# Real-ESRGAN

Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.

Repository: https://github.com/xinntao/Real-ESRGAN
Canonical: https://ross.abutalabs.com/products/real-esrgan
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: esrgan, pytorch, real-esrgan, super-resolution, image-restoration, denoise, jpeg-compression, amine
Last push: 2024-08-06T13:15:18+00:00
Link (homepage): https://github.com/xinntao/Real-ESRGAN

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 1871, "days_push": 757, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 36593, forks 4451 (observed 2026-08-28T04:12:01.163343+00:00)

## What it is
Real-ESRGAN is a deep learning project for practical image and video restoration via super-resolution, with pretrained models for photos and anime. It ships as a Python library (pip) plus portable ncnn-Vulkan executables for Intel/AMD/Nvidia GPUs.

## Use cases
- upscale low-resolution photos to 4x
- restore and denoise old or compressed images
- upscale anime images and illustrations
- enhance anime video quality frame by frame
- batch upscale images from the command line without a Python setup
- integrate super-resolution into an image processing pipeline

## When to choose
- you need state-of-the-art general-purpose image upscaling
- you work with anime artwork or anime video
- you want a portable GPU executable that runs on any Vulkan-capable GPU
- you want a pip-installable Python library for restoration tasks

## When to avoid
- you need real-time upscaling on weak hardware without a GPU
- you only need simple resizing without learned detail reconstruction
- you need text-focused or face-specific restoration with dedicated models
- you cannot accept GPU memory usage for large images

## Facets
- artifact type: library
- maturity: stable
- function: image-processing, machine-learning, deep-learning, cli
- domain: image-processing, computer-vision, machine-learning, media
- platform: python, windows, cross-platform
- tags: super-resolution, upscaling, image-restoration, denoising, anime, video-upscaling, ncnn, vulkan, pytorch, linux, macos, gpu

## Member repositories
- xinntao/Real-ESRGAN (main) score 23
- xinntao/Real-ESRGAN-ncnn-vulkan (sdk) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:01.163343+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 2026-08-29T16:26:10.248613+00:00, confidence not recorded.
  - readme: https://github.com/xinntao/Real-ESRGAN (fetched 2026-08-28T04:12:01.163343+00:00, sha fe1dea8c592e)
  - homepage: https://github.com/xinntao/Real-ESRGAN (fetched 2026-08-29T07:47:29.136413+00:00, sha 062617d9c198)
- Data as of 2026-08-30T08:39:29.467469+00:00.