Merge pull request #12457 from rubberbaron/shared-hires-prompt-test

        self.main_prompt = self.all_prompts[0]

        self.main_negative_prompt = self.all_negative_prompts[0]

    def cached_params(self, required_prompts, steps, extra_network_data):

    def cached_params(self, required_prompts, steps, extra_network_data, hires_steps=None, use_old_scheduling=False):

        """Returns parameters that invalidate the cond cache if changed"""

        return (

            required_prompts,

            steps,

            hires_steps,

            use_old_scheduling,

            opts.CLIP_stop_at_last_layers,

            shared.sd_model.sd_checkpoint_info,

            extra_network_data,

            self.height,

        )

    def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data):

    def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data, hires_steps=None):

        """

        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.

        caches is a list with items described above.

        """

        cached_params = self.cached_params(required_prompts, steps, extra_network_data)

        cached_params = self.cached_params(required_prompts, steps, extra_network_data, hires_steps, shared.opts.use_old_scheduling)

        for cache in caches:

            if cache[0] is not None and cached_params == cache[0]:

        cache = caches[0]

        with devices.autocast():

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

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

        cache[0] = cached_params

        return cache[1]

        sampler_config = sd_samplers.find_sampler_config(self.sampler_name)

        total_steps = sampler_config.total_steps(self.steps) if sampler_config else self.steps

        self.step_multiplier = total_steps // self.steps

        self.firstpass_steps = total_steps

        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, total_steps, [self.cached_uc], self.extra_network_data)

        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, total_steps, [self.cached_c], self.extra_network_data)

        steps = self.hr_second_pass_steps or self.steps

        total_steps = sampler_config.total_steps(steps) if sampler_config else steps

        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, total_steps, [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, hr_prompts, total_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)

        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.firstpass_steps, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data, total_steps)

        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.firstpass_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data, total_steps)

    def setup_conds(self):

        if self.is_hr_pass:

%import common.SIGNED_NUMBER -> NUMBER

""")

def get_learned_conditioning_prompt_schedules(prompts, steps):

def get_learned_conditioning_prompt_schedules(prompts, base_steps, hires_steps=None, use_old_scheduling=False):

    """

    >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]

    >>> g("test")

    [[1, 'female'], [2, 'male'], [3, 'female'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'female'], [8, 'male'], [9, 'female'], [10, 'male']]

    >>> g("[fe|||]male")

    [[1, 'female'], [2, 'male'], [3, 'male'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'male'], [8, 'male'], [9, 'female'], [10, 'male']]

    >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10, 10)[0]

    >>> g("a [b:.5] c")

    [[10, 'a b c']]

    >>> g("a [b:1.5] c")

    [[5, 'a  c'], [10, 'a b c']]

    """

    if hires_steps is None or use_old_scheduling:

        int_offset = 0

        flt_offset = 0

        steps = base_steps

    else:

        int_offset = base_steps

        flt_offset = 1.0

        steps = hires_steps

    def collect_steps(steps, tree):

        res = [steps]

        class CollectSteps(lark.Visitor):

            def scheduled(self, tree):

                tree.children[-2] = float(tree.children[-2])

                if tree.children[-2] < 1:

                    tree.children[-2] *= steps

                tree.children[-2] = min(steps, int(tree.children[-2]))

                s = tree.children[-2]

                v = float(s)

                if use_old_scheduling:

                    v = v*steps if v<1 else v

                else:

                    if "." in s:

                        v = (v - flt_offset) * steps

                    else:

                        v = (v - int_offset)

                tree.children[-2] = min(steps, int(v))

                if tree.children[-2] >= 1:

                    res.append(tree.children[-2])

            def alternate(self, tree):

def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps):

def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps, hires_steps=None, use_old_scheduling=False):

    """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),

    and the sampling step at which this condition is to be replaced by the next one.

    """

    res = []

    prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)

    prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps, hires_steps, use_old_scheduling)

    cache = {}

    for prompt, prompt_schedule in zip(prompts, prompt_schedules):

        self.batch: List[List[ComposableScheduledPromptConditioning]] = batch

def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:

def get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None, use_old_scheduling=False) -> MulticondLearnedConditioning:

    """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.

    For each prompt, the list is obtained by splitting the prompt using the AND separator.

    res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts)

    learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)

    learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps, hires_steps, use_old_scheduling)

    res = []

    for indexes in res_indexes:

    "use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),

    "dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),

    "hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),

    "use_old_scheduling": OptionInfo(False, "Use old prompt where first pass and hires both used the same timeline, and < 1 meant relative and >= 1 meant absolute"),

}))

options_templates.update(options_section(('interrogate', "Interrogate"), {