hr conditioning (a9f0e7d5) · Commits · github_fork / Stable Diffusion Webui

modules/processing.py

+46 −26

Original line number	Diff line number	Diff line
		@@ -235,7 +235,7 @@ class StableDiffusionProcessing:
		def init(self, all_prompts, all_seeds, all_subseeds):
		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()

		def close(self):
		@@ -516,25 +516,25 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
		else:
		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 p.enable_hr and p.is_hr_pass:
		# logging.info("Running hr pass with custom prompt")
		# if p.hr_prompt:
		# if type(p.prompt) == list:
		# p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.hr_prompt]
		# else:
		# p.all_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:
		# p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in
		# p.hr_negative_prompt]
		# else:
		# p.all_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(p) == StableDiffusionProcessingTxt2Img:
		if p.enable_hr and p.is_hr_pass:
		logging.info("Running hr pass with custom prompt")
		if p.hr_prompt:
		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]
		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:
		p.all_hr_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in
		p.hr_negative_prompt]
		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:
		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]
		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]
		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)
		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:
		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}"

		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))]
		for x in x_samples_ddim:
		@@ -744,6 +765,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
		self.hr_sampler = hr_sampler
		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.all_hr_prompts = None
		self.all_hr_negative_prompts = None

		if firstphase_width != 0 or firstphase_height != 0:
		self.hr_upscale_to_x = self.width
		@@ -817,7 +840,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
		if self.hr_upscaler is not None:
		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)

		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:
		return samples

		self.prompt = self.hr_prompt
		self.negative_prompt = self.hr_negative_prompt

		target_width = self.hr_upscale_to_x
		target_height = self.hr_upscale_to_y

		@@ -904,7 +924,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
		x = None
		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

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