# yangxy/GPEN

Repository: https://github.com/yangxy/GPEN
Canonical: https://ross.abutalabs.com/products/gpen
Language: Jupyter Notebook
License Family: other
Last push: 2026-03-10T03:45:58+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 71, release rhythm 35, longevity 100
- inputs: {"age_days": 1974, "days_push": 176, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2612, forks 452 (observed 2026-08-28T04:07:04.450537+00:00)

## What it is
GPEN (GAN Prior Embedded Network) is a deep learning library for blind face restoration of low-quality, in-the-wild photos. It also supports face colorization, face inpainting, super-resolution, and segmentation-to-face synthesis.

## Use cases
- restore blurry old photos of faces
- enhance low-resolution selfies
- colorize black and white portraits
- inpaint missing regions in face images
- upscale face images 4x
- generate faces from segmentation maps

## When to choose
- you need state-of-the-art blind face restoration on real-world degraded photos
- you want high-resolution face enhancement up to 1024/2048
- you need face colorization or inpainting alongside restoration

## When to avoid
- you need general non-face image restoration
- you require a permissive license for commercial use (no license is specified)
- you lack a GPU and want fast CPU inference

## Facets
- artifact type: library
- maturity: maintenance
- function: image-processing, machine-learning, deep-learning
- domain: computer-vision, image-processing, deep-learning, artificial-intelligence
- platform: python, cross-platform
- tags: face-restoration, gan, blind-face-restoration, face-inpainting, face-colorization, image-enhancement, super-resolution, cvpr2021, gpu

## Member repositories
- yangxy/GPEN (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:04.450537+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:20:41.374935+00:00, confidence not recorded.
  - readme: https://github.com/yangxy/GPEN (fetched 2026-08-28T04:07:04.450537+00:00, sha 829f41c25025)
- Data as of 2026-08-30T08:39:29.467469+00:00.