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Enhance Your Images with Effortless Cross-Platform Super-Resolution at Any Scale

This is a powerful tool that allows for image super-resolution to arbitrary sizes using multiple models, designed to enhance the resolution and quality of images, making them clearer and more detailed. Presently, it supports several models, such as RealCUGAN, RealESRGAN, and Waifu2x.

Documentation

Comparison

use Final2x to perform 4x super-resolution on a 256x256 Hutao RGBA image

Screenshots

Installation

Download the latest release from here.

Windows

Just Run! Furthermore, you can use package mananger to install and upgrade.

winget

winget install Final2x

MacOS

sudo spctl --master-disable
# Disable Gatekeeper, then allow applications downloaded from anywhere in System Preferences > Security & Privacy > General
xattr -cr /Applications/Final2x.app

In first time, you need to run the command above in terminal to allow the app to run, and first process may very slow because of Apple's Check.

Linux

For General Linux (Suggested, download linux-pip)

Python >= 3.8, then check install in terminal.

pip install Final2x-core
Final2x-core -c # cache models
apt install -y libomp5 xdg-utils

For Deb/Ubuntu

Set 777 permission for the file and resources folder.

Features

• Cross-platform: The tool is available for Windows x64/arm64, MacOS x64/arm64, and Linux x64, allowing users to enjoy the benefits of super-resolution regardless of their operating system.
• Super-resolution: The tool employs advanced algorithms and models to upscale images, significantly increasing their resolution without sacrificing quality.
• Multiple models: It offers a wide range of models that can be used to achieve different levels of super-resolution, allowing users to choose the one that best suits their specific needs.
• Customizable Scale: Users have the flexibility to specify the desired output size for their images, ranging from small enhancements to large-scale super-resolution.
• i18n: The tool is available in ['English', 'Chinese', 'Japanese', 'French'] allowing users from different countries to enjoy the benefits of super-resolution.

Reference

The following references were referenced in the development of this project:

• Final2x-core - This project provided the core of the Final2x algorithm using the ncnn and Vulkan libraries.
• naive-ui - This project provided the UI framework for the Final2x.
• electron-vite - This project provided the build framework for the Final2x.
• ncnn - ncnn is a high-performance neural network inference framework developed by Tencent AI Lab.
• nihui/realcugan-ncnn-vulkan - This project provided the core implementation of the Real-CUGAN algorithm using the ncnn and Vulkan libraries.
• xinntao/Real-ESRGAN-ncnn-vulkan - This project provided the core implementation of the Real-ESRGAN algorithm using the ncnn and Vulkan. libraries.
• nihui/waifu2x-ncnn-vulkan - This project provided the core implementation of the Waifu2x algorithm using the ncnn and Vulkan libraries.

License

This project is licensed under the BSD 3-Clause - see the LICENSE file for details.

Acknowledgements

Feel free to reach out to the project maintainers with any questions or concerns. Happy image super-resolution!