GFPGAN

Based on the available evidence, GFPGAN is worth adopting if you evaluate it as a specialized face-restoration component, not as a general-purpose image enhancer. Popularity proof is strong: multiple high-engagement X posts recommend it, and it is repeatedly mentioned alongside Real-ESRGAN and CodeFormer. That shows durable attention and common workflow usage, but not universal superiority. The better proof of usefulness comes from comparison writeups, hands-on posts, and sample-based feedback showing that GFPGAN often balances facial detail recovery, identity resemblance, and speed reasonably well.

In practice, it is most useful when the face is the main problem: old photos with blurred facial features, low-resolution selfies, surveillance-like portraits, or AI-generated faces with broken eyes, mouths, or facial structure. Several sources also describe a common pairing with Real-ESRGAN: GFPGAN restores the face, while Real-ESRGAN handles super-resolution and broader upscaling. So this is not a Photoshop-style full-image retouching suite, and not a universal denoiser.