Finegrain/refiners
1
0
Fork 0
mirror of https://github.com/finegrain-ai/refiners.git synced 2024-11-23 22:58:45 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

update gaussian_blur fluxion util, see 45e053b2ae

Browse source
This commit is contained in:
Laurent 2024-09-09 14:30:13 +00:00 committed by Laureηt
parent a51d695523
commit 2395e666d5
1 changed files with 6 additions and 4 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

10
src/refiners/fluxion/utils.py
View file

@ -70,9 +70,11 @@ def gaussian_blur(
) -> Float[Tensor, "*batch channels height width"]: ) -> Float[Tensor, "*batch channels height width"]:
assert torch.is_floating_point(tensor) 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 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)) pdf = torch.exp(-0.5 * (x / sigma).pow(2))
kernel1d = pdf / pdf.sum() kernel1d = pdf / pdf.sum()
return kernel1d return kernel1d
@ -80,8 +82,8 @@ def gaussian_blur(
def get_gaussian_kernel2d( def get_gaussian_kernel2d(
kernel_size_x: int, kernel_size_y: int, sigma_x: float, sigma_y: float, dtype: DType, device: Device 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"]: ) -> Float[Tensor, "kernel_size_y kernel_size_x"]:
kernel1d_x = get_gaussian_kernel1d(kernel_size_x, sigma_x).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).to(device, dtype=dtype) kernel1d_y = get_gaussian_kernel1d(kernel_size_y, sigma_y, dtype, device)
kernel2d = torch.mm(kernel1d_y[:, None], kernel1d_x[None, :]) kernel2d = torch.mm(kernel1d_y[:, None], kernel1d_x[None, :])
return kernel2d return kernel2d