| 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 = [] |
|
|
| |
| 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 |
|
|
