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| 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 | |
| from modules import deepbooru | |
| 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")) | |
| denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1, elem_id=self.elem_id("denoising_strength_change_factor")) | |
| append_interrogation = gr.Dropdown(label="Append interrogated prompt at each iteration", choices=["None", "CLIP", "DeepBooru"], value="None") | |
| return [loops, denoising_strength_change_factor, append_interrogation] | |
| def run(self, p, loops, denoising_strength_change_factor, append_interrogation): | |
| 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 | |
| original_prompt = p.prompt | |
| 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 | |
| if append_interrogation != "None": | |
| p.prompt = 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 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 | |