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.

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)