prevent calculating cons for second pass of hires fix when they are the same as for the first pass

        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]

    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)

        using a cache to store the result if the same arguments have been used before.

        representing the previously used arguments, or None if no arguments

        have been used before. The second element is where the previously

        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]:

                return cache[1]

        cache = caches[0]

        with devices.autocast():

            cache[1] = function(shared.sd_model, required_prompts, steps)

        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.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.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)

    def parse_extra_network_prompts(self):

        self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)

        if self.hr_c is not None:

            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_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_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.cached_c], self.hr_extra_network_data)

    def setup_conds(self):

        super().setup_conds()