---
license: apache-2.0
tags:
- image-to-image
- gan
- facial-restoration
- pytorch
datasets:
- CelebA
- FFHQ
metrics:
- psnr
- ssim
- lpips
pipeline_tag: image-to-image
library_name: pytorch
---

# SocialFace-Restore-GAN

A Component-Based Hybrid GAN architecture designed to restore high-fidelity facial features (eyes, nose, mouth) in images degraded by social media compression, motion blur, and sensor noise.

## 🎯 Goal

The primary objective is to achieve perceptual realism while strictly preserving the subject's unique identity. This model leverages specialized discriminators for local facial components and an identity-consistency module.

## 🧵 Dataset

The model was trained using paired training data generated via synthetic distortion from:
- **CelebA**: Large-scale face attributes dataset.
- **FFHQ**: Flickr-Faces-HQ dataset.

## 🧾 Description

Images on social media often suffer from aggressive lossy compression. This project implements a **Hybrid GAN** featuring:
- **Generator (U-Net)**: A symmetric encoder-decoder with residual learning.
- **Global Discriminator (PatchGAN)**: For full-image texture realism.
- **Local Feature Discriminator**: Specialized networks focusing on eyes, nose, and mouth.
- **Identity Preserver**: VGG-based feature extraction for identity-consistency loss.

## 📊 Performance Metrics

The model achieves the following target benchmarks:
- **PSNR**: > 28dB
- **SSIM**: > 0.85
- **LPIPS**: < 0.15 (indicating high human-perceived realism)

## 🚀 Usage

You can load the model in PyTorch using:

```python
import torch
# Assuming your model architecture class 'Generator' is defined
model = Generator()
model.load_state_dict(torch.load("socialface_restore_gan.pth", map_location="cpu"))
model.eval()