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Repair faces

Finds faces in the image and redraws them, which is the usual repair for the melted features small renders produce.

GFPGAN rebuilds a face aggressively, and will replace one that was already nearly correct. CodeFormers keeps more of the original and is the safer choice on a portrait you mostly like. Running either over a face that was already correct still changes it, so use one only where a face needs repair.
GFPGAN (face fix)
Fast, general-purpose face restoration. A good default; makes only subtle changes to identity.
GFPGAN v1.3 (face fix)
An earlier GFPGAN version, kept for compatibility. Slightly softer results than current GFPGAN.
CodeFormers (face fix)
Stronger restoration for badly damaged or very low-resolution faces. Can alter identity or fine details more than GFPGAN.
RestoreFormer (face fix)
Favours fidelity to the original face over aggressive fixing, a gentler alternative to CodeFormers.

Examples

GFPGAN (face fix) GFPGAN
The image that was submitted 806 × 808 Source What GFPGAN (face fix) returned 806 × 808 Result

The source is the degraded portrait hordelib's face fix tests run on, the kind of blurred, compressed photograph the blind face restoration line the GFPGAN and CodeFormer weights come from was built to answer, because a face that is already sharp shows nothing.

CodeFormers (face fix) CodeFormers
The image that was submitted 806 × 808 Source What CodeFormers (face fix) returned 806 × 808 Result

The same degraded portrait as the GFPGAN entry. CodeFormer trades identity for fidelity differently, which is the comparison the shared source is here to make.

GFPGAN v1.3 (face fix) GFPGANv1.3
The image that was submitted 806 × 808 Source What GFPGAN v1.3 (face fix) returned 806 × 808 Result

The predecessor GFPGAN weight, more faithful to the face it was given than the default.

RestoreFormer (face fix) RestoreFormer
The image that was submitted 806 × 808 Source What RestoreFormer (face fix) returned 806 × 808 Result

A transformer-based restorer that leans towards realism rather than towards smoothing.

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