|
|
| import math |
| import os |
| import sys |
| import traceback |
| import random |
|
|
| import modules.scripts as scripts |
| import modules.images as images |
| import gradio as gr |
|
|
| from modules.processing import Processed, process_images |
| from PIL import Image |
| from modules.shared import opts, cmd_opts, state |
|
|
| class Script(scripts.Script): |
| def title(self): |
| return "Lanczos quick upscale" |
|
|
| def ui(self, is_img2img): |
| upscale_factor = gr.Slider(minimum=1, maximum=4, step=0.1, label='Upscale factor', value=2) |
| return [upscale_factor] |
|
|
| def run(self, p, upscale_factor): |
| infotexts = [] |
| def simple_upscale(img, upscale_factor): |
| w, h = img.size |
| w = int(w * upscale_factor) |
| h = int(h * upscale_factor) |
| return img.resize((w, h), Image.Resampling.LANCZOS) |
|
|
| state.job_count = p.n_iter |
| p.n_iter = 1 |
| p.do_not_save_samples = True |
| output_images = [] |
| for batch_no in range(state.job_count): |
| |
| proc = process_images(p) |
| infotexts.append(proc.info) |
| proc.images[0] = simple_upscale(proc.images[0], upscale_factor) |
| images.save_image(proc.images[0], p.outpath_samples, "", proc.seed, proc.prompt, opts.samples_format, info= proc.info, p=p) |
| output_images += proc.images |
| p.seed = proc.seed + 1 |
|
|
| return Processed(p, images, infotexts=infotexts,index_of_first_image=0) |
|
|
