diff options
| author | Splendide Imaginarius <119545140+Splendide-Imaginarius@users.noreply.github.com> | 2023-08-05 04:54:23 +0000 |
|---|---|---|
| committer | Splendide Imaginarius <119545140+Splendide-Imaginarius@users.noreply.github.com> | 2023-08-05 04:54:23 +0000 |
| commit | 56888644a67298253260eda84ceb2d6cd0ce5099 (patch) | |
| tree | b21b592cceffc1f6cfcc82cbad14a14a20486905 | |
| parent | a1825ee741bb21b35561d58db8cb316d7f5d0c79 (diff) | |
Reduce mask blur kernel size to 2.5 sigmas
This more closely matches the old behavior of PIL's Gaussian blur, and
fixes breakage when tiling.
See https://github.com/Coyote-A/ultimate-upscale-for-automatic1111/issues/111#issuecomment-1663504109
Thanks to Алексей Трофимов and eunnone for reporting the issue.
| -rwxr-xr-x | modules/processing.py | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/modules/processing.py b/modules/processing.py index 44d20fb7..63cd025c 100755 --- a/modules/processing.py +++ b/modules/processing.py @@ -1275,13 +1275,13 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): if self.mask_blur_x > 0:
np_mask = np.array(image_mask)
- kernel_size = 2 * int(4 * self.mask_blur_x + 0.5) + 1
+ kernel_size = 2 * int(2.5 * self.mask_blur_x + 0.5) + 1
np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), self.mask_blur_x)
image_mask = Image.fromarray(np_mask)
if self.mask_blur_y > 0:
np_mask = np.array(image_mask)
- kernel_size = 2 * int(4 * self.mask_blur_y + 0.5) + 1
+ kernel_size = 2 * int(2.5 * self.mask_blur_y + 0.5) + 1
np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur_y)
image_mask = Image.fromarray(np_mask)
|