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hr conditioning

This commit is contained in:
invincibledude 2023-01-22 15:12:00 +03:00
parent f774a8d24e
commit a9f0e7d536
1 changed files with 46 additions and 26 deletions
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72
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, seeds, subseeds, subseed_strength, prompts): def sample(self, conditioning, unconditional_conditioning, hr_conditioning, hr_uconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
raise NotImplementedError() raise NotImplementedError()
def close(self): def close(self):
@ -516,25 +516,25 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
else: else:
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 and p.is_hr_pass:
# logging.info("Running hr pass with custom prompt") logging.info("Running hr pass with custom prompt")
# if p.hr_prompt: if p.hr_prompt:
# if type(p.prompt) == list: if type(p.prompt) == list:
# p.all_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:
# p.all_prompts = p.batch_size * p.n_iter * [ p.all_hr_prompts = p.batch_size * p.n_iter * [
# shared.prompt_styles.apply_styles_to_prompt(p.hr_prompt, p.styles)] shared.prompt_styles.apply_styles_to_prompt(p.hr_prompt, p.styles)]
# logging.info(p.all_prompts) logging.info(p.all_prompts)
#
# if p.hr_negative_prompt: if p.hr_negative_prompt:
# if type(p.negative_prompt) == list: if type(p.negative_prompt) == list:
# p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in
# p.hr_negative_prompt] p.hr_negative_prompt]
# else: else:
# p.all_negative_prompts = p.batch_size * p.n_iter * [ 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)] shared.prompt_styles.apply_negative_styles_to_prompt(p.hr_negative_prompt, p.styles)]
# logging.info(p.all_negative_prompts) logging.info(p.all_negative_prompts)
if type(seed) == list: if type(seed) == list:
p.all_seeds = seed p.all_seeds = seed
@ -607,6 +607,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size] prompts = p.all_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] negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
if type(p) == StableDiffusionProcessingTxt2Img:
if p.enable_hr:
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]
seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size] seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size] subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
@ -620,6 +626,12 @@ 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 p.enable_hr:
hr_uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps,
cached_uc)
hr_c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps,
cached_c)
if len(model_hijack.comments) > 0: if len(model_hijack.comments) > 0:
for comment in model_hijack.comments: for comment in model_hijack.comments:
@ -629,7 +641,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
shared.state.job = f"Batch {n+1} out of {p.n_iter}" shared.state.job = f"Batch {n+1} out of {p.n_iter}"
with devices.autocast(): with devices.autocast():
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts) if type(p) == StableDiffusionProcessingTxt2Img:
if p.enable_hr:
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, hr_conditioning=hr_c, hr_uconditional_conditioning=hr_uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds,
subseeds=subseeds,
subseed_strength=p.subseed_strength, prompts=prompts)
else:
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds,
subseeds=subseeds,
subseed_strength=p.subseed_strength, prompts=prompts)
x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))] x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
for x in x_samples_ddim: for x in x_samples_ddim:
@ -744,6 +765,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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.prompt
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.negative_prompt
self.all_hr_prompts = None
self.all_hr_negative_prompts = None
if firstphase_width != 0 or firstphase_height != 0: if firstphase_width != 0 or firstphase_height != 0:
self.hr_upscale_to_x = self.width self.hr_upscale_to_x = self.width
@ -817,7 +840,7 @@ 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, seeds, subseeds, subseed_strength, prompts): def sample(self, conditioning, unconditional_conditioning, hr_conditioning, hr_uconditional_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")
@ -830,9 +853,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if not self.enable_hr: if not self.enable_hr:
return samples return samples
self.prompt = self.hr_prompt
self.negative_prompt = self.hr_negative_prompt
target_width = self.hr_upscale_to_x target_width = self.hr_upscale_to_x
target_height = self.hr_upscale_to_y target_height = self.hr_upscale_to_y
@ -904,7 +924,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
x = None x = None
devices.torch_gc() devices.torch_gc()
samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning) samples = self.sampler.sample_img2img(self, samples, noise, hr_conditioning, hr_unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
return samples return samples