Real-ESRGAN

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About

Real-ESRGAN is ranked #12 of 99 in image upscaling software on Inferse. It runs on Linux, macOS, Self-hosted, Web, Windows.

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