import math import modules.scripts as scripts import gradio as gr from PIL import Image from modules import processing, shared, sd_samplers, images, devices from modules.processing import Processed from modules.shared import opts, cmd_opts, state class Script(scripts.Script): def title(self): return "SD upscale" def show(self, is_img2img): return is_img2img def ui(self, is_img2img): elem_prefix = ('i2i' if is_img2img else 't2i') + '_script_sd_upscale_' info = gr.HTML("

Will upscale the image by the selected scale factor; use width and height sliders to set tile size

") overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, elem_id=elem_prefix + "overlap") scale_factor = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label='Scale Factor', value=2.0, elem_id=elem_prefix + "scale_factor") upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index", elem_id=elem_prefix + "upscaler_index") return [info, overlap, upscaler_index, scale_factor] def run(self, p, _, overlap, upscaler_index, scale_factor): processing.fix_seed(p) upscaler = shared.sd_upscalers[upscaler_index] p.extra_generation_params["SD upscale overlap"] = overlap p.extra_generation_params["SD upscale upscaler"] = upscaler.name initial_info = None seed = p.seed init_img = p.init_images[0] init_img = images.flatten(init_img, opts.img2img_background_color) if upscaler.name != "None": img = upscaler.scaler.upscale(init_img, scale_factor, upscaler.data_path) else: img = init_img devices.torch_gc() grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=overlap) batch_size = p.batch_size 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) / 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.batch_size = batch_size p.init_images = work[i * batch_size:(i + 1) * batch_size] state.job = f"Batch {i + 1 + n * batch_count} out of {state.job_count}" processed = processing.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, p.prompt, opts.samples_format, info=initial_info, p=p) processed = Processed(p, result_images, seed, initial_info) return processed