attempt 2

        "Sampler": p.sampler_name,

        "CFG scale": p.cfg_scale,

        "Image CFG scale": getattr(p, 'image_cfg_scale', None),

        "Seed": all_seeds[index],

        "Seed": p.all_seeds[0] if use_main_prompt else all_seeds[index],

        "Face restoration": (opts.face_restoration_model if p.restore_faces else None),

        "Size": f"{p.width}x{p.height}",

        "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),

        "Model": (None if not opts.add_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),

        "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),

        "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),

        "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),

        "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),

        "Denoising strength": getattr(p, 'denoising_strength', None),

                    p.subseeds,

                )

            if p.scripts is not None:

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

                x_samples_ddim = batch_params.images

                p.prompts = batch_params.prompts

                p.negative_prompts = batch_params.negative_prompts

                p.seeds = batch_params.seeds

                p.subseeds = batch_params.subseeds

            def infotext(index=0, use_main_prompt=False):

                return create_infotext(p, batch_params.prompts, batch_params.seeds, batch_params.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=batch_params.negative_prompts)

                return create_infotext(p, p.prompts, p.seeds, p.subseeds, use_main_prompt=use_main_prompt, index=index, all_negative_prompts=p.negative_prompts)

            for i, x_sample in enumerate(x_samples_ddim):

                p.batch_index = i

        p,

        images_list=output_images,

        seed=p.all_seeds[0],

        info=infotext(),

        info=infotexts[0],

        comments="".join(f"{comment}\n" for comment in comments),

        subseed=p.all_subseeds[0],

        index_of_first_image=index_of_first_image,