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Reduce mask blur kernel size to 2.5 sigmas

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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.
This commit is contained in:
Splendide Imaginarius 2023-08-05 04:54:23 +00:00
parent a1825ee741
commit 56888644a6
1 changed files with 2 additions and 2 deletions
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4
modules/processing.py
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@ -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)