Real-ESRGAN

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Score9.4

Rank#1 of 100

Free planNo

Runs onLinux, macOS, Self-hosted, Web, Windows

SourceOpen source

Summary

Real-ESRGAN is an open-source project developing algorithms for image and video restoration. It provides pretrained models for general images, anime images, and animation video, with image inputs including JPG, PNG, and WEBP. Portable inference supports 2×, 3×, or 4× scaling, while the Python implementation allows arbitrary output scaling. Supported image types include alpha-channel, grayscale, and 16-bit images, and GFPGAN is integrated for face enhancement. Portable NCNN executables are available for Windows, Linux, and macOS, with required binaries and models included; they do not require a CUDA or PyTorch environment. These portable builds do not include every function in the Python inference script, including its outscale option. Python use requires Python 3.7 or newer and PyTorch 1.7 or newer. Online inference is available through a Tencent ARC demo and Colab demos. The project uses pure synthetic training data, and its training code supports fine-tuning on a user’s own data or paired data. Real-ESRGAN is released under the BSD 3-Clause license.

Who it is for

Real-ESRGAN may suit developers and users seeking image or video restoration models, including tools for general images and anime. Its portable executables offer a route that does not require CUDA or PyTorch, while Python users can access arbitrary output scaling and training customization.

What is good

Open-source under the BSD 3-Clause license.
Portable inference supports 2×, 3×, or 4× scaling.
Portable builds include required binaries and models.
Supports alpha-channel, grayscale, and 16-bit images.
Training code supports fine-tuning on user data.

What to know first

Portable executables omit some Python features, including outscale.
Python implementation requires Python 3.7 or newer.
Python implementation requires PyTorch 1.7 or newer.

Verdict

Real-ESRGAN offers pretrained restoration models and portable executables, with additional scaling and customization options in Python. Choose the implementation based on whether its portable feature limits or Python requirements fit your workflow.

Compared on image upscaling software

Free plan: Yesgithub.com
Batch processing: Yesgithub.com
Desktop app: Yesgithub.com

Facts

Free plan: Yesgithub.com · 20 Sept 2026
Upscaling method: aigithub.com · 20 Sept 2026
Maximum scale: customgithub.com · 20 Sept 2026
Batch processing: Yesgithub.com · 20 Sept 2026
Desktop app: Yesgithub.com · 20 Sept 2026
Input formats: JPG, PNG, WEBPgithub.com · 20 Sept 2026
Project purpose: Develops practical algorithms for general image and video restoration.github.com · 27 Sept 2026
Open-source license: Released under the BSD-3-Clause license.github.com · 27 Sept 2026
Repository stars: The repository has 36.9k stars.github.com · 27 Sept 2026
Repository forks: The repository has 4.5k forks.github.com · 27 Sept 2026
Training data: The project is trained with pure synthetic data.github.com · 27 Sept 2026
Online inference: Online inference is available through an ARC Demo and Colab demos.github.com · 27 Sept 2026
Portable runtime: The executable includes required binaries and models and needs no CUDA or PyTorch environment.github.com · 27 Sept 2026
Input formats: The executable accepts JPG, PNG, and WebP images.github.com · 27 Sept 2026
Output formats: The executable can output JPG, PNG, and WebP images.github.com · 27 Sept 2026
Supported scales: Portable inference supports scale ratios of 2, 3, or 4, with 4 as the default.github.com · 27 Sept 2026
Image support: Inference supports alpha-channel, grayscale, and 16-bit images.github.com · 27 Sept 2026
Video models: The project includes AnimeVideo-v3 and small models for anime videos.github.com · 27 Sept 2026
Model options: Provided models include general, anime-image, anime-video, and Real-ESRNet variants.github.com · 27 Sept 2026
Custom training: The project supports finetuning on users' own data or paired data.github.com · 27 Sept 2026
Support contact: Questions can be sent by email to xintao.wang at Outlook or Tencent.github.com · 27 Sept 2026
Pricing page status: The supplied GitHub pricing URL displays a Page not found result.github.com · 27 Sept 2026
Purpose: Real-ESRGAN develops practical algorithms for general image and video restoration.github.com · 1 Oct 2026
Training: The application is trained with pure synthetic data.github.com · 1 Oct 2026
Models: The project provides models for general images, anime images, and animation video.github.com · 1 Oct 2026
Face enhancement: GFPGAN is integrated to support face enhancement.github.com · 1 Oct 2026
Image support: The inference code supports tile options, alpha-channel images, grayscale images, and 16-bit images.github.com · 1 Oct 2026
Scale control: The project supports arbitrary output scaling with the --outscale option and includes a RealESRGAN_x2plus model.github.com · 1 Oct 2026
Online demos: Online inference is available through the Tencent ARC Demo and two Colab demos.github.com · 1 Oct 2026
Portable downloads: Portable NCNN executables are provided for Windows, Linux, and MacOS for Intel, AMD, and Nvidia GPUs.github.com · 1 Oct 2026
Portable dependencies: The portable executable includes required binaries and models and does not need CUDA or a PyTorch environment.github.com · 1 Oct 2026
Portable limitation: The portable executable does not support all functions available in the Python inference script, including outscale.github.com · 1 Oct 2026
Runtime requirements: The Python implementation requires Python 3.7 or newer and PyTorch 1.7 or newer.github.com · 1 Oct 2026
Integrations: The project is integrated with Hugging Face Spaces through Gradio and lists NCNN-Android, VapourSynth, and NCNN projects that use it.github.com · 1 Oct 2026
Licensing: The repository is distributed under the BSD 3-Clause License, which permits redistribution and use in source and binary forms with conditions.github.com · 1 Oct 2026
Support: The maintainers invite questions by email at [email protected] or [email protected].github.com · 1 Oct 2026
Training customization: The released training code supports finetuning on a user's own data or paired data.github.com · 1 Oct 2026
Purpose: Real-ESRGAN develops practical algorithms for general image and video restoration, extending ESRGAN using training with synthetic data.github.com · 2 Oct 2026
Image enhancement: The project provides pretrained models for general images and anime images, including a smaller anime model.github.com · 2 Oct 2026
Face enhancement: Its inference code can use GFPGAN to enhance faces.github.com · 2 Oct 2026
Image options: The inference code supports tiling, alpha-channel images, grayscale images, and 16-bit images.github.com · 2 Oct 2026
Output scaling: The Python script supports arbitrary output scaling with the --outscale option and resizes after model inference.github.com · 2 Oct 2026
Video models: The project includes AnimeVideo-v3 models for animation video.github.com · 2 Oct 2026
Ways to use it: The README lists online inference, portable NCNN executable files, and a Python script as inference options.github.com · 2 Oct 2026
Operating systems: The project lists portable executable files for Windows, Linux, and macOS.github.com · 2 Oct 2026
Portable requirements: The portable executable includes the required binaries and models and does not require a CUDA or PyTorch environment.github.com · 2 Oct 2026
Dependencies: The Python installation instructions specify Python 3.7 or later and PyTorch 1.7 or later.github.com · 2 Oct 2026
Integrations: The README says Real-ESRGAN was integrated into Hugging Face Spaces with Gradio and links to a web demo.github.com · 2 Oct 2026
License: The repository includes a BSD 3-Clause license that permits redistribution and use in source and binary forms subject to its conditions.github.com · 2 Oct 2026
Limitations: The README says the portable NCNN executable lacks some Python-script features, such as arbitrary outscale, and tile processing can cause block inconsistencies.github.com · 2 Oct 2026
Support: The README directs questions to the listed email addresses [email protected] and [email protected].github.com · 2 Oct 2026