stable-diffusion-webui/scripts/loopback.py

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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
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)
denoising_strength_change_factor = gr.Slider(minimum=0.9, maximum=1.1, step=0.01, label='Denoising strength change factor', value=1)
return [loops, denoising_strength_change_factor]
def run(self, p, loops, denoising_strength_change_factor):
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
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
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