SeeSR
SeeSR is a state-of-the-art deep learning model for real-world image super-resolution, presented as a CVPR 2024 paper by researchers from the Hong Kong Polytechnic University, OPPO Research Institute, and ByteDance. The model is designed to recover high-resolution images from low-quality inputs by integrating semantic awareness into the super-resolution process. Its core framework leverages a Stable Diffusion-2 base architecture combined with a specialized diffusion enhancement module to generate realistic textures and fine details. Key features include support for accelerated inference using SD-Turbo with just two steps, compatibility with Gradio and Replicate online demos, and the provision of a real-world low-resolution dataset called RealLR200 for benchmarking. The software provides pretrained models and scripts for easy installation via Python and pip, allowing users to process testing images through a simple command-line interface. It is suitable for applications requiring the enhancement of blurry, noi