metadata
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:
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()