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hr conditioning

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This commit is contained in:
invincibledude 2023-01-22 15:28:38 +03:00
parent c5d4c87c02
commit 2ab2bce74d
1 changed files with 12 additions and 7 deletions
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19
modules/processing.py
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@ -235,7 +235,7 @@ class StableDiffusionProcessing:
def init(self, all_prompts, all_seeds, all_subseeds): def init(self, all_prompts, all_seeds, all_subseeds):
pass pass
def sample(self, conditioning, unconditional_conditioning, hr_conditioning, hr_uconditional_conditioning, seeds, subseeds, subseed_strength, prompts): def sample(self, conditioning, unconditional_conditioning, hr_conditioning=None, hr_unconditional_conditioning=None, seeds, subseeds, subseed_strength, prompts):
raise NotImplementedError() raise NotImplementedError()
def close(self): def close(self):
@ -517,7 +517,7 @@ 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: if p.enable_hr and p.hr_prompt != '':
if type(p.prompt) == list: 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] p.all_hr_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.hr_prompt]
else: else:
@ -601,7 +601,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size] negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
if type(p) == StableDiffusionProcessingTxt2Img: if type(p) == StableDiffusionProcessingTxt2Img:
if p.enable_hr: if p.enable_hr and p.hr_prompt != '':
hr_prompts = p.all_hr_prompts[n * p.batch_size:(n + 1) * p.batch_size] hr_prompts = p.all_hr_prompts[n * p.batch_size:(n + 1) * p.batch_size]
hr_negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size] hr_negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
@ -619,7 +619,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc) uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
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 and p.hr_prompt != '':
hr_uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_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, hr_prompts, p.steps, hr_c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, p.steps,
@ -635,7 +635,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
with devices.autocast(): with devices.autocast():
if type(p) == StableDiffusionProcessingTxt2Img: if type(p) == StableDiffusionProcessingTxt2Img:
if p.enable_hr: if p.enable_hr:
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, hr_conditioning=hr_c, hr_unconditional_conditioning=hr_uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts) if p.hr_prompts != '':
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, hr_conditioning=hr_c, hr_unconditional_conditioning=hr_uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
else:
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, hr_conditioning=c,
hr_unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds,
subseed_strength=p.subseed_strength, prompts=prompts)
else: else:
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds,
subseed_strength=p.subseed_strength, prompts=prompts) subseed_strength=p.subseed_strength, prompts=prompts)
@ -756,8 +761,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
self.hr_upscale_to_x = hr_resize_x self.hr_upscale_to_x = hr_resize_x
self.hr_upscale_to_y = hr_resize_y self.hr_upscale_to_y = hr_resize_y
self.hr_sampler = hr_sampler self.hr_sampler = hr_sampler
self.hr_prompt = hr_prompt if hr_prompt != '' else self.prompt self.hr_prompt = hr_prompt if hr_prompt != '' else ''
self.hr_negative_prompt = hr_negative_prompt if hr_negative_prompt != '' else self.negative_prompt self.hr_negative_prompt = hr_negative_prompt if hr_negative_prompt != '' else ''
self.all_hr_prompts = None self.all_hr_prompts = None
self.all_hr_negative_prompts = None self.all_hr_negative_prompts = None