import math import cv2 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 img2img(prompt: str, negative_prompt: str, prompt_style: str, init_img, init_img_with_mask, init_mask, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, denoising_strength_change_factor: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, height: int, width: int, resize_mode: int, upscaler_index: str, upscale_overlap: int, inpaint_full_res: bool, inpainting_mask_invert: int, *args): is_inpaint = mode == 1 is_loopback = mode == 2 is_upscale = mode == 3 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 mask = init_mask 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, prompt_style=prompt_style, 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_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, inpainting_mask_invert=inpainting_mask_invert, extra_generation_params={ "Denoising strength": denoising_strength, "Denoising strength change factor": (denoising_strength_change_factor if is_loopback else None) } ) print(f"\nimg2img: {prompt}", file=shared.progress_print_out) if is_loopback: output_images, info = None, None history = [] initial_seed = None initial_info = None state.job_count = n_iter do_color_correction = False try: from skimage import exposure do_color_correction = True except: print("Install scikit-image to perform color correction on loopback") for i in range(n_iter): if do_color_correction and i == 0: correction_target = cv2.cvtColor(np.asarray(init_img.copy()), cv2.COLOR_RGB2LAB) p.n_iter = 1 p.batch_size = 1 p.do_not_save_grid = True state.job = f"Batch {i + 1} out of {n_iter}" processed = process_images(p) if initial_seed is None: initial_seed = processed.seed initial_info = processed.info init_img = processed.images[0] if do_color_correction and correction_target is not None: init_img = Image.fromarray(cv2.cvtColor(exposure.match_histograms( cv2.cvtColor( np.asarray(init_img), cv2.COLOR_RGB2LAB ), correction_target, channel_axis=2 ), cv2.COLOR_LAB2RGB).astype("uint8")) p.init_images = [init_img] p.seed = processed.seed + 1 p.denoising_strength = min(max(p.denoising_strength * denoising_strength_change_factor, 0.1), 1) history.append(processed.images[0]) grid = images.image_grid(history, batch_size, rows=1) images.save_image(grid, p.outpath_grids, "grid", initial_seed, prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, p=p) processed = Processed(p, history, initial_seed, initial_info) elif is_upscale: initial_info = None processing.fix_seed(p) seed = p.seed upscaler = shared.sd_upscalers[upscaler_index] img = upscaler.upscale(init_img, init_img.width * 2, init_img.height * 2) devices.torch_gc() grid = images.split_grid(img, tile_w=width, tile_h=height, overlap=upscale_overlap) upscale_count = p.n_iter p.n_iter = 1 p.do_not_save_grid = True p.do_not_save_samples = True work = [] for y, h, row in grid.tiles: for tiledata in row: work.append(tiledata[2]) batch_count = math.ceil(len(work) / p.batch_size) state.job_count = batch_count * upscale_count print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.") result_images = [] for n in range(upscale_count): start_seed = seed + n p.seed = start_seed work_results = [] for i in range(batch_count): p.init_images = work[i*p.batch_size:(i+1)*p.batch_size] state.job = f"Batch {i + 1} out of {state.job_count}" processed = process_images(p) if initial_info is None: initial_info = processed.info p.seed = processed.seed + 1 work_results += processed.images image_index = 0 for y, h, row in grid.tiles: for tiledata in row: tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height)) image_index += 1 combined_image = images.combine_grid(grid) result_images.append(combined_image) if opts.samples_save: images.save_image(combined_image, p.outpath_samples, "", start_seed, prompt, opts.samples_format, info=initial_info, p=p) processed = Processed(p, result_images, seed, initial_info) else: processed = modules.scripts.scripts_img2img.run(p, *args) if processed is None: processed = process_images(p) shared.total_tqdm.clear() return processed.images, processed.js(), plaintext_to_html(processed.info)