What Is CodeFormer?

CodeFormer is an AI model specifically designed to restore faces in old, low-quality, or damaged photographs. It was developed by researchers at Nanyang Technological University (Singapore) and released in 2022. It has quickly become the best-performing open model for face restoration in historical photographs.

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Why CodeFormer Was Created

Previous AI face restoration models worked well on modern, slightly blurry photos — but struggled with the specific type of degradation in old printed photographs. The challenge:

Historical photographic paper aging produces a specific type of face degradation: fine detail (eyebrow hairs, iris texture, pore structure, individual eyelashes) deteriorates over decades in ways that are difficult to distinguish from lost-resolution blur. Standard sharpening amplifies noise without recovering the original structure.

Existing face restoration models (GFPGAN, earlier versions of Real-ESRGAN with face detection) produced good results but sometimes over-smoothed or generated faces that looked different from the original — "hallucinating" features rather than recovering them.

CodeFormer was designed to solve the fidelity-quality tradeoff: producing sharper, more detailed restored faces while staying closer to the original person's actual appearance.

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How CodeFormer Works (Plain English)

Step 1 — Face detection: CodeFormer identifies and isolates faces in the image.

Step 2 — Codebook lookup: CodeFormer uses a "codebook" — a learned dictionary of high-quality face components (eyes, nose regions, skin textures, etc.) assembled during training. The degraded face is analyzed and matched to entries in this codebook.

Step 3 — Transformer refinement: A transformer network (the same type of architecture that powers ChatGPT) refines the reconstruction by attending to the global face structure — ensuring that the reconstructed eye is consistent with the surrounding face structure, that skin texture is consistent across the face, etc.

Step 4 — Fidelity weighting: CodeFormer has a parameter (w) that controls the fidelity-quality tradeoff. Higher fidelity = closer to the original (even if degraded). Higher quality = cleaner reconstruction (even if slightly departing from the original). For old photo restoration, a balanced setting recovers detail while maintaining recognizability.

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CodeFormer vs. GFPGAN

Both are face restoration models; they excel at different things:

| | CodeFormer | GFPGAN | |-|-|-| | Primary strength | Face reconstruction from heavy degradation | Overall image fading/color correction | | Face recovery | Better on heavily degraded historical photos | Better on lightly degraded modern photos | | Full image | Face-focused (regions outside faces handled separately) | Full image correction | | Release | 2022 | 2021 |

In practice, the best pipeline uses both: CodeFormer for face reconstruction, GFPGAN for image-wide fading correction. This is what ArtImageHub runs.

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Why CodeFormer Produces Better Results Than General AI Filters

Photoshop's "Neural Filters > Photo Restoration" applies a general face enhancement trained on diverse image quality issues. It works but isn't specifically optimized for the type of face degradation in 60-year-old printed photographs.

CodeFormer's codebook approach allows it to reconstruct structural face features (eye shape, iris detail, skin texture at appropriate age) from severely degraded input, while the fidelity control prevents over-reconstruction.

The difference is visible on 1940s–1960s portraits where faces have lost significant detail: CodeFormer recovers specific facial structure; general AI filters sharpen whatever is there without fully reconstructing what was lost.

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CodeFormer in ArtImageHub

ArtImageHub runs CodeFormer as the face reconstruction step in its pipeline:

1. CodeFormer reconstructs face detail
2. GFPGAN corrects full-image fading and yellowing
3. Real-ESRGAN upscales the restored image

The integration is invisible to users — upload, wait 90 seconds, download. The technical stack is what produces the results.

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Try CodeFormer-powered restoration at ArtImageHub — $4.99 one-time →

Results in 30–90 seconds · HD download · 30-day guarantee

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Related

• How Does AI Photo Restoration Work? — full pipeline explainer
• Best AI Tools for Old Photo Restoration in 2026 — 7-tool ranked comparison
• Old Photo Restoration Before and After — what to expect
• How to Restore Black and White Photos — practical guide