#!/usr/bin/env python # -*- coding:utf-8 -*- # Power by Zongsheng Yue 2024-12-11 17:17:41 import spaces import warnings warnings.filterwarnings("ignore") import argparse import numpy as np import gradio as gr from pathlib import Path from omegaconf import OmegaConf from sampler_invsr import InvSamplerSR from utils import util_common from utils import util_image from basicsr.utils.download_util import load_file_from_url def get_configs(num_steps=1, chopping_size=128, seed=12345): configs = OmegaConf.load("./configs/sample-sd-turbo.yaml") if num_steps == 1: configs.timesteps = [200,] elif num_steps == 2: configs.timesteps = [200, 100] elif num_steps == 3: configs.timesteps = [200, 100, 50] elif num_steps == 4: configs.timesteps = [200, 150, 100, 50] elif num_steps == 5: configs.timesteps = [250, 200, 150, 100, 50] else: assert num_steps <= 250 configs.timesteps = np.linspace( start=250, stop=0, num=num_steps, endpoint=False, dtype=np.int64() ).tolist() print(f'Setting timesteps for inference: {configs.timesteps}') # path to save noise predictor started_ckpt_path = "noise_predictor_sd_turbo_v5.pth" # started_ckpt_dir = "./weights" # util_common.mkdir(started_ckpt_dir, delete=False, parents=True) # started_ckpt_path = Path(started_ckpt_dir) / started_ckpt_name # if not started_ckpt_path.exists(): # load_file_from_url( # url="https://huggingface.co/OAOA/InvSR/resolve/main/noise_predictor_sd_turbo_v5.pth", # model_dir=started_ckpt_dir, # progress=True, # file_name=started_ckpt_name, # ) configs.model_start.ckpt_path = started_ckpt_path configs.bs = 1 configs.seed = 12345 configs.basesr.chopping.pch_size = chopping_size return configs @spaces.GPU def predict(in_path, num_steps=1, chopping_size=128, seed=12345): configs = get_configs(num_steps=num_steps, chopping_size=chopping_size, seed=12345) sampler = InvSamplerSR(configs) out_dir = Path('invsr_output') if not out_dir.exists(): out_dir.mkdir() sampler.inference(in_path, out_path=out_dir, bs=1) out_path = out_dir / f"{Path(in_path).stem}.png" assert out_path.exists(), 'Super-resolution failed!' im_sr = util_image.imread(out_path, chn="rgb", dtype="uint8") return im_sr, str(out_path) title = "Arbitrary-steps Image Super-resolution via Diffusion Inversion" description = r""" Official Gradio demo for Arbitrary-steps Image Super-resolution via Diffuion Inversion.
🔥 InvSR is an image super-resolution method via Diffusion Inversion, supporting arbitrary sampling steps.
""" article = r""" If you've found InvSR useful for your research or projects, please show your support by ⭐ the Github Repo. Thanks! [![GitHub Stars](https://img.shields.io/github/stars/zsyOAOA/InvSR?affiliations=OWNER&color=green&style=social)](https://github.com/zsyOAOA/InvSR) --- If our work is useful for your research, please consider citing: ```bibtex @article{yue2024InvSR, title={Arbitrary-steps Image Super-resolution via Diffusion Inversion}, author={Yue, Zongsheng and Kang, Liao and Loy, Chen Change}, journal = {arXiv preprint arXiv:2412.09013}, year={2024}, } ``` 📋 **License** This project is licensed under S-Lab License 1.0. Redistribution and use for non-commercial purposes should follow this license. 📧 **Contact** If you have any questions, please feel free to contact me via zsyzam@gmail.com. ![visitors](https://visitor-badge.laobi.icu/badge?page_id=zsyOAOA/InvSR) """ demo = gr.Interface( fn=predict, inputs=[ gr.Image(type="filepath", label="Input: Low Quality Image"), gr.Dropdown( choices=[1,2,3,4,5], value=1, label="Number of steps", ), gr.Dropdown( choices=[128, 256], value=128, label="Chopping size", ), gr.Number(value=12345, precision=0, label="Ranom seed") ], outputs=[ gr.Image(type="numpy", label="Output: High Quality Image"), gr.File(label="Download the output") ], title=title, description=description, article=article, examples=[ ['./testdata/RealSet80/29.jpg', 3, 128, 12345], ['./testdata/RealSet80/32.jpg', 1, 128, 12345], ['./testdata/RealSet80/0030.jpg', 1, 128, 12345], ['./testdata/RealSet80/2684538-PH.jpg', 1, 128, 12345], ['./testdata/RealSet80/oldphoto6.png', 1, 128, 12345], ] ) demo.queue(max_size=5) demo.launch(share=True)