import math import os import sys import traceback import numpy as np from PIL import Image, ImageOps, ImageChops from modules import devices 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.images as images import modules.scripts def process_batch(p, input_dir, output_dir, args): processing.fix_seed(p) images = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)] print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.") save_normally = output_dir == '' p.do_not_save_grid = True p.do_not_save_samples = not save_normally state.job_count = len(images) * p.n_iter for i, image in enumerate(images): state.job = f"{i+1} out of {len(images)}" if state.interrupted: break img = Image.open(image) p.init_images = [img] * p.batch_size proc = modules.scripts.scripts_img2img.run(p, *args) if proc is None: proc = process_images(p) for n, processed_image in enumerate(proc.images): filename = os.path.basename(image) if n > 0: left, right = os.path.splitext(filename) filename = f"{left}-{n}{right}" if not save_normally: processed_image.save(os.path.join(output_dir, filename)) def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, 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, height: int, width: int, 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, *args): is_inpaint = mode == 1 is_batch = mode == 2 if is_inpaint: if mask_mode == 0: 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') else: image = init_img_inpaint mask = init_mask_inpaint else: image = init_img mask = None 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_style, prompt_style2], 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_index=sampler_index, 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, inpaint_full_res=inpaint_full_res, inpaint_full_res_padding=inpaint_full_res_padding, inpainting_mask_invert=inpainting_mask_invert, ) if shared.cmd_opts.enable_console_prompts: print(f"\nimg2img: {prompt}", file=shared.progress_print_out) p.extra_generation_params["Mask blur"] = mask_blur 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, args) 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) return processed.images, generation_info_js, plaintext_to_html(processed.info)