elf.all_negative_prompts]

        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, 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,
            opts.sdxl_crop_left,
            opts.sdxl_crop_top,
            self.width,
            self.height,
        )

    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.

        cache is an array containing two elements. The first element is a tuple
        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.
        """

        if shared.opts.use_old_scheduling:
            old_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(required_prompts, steps, hires_steps, False)
            new_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(required_prompts, steps, hires_steps, True)
            if old_schedules != new_schedules:
                self.extra_generation_params["Old prompt editing timelines"] = True

        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]:
                return cache[1]

        cache = caches[0]

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

        cache[0] = cached_params
        return cache[1]

    def setup_conds(self):
        prompts = prompt_parser.SdConditioning(self.prompts, width=self.width, height=self.height)
        negative_prompts = prompt_parser.SdConditioning(self.negative_prompts, width=self.width, height=self.height, is_negative_prompt=True)

        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)

    def get_conds(self):
        return self.c, self.uc

    def parse_extra_network_prompts(self):
        self.prompts, self.extra_network_data = extra_networks.parse_prompts(self.prompts)

    def save_samples(self) -> bool:
        """Returns whether generated images need to be written to disk"""
        return opts.samples_save and not self.do_not_save_samples and (opts.save_incomplete_images or not state.interrupted and not state.skipped)


class Processed:
    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
        self.images = images_list
        self.prompt = p.prompt
        self.negative_prompt = p.negative_prompt
        self.seed = seed
        self.subseed = subseed
        self.subseed_strength = p.subseed_strength
        self.info = info
        self.comments = "".join(f"{comment}\n" for comment in p.comments)
        self.width = p.width
        self.height = p.height
        self.sampler_name = p.sampler_name
        self.cfg_scale = p.cfg_scale
        self.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
        self.steps = p.steps
        self.batch_size = p.batch_size
        self.restore_faces = p.restore_faces
        self.face_restoration_model = opts.face_restoration_model if p.restore_faces else None
        self.sd_model_name = p.sd_model_name
        self.sd_model_hash = p.sd_model_hash
        self.sd_vae_name = p.sd_vae_name
        self.sd_vae_hash = p.sd_vae_hash
        self.seed_resize_from_w = p.seed_resize_from_w
        self.seed_resize_from_h = p.seed_resize_from_h
        self.den