| 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): |
| |
| p.init_images = original_init_image |
|
|
| |
| 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) |
|
|
| |
| 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 |
|
|
| 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 |
|
|
