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hr conditioning

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invincibledude 2023-01-22 15:15:08 +03:00
parent a9f0e7d536
commit 4e0cf7d4ed
1 changed files with 12 additions and 20 deletions
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32
modules/processing.py
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@ -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)] 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 type(p) == StableDiffusionProcessingTxt2Img:
if p.enable_hr and p.is_hr_pass: if p.enable_hr:
logging.info("Running hr pass with custom prompt") if type(p.prompt) == list:
if p.hr_prompt: p.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.hr_prompt]
if type(p.prompt) == list: else:
p.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.hr_prompt] p.all_hr_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.hr_prompt, p.styles)]
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)
if p.hr_negative_prompt: if type(p.negative_prompt) == list:
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 else:
p.hr_negative_prompt] 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)]
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)
if type(seed) == list: if type(seed) == list:
p.all_seeds = seed 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) c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
if type(p) == StableDiffusionProcessingTxt2Img: if type(p) == StableDiffusionProcessingTxt2Img:
if p.enable_hr: 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) 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) cached_c)
if len(model_hijack.comments) > 0: if len(model_hijack.comments) > 0:
@ -840,7 +832,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if self.hr_upscaler is not None: if self.hr_upscaler is not None:
self.extra_generation_params["Hires upscaler"] = self.hr_upscaler 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) 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") 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")