Fixed outpainting_mk2 output cropping

            pixels_vert = expand_pixels if is_vert else 0

            images_to_process = []

            output_images = []

            for n in range(count):

                res_w = init[n].width + pixels_horiz

                res_h = init[n].height + pixels_vert

                np_image = (np.asarray(img) / 255.0).astype(np.float64)

                np_mask = (np.asarray(mask) / 255.0).astype(np.float64)

                noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)

                out = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")

                output_images.append(Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB"))

                target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width

                target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height

                p.height = target_height if is_vert else img.height

                crop_region = (

                    0 if is_left else out.width - target_width,

                    0 if is_top else out.height - target_height,

                    target_width if is_left else out.width,

                    target_height if is_top else out.height,

                    0 if is_left else output_images[n].width - target_width,

                    0 if is_top else output_images[n].height - target_height,

                    target_width if is_left else output_images[n].width,

                    target_height if is_top else output_images[n].height,

                )

                mask = mask.crop(crop_region)

                p.image_mask = mask

                image_to_process = out.crop(crop_region)

                image_to_process = output_images[n].crop(crop_region)

                images_to_process.append(image_to_process)

            p.init_images = images_to_process

                initial_seed_and_info[0] = proc.seed

                initial_seed_and_info[1] = proc.info

            for proc_img in proc.images:

                out.paste(proc_img, (0 if is_left else out.width - proc_img.width, 0 if is_top else out.height - proc_img.height))

                out = out.crop((0, 0, res_w, res_h))

            for n in range(count):

                output_images[n].paste(proc.images[n], (0 if is_left else output_images[n].width - proc.images[n].width, 0 if is_top else output_images[n].height - proc.images[n].height))

                output_images[n] = output_images[n].crop((0, 0, res_w, res_h))

            return proc.images

            return output_images

        batch_count = p.n_iter

        batch_size = p.batch_size