add postprocess_batch_list callback

        p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]

    def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):

        return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch, use_main_prompt)

        all_prompts = p.all_prompts[:]

        all_seeds = p.all_seeds[:]

        all_subseeds = p.all_subseeds[:]

        # apply changes to generation data

        all_prompts[n * p.batch_size:(n + 1) * p.batch_size] = p.prompts

        all_seeds[n * p.batch_size:(n + 1) * p.batch_size] = p.seeds

        all_subseeds[n * p.batch_size:(n + 1) * p.batch_size] = p.subseeds

        # update p.all_negative_prompts in case extensions changed the size of the batch

        # create_infotext below uses it

        old_negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]

        p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size] = p.negative_prompts

        try:

            return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch, use_main_prompt)

        finally:

            # restore p.all_negative_prompts in case extensions changed the size of the batch

            p.all_negative_prompts[n * p.batch_size:n * p.batch_size + len(p.negative_prompts)] = old_negative_prompts

    if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:

        model_hijack.embedding_db.load_textual_inversion_embeddings()

            if p.scripts is not None:

                p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)

                postprocess_batch_list_args = scripts.PostprocessBatchListArgs(list(x_samples_ddim))

                p.scripts.postprocess_batch_list(p, postprocess_batch_list_args, batch_number=n)

                x_samples_ddim = postprocess_batch_list_args.images

            for i, x_sample in enumerate(x_samples_ddim):

                p.batch_index = i

        self.image = image

class PostprocessBatchListArgs:

    def __init__(self, images):

        self.images = images

class Script:

    name = None

    """script's internal name derived from title"""

        pass

    def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, *args, **kwargs):

        """

        Same as postprocess_batch(), but receives batch images as a list of 3D tensors instead of a 4D tensor.

        This is useful when you want to update the entire batch instead of individual images.

        You can modify the postprocessing object (pp) to update the images in the batch, remove images, add images, etc.

        If the number of images is different from the batch size when returning,

        then the script has the responsibility to also update the following attributes in the processing object (p):

          - p.prompts

          - p.negative_prompts

          - p.seeds

          - p.subseeds

        **kwargs will have same items as process_batch, and also:

          - batch_number - index of current batch, from 0 to number of batches-1

        """

        pass

    def postprocess_image(self, p, pp: PostprocessImageArgs, *args):

        """

        Called for every image after it has been generated.

            except Exception:

                errors.report(f"Error running postprocess_batch: {script.filename}", exc_info=True)

    def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, **kwargs):

        for script in self.alwayson_scripts:

            try:

                script_args = p.script_args[script.args_from:script.args_to]

                script.postprocess_batch_list(p, pp, *script_args, **kwargs)

            except Exception:

                errors.report(f"Error running postprocess_batch_list: {script.filename}", exc_info=True)

    def postprocess_image(self, p, pp: PostprocessImageArgs):

        for script in self.alwayson_scripts:

            try: