GFPGAN addresses real-world blind face restoration by leveraging generative priors from a pretrained face GAN. It is a Python library and CLI tool that developers can run locally to restore degraded face images.
Project overview
It uses generative facial priors from a pretrained face GAN specifically for blind face restoration, a targeted approach to recovering facial details in degraded images.
Project type
Model Runtime · Image & Vision
Use cases
Image Processing
Deployment
Refer to project documentation
License
License pending
Best for
Developers and AI engineers needing a scriptable or library-based tool for image editing and model serving to perform blind face restoration.
Key capabilities
Performs blind face restoration using generative facial priors from a pretrained face GAN.
Provides training codes to improve or customize the model.
Limitations and risks
The V1.3 model is not very sharp and has a slight change on identity.
The V1.2 model may produce some outputs that are unnatural.
Getting started
Setup involves medium difficulty, requiring users to clone the repository and install dependencies. The first success path includes cloning the repo, installing dependent packages (basicsr, facexlib), installing the realesrgan package, downloading the required pre-trained GFPGAN model files, and running python inference_gfpgan.py.
Evidence and sources
GitHub project description: GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
README: GFPGAN aims at developing a **Practical Algorithm for Real-world Face Restoration**.<br> It leverages rich and diverse priors encapsulated in a pretrained face GAN (*e.g.*, StyleG…