import os import itertools # SBM Batch frames import numpy as np from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError import modules.scripts from modules import shared, processing, images from modules.generation_parameters_copypaste import create_override_settings_dict from modules.ui import plaintext_to_html from modules.memstats import memory_stats debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None debug('Trace: PROCESS') def process_batch(p, input_files, input_dir, output_dir, inpaint_mask_dir, args): shared.log.debug(f'batch: {input_files}|{input_dir}|{output_dir}|{inpaint_mask_dir}') processing.fix_seed(p) if input_files is not None and len(input_files) > 0: image_files = [f.name for f in input_files] else: if not os.path.isdir(input_dir): shared.log.error(f"Process batch: directory not found: {input_dir}") return image_files = os.listdir(input_dir) image_files = [os.path.join(input_dir, f) for f in image_files] is_inpaint_batch = False if inpaint_mask_dir: inpaint_masks = os.listdir(inpaint_mask_dir) inpaint_masks = [os.path.join(inpaint_mask_dir, f) for f in inpaint_masks] is_inpaint_batch = len(inpaint_masks) > 0 if is_inpaint_batch: shared.log.info(f"Process batch: inpaint batch masks={len(inpaint_masks)}") save_normally = output_dir == '' p.do_not_save_grid = True p.do_not_save_samples = not save_normally shared.state.job_count = len(image_files) * p.n_iter if shared.opts.batch_frame_mode: # SBM Frame mode is on, process each image in batch with same seed window_size = p.batch_size btcrept = 1 p.seed = [p.seed] * window_size # SBM MONKEYPATCH: Need to change processing to support a fixed seed value. p.subseed = [p.subseed] * window_size # SBM MONKEYPATCH shared.log.info(f"Process batch: inputs={len(image_files)} parallel={window_size} outputs={p.n_iter} per input ") else: # SBM Frame mode is off, standard operation of repeating same images with sequential seed. window_size = 1 btcrept = p.batch_size shared.log.info(f"Process batch: inputs={len(image_files)} outputs={p.n_iter * p.batch_size} per input") for i in range(0, len(image_files), window_size): if shared.state.skipped: shared.state.skipped = False if shared.state.interrupted: break batch_image_files = image_files[i:i+window_size] batch_images = [] for image_file in batch_image_files: try: img = Image.open(image_file) if p.scale_by != 1: p.width = int(img.width * p.scale_by) p.height = int(img.height * p.scale_by) except UnidentifiedImageError as e: shared.log.error(f"Image error: {e}") continue img = ImageOps.exif_transpose(img) batch_images.append(img) batch_images = batch_images * btcrept # Standard mode sends the same image per batchsize. p.init_images = batch_images if is_inpaint_batch: # try to find corresponding mask for an image using simple filename matching batch_mask_images = [] for image_file in batch_image_files: mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image_file)) # if not found use first one ("same mask for all images" use-case) if mask_image_path not in inpaint_masks: mask_image_path = inpaint_masks[0] mask_image = Image.open(mask_image_path) batch_mask_images.append(mask_image) batch_mask_images = batch_mask_images * btcrept p.image_mask = batch_mask_images batch_image_files = batch_image_files * btcrept # List used for naming later. proc = modules.scripts.scripts_img2img.run(p, *args) if proc is None: proc = processing.process_images(p) for n, (image, image_file) in enumerate(itertools.zip_longest(proc.images,batch_image_files)): basename = '' if shared.opts.use_original_name_batch: forced_filename, ext = os.path.splitext(os.path.basename(image_file)) else: forced_filename = None ext = shared.opts.samples_format if len(proc.images) > 1: basename = f'{n + i}' if shared.opts.batch_frame_mode else f'{n}' else: basename = '' if output_dir == '': output_dir = shared.opts.outdir_img2img_samples if not save_normally: os.makedirs(output_dir, exist_ok=True) geninfo, items = images.read_info_from_image(image) for k, v in items.items(): image.info[k] = v images.save_image(image, path=output_dir, basename=basename, seed=None, prompt=None, extension=ext, info=geninfo, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=image.info, forced_filename=forced_filename) shared.log.debug(f'Processed: images={len(batch_image_files)} memory={memory_stats()} batch') def img2img(id_task: str, mode: int, prompt, negative_prompt, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps, sampler_index, mask_blur, mask_alpha, inpainting_fill, full_quality, restore_faces, tiling, n_iter, batch_size, cfg_scale, image_cfg_scale, diffusers_guidance_rescale, sag_scale, cfg_end, refiner_start, clip_skip, denoising_strength, seed, subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, selected_scale_tab, height, width, scale_by, resize_mode, resize_name, inpaint_full_res, inpaint_full_res_padding, inpainting_mask_invert, img2img_batch_files, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, hdr_mode, hdr_brightness, hdr_color, hdr_sharpen, hdr_clamp, hdr_boundary, hdr_threshold, hdr_maximize, hdr_max_center, hdr_max_boundry, hdr_color_picker, hdr_tint_ratio, override_settings_texts, *args): # pylint: disable=unused-argument if shared.sd_model is None: shared.log.warning('Model not loaded') return [], '', '', 'Error: model not loaded' debug(f'img2img: id_task={id_task}|mode={mode}|prompt={prompt}|negative_prompt={negative_prompt}|prompt_styles={prompt_styles}|init_img={init_img}|sketch={sketch}|init_img_with_mask={init_img_with_mask}|inpaint_color_sketch={inpaint_color_sketch}|inpaint_color_sketch_orig={inpaint_color_sketch_orig}|init_img_inpaint={init_img_inpaint}|init_mask_inpaint={init_mask_inpaint}|steps={steps}|sampler_index={sampler_index}||mask_blur={mask_blur}|mask_alpha={mask_alpha}|inpainting_fill={inpainting_fill}|full_quality={full_quality}|restore_faces={restore_faces}|tiling={tiling}|n_iter={n_iter}|batch_size={batch_size}|cfg_scale={cfg_scale}|image_cfg_scale={image_cfg_scale}|clip_skip={clip_skip}|denoising_strength={denoising_strength}|seed={seed}|subseed{subseed}|subseed_strength={subseed_strength}|seed_resize_from_h={seed_resize_from_h}|seed_resize_from_w={seed_resize_from_w}|selected_scale_tab={selected_scale_tab}|height={height}|width={width}|scale_by={scale_by}|resize_mode={resize_mode}|resize_name={resize_name}|inpaint_full_res={inpaint_full_res}|inpaint_full_res_padding={inpaint_full_res_padding}|inpainting_mask_invert={inpainting_mask_invert}|img2img_batch_files={img2img_batch_files}|img2img_batch_input_dir={img2img_batch_input_dir}|img2img_batch_output_dir={img2img_batch_output_dir}|img2img_batch_inpaint_mask_dir={img2img_batch_inpaint_mask_dir}|override_settings_texts={override_settings_texts}') if mode == 5: if img2img_batch_files is None or len(img2img_batch_files) == 0: shared.log.debug('Init bactch images not set') elif init_img: shared.log.debug('Init image not set') if sampler_index is None: sampler_index = 0 override_settings = create_override_settings_dict(override_settings_texts) if mode == 0: # img2img if init_img is None: return [], '', '', 'Error: init image not provided' image = init_img.convert("RGB") mask = None elif mode == 1: # img2img sketch if sketch is None: return [], '', '', 'Error: sketch image not provided' image = sketch.convert("RGB") mask = None elif mode == 2: # inpaint if init_img_with_mask is None: return [], '', '', 'Error: init image with mask not provided' image = init_img_with_mask["image"] mask = init_img_with_mask["mask"] alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1') mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L') image = image.convert("RGB") elif mode == 3: # inpaint sketch if inpaint_color_sketch is None: return [], '', '', 'Error: color sketch image not provided' 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((255.0 * pred).astype(np.uint8), "L") mask = ImageEnhance.Brightness(mask).enhance(mask_alpha) 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 if init_img_inpaint is None: return [], '', '', 'Error: inpaint image not provided' image = init_img_inpaint mask = init_mask_inpaint else: shared.log.error(f'Image processing unknown mode: {mode}') image = None mask = None if image is not None: image = ImageOps.exif_transpose(image) if selected_scale_tab == 1 and resize_mode != 0: width = int(image.width * scale_by) height = int(image.height * scale_by) p = processing.StableDiffusionProcessingImg2Img( sd_model=shared.sd_model, outpath_samples=shared.opts.outdir_samples or shared.opts.outdir_img2img_samples, outpath_grids=shared.opts.outdir_grids or shared.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=True, sampler_name = processing.get_sampler_name(sampler_index, img=True), batch_size=batch_size, n_iter=n_iter, steps=steps, cfg_scale=cfg_scale, cfg_end=cfg_end, clip_skip=clip_skip, width=width, height=height, full_quality=full_quality, restore_faces=restore_faces, tiling=tiling, init_images=[image], mask=mask, mask_blur=mask_blur, inpainting_fill=inpainting_fill, resize_mode=resize_mode, resize_name=resize_name, denoising_strength=denoising_strength, image_cfg_scale=image_cfg_scale, diffusers_guidance_rescale=diffusers_guidance_rescale, sag_scale=sag_scale, refiner_start=refiner_start, inpaint_full_res=inpaint_full_res != 0, inpaint_full_res_padding=inpaint_full_res_padding, inpainting_mask_invert=inpainting_mask_invert, hdr_mode=hdr_mode, hdr_brightness=hdr_brightness, hdr_color=hdr_color, hdr_sharpen=hdr_sharpen, hdr_clamp=hdr_clamp, hdr_boundary=hdr_boundary, hdr_threshold=hdr_threshold, hdr_maximize=hdr_maximize, hdr_max_center=hdr_max_center, hdr_max_boundry=hdr_max_boundry, hdr_color_picker=hdr_color_picker, hdr_tint_ratio=hdr_tint_ratio, override_settings=override_settings, ) if selected_scale_tab == 1 and resize_mode != 0: p.scale_by = scale_by p.scripts = modules.scripts.scripts_img2img p.script_args = args if mask: p.extra_generation_params["Mask blur"] = mask_blur p.extra_generation_params["Mask alpha"] = mask_alpha p.extra_generation_params["Mask invert"] = inpainting_mask_invert p.extra_generation_params["Mask content"] = inpainting_fill p.extra_generation_params["Mask area"] = inpaint_full_res p.extra_generation_params["Mask padding"] = inpaint_full_res_padding p.is_batch = mode == 5 if p.is_batch: process_batch(p, img2img_batch_files, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args) processed = processing.Processed(p, [], p.seed, "") else: processed = modules.scripts.scripts_img2img.run(p, *args) if processed is None: processed = processing.process_images(p) p.close() generation_info_js = processed.js() if processed is not None else '' if processed is None: return [], generation_info_js, '', 'Error: no images' return processed.images, generation_info_js, processed.info, plaintext_to_html(processed.comments)