prevent StableDiffusionProcessingImg2Img changing image_mask field as an... (413c0779) · Commits · github_fork / Stable Diffusion Webui

modules/processing.py

+16 −17

Original line number	Diff line number	Diff line
		@@ -740,7 +740,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
		self.denoising_strength: float = denoising_strength
		self.init_latent = None
		self.image_mask = mask
		#self.image_unblurred_mask = None
		self.latent_mask = None
		self.mask_for_overlay = None
		self.mask_blur = mask_blur
		@@ -756,36 +755,36 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
		self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
		crop_region = None

		if self.image_mask is not None:
		self.image_mask = self.image_mask.convert('L')
		image_mask = self.image_mask

		if self.inpainting_mask_invert:
		self.image_mask = ImageOps.invert(self.image_mask)
		if image_mask is not None:
		image_mask = image_mask.convert('L')

		#self.image_unblurred_mask = self.image_mask
		if self.inpainting_mask_invert:
		image_mask = ImageOps.invert(image_mask)

		if self.mask_blur > 0:
		self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
		image_mask = image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))

		if self.inpaint_full_res:
		self.mask_for_overlay = self.image_mask
		mask = self.image_mask.convert('L')
		self.mask_for_overlay = image_mask
		mask = image_mask.convert('L')
		crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
		crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
		x1, y1, x2, y2 = crop_region

		mask = mask.crop(crop_region)
		self.image_mask = images.resize_image(2, mask, self.width, self.height)
		image_mask = images.resize_image(2, mask, self.width, self.height)
		self.paste_to = (x1, y1, x2-x1, y2-y1)
		else:
		self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height)
		np_mask = np.array(self.image_mask)
		image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
		np_mask = np.array(image_mask)
		np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)
		self.mask_for_overlay = Image.fromarray(np_mask)

		self.overlay_images = []

		latent_mask = self.latent_mask if self.latent_mask is not None else self.image_mask
		latent_mask = self.latent_mask if self.latent_mask is not None else image_mask

		add_color_corrections = opts.img2img_color_correction and self.color_corrections is None
		if add_color_corrections:
		@@ -797,7 +796,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
		if crop_region is None:
		image = images.resize_image(self.resize_mode, image, self.width, self.height)

		if self.image_mask is not None:
		if image_mask is not None:
		image_masked = Image.new('RGBa', (image.width, image.height))
		image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))

		@@ -807,7 +806,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
		image = image.crop(crop_region)
		image = images.resize_image(2, image, self.width, self.height)

		if self.image_mask is not None:
		if image_mask is not None:
		if self.inpainting_fill != 1:
		image = masking.fill(image, latent_mask)

		@@ -839,7 +838,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):

		self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))

		if self.image_mask is not None:
		if image_mask is not None:
		init_mask = latent_mask
		latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
		latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
		@@ -856,7 +855,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
		elif self.inpainting_fill == 3:
		self.init_latent = self.init_latent * self.mask

		self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
		self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, image_mask)

		def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
		x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

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