pow(2))
         kernel1d = pdf / pdf.sum()
         return kernel1d
@@ -80,8 +82,8 @@ def gaussian_blur(
     def get_gaussian_kernel2d(
         kernel_size_x: int, kernel_size_y: int, sigma_x: float, sigma_y: float, dtype: DType, device: Device
     ) -> Float[Tensor, "kernel_size_y kernel_size_x"]:
-        kernel1d_x = get_gaussian_kernel1d(kernel_size_x, sigma_x).to(device, dtype=dtype)
-        kernel1d_y = get_gaussian_kernel1d(kernel_size_y, sigma_y).to(device, dtype=dtype)
+        kernel1d_x = get_gaussian_kernel1d(kernel_size_x, sigma_x, dtype, device)
+        kernel1d_y = get_gaussian_kernel1d(kernel_size_y, sigma_y, dtype, device)
         kernel2d = torch.mm(kernel1d_y[:, None], kernel1d_x[None, :])
         return kernel2d