Restored original formatting. (60c60223) · Commits · github_fork / Stable Diffusion Webui

modules/processing.py

+11 −25

Original line number	Diff line number	Diff line
		@@ -370,10 +370,7 @@ class StableDiffusionProcessing:
		return self.edit_image_conditioning(source_image)

		if self.sampler.conditioning_key in {'hybrid', 'concat'}:
		return self.inpainting_image_conditioning(source_image,
		latent_image,
		image_mask=image_mask,
		round_image_mask=round_image_mask)
		return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask, round_image_mask=round_image_mask)

		if self.sampler.conditioning_key == "crossattn-adm":
		return self.unclip_image_conditioning(source_image)
		@@ -885,7 +882,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:

		if getattr(samples_ddim, 'already_decoded', False):
		x_samples_ddim = samples_ddim
		# todo: generate masks the old fashioned way
		# todo: generate adaptive masks based on pixel differences.
		# if p.masks_for_overlay is used, it will already be populated with masks
		else:
		if opts.sd_vae_decode_method != 'Full':
		p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
		@@ -900,9 +898,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		height=p.height,
		paste_to=p.paste_to)

		x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim,
		target_device=devices.cpu,
		check_for_nans=True)
		x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)

		x_samples_ddim = torch.stack(x_samples_ddim).float()
		x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
		@@ -927,9 +923,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		x_samples_ddim = batch_params.images

		def infotext(index=0, use_main_prompt=False):
		return create_infotext(p, p.prompts, p.seeds, p.subseeds,
		use_main_prompt=use_main_prompt, index=index,
		all_negative_prompts=p.negative_prompts)
		return create_infotext(p, p.prompts, p.seeds, p.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=p.negative_prompts)

		save_samples = p.save_samples()

		@@ -972,8 +966,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		image = apply_overlay(image, p.paste_to, i, p.overlay_images)

		if save_samples:
		images.save_image(image, p.outpath_samples, "", p.seeds[i],
		p.prompts[i], opts.samples_format, info=infotext(i), p=p)
		images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)

		text = infotext(i)
		infotexts.append(text)
		@@ -983,14 +976,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		if save_samples and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
		if hasattr(p, 'masks_for_overlay') and p.masks_for_overlay:
		image_mask = p.masks_for_overlay[i].convert('RGB')
		image_mask_composite = Image.composite(
		original_denoised_image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size),
		images.resize_image(2, p.masks_for_overlay[i], image.width, image.height).convert('L')).convert('RGBA')
		image_mask_composite = Image.composite(original_denoised_image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.masks_for_overlay[i], image.width, image.height).convert('L')).convert('RGBA')
		elif hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
		image_mask = p.mask_for_overlay.convert('RGB')
		image_mask_composite = Image.composite(
		original_denoised_image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size),
		images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
		image_mask_composite = Image.composite(original_denoised_image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
		else:
		image_mask = None
		image_mask_composite = None
		@@ -1515,8 +1504,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
		self.masks_for_overlay.append(image_mask)
		else:
		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')))
		image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))

		self.overlay_images.append(image_masked.convert('RGBA'))

		# crop_region is not None if we are doing inpaint full res
		@@ -1583,10 +1572,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
		elif self.inpainting_fill == 3:
		self.init_latent = self.init_latent * self.mask

		self.image_conditioning = self.img2img_image_conditioning(image * 2 - 1,
		self.init_latent,
		image_mask,
		self.soft_inpainting is None)
		self.image_conditioning = self.img2img_image_conditioning(image * 2 - 1, self.init_latent, image_mask, self.soft_inpainting is None)

		def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
		x = self.rng.next()

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