hr conditioning
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invincibledude
parent
f774a8d24e
commit
a9f0e7d536
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@ -235,7 +235,7 @@ class StableDiffusionProcessing:
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def init(self, all_prompts, all_seeds, all_subseeds):
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pass
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
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def sample(self, conditioning, unconditional_conditioning, hr_conditioning, hr_uconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
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raise NotImplementedError()
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def close(self):
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@ -516,25 +516,25 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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else:
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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 type(p.prompt) == list:
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# p.all_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_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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#
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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_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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# else:
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# p.all_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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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 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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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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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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if type(seed) == list:
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p.all_seeds = seed
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@ -607,6 +607,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
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negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
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if type(p) == StableDiffusionProcessingTxt2Img:
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if p.enable_hr:
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hr_prompts = p.all_hr_prompts[n * p.batch_size:(n + 1) * p.batch_size]
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hr_negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
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seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
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subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
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@ -620,6 +626,12 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
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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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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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cached_c)
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if len(model_hijack.comments) > 0:
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for comment in model_hijack.comments:
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@ -629,7 +641,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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shared.state.job = f"Batch {n+1} out of {p.n_iter}"
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with devices.autocast():
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samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
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if type(p) == StableDiffusionProcessingTxt2Img:
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if p.enable_hr:
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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)
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samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds,
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subseeds=subseeds,
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subseed_strength=p.subseed_strength, prompts=prompts)
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else:
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samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds,
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subseeds=subseeds,
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subseed_strength=p.subseed_strength, prompts=prompts)
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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))]
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for x in x_samples_ddim:
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@ -744,6 +765,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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self.hr_sampler = hr_sampler
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self.hr_prompt = hr_prompt if hr_prompt != '' else self.prompt
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self.hr_negative_prompt = hr_negative_prompt if hr_negative_prompt != '' else self.negative_prompt
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self.all_hr_prompts = None
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self.all_hr_negative_prompts = None
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if firstphase_width != 0 or firstphase_height != 0:
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self.hr_upscale_to_x = self.width
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@ -817,7 +840,7 @@ 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, seeds, subseeds, subseed_strength, prompts):
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def sample(self, conditioning, unconditional_conditioning, hr_conditioning, hr_uconditional_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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@ -830,9 +853,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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if not self.enable_hr:
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return samples
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self.prompt = self.hr_prompt
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self.negative_prompt = self.hr_negative_prompt
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target_width = self.hr_upscale_to_x
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target_height = self.hr_upscale_to_y
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@ -904,7 +924,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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x = None
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devices.torch_gc()
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samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
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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)
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return samples
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