GFPGAN vs CodeFormer: Which AI Face Restorer Is Better?

GFPGAN and CodeFormer are the two dominant AI models for face restoration. Both can take a blurry, damaged, or low-resolution face and produce a sharp, natural result — but they take different approaches and excel in different scenarios.

The Technology Behind Each

### GFPGAN (Generative Facial Prior GAN) Developed by Tencent ARC, GFPGAN uses a pre-trained face generation model (StyleGAN2) as a "prior" — essentially a reference library of what high-quality faces look like. When it receives a degraded face, it finds the closest match in this prior and uses it to guide the restoration.

### CodeFormer (Code-based Face Restoration) Developed by Shanghai AI Laboratory, CodeFormer takes a different approach. It learns a discrete codebook of facial components (eyes, nose, mouth, etc.) and maps degraded facial features to their closest high-quality codes. Think of it like having a dictionary of perfect facial parts that it assembles.

Head-to-Head Comparison

### Quality of Enhancement - GFPGAN: Produces very sharp results with enhanced detail. Skin textures look natural and high-resolution. Tends to generate slightly idealized features. - CodeFormer: Produces natural-looking results that are closer to the original face. Less aggressive enhancement means more realistic output.

### Identity Preservation - GFPGAN: Good identity preservation in most cases, but can sometimes shift features slightly on heavily damaged photos. - CodeFormer: Superior identity preservation due to its codebook approach. The enhanced face looks more like the original person.

### Speed - GFPGAN: Approximately 5 seconds per image - CodeFormer: Approximately 6 seconds per image

Both are fast enough for practical use.

### Flexibility - GFPGAN: Choose from 4 model versions, plus adjustable upscale factor - CodeFormer: Adjustable fidelity slider (0.0-1.0) plus upscale factor — more granular control

When to Choose GFPGAN

1. The face is only slightly blurry or soft
2. You want maximum sharpness and detail
3. The photo is a recent selfie or social media image
4. Processing speed matters (marginally faster)
5. You want to try different model versions

When to Choose CodeFormer

1. The photo is old, damaged, or heavily degraded
2. Preserving the person's identity is critical
3. You want control over the quality-fidelity tradeoff
4. The face has significant damage (scratches, tears, water damage)
5. You're restoring photos for family members who would notice identity changes

Our Recommendation

For most users: Start with GFPGAN v1.4. It's the fastest path to sharp, enhanced faces and works great for the majority of photos.

For old photo restoration: Use CodeFormer with fidelity around 0.7. If the result doesn't look enough like the original person, increase fidelity. If the quality isn't sufficient, decrease fidelity.

For critical identity preservation: CodeFormer at fidelity 0.8-1.0. This keeps the face as close to the original as possible while still improving quality.

Try Both for Free

UpscaleFast offers both GFPGAN (Face Enhancer) and CodeFormer (Face Restorer Pro) as free online tools. Each gives you 3 free uses per day with no account required and no watermarks. The best approach is to try both on the same photo and pick the result you prefer.

Try the Face Enhancer (GFPGAN) or Face Restorer Pro (CodeFormer) free on UpscaleFast. You can also upscale the result to 4K afterward for maximum clarity.