prevent calculating cons for second pass of hires fix when they are the same as for the first pass (fbf88343) · Commits · github_fork / Stable Diffusion Webui

modules/processing.py

+13 −7

Original line number	Original line	Diff line number	Diff line
	@@ -312,7 +312,7 @@ class StableDiffusionProcessing:
	self.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_prompts]		self.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, self.styles) for x in self.all_prompts]
	self.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_negative_prompts]		self.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, self.styles) for x in self.all_negative_prompts]

	def get_conds_with_caching(self, function, required_prompts, steps, cache, extra_network_data):		def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data):
	"""		"""
	Returns the result of calling function(shared.sd_model, required_prompts, steps)		Returns the result of calling function(shared.sd_model, required_prompts, steps)
	using a cache to store the result if the same arguments have been used before.		using a cache to store the result if the same arguments have been used before.
	@@ -321,10 +321,16 @@ class StableDiffusionProcessing:
	representing the previously used arguments, or None if no arguments		representing the previously used arguments, or None if no arguments
	have been used before. The second element is where the previously		have been used before. The second element is where the previously
	computed result is stored.		computed result is stored.

			caches is a list with items described above.
	"""		"""

			for cache in caches:
	if cache[0] is not None and (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data) == cache[0]:		if cache[0] is not None and (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info, extra_network_data) == cache[0]:
	return cache[1]		return cache[1]

			cache = caches[0]

	with devices.autocast():		with devices.autocast():
	cache[1] = function(shared.sd_model, required_prompts, steps)		cache[1] = function(shared.sd_model, required_prompts, steps)

	@@ -335,8 +341,8 @@ class StableDiffusionProcessing:
	sampler_config = sd_samplers.find_sampler_config(self.sampler_name)		sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
	self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1		self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1

	self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.negative_prompts, self.steps * self.step_multiplier, self.cached_uc, self.extra_network_data)		self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.negative_prompts, self.steps * self.step_multiplier, [self.cached_uc], self.extra_network_data)
	self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.prompts, self.steps * self.step_multiplier, self.cached_c, self.extra_network_data)		self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.prompts, self.steps * self.step_multiplier, [self.cached_c], self.extra_network_data)

	def parse_extra_network_prompts(self):		def parse_extra_network_prompts(self):
	self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)		self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)
	@@ -1106,8 +1112,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
	if self.hr_c is not None:		if self.hr_c is not None:
	return		return

	self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, self.cached_hr_uc, self.hr_extra_network_data)		self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, self.hr_negative_prompts, self.steps * self.step_multiplier, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
	self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, self.cached_hr_c, self.hr_extra_network_data)		self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, self.hr_prompts, self.steps * self.step_multiplier, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)

	def setup_conds(self):		def setup_conds(self):
	super().setup_conds()		super().setup_conds()

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