')
with gr.Row():
gr.HTML('
The purpose of this script is to assist in Img2Img of folders containing incrementally named images such as one would use when extracting frames from video.
It can use any model though is best done with high consistency models such as Flux.
Note the full path of your folder containing incrementally numbered images such as Frame000.png to Frame900.png and the script will run Inference on each image in order saving the images with identical names in an output subfolder.
') with gr.Row(): folder = gr.Textbox( label="Input folder", placeholder="Path to folder containing PNG images", elem_id=self.elem_id("folder"), ) with gr.Row(): output_dir = gr.Textbox( label="Output folder", placeholder="Leave empty to save alongside inputs in /output/", elem_id=self.elem_id("output_dir"), ) with gr.Row(): prompt_override = gr.Textbox( label="Prompt override", placeholder="Leave empty to use the prompt from the main panel", elem_id=self.elem_id("prompt_override"), ) with gr.Row(): negative_override = gr.Textbox( label="Negative prompt override", placeholder="Leave empty to use the negative prompt from the main panel", elem_id=self.elem_id("negative_override"), ) with gr.Row(): seed_override = gr.Textbox( label="Seed override (-1 = use panel seed)", value="-1", elem_id=self.elem_id("seed_override"), ) with gr.Row(): steps_override = gr.Slider( minimum=0, maximum=150, step=1, value=0, label="Steps override (0 = use panel steps)", elem_id=self.elem_id("steps_override"), ) with gr.Row(): cfg_scale_override = gr.Slider( minimum=0.0, maximum=30.0, step=0.5, value=0.0, label="Guidance scale override (0.0 = use panel value)", elem_id=self.elem_id("cfg_scale_override"), ) with gr.Row(): sampler_override = gr.Dropdown( label="Sampler override (empty = use panel sampler)", choices=[""] + [s.name for s in sd_samplers.samplers_for_img2img], value="", elem_id=self.elem_id("sampler_override"), ) with gr.Row(): strength_override = gr.Slider( minimum=0.0, maximum=1.0, step=0.01, value=0.0, label="Denoising strength override (0.0 = use panel value)", elem_id=self.elem_id("strength_override"), ) with gr.Row(): gr.HTML('Post-inference resize') with gr.Row(): resize_enabled = gr.Checkbox( label="Enable post-inference resize", value=False, elem_id=self.elem_id("resize_enabled"), ) with gr.Row(): _upscaler_choices = [x.name for x in shared.sd_upscalers] or ["None"] resize_mode = gr.Dropdown( label="Resize mode", choices=shared.resize_modes, type="index", value="None", elem_id=self.elem_id("resize_mode"), ) resize_name = gr.Dropdown( label="Resize method", choices=_upscaler_choices, value=_upscaler_choices[0], elem_id=self.elem_id("resize_name"), ) with gr.Row(): resize_scale = gr.Slider( minimum=1.0, maximum=8.0, step=0.05, value=2.0, label="Scale factor", elem_id=self.elem_id("resize_scale"), ) return [folder, output_dir, prompt_override, negative_override, seed_override, steps_override, cfg_scale_override, sampler_override, strength_override, resize_enabled, resize_mode, resize_name, resize_scale] def run(self, p, folder, output_dir, prompt_override, negative_override, seed_override, steps_override, cfg_scale_override, sampler_override, strength_override, resize_enabled, resize_mode, resize_name, resize_scale): # pylint: disable=arguments-differ folder = (folder or "").strip() if not folder or not os.path.isdir(folder): log.error(f"Image folder batch: invalid or missing folder: {folder!r}") return Processed(p, [], p.seed, "Invalid or missing folder") files = sorted(glob.glob(os.path.join(folder, "*.png"))) if not files: log.error(f"Image folder batch: no PNG files found in: {folder!r}") return Processed(p, [], p.seed, "No PNG files found") out_dir = (output_dir or "").strip() or os.path.join(folder, "output") os.makedirs(out_dir, exist_ok=True) resize_out_dir = os.path.join(os.path.dirname(out_dir), "output-resized") if resize_enabled else None if resize_out_dir: os.makedirs(resize_out_dir, exist_ok=True) log.info(f"Image folder batch: folder={folder!r} images={len(files)} output={out_dir!r}") processing.fix_seed(p) try: seed_val = int(str(seed_override).strip()) except (ValueError, TypeError): seed_val = -1 if seed_val >= 0: p.seed = seed_val if int(steps_override) > 0: p.steps = int(steps_override) if float(cfg_scale_override) > 0.0: p.cfg_scale = float(cfg_scale_override) if str(sampler_override).strip(): p.sampler_name = str(sampler_override).strip() if float(strength_override) > 0.0: p.denoising_strength = float(strength_override) if prompt_override.strip(): p.prompt = prompt_override.strip() if negative_override.strip(): p.negative_prompt = negative_override.strip() state.job_count = len(files) all_images = [] all_prompts = [] all_seeds = [] all_negative = [] infotexts = [] for i, filepath in enumerate(files): if state.interrupted: break state.job = f"{i + 1}/{len(files)}" state.job_no = i img = Image.open(filepath) if img.mode not in ('RGB', 'L'): img = img.convert('RGB') cp = copy.copy(p) cp.init_images = [img] cp.width = img.width cp.height = img.height cp.batch_size = 1 cp.n_iter = 1 cp.do_not_save_samples = True cp.do_not_save_grid = True log.info(f"Image folder batch: [{i + 1}/{len(files)}] file={os.path.basename(filepath)} size={img.size} seed={cp.seed}") proc = processing.process_images(cp) img.close() if proc is None or not proc.images: log.warning(f"Image folder batch: no output for {filepath!r}") continue out_img = proc.images[0] if resize_enabled and resize_mode != 0 and resize_name != 'None': target_w = int(out_img.width * resize_scale) target_h = int(out_img.height * resize_scale) resized_img = images.resize_image(resize_mode, out_img, target_w, target_h, resize_name) log.info(f"Image folder batch: resized to {resized_img.size} mode={shared.resize_modes[resize_mode]!r} method={resize_name!r}") res_name = os.path.splitext(os.path.basename(filepath))[0] + ".png" resized_img.save(os.path.join(resize_out_dir, res_name)) out_name = os.path.splitext(os.path.basename(filepath))[0] + ".png" out_path = os.path.join(out_dir, out_name) out_img.save(out_path) log.info(f"Image folder batch: saved {out_path!r}") all_images.append(out_img) all_prompts += proc.all_prompts all_seeds += proc.all_seeds all_negative += proc.all_negative_prompts infotexts += proc.infotexts return get_processed(p, all_images, p.seed, "", all_prompts=all_prompts, all_seeds=all_seeds, all_negative_prompts=all_negative, infotexts=infotexts)