import os import itertools # SBM Batch frames import numpy as np import filetype from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError from modules import scripts_manager, shared, processing, images, errors from modules.generation_parameters_copypaste import create_override_settings_dict from modules.ui_common import plaintext_to_html from modules.memstats import memory_stats from modules.paths import resolve_output_path debug = shared.log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None debug('Trace: PROCESS') def validate_inputs(inputs): outputs = [] for image in inputs: if filetype.is_image(image): outputs.append(image) else: shared.log.warning(f'Input skip: file="{image}" filetype={filetype.guess(image)}') return outputs 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) image_files = [] if input_files is not None and len(input_files) > 0: image_files = [f.name for f in input_files] image_files = validate_inputs(image_files) shared.log.info(f'Process batch: input images={len(image_files)}') elif os.path.isdir(input_dir): image_files = [os.path.join(input_dir, f) for f in os.listdir(input_dir)] image_files = validate_inputs(image_files) shared.log.info(f'Process batch: input folder="{input_dir}" images={len(image_files)}') is_inpaint_batch = False if inpaint_mask_dir and os.path.isdir(inpaint_mask_dir): inpaint_masks = [os.path.join(inpaint_mask_dir, f) for f in os.listdir(inpaint_mask_dir)] inpaint_masks = validate_inputs(inpaint_masks) is_inpaint_batch = len(inpaint_masks) > 0 shared.log.info(f'Process batch: mask folder="{input_dir}" images={len(inpaint_masks)}') p.do_not_save_grid = True p.do_not_save_samples = True p.default_prompt = p.prompt if p.n_iter > 1: p.n_iter = 1 shared.log.warning(f'Process batch: batch_count={p.n_iter} forced to 1') 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)} outputs={p.n_iter}x{len(image_files)} parallel={window_size}") 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}x{len(image_files)}") 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) img = ImageOps.exif_transpose(img) batch_images.append(img) # p.init() p.width = int(img.width * p.scale_by) p.height = int(img.height * p.scale_by) caption_file = os.path.splitext(image_file)[0] + '.txt' prompt_type='default' if os.path.exists(caption_file): with open(caption_file, 'r', encoding='utf8') as f: p.prompt = f.read() prompt_type='file' else: p.prompt = p.default_prompt p.all_prompts = None p.all_negative_prompts = None p.all_seeds = None p.all_subseeds = None shared.log.debug(f'Process batch: image="{image_file}" prompt={prompt_type} i={i+1}/{len(image_files)}') except UnidentifiedImageError as e: shared.log.error(f'Process batch: image="{image_file}" {e}') if len(batch_images) == 0: shared.log.warning("Process batch: no images found in batch") continue 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. try: processed = scripts_manager.scripts_img2img.run(p, *args) if processed is None: processed = processing.process_images(p) except Exception as e: shared.log.error(f'Process batch: {e}') errors.display(e, 'batch') processed = None if processed is None or len(processed.images) == 0: shared.log.warning(f'Process batch: i={i+1}/{len(image_files)} no images processed') continue for n, (image, image_file) in enumerate(itertools.zip_longest(processed.images, batch_image_files)): if image is None: continue 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(processed.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 os.makedirs(output_dir, exist_ok=True) info, 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=info, grid=False, pnginfo_section_name="extras", existing_info=image.info, forced_filename=forced_filename) processed = scripts_manager.scripts_img2img.after(p, processed, *args) shared.log.debug(f'Processed: images={len(batch_image_files)} memory={memory_stats()} batch') def img2img(id_task: str, state: 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, vae_type, tiling, hidiffusion, detailer_enabled, detailer_prompt, detailer_negative, detailer_steps, detailer_strength, detailer_resolution, n_iter, batch_size, guidance_name, guidance_scale, guidance_rescale, guidance_start, guidance_stop, cfg_scale, image_cfg_scale, diffusers_guidance_rescale, pag_scale, pag_adaptive, 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, resize_context, 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_boundary, hdr_color_picker, hdr_tint_ratio, enable_hr, hr_sampler_index, hr_denoising_strength, hr_resize_mode, hr_resize_context, hr_upscaler, hr_force, hr_second_pass_steps, hr_scale, hr_resize_x, hr_resize_y, refiner_steps, hr_refiner_start, refiner_prompt, refiner_negative, override_settings_texts, *args): debug(f'img2img: {id_task}') if shared.sd_model is None: shared.log.warning('Aborted: op=img model not loaded') return [], '', '', 'Error: model not loaded' if sampler_index is None: shared.log.warning('Sampler: invalid') sampler_index = 0 mode = int(mode) image = None mask = None 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") elif mode == 1: # 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 == 2: # sketch if sketch is None: return [], '', '', 'Error: sketch image not provided' image = sketch.convert("RGB") elif mode == 3: # composite 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 elif mode == 5: # process batch pass # handled later else: shared.log.error(f'Image processing unknown mode: {mode}') 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=resolve_output_path(shared.opts.outdir_samples, shared.opts.outdir_img2img_samples), outpath_grids=resolve_output_path(shared.opts.outdir_grids, 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, sampler_name = processing.get_sampler_name(sampler_index, img=True), batch_size=batch_size, n_iter=n_iter, steps=steps, guidance_name=guidance_name, guidance_scale=guidance_scale, guidance_rescale=guidance_rescale, guidance_start=guidance_start, guidance_stop=guidance_stop, cfg_scale=cfg_scale, cfg_end=cfg_end, clip_skip=clip_skip, width=width, height=height, vae_type=vae_type, tiling=tiling, hidiffusion=hidiffusion, detailer_enabled=detailer_enabled, detailer_prompt=detailer_prompt, detailer_negative=detailer_negative, detailer_steps=detailer_steps, detailer_strength=detailer_strength, detailer_resolution=detailer_resolution, init_images=[image], mask=mask, mask_blur=mask_blur, resize_mode=resize_mode, resize_name=resize_name, resize_context=resize_context, scale_by=scale_by, denoising_strength=denoising_strength, image_cfg_scale=image_cfg_scale, diffusers_guidance_rescale=diffusers_guidance_rescale, pag_scale=pag_scale, pag_adaptive=pag_adaptive, 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_boundary=hdr_max_boundary, hdr_color_picker=hdr_color_picker, hdr_tint_ratio=hdr_tint_ratio, # refiner enable_hr=enable_hr, hr_denoising_strength=hr_denoising_strength, hr_scale=hr_scale, hr_resize_mode=hr_resize_mode, hr_resize_context=hr_resize_context, hr_upscaler=hr_upscaler, hr_force=hr_force, hr_second_pass_steps=hr_second_pass_steps, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y, hr_sampler_name = processing.get_sampler_name(hr_sampler_index), refiner_steps=refiner_steps, hr_refiner_start=hr_refiner_start, refiner_prompt=refiner_prompt, refiner_negative=refiner_negative, # override override_settings=override_settings, ) p.scripts = scripts_manager.scripts_img2img p.script_args = args p.state = state if mask: p.extra_generation_params["Mask blur"] = mask_blur p.extra_generation_params["Mask alpha"] = mask_alpha p.extra_generation_params["Mask padding"] = inpaint_full_res_padding p.extra_generation_params["Mask invert"] = ['masked', 'invert'][inpainting_mask_invert] p.extra_generation_params["Mask area"] = ["full", "masked"][inpaint_full_res] 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.get_processed(p, [], p.seed, "") else: processed = scripts_manager.scripts_img2img.run(p, *args) if processed is None: processed = processing.process_images(p) processed = scripts_manager.scripts_img2img.after(p, processed, *args) 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)