import math from PIL import Image 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, init_img, init_img_with_mask, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, use_GFPGAN: bool, mode: int, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, height: int, width: int, resize_mode: int, upscaler_name: str, upscale_overlap: int, inpaint_full_res: bool, *args): is_inpaint = mode == 1 is_loopback = mode == 2 is_upscale = mode == 3 if is_inpaint: image = init_img_with_mask['image'] mask = init_img_with_mask['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, seed=seed, sampler_index=sampler_index, batch_size=batch_size, n_iter=n_iter, steps=steps, cfg_scale=cfg_scale, width=width, height=height, use_GFPGAN=use_GFPGAN, 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, extra_generation_params={"Denoising Strength": denoising_strength} ) if is_loopback: output_images, info = None, None history = [] initial_seed = None initial_info = None for i in range(n_iter): 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 p.init_images = [processed.images[0]] p.seed = processed.seed + 1 p.denoising_strength = max(p.denoising_strength * 0.95, 0.1) 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) processed = Processed(p, history, initial_seed, initial_info) elif is_upscale: initial_seed = None initial_info = None upscaler = shared.sd_upscalers.get(upscaler_name, next(iter(shared.sd_upscalers.values()))) img = upscaler(init_img) processing.torch_gc() grid = images.split_grid(img, tile_w=width, tile_h=height, overlap=upscale_overlap) p.n_iter = 1 p.do_not_save_grid = True p.do_not_save_samples = True work = [] work_results = [] for y, h, row in grid.tiles: for tiledata in row: work.append(tiledata[2]) batch_count = math.ceil(len(work) / p.batch_size) print(f"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} in a total of {batch_count} batches.") 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 {batch_count}" processed = process_images(p) if initial_seed is None: initial_seed = processed.seed 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) if opts.samples_save: images.save_image(combined_image, p.outpath_samples, "", initial_seed, prompt, opts.grid_format, info=initial_info) processed = Processed(p, [combined_image], initial_seed, initial_info) else: processed = modules.scripts.run(p, *args) if processed is None: processed = process_images(p) return processed.images, processed.js(), plaintext_to_html(processed.info)