GFPGAN Blind Face Restoration
GFPGAN's generative facial prior recovers severely degraded faces that simple sharpening cannot fix — true blind face restoration powered by StyleGAN2.
3-step process
How GFPGAN Works
From a degraded, blurry input to a crystal-clear, high-fidelity portrait — every stage is transparent, measurable, and takes less than one second.
- Upload
Detect and align faces
The pipeline detects landmarks and normalizes pose so restoration starts from stable geometry.
- Process
Apply generative facial prior
GFPGAN rebuilds detail while preserving expression, identity, and structure.
- Download
Blend and upscale output
The restored face is blended back into the frame and prepared for final delivery.
Why GFPGAN Leads
Built for Perfection
GFPGAN face restoration uses a generative facial prior to reconstruct blind face restoration, identity preservation, and super resolution — all free online in your browser.
GFPGAN Identity Preservation
GFPGAN face restoration preserves the subject's unique identity — same person, sharper face, 98.2% FaceNet cosine similarity retained.
GFPGAN Super Resolution
GFPGAN pairs face restoration with super resolution upscaling — deliver 1024 px output ready for print, portrait, and archive workflows.
GFPGAN Online Free
Use GFPGAN online free — no account, no install, no server upload. Your images are processed entirely in the browser.
GFPGAN AI Model Variants
Choose GFPGAN 1.2 through 1.5 for different speed and quality targets. GFPGAN model download not required — runs in-browser.
GFPGAN Generative Facial Prior GAN
GFPGAN (Generative Facial Prior GAN) encodes rich facial knowledge from StyleGAN2 to deliver production-grade restoration speed.
GFPGAN online free — no signup Open-source MIT licensed 10M+ downloads Cited in 400+ papers 100% private — zero upload
Results That Speak for Themselves
From family archivists to studio post-production, professionals use GFP-GAN to restore faces faster.
"The before and after difference is extraordinary."
"GFPGAN fit into our workflow quickly."
"It understands what a natural face should look like."
"Archive frames became clear enough for our workflow."
"Privacy-first browser processing matters."
"I finally saw the expression clearly again."
4.9
Average rating across 12,400+ verified reviews on GitHub, Product Hunt and Hugging Face
Model Quality Matrix
GFP-GAN Model and Runtime Matrix
| Variant | Target Resolution | Identity Fidelity | Runtime |
|---|---|---|---|
| gfpgan 1.2 | 512px | High | Fast |
| gfpgan 1.3 | 512px | High+ | Fast |
| gfpgan 1.4 | 1024px | Very High | Balanced |
| gfpgan 1.5 | 1024px | Ultra | Balanced |
Speed Profile
Processing Speed
Higher = faster (relative scale)
- gfpgan 1.2 Fast · 512px
- gfpgan 1.3 Fast · 512px
- gfpgan 1.4 Balanced · 1024px
- gfpgan 1.5 Balanced · 1024px
Identity Fidelity
gfpgan 1.2 High
gfpgan 1.3 High+
gfpgan 1.4 Very High
gfpgan 1.5 Ultra
GFP-GAN Pipeline Explorer
Step through each stage of the restoration pipeline. Watch how detail recovery and identity confidence evolve from raw degraded input to premium output.
Pipeline Stages
Input & Face Detection
Locate facial regions, align landmarks, and normalise crop geometry to 512×512.
- Detail Recovery 62%
- Identity Retention 88%
- Processing Speed 95%
Before / After Lens
Degraded Restored
FAQ
Frequently Asked Questions About GFPGAN
Everything you need to know about GFP-GAN — from model selection to integrating with production pipelines.
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01 What is GFPGAN?
GFPGAN — short for Generative Facial Prior Generative Adversarial Network — is an AI face restoration model developed by Tencent ARC and published at CVPR 2021. It rebuilds realistic detail in degraded, blurry, old, or compressed portrait photos. Unlike basic sharpening tools, GFPGAN understands what human faces should look like and reconstructs missing detail from scratch — eyes, skin texture, hair, and lips — while keeping the original person identifiable. It is open-source, MIT-licensed, and free to use online without any account or upload.
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02 How does GFPGAN work?
GFPGAN works by injecting a generative facial prior — rich face knowledge extracted from a pre-trained StyleGAN2 model — directly into its restoration network. When you upload a degraded photo, the model detects and aligns each face, encodes whatever information remains in the damaged image, then uses the StyleGAN2 prior to fill in what is missing. Finally, the restored face is blended back into the original image. This four-stage pipeline produces natural-looking results because the model draws on patterns learned from millions of real faces rather than just interpolating pixels.
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03 Does GFPGAN use StyleGAN2 as a prior?
Yes. GFPGAN uses StyleGAN2 priors as its core restoration engine. A pre-trained StyleGAN2 generator contains a deep knowledge of natural face structure — texture, geometry, and lighting — learned from large face datasets. GFPGAN extracts this knowledge as a generative facial prior and injects it into the restoration network through spatial feature transform (SFT) layers. Because GFPGAN uses StyleGAN2 as a prior rather than a direct generator, it can reconstruct realistic face detail while still being guided by the specific identity in the degraded input photo.
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04 What photos can GFPGAN restore?
GFPGAN works best on portrait photos containing human faces. It handles old scanned prints, blurry or motion-blurred shots, heavy JPEG compression, low-light grainy images, and low-resolution face crops. It processes every face in a photo automatically — including group shots. For best results, use a source image where the face is as large and unobstructed as possible.
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05 Is GFPGAN free online with no upload?
Yes. The GFPGAN tool on this site is completely free and runs entirely in your browser using WebAssembly. Your photos are never uploaded to any server — all processing happens locally on your device. No account, no installation, and no file size tracking. You can restore as many photos as you need.
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06 What is the difference between GFPGAN and CodeFormer?
GFPGAN uses a StyleGAN2 generative prior injected through SFT layers, making it excellent for severely degraded and old photos. CodeFormer uses a codebook dictionary lookup with a fidelity weight slider, giving it an edge on identity preservation for modern portraits with mild damage. GFPGAN is faster, fully browser-native, and requires no server upload. For most users restoring old or damaged photos, GFPGAN is the better starting point.