hr conditioning (4e0cf7d4) · Commits · github_fork / Stable Diffusion Webui

modules/processing.py

+13 −21

Original line number	Diff line number	Diff line
		@@ -517,24 +517,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]

		if type(p) == StableDiffusionProcessingTxt2Img:
		if p.enable_hr and p.is_hr_pass:
		logging.info("Running hr pass with custom prompt")
		if p.hr_prompt:
		if p.enable_hr:
		if type(p.prompt) == list:
		p.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.hr_prompt]
		else:
		p.all_hr_prompts = p.batch_size * p.n_iter * [
		shared.prompt_styles.apply_styles_to_prompt(p.hr_prompt, p.styles)]
		logging.info(p.all_prompts)
		p.all_hr_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.hr_prompt, p.styles)]

		if p.hr_negative_prompt:
		if type(p.negative_prompt) == list:
		p.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in
		p.hr_negative_prompt]
		p.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.hr_negative_prompt]
		else:
		p.all_hr_negative_prompts = p.batch_size * p.n_iter * [
		shared.prompt_styles.apply_negative_styles_to_prompt(p.hr_negative_prompt, p.styles)]
		logging.info(p.all_negative_prompts)
		p.all_hr_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.hr_negative_prompt, p.styles)]

		if type(seed) == list:
		p.all_seeds = seed
		@@ -628,9 +620,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
		if type(p) == StableDiffusionProcessingTxt2Img:
		if p.enable_hr:
		hr_uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps,
		hr_uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, p.steps,
		cached_uc)
		hr_c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps,
		hr_c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, p.steps,
		cached_c)

		if len(model_hijack.comments) > 0:
		@@ -840,7 +832,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
		if self.hr_upscaler is not None:
		self.extra_generation_params["Hires upscaler"] = self.hr_upscaler

		def sample(self, conditioning, unconditional_conditioning, hr_conditioning, hr_uconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
		def sample(self, conditioning, unconditional_conditioning, hr_conditioning, hr_unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
		self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)

		latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")

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