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 to Frame900 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, jpg, jpeg, webp or jxl images", elem_id=self.elem_id("folder"), ) with gr.Row(): output_dir = gr.Textbox( label="Output folder", placeholder="/output/", elem_id=self.elem_id("output_dir"), ) with gr.Row(): upscale_only = gr.Checkbox( label="Upscale only (skip inference, apply post-resize)", value=False, elem_id=self.elem_id("upscale_only"), ) with gr.Row(): prompt_override = gr.Textbox( label="Prompt", placeholder="Leave empty to use the panel prompt", elem_id=self.elem_id("prompt_override"), ) with gr.Row(): negative_override = gr.Textbox( label="Negative prompt", placeholder="Leave empty to use the panel value", elem_id=self.elem_id("negative_override"), ) with gr.Row(): seed_override = gr.Textbox( label="Seed (-1 = use panel)", 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 (0 = use panel)", 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 (0.0 = use panel)", elem_id=self.elem_id("cfg_scale_override"), ) with gr.Row(): sampler_override = gr.Dropdown( label="Sampler (empty = use panel)", 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 (0.0 = use panel)", elem_id=self.elem_id("strength_override"), ) # with gr.Row(): # gr.HTML('Pre-inference resize') _upscaler_choices = [x.name for x in shared.sd_upscalers] or ["None"] # with gr.Row(): # pre_resize_enabled = gr.Checkbox( # label="Enable pre-inference resize", # value=False, # elem_id=self.elem_id("pre_resize_enabled"), # ) # with gr.Row(): # pre_resize_mode = gr.Dropdown( # label="Resize mode", # choices=shared.resize_modes, # type="index", # value="None", # elem_id=self.elem_id("pre_resize_mode"), # ) # pre_resize_name = gr.Dropdown( # label="Resize method", # choices=_upscaler_choices, # value=_upscaler_choices[0], # elem_id=self.elem_id("pre_resize_name"), # ) # with gr.Row(): # pre_resize_scale = gr.Slider( # minimum=0.25, maximum=4.0, step=0.05, value=1.0, # label="Scale factor (ignored if width/height set)", # elem_id=self.elem_id("pre_resize_scale"), # ) # with gr.Row(): # pre_resize_width = gr.Number( # label="Width (0 = use scale factor)", # value=0, precision=0, # elem_id=self.elem_id("pre_resize_width"), # ) # pre_resize_height = gr.Number( # label="Height (0 = use scale factor)", # value=0, precision=0, # elem_id=self.elem_id("pre_resize_height"), # ) 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(): 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 (ignored if width/height set)", elem_id=self.elem_id("resize_scale"), ) with gr.Row(): resize_width = gr.Number( label="Width (0 = use scale factor)", value=0, precision=0, elem_id=self.elem_id("resize_width"), ) resize_height = gr.Number( label="Height (0 = use scale factor)", value=0, precision=0, elem_id=self.elem_id("resize_height"), ) return [folder, output_dir, upscale_only, prompt_override, negative_override, seed_override, steps_override, cfg_scale_override, sampler_override, strength_override, resize_enabled, resize_mode, resize_name, resize_scale, resize_width, resize_height] def run(self, p, folder, output_dir, upscale_only, prompt_override, negative_override, seed_override, steps_override, cfg_scale_override, sampler_override, strength_override, resize_enabled, resize_mode, resize_name, resize_scale, resize_width, resize_height): # 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 processing.Processed(p, [], p.seed, "Invalid or missing folder") files = sorted(list_files(folder, ext_filter=['.png', '.jpg', '.jpeg', '.webp', '.jxl'], recursive=False)) if not files: log.error(f"Image folder batch: no image files found in: {folder!r}") return processing.Processed(p, [], p.seed, "No image files found") out_dir = (output_dir or "").strip() or os.path.join(folder, "output") os.makedirs(out_dir, exist_ok=True) # pre_resize_out_dir = os.path.join(out_dir, "pre-resize") if pre_resize_enabled else None # if pre_resize_out_dir: # os.makedirs(pre_resize_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() shared.state.job_count = len(files) all_images = [] all_prompts = [] all_seeds = [] all_negative = [] infotexts = [] for i, filepath in enumerate(files): if shared.state.interrupted: break shared.state.job = f"{i + 1}/{len(files)}" shared.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 # if pre_resize_enabled and pre_resize_mode != 0 and pre_resize_name not in ('None', '') and shared.sd_upscalers: # pre_w = int(pre_resize_width) if int(pre_resize_width) > 0 else int(img.width * pre_resize_scale) # pre_h = int(pre_resize_height) if int(pre_resize_height) > 0 else int(img.height * pre_resize_scale) # img = images.resize_image(pre_resize_mode, img, pre_w, pre_h, pre_resize_name) # cp.init_images = [img] # cp.width = img.width # cp.height = img.height # log.info(f"Image folder batch: pre-resize to {img.size} mode={shared.resize_modes[pre_resize_mode]!r} method={pre_resize_name!r}") # pre_out_path = os.path.join(pre_resize_out_dir, os.path.basename(filepath)) # img.save(pre_out_path) # log.info(f"Image folder batch: pre-resize saved {pre_out_path!r}") if upscale_only: log.info(f"Image folder batch: [{i + 1}/{len(files)}] upscale-only file={os.path.basename(filepath)} size={img.size}") out_img = img else: 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] all_prompts += proc.all_prompts all_seeds += proc.all_seeds all_negative += proc.all_negative_prompts infotexts += proc.infotexts if resize_enabled and resize_mode != 0 and resize_name not in ('None', '') and shared.sd_upscalers: target_w = int(resize_width) if int(resize_width) > 0 else int(out_img.width * resize_scale) target_h = int(resize_height) if int(resize_height) > 0 else 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.basename(filepath) resized_img.save(os.path.join(resize_out_dir, res_name)) out_name = os.path.basename(filepath) 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) return processing.get_processed(p, all_images, p.seed, "", all_prompts=all_prompts, all_seeds=all_seeds, all_negative_prompts=all_negative, infotexts=infotexts)