import numpy as np from tqdm import trange import modules.scripts as scripts import gradio as gr from modules import processing, shared, sd_samplers, images from modules.processing import Processed from modules.sd_samplers import samplers from modules.shared import opts, cmd_opts, state class Script(scripts.Script): def title(self): return "Loopback" def show(self, is_img2img): return is_img2img def ui(self, is_img2img): loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4) denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1) return [loops, denoising_strength_change_factor] def run(self, p, loops, denoising_strength_change_factor): processing.fix_seed(p) batch_count = p.n_iter p.extra_generation_params = { "Denoising strength change factor": denoising_strength_change_factor, } p.batch_size = 1 p.n_iter = 1 output_images, info = None, None initial_seed = None initial_info = None grids = [] all_images = [] original_init_image = p.init_images state.job_count = loops * batch_count initial_color_corrections = [processing.setup_color_correction(p.init_images[0])] for n in range(batch_count): history = [] # Reset to original init image at the start of each batch p.init_images = original_init_image for i in range(loops): p.n_iter = 1 p.batch_size = 1 p.do_not_save_grid = True if opts.img2img_color_correction: p.color_corrections = initial_color_corrections state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}" processed = processing.process_images(p) if initial_seed is None: initial_seed = processed.seed initial_info = processed.info init_img = processed.images[0] 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, rows=1) if opts.grid_save: 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) grids.append(grid) all_images += history if opts.return_grid: all_images = grids + all_images processed = Processed(p, all_images, initial_seed, initial_info) return processed