Blind Face Restoration
GFPGAN's generative facial prior recovers severely degraded faces that simple sharpening cannot fix — true blind face restoration built on StyleGAN2.
3-step process
GFPGAN chetor kar mikonad
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.
What We Cover
Everything About GFPGAN
GFPGAN uses a generative facial prior to rebuild degraded faces. We document how that works, how to run it, and where it falls short — sourced to the original research and our own testing.
Identity Preservation
GFPGAN is built around preserving the subject's identity — the same person with a sharper face, rather than a plausible stranger.
Pairs With Super Resolution
GFPGAN restores the face, then hands the frame to Real-ESRGAN for upscaling. We explain why that order matters, and when you need only one of them.
Free and Open Source
The code and weights are published openly by Tencent ARC. You can run GFPGAN on your own machine at no cost — our install guide walks through every step.
Model Variants Explained
v1.2, v1.3 and v1.4 trade speed against output character. We cover which checkpoint suits which kind of photo, and where to get each one.
Real Troubleshooting
Dependency breakage, missing weights, CUDA memory limits — we document the errors that actually stop people, with the fix and the reason for each.
Sourced to the CVPR 2021 paper Open-source on GitHub Built by Tencent ARC Runs offline locally No photo ever uploaded
Where This Information Comes From
We publish guides about a model we did not build. Every technical claim on this site traces back to the original research, the reference implementation, or our own documented testing — and each one is linked so you can check it yourself.
Peer-reviewed paper
Towards Real-World Blind Face Restoration with Generative Facial Prior
The paper that introduced GFPGAN. Xintao Wang, Yu Li, Honglun Zhang and Ying Shan present the Generative Facial Prior — using a pretrained face GAN to supply realistic facial detail during restoration.
Reference implementation
TencentARC/GFPGAN on GitHub
The official implementation and released model weights, maintained by the ARC Lab at Tencent PCG. This is the canonical source for the v1.2, v1.3 and v1.4 checkpoints and for the current licence terms.
Hosted demo
Run the real model without installing anything
GFPGAN runs as a hosted inference endpoint on Replicate and as a Hugging Face Space. Both execute the actual network on a server GPU — unlike the browser filter on this page, which is a local image enhancer.
Downstream adoption
Built into Stable Diffusion tooling
GFPGAN ships as a selectable face-restoration backend in AUTOMATIC1111's Stable Diffusion WebUI and is available through ComfyUI extensions — one of the clearest signals of the model's practical adoption.
GFPGAN is developed by the ARC Lab at Tencent PCG and is not affiliated with this website. We are an independent documentation site. Read our editorial policy for how we test, source, and correct what we publish.
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
Soal haye motadavel darbare 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 is an AI face restoration model that rebuilds realistic detail in degraded portraits.
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02 What photos can it restore?
It works best on portraits, old family photos, compressed images, blurry selfies, and frames with visible faces.
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03 Are my images uploaded?
No. The browser tool processes images locally on your device.