GFPGAN Hands-On: Can This 37K-Star Face Restoration Tool Save Your Old Photos?

Have you ever dug through old photos only to find faces blurred into smudges, pixelated beyond recognition, with no clear way to fix them? I’d tried several online restoration tools before — either ridiculously expensive or producing results that looked like someone swapped in a completely different face. Then I found GFPGAN, an open-source project from Tencent’s ARC Lab with over 37,000 GitHub stars. In the computer vision world, those numbers put it at the top tier.

Project Background

GFPGAN stands for Generative Facial Prior-GAN, released by Tencent ARC Lab in 2021. Its goal is tightly focused: face restoration in real-world scenarios. Not beautification, not skin smoothing — taking blurry, low-resolution, damaged facial photos and restoring them to clear, natural versions.

Built on PyTorch, it uses generative adversarial networks (GANs) at its core. The key innovation is incorporating a “pre-trained face generator” as prior knowledge. In plain terms, it “knows” what a human face should look like, so it doesn’t guess blindly during restoration.

37k+ stars, Python-based, backed by Tencent ARC’s ongoing maintenance — that’s a solid foundation.

Core Features

Real Face Restoration

GFPGAN specializes in real-world old photos, not pristine lab-quality headshots. I tested it on a dozen family photos from the 1990s, where face regions typically had compression damage and blur. After restoration, eyes, noses, and mouths became clearly defined, and skin texture looked natural — not that plastic influencer-filter look.

Face Enhancement

Beyond restoration, it does super-resolution too. Taking a 64×64 tiny face and upscaling to 512×512 while preserving facial structure. That’s miles ahead of traditional interpolation algorithms.

Fast Inference

On an RTX 3060, inference takes roughly 0.5 seconds per image. Batch processing is even more efficient. For comparison, some commercial web tools make you wait half a minute after uploading.

Multiple Degradation Models

Real-world photo damage comes in many flavors: compression noise, motion blur, low resolution, color distortion. GFPGAN was trained on multiple degradation scenarios, so it generalizes well.

Quick Start

Installation is straightforward if you have a PyTorch environment:

# Clone the repo
git clone https://github.com/TencentARC/GFPGAN.git
cd GFPGAN

# Install dependencies
pip install basicsr facexlib
pip install -r requirements.txt
python setup.py develop

# Download pretrained model
wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P experiments/pretrained_models

# Run inference
python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.3 -s 2

Parameter notes:

• -i: input image directory
• -o: output directory
• -v: model version (1.3 is currently recommended)
• -s: upscale factor

It also supports processing only face regions (leaving backgrounds untouched). Add --bg_upsampler realesrgan if you want the whole image sharpened too.

Real Test Results

I tested three typical scenarios:

Old Photo Restoration

A 1995 ID photo with a face region around 150×150 pixels, showing obvious compression artifacts. After GFPGAN processing, eyes, nose, and mouth contours emerged clearly, and skin texture recovered. But it’s not magic — if the original face is down to a handful of pixels, even the best AI can’t do much.

Video Screenshot Restoration

Screenshots from an old TV drama, with tiny blurry faces. GFPGAN cleaned up the faces while leaving backgrounds untouched (since it only processes face regions). This behavior makes sense — otherwise backgrounds would get hallucinated into weirdness.

Slightly Blurry Smartphone Photos

A selfie blurred by hand shake. GFPGAN improved it somewhat, but not as dramatically as with old photos. Probably because mild motion blur doesn’t quite match the degradation models it was trained on.

Pros and Cons

Pros:

• Restoration quality ranks among the best open-source tools
• Fast inference, runs locally
• Solid model training from Tencent ARC
• Fully open source under Apache-2.0, safe for commercial use

Cons:

• Only processes faces; backgrounds are left alone (though you can pair it with Real-ESRGAN)
• Limited effectiveness on extremely low-resolution faces (below 50×50)
• Occasionally over-smooths faces, making them look artificially polished
• Environment setup can be unfriendly for beginners; PyTorch version conflicts are common

Comparison with Similar Tools

Tool	Restoration Quality	Speed	Open Source	Best For
GFPGAN	High	Fast	Yes	Real-world face restoration
Real-ESRGAN	Medium	Fast	Yes	General image super-resolution
CodeFormer	High	Medium	Yes	Face restoration + coding
Commercial (e.g., Remini)	Very High	Slow (cloud)	No	One-tap mobile restoration

GFPGAN and CodeFormer are contemporaries and both produce strong results. CodeFormer edges ahead on extreme degradation cases, but GFPGAN is faster at inference. Remini’s results are genuinely impressive, but it’s subscription-based and requires uploading your photos to their servers.

Who Should Use It

• Families looking to batch-restore old photos
• Professionals in the old photo restoration business
• Computer vision researchers (the paper and code are clean, great as a baseline)
• Video restoration teams (can be piped with ffmpeg for batch frame processing)

Conclusion

GFPGAN is the most balanced open-source face restoration tool I’ve used — good enough results, fast enough speed, clean enough code. Those 37,000 stars weren’t handed out for nothing.

But let me temper expectations: it’s not magic. When the original image is missing too much information, even the best AI can only “guess,” and guesses aren’t always right. Treat it as a powerful assistant, not a universal fixer. The clearer the original and the larger the face, the better the results.

Final reminder: always back up originals before processing. AI restoration is irreversible, and if you’re not happy with the result, at least you still have the source.

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About the Author

Liudingyu is a full-stack developer and heavy GitHub user. With 900+ starred repos over the past 3 years, this site only covers tools I’ve actually used or deeply researched.

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