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| 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: | |
| 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: | |
| 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 | |