1. Tutorial Introduction

SUPIR (Scaling-UP Image Restoration) is a breakthrough image restoration and image quality enhancement method. The constructed model utilizes the large-scale generative model StableDiffusion-XL (SDXL) and model expansion technology, and achieves high-quality restoration of low-quality images through deep learning and multimodal methods. It can enlarge low-resolution images to high resolution while maintaining the details and realism of the image. SUPIR can handle various complex degradation situations such as blur, noise, compression, etc., to achieve high-quality image restoration, enlarge low-resolution images to high resolution while maintaining the details and realism of the image.

The method also supports fine-grained control of image restoration through text prompts, which can adjust various aspects of restoration based on the user's input. SUPIR was jointly launched by researchers from the Shenzhen Institutes of Advanced Technology of the Chinese Academy of Sciences, Shanghai A1 Laboratory, the University of Sydney, the Hong Kong Polytechnic University, Tencent, ARC Labs and the Chinese University of Hong Kong.

This tutorial uses the resources for the dual-SIM A6000.

👉 The project provides two types of models:

• SUPIR-v0Q: Default training settings. Has higher generalization ability and higher image quality in most cases.
• SUPIR-v0F: Trained with degraded lighting. The Stage 1 encoder SUPIR-v0F preserves more details when the lighting quality is degraded.

Project Examples

2. Operation steps

1. After starting the container, click the API address to enter the Web interface

If "Model" is not displayed, it means the model is being initialized. Since the model is large, please wait about 1-2 minutes and refresh the page.

2. After entering the webpage, you can start a conversation with the model

How to use

Exchange and discussion

🖌️ If you see a high-quality project, please leave a message in the background to recommend it! In addition, we have also established a tutorial exchange group. Welcome friends to scan the QR code and remark [SD Tutorial] to join the group to discuss various technical issues and share application effects↓

Citation Information

@misc{yu2024scaling, title={Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild}, author={Fanghua Yu and Jinjin Gu and Zheyuan Li and Jinfan Hu and Xiangtao Kong and Xintao Wang and Jingwen He and Yu Qiao and Chao Dong}, year={2024}, eprint={2401.13627}, archivePrefix={arXiv}, primaryClass={cs.CV} }