scripts/loopback.py

import math

import gradio as gr
from modules import images, processing, scripts
from modules.processing import Processed
from modules.shared import opts, state


class Loopback(scripts.Script):
    def title(self):
        return "Loopback"

    def show(self, is_img2img):
        return is_img2img

    def ui(self, is_img2img):
        with gr.Row():
            loops = gr.Slider(
                minimum=1,
                maximum=8,
                step=1,
                label="Loops",
                value=2,
                elem_id=self.elem_id("loops"),
            )
            final_denoising_strength = gr.Slider(
                label="Final Denoising Strength",
                minimum=0,
                maximum=1,
                step=0.01,
                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",
                elem_id=self.elem_id("denoising_strength_curve"),
            )

        return [loops, final_denoising_strength, denoising_curve]

    def run(self, p, loops: int, final_denoising_strength: float, denoising_curve: str):
        processing.fix_seed(p)
        p.extra_generation_params = {
            "Final Denoising Strength": final_denoising_strength,
            "Denoising Strength Curve": denoising_curve,
        }

        batch_count = p.n_iter
        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_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

                state.job = f"Iteration {i + 1}/{loops}, batch {n + 1}/{batch_count}"

                processed = processing.process_images(p)

                # Generation cancelled
                if state.interrupted or state.stopping_generation:
                    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]
                # Set "masked content" to "original" for next loop
                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 or state.stopping_generation:
                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

        return Processed(p, all_images, initial_seed, initial_info)