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Merge pull request #12470 from Splendide-Imaginarius/mask-blur-property+kernel

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Make `StableDiffusionProcessingImg2Img.mask_blur` a property, make more inline with PIL `GaussianBlur`
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AUTOMATIC1111 2023-08-11 15:03:18 +03:00 committed by GitHub
commit ae6b30907d
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1 changed files with 20 additions and 5 deletions
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modules/processing.py Normal file → Executable file
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@ -1243,11 +1243,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.image_mask = mask
self.latent_mask = None
self.mask_for_overlay = None
if mask_blur is not None:
mask_blur_x = mask_blur
mask_blur_y = mask_blur
self.mask_blur_x = mask_blur_x
self.mask_blur_y = mask_blur_y
if mask_blur is not None:
self.mask_blur = mask_blur
self.inpainting_fill = inpainting_fill
self.inpaint_full_res = inpaint_full_res
self.inpaint_full_res_padding = inpaint_full_res_padding
@ -1257,6 +1256,22 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.nmask = None
self.image_conditioning = None
@property
def mask_blur(self):
if self.mask_blur_x == self.mask_blur_y:
return self.mask_blur_x
return None
@mask_blur.setter
def mask_blur(self, value):
self.mask_blur_x = value
self.mask_blur_y = value
@mask_blur.deleter
def mask_blur(self):
del self.mask_blur_x
del self.mask_blur_y
def init(self, all_prompts, all_seeds, all_subseeds):
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
crop_region = None
@ -1271,13 +1286,13 @@ 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)