Merge pull request #12023 from AUTOMATIC1111/create_infotext_fix

    return res

def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False):

def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0, use_main_prompt=False, index=None, all_negative_prompts=None):

    if index is None:

        index = position_in_batch + iteration * p.batch_size

    if all_negative_prompts is None:

        all_negative_prompts = p.all_negative_prompts

    clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)

    enable_hr = getattr(p, 'enable_hr', False)

    token_merging_ratio = p.get_token_merging_ratio()

        "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),

    generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])

    prompt_text = p.prompt if use_main_prompt else all_prompts[index]

    negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""

    negative_prompt_text = f"\nNegative prompt: {all_negative_prompts[index]}" if all_negative_prompts[index] else ""

    return f"{prompt_text}{negative_prompt_text}\n{generation_params_text}".strip()

    else:

        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):

        all_prompts = p.all_prompts[:]

        all_negative_prompts = p.all_negative_prompts[:]

        all_seeds = p.all_seeds[:]

        all_subseeds = p.all_subseeds[:]

        # apply changes to generation data

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

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

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

        all_subseeds[iteration * p.batch_size:(iteration + 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

        p.all_negative_prompts = all_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 = 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

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

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

                batch_params = scripts.PostprocessBatchListArgs(list(x_samples_ddim))

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

                x_samples_ddim = batch_params.images

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

                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

                if p.restore_faces:

                    if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:

                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")

                        images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-face-restoration")

                    devices.torch_gc()

                if p.color_corrections is not None and i < len(p.color_corrections):

                    if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:

                        image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)

                        images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")

                        images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-color-correction")

                    image = apply_color_correction(p.color_corrections[i], image)

                image = apply_overlay(image, p.paste_to, i, p.overlay_images)

                if opts.samples_save and not p.do_not_save_samples:

                    images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p)

                    images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)

                text = infotext(n, i)

                text = infotext(i)

                infotexts.append(text)

                if opts.enable_pnginfo:

                    image.info["parameters"] = text

                    image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')

                    if opts.save_mask:

                        images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")

                        images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")

                    if opts.save_mask_composite:

                        images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")

                        images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")

                    if opts.return_mask:

                        output_images.append(image_mask)

        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,