| | 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 |
| |
|
