experimental optimization

    infotexts = []

    output_images = []

    cached_uc = [None, None]

    cached_c = [None, None]

    def get_conds_with_caching(function, required_prompts, steps, cache):

        """

        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.

        """

        if cache[0] is not None and (required_prompts, steps) == cache[0]:

            return cache[1]

        with devices.autocast():

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

        cache[0] = (required_prompts, steps)

        return cache[1]

    with torch.no_grad(), p.sd_model.ema_scope():

        with devices.autocast():

            p.init(p.all_prompts, p.all_seeds, p.all_subseeds)

            if p.scripts is not None:

                p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)

            with devices.autocast():

                uc = prompt_parser.get_learned_conditioning(shared.sd_model, negative_prompts, p.steps)

                c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)

            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 len(model_hijack.comments) > 0:

                for comment in model_hijack.comments: