import math import gradio as gr import modules.scripts as scripts from modules import deepbooru, images, processing, shared from modules.processing import Processed from modules.shared import 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, elem_id=self.elem_id("loops")) final_denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01, label='Final denoising strength', value=0.5, elem_id=self.elem_id("final_denoising_strength")) denoising_curve = gr.Dropdown(label="Denoising strength curve", choices=["Aggressive", "Linear", "Lazy"], value="Linear") append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None") return [loops, final_denoising_strength, denoising_curve, append_interrogation] def run(self, p, loops, final_denoising_strength, denoising_curve, append_interrogation): processing.fix_seed(p) batch_count = p.n_iter p.extra_generation_params = { "Final denoising strength": final_denoising_strength, "Denoising curve": denoising_curve } p.batch_size = 1 p.n_iter = 1 info = None initial_seed = None initial_info = None initial_denoising_strength = p.denoising_strength grids = [] all_images = [] original_init_image = p.init_images original_prompt = p.prompt original_inpainting_fill = p.inpainting_fill state.job_count = loops * batch_count initial_color_corrections = [processing.setup_color_correction(p.init_images[0])] def calculate_denoising_strength(loop): strength = initial_denoising_strength if loops == 1: return strength progress = loop / (loops - 1) if denoising_curve == "Aggressive": strength = math.sin((progress) * math.pi * 0.5) elif denoising_curve == "Lazy": strength = 1 - math.cos((progress) * math.pi * 0.5) else: strength = progress change = (final_denoising_strength - initial_denoising_strength) * strength return initial_denoising_strength + change history = [] for n in range(batch_count): # Reset to original init image at the start of each batch p.init_images = original_init_image # Reset to original denoising strength p.denoising_strength = initial_denoising_strength last_image = None 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 if append_interrogation != "None": p.prompt = f"{original_prompt}, " if original_prompt else "" if append_interrogation == "CLIP": p.prompt += shared.interrogator.interrogate(p.init_images[0]) elif append_interrogation == "DeepBooru": p.prompt += deepbooru.model.tag(p.init_images[0]) state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}" processed = processing.process_images(p) # Generation cancelled. if state.interrupted or state.interrupted_next: break if initial_seed is None: initial_seed = processed.seed initial_info = processed.info p.seed = processed.seed + 1 p.denoising_strength = calculate_denoising_strength(i + 1) if state.skipped: break last_image = processed.images[0] p.init_images = [last_image] p.inpainting_fill = 1 # Set "masked content" to "original" for next loop. if batch_count == 1: history.append(last_image) all_images.append(last_image) if batch_count > 1 and not state.skipped and not state.interrupted: history.append(last_image) all_images.append(last_image) p.inpainting_fill = original_inpainting_fill if state.interrupted or state.interrupted_next: break if len(history) > 1: 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) if opts.return_grid: grids.append(grid) all_images = grids + all_images processed = Processed(p, all_images, initial_seed, initial_info) return processed