import os from contextlib import closing from pathlib import Path import numpy as np from PIL import Image, ImageOps, ImageFilter, ImageEnhance, UnidentifiedImageError import gradio as gr from modules import sd_samplers, images as imgutil from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images from modules.shared import opts, state import modules.shared as shared import modules.processing as processing from modules.ui import plaintext_to_html import modules.scripts def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None): output_dir = output_dir.strip() processing.fix_seed(p) images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff"))) is_inpaint_batch = False if inpaint_mask_dir: inpaint_masks = shared.listfiles(inpaint_mask_dir) is_inpaint_batch = bool(inpaint_masks) if is_inpaint_batch: print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.") print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.") state.job_count = len(images) * p.n_iter # extract "default" params to use in case getting png info fails prompt = p.prompt negative_prompt = p.negative_prompt seed = p.seed cfg_scale = p.cfg_scale sampler_name = p.sampler_name steps = p.steps for i, image in enumerate(images): state.job = f"{i+1} out of {len(images)}" if state.skipped: state.skipped = False if state.interrupted: break try: img = Image.open(image) except UnidentifiedImageError as e: print(e) continue # Use the EXIF orientation of photos taken by smartphones. img = ImageOps.exif_transpose(img) if to_scale: p.width = int(img.width * scale_by) p.height = int(img.height * scale_by) p.init_images = [img] * p.batch_size image_path = Path(image) if is_inpaint_batch: # try to find corresponding mask for an image using simple filename matching if len(inpaint_masks) == 1: mask_image_path = inpaint_masks[0] else: # try to find corresponding mask for an image using simple filename matching mask_image_dir = Path(inpaint_mask_dir) masks_found = list(mask_image_dir.glob(f"{image_path.stem}.*")) if len(masks_found) == 0: print(f"Warning: mask is not found for {image_path} in {mask_image_dir}. Skipping it.") continue # it should contain only 1 matching mask # otherwise user has many masks with the same name but different extensions mask_image_path = masks_found[0] mask_image = Image.open(mask_image_path) p.image_mask = mask_image if use_png_info: try: info_img = img if png_info_dir: info_img_path = os.path.join(png_info_dir, os.path.basename(image)) info_img = Image.open(info_img_path) geninfo, _ = imgutil.read_info_from_image(info_img) parsed_parameters = parse_generation_parameters(geninfo) parsed_parameters = {k: v for k, v in parsed_parameters.items() if k in (png_info_props or {})} except Exception: parsed_parameters = {} p.prompt = prompt + (" " + parsed_parameters["Prompt"] if "Prompt" in parsed_parameters else "") p.negative_prompt = negative_prompt + (" " + parsed_parameters["Negative prompt"] if "Negative prompt" in parsed_parameters else "") p.seed = int(parsed_parameters.get("Seed", seed)) p.cfg_scale = float(parsed_parameters.get("CFG scale", cfg_scale)) p.sampler_name = parsed_parameters.get("Sampler", sampler_name) p.steps = int(parsed_parameters.get("Steps", steps)) proc = modules.scripts.scripts_img2img.run(p, *args) if proc is None: if output_dir: p.outpath_samples = output_dir p.override_settings['save_to_dirs'] = False if p.n_iter > 1 or p.batch_size > 1: p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]' else: p.override_settings['samples_filename_pattern'] = f'{image_path.stem}' process_images(p) def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args): override_settings = create_override_settings_dict(override_settings_texts) is_batch = mode == 5 if mode == 0: # img2img image = init_img.convert("RGB") mask = None elif mode == 1: # img2img sketch image = sketch.convert("RGB") mask = None elif mode == 2: # inpaint image, mask = init_img_with_mask["image"], init_img_with_mask["mask"] mask = mask.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0) image = image.convert("RGB") elif mode == 3: # inpaint sketch image = inpaint_color_sketch orig = inpaint_color_sketch_orig or inpaint_color_sketch pred = np.any(np.array(image) != np.array(orig), axis=-1) mask = Image.fromarray(pred.astype(np.uint8) * 255, "L") mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100) blur = ImageFilter.GaussianBlur(mask_blur) image = Image.composite(image.filter(blur), orig, mask.filter(blur)) image = image.convert("RGB") elif mode == 4: # inpaint upload mask image = init_img_inpaint mask = init_mask_inpaint else: image = None mask = None # Use the EXIF orientation of photos taken by smartphones. if image is not None: image = ImageOps.exif_transpose(image) if selected_scale_tab == 1 and not is_batch: assert image, "Can't scale by because no image is selected" width = int(image.width * scale_by) height = int(image.height * scale_by) assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]' p = StableDiffusionProcessingImg2Img( sd_model=shared.sd_model, outpath_samples=opts.outdir_samples or opts.outdir_img2img_samples, outpath_grids=opts.outdir_grids or opts.outdir_img2img_grids, prompt=prompt, negative_prompt=negative_prompt, styles=prompt_styles, seed=seed, subseed=subseed, subseed_strength=subseed_strength, seed_resize_from_h=seed_resize_from_h, seed_resize_from_w=seed_resize_from_w, seed_enable_extras=seed_enable_extras, sampler_name=sd_samplers.samplers_for_img2img[sampler_index].name, batch_size=batch_size, n_iter=n_iter, steps=steps, cfg_scale=cfg_scale, width=width, height=height, restore_faces=restore_faces, tiling=tiling, init_images=[image], mask=mask, mask_blur=mask_blur, inpainting_fill=inpainting_fill, resize_mode=resize_mode, denoising_strength=denoising_strength, image_cfg_scale=image_cfg_scale, inpaint_full_res=inpaint_full_res, inpaint_full_res_padding=inpaint_full_res_padding, inpainting_mask_invert=inpainting_mask_invert, override_settings=override_settings, ) p.scripts = modules.scripts.scripts_img2img p.script_args = args p.user = request.username if shared.cmd_opts.enable_console_prompts: print(f"\nimg2img: {prompt}", file=shared.progress_print_out) if mask: p.extra_generation_params["Mask blur"] = mask_blur with closing(p): if is_batch: assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled" process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir) processed = Processed(p, [], p.seed, "") else: processed = modules.scripts.scripts_img2img.run(p, *args) if processed is None: processed = process_images(p) shared.total_tqdm.clear() generation_info_js = processed.js() if opts.samples_log_stdout: print(generation_info_js) if opts.do_not_show_images: processed.images = [] return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments, classname="comments")