# GFPGAN — AI Face Restoration with Generative Facial Prior

> Open-source face restoration algorithm that leverages generative facial priors from a pretrained face GAN to recover realistic faces from degraded inputs.

## Install

Save the content below to `.claude/skills/` or append to your `CLAUDE.md`:

# GFPGAN — AI Face Restoration with Generative Facial Prior

## Quick Use
```bash
pip install gfpgan
wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P experiments/pretrained_models
python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.4 -s 2
```

## Introduction
GFPGAN is a face restoration algorithm developed by Tencent ARC Lab that uses Generative Facial Prior embedded in a pretrained face GAN for blind face restoration. It handles real-world degradation including blur, noise, compression artifacts, and low resolution without requiring paired training data.

## What GFPGAN Does
- Restores faces from severely degraded images (old photos, low-resolution video frames)
- Leverages rich facial geometry and texture priors from a pretrained StyleGAN2
- Handles both aligned face crops and full images with face detection
- Supports adjustable restoration strength for balancing fidelity and quality
- Provides a real-world inference pipeline with background enhancement via Real-ESRGAN

## Architecture Overview
GFPGAN uses a U-Net degradation removal module connected to a pretrained StyleGAN2 face generator through spatial feature transform (SFT) layers. Channel-split spatial feature transforms inject multi-resolution identity-preserving features into the generator, balancing realness from the GAN prior with fidelity to the input face. A local discriminator on facial components (eyes, mouth) ensures detailed texture recovery.

## Self-Hosting & Configuration
- Install via pip: `pip install gfpgan` or clone the repo and install from source
- Download pretrained models (GFPGANv1.4 recommended) from GitHub releases
- Requires PyTorch >= 1.7 and a CUDA-capable GPU for real-time processing
- Adjust upscale factor with `-s` and model version with `-v` flags
- Integrates with Real-ESRGAN for background enhancement in full images

## Key Features
- Blind face restoration without requiring degradation-type knowledge
- Generative facial prior from StyleGAN2 produces realistic textures
- Identity-preserving through spatial feature transforms
- Works on both cropped faces and whole images with automatic face detection
- Color correction and paste-back for natural-looking full-image output

## Comparison with Similar Tools
- **Real-ESRGAN** — general-purpose image super-resolution; GFPGAN focuses specifically on faces
- **CodeFormer** — similar face restoration using codebook lookup; slightly different quality-fidelity tradeoff
- **DFDNet** — dictionary-based face restoration with component dictionaries
- **VQFR** — uses vector-quantized codebook for face restoration
- **PULSE** — generates high-resolution faces via StyleGAN but less faithful to input

## FAQ
**Q: Does GFPGAN work on non-face images?**
A: No, it is specialized for face restoration. For general images, use Real-ESRGAN or similar super-resolution tools.

**Q: What GPU memory is required?**
A: Approximately 4 GB VRAM for processing single faces at standard resolution. Full images with background enhancement need more.

**Q: Can I use GFPGAN in a commercial product?**
A: The code is released under Apache 2.0 license, but check the pretrained model licenses separately.

**Q: How does it handle multiple faces in one image?**
A: The pipeline uses face detection to locate and restore each face individually, then pastes them back into the enhanced background.

## Sources
- https://github.com/TencentARC/GFPGAN
- https://xinntao.github.io/projects/gfpgan

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Source: https://tokrepo.com/en/workflows/gfpgan-ai-face-restoration-generative-facial-prior-5e5feceb
Author: Script Depot