cymic/Waifu_Diffusion_Webui

1import numpy as np2from tqdm import trange3 4import modules.scripts as scripts5import gradio as gr6 7from modules import processing, shared, sd_samplers, images8from modules.processing import Processed9from modules.sd_samplers import samplers10from modules.shared import opts, cmd_opts, state11 12class Script(scripts.Script):13    def title(self):14        return "Loopback"15 16    def show(self, is_img2img):17        return is_img2img18 19    def ui(self, is_img2img):20        loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4)21        denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1)22 23        return [loops, denoising_strength_change_factor]24 25    def run(self, p, loops, denoising_strength_change_factor):26        processing.fix_seed(p)27        batch_count = p.n_iter28        p.extra_generation_params = {29            "Denoising strength change factor": denoising_strength_change_factor,30        }31 32        p.batch_size = 133        p.n_iter = 134 35        output_images, info = None, None36        initial_seed = None37        initial_info = None38 39        grids = []40        all_images = []41        state.job_count = loops * batch_count42 43        initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]44 45        for n in range(batch_count):46            history = []47 48            for i in range(loops):49                p.n_iter = 150                p.batch_size = 151                p.do_not_save_grid = True52 53                if opts.img2img_color_correction:54                    p.color_corrections = initial_color_corrections55 56                state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"57 58                processed = processing.process_images(p)59 60                if initial_seed is None:61                    initial_seed = processed.seed62                    initial_info = processed.info63 64                init_img = processed.images[0]65 66                p.init_images = [init_img]67                p.seed = processed.seed + 168                p.denoising_strength = min(max(p.denoising_strength * denoising_strength_change_factor, 0.1), 1)69                history.append(processed.images[0])70 71            grid = images.image_grid(history, rows=1)72            if opts.grid_save:73                images.save_image(grid, p.outpath_grids, "grid", initial_seed, p.prompt, opts.grid_format, info=info, short_filename=not opts.grid_extended_filename, grid=True, p=p)74 75            grids.append(grid)76            all_images += history77 78        if opts.return_grid:79            all_images = grids + all_images80 81        processed = Processed(p, all_images, initial_seed, initial_info)82 83        return processed84