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https://github.com/openvinotoolkit/stable-diffusion-webui.git
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hr conditioning
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@ -517,24 +517,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
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if type(p) == StableDiffusionProcessingTxt2Img:
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if p.enable_hr and p.is_hr_pass:
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logging.info("Running hr pass with custom prompt")
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if p.hr_prompt:
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if p.enable_hr:
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if type(p.prompt) == list:
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p.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.hr_prompt]
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else:
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p.all_hr_prompts = p.batch_size * p.n_iter * [
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shared.prompt_styles.apply_styles_to_prompt(p.hr_prompt, p.styles)]
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logging.info(p.all_prompts)
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p.all_hr_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.hr_prompt, p.styles)]
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if p.hr_negative_prompt:
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if type(p.negative_prompt) == list:
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p.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in
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p.hr_negative_prompt]
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p.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.hr_negative_prompt]
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else:
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p.all_hr_negative_prompts = p.batch_size * p.n_iter * [
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shared.prompt_styles.apply_negative_styles_to_prompt(p.hr_negative_prompt, p.styles)]
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logging.info(p.all_negative_prompts)
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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)]
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if type(seed) == list:
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p.all_seeds = seed
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@ -628,9 +620,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
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if type(p) == StableDiffusionProcessingTxt2Img:
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if p.enable_hr:
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hr_uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps,
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hr_uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, p.steps,
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cached_uc)
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hr_c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps,
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hr_c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, p.steps,
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cached_c)
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if len(model_hijack.comments) > 0:
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@ -840,7 +832,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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if self.hr_upscaler is not None:
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self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
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def sample(self, conditioning, unconditional_conditioning, hr_conditioning, hr_uconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
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def sample(self, conditioning, unconditional_conditioning, hr_conditioning, hr_unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
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self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
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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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