back to top
HomeSoftwareAI ToolsFinal2x – AI Image Super-Resolution Tool for Windows, macOS and Linux

Final2x – AI Image Super-Resolution Tool for Windows, macOS and Linux

- Advertisement -

File Information

PropertyDetails
Software NameFinal2x
Version4.0.0
LicenseBSD 3-Clause License
File Size~2 GB (may vary by OS)
Developer / PublisherEutropicAI
GitHub RepositoryFinal2x
Supported PlatformsWindows, macOS (Intel/ARM), Linux
CategoryAI Image Upscaler / Super-Resolution Tool

Description

Final2x is a cross-platform, AI-powered image super-resolution tool designed to upscale low-quality images into ultra-clear, high-resolution masterpieces.
Built with modern deep learning techniques, Final2x enhances images locally on your system, ensuring offline privacy and fast GPU acceleration, no internet or cloud dependency required.

It’s the perfect open-source alternative to commercial tools like Topaz Gigapixel AI, offering professional-grade upscaling completely free under the BSD 3-Clause License.

Features of Final2x

FeatureDescription
AI-Powered Super ResolutionUpscales images using cutting-edge AI models for sharp, detailed results.
Offline ProcessingAll operations run locally — no cloud, no data sharing.
GPU AccelerationUtilizes NVIDIA, AMD, and Apple Silicon GPUs for faster rendering.
Batch Image UpscalingProcess multiple images simultaneously for maximum efficiency.
Custom Model SupportLoad and integrate your own deep learning models.
Cross-Platform InterfaceSimple, elegant, and responsive UI for Windows, macOS, and Linux.
Semantic UpscalingIntelligent recognition of patterns and textures for more natural detail.
Open Source & ExtensibleFreely available for modification under BSD 3-Clause license.
Multilingual SupportAvailable in multiple languages for global users.
Support custom modelsIt use the cccv backend, supporting custom models and more.

Screenshots

System Requirements

ComponentMinimumRecommended
Operating SystemWindows 10 / macOS 11 / Ubuntu 20.04+Windows 11 / macOS 13 / Ubuntu 22.04+
CPUIntel Core i5 / AMD Ryzen 5Intel Core i7 / Ryzen 7 or higher
GPUNVIDIA GTX 10 Series / AMD RX 500NVIDIA RTX / Apple M1+ / AMD RX 6000
RAM8 GB16 GB or more
Storage2 GB free space4 GB+ for optimal model caching
Python (for Linux Core)3.9+3.11+
PyTorch2.0+Latest stable build

How to Install Final2x Super Resolution AI Tool??

Windows Installation

  1. Download Final2x-windows-x64-unpacked.7z From Download Section Below.
  2. Extract using 7-Zip or WinRAR.
  3. Open the extracted folder and run Final2x.exe.
  4. (Optional) Create a desktop shortcut or pin to Start.

Alternative Installation (via winget or scoop):

winget install Final2x

macOS Installation

  1. Download the appropriate .dmg file (Intel or ARM).
  2. Open and drag Final2x.app to the Applications folder.
  3. If Gatekeeper blocks it, run:
sudo spctl --master-disable
xattr -cr /Applications/Final2x.app
  1. Launch Final2x normally.

Linux Installation

Option 1 – AppImage

chmod +x Final2x*.AppImage
./Final2x*.AppImage

Option 2 – Debian/Ubuntu (.deb)

sudo dpkg -i Final2x*.deb
sudo apt-get install -f

Option 3 – Python Core (Advanced Users)

pip install Final2x-core
Final2x-core -h

Replace * with the file name you downloaded

Download Final2x For Windows , macOS & Linux For Free

Conclusion

If you’re looking for a fast, free, and open-source AI upscaler, Final2x is the perfect choice.
Whether you’re a photographer, graphic designer, or AI enthusiast, Final2x helps you turn every pixel into perfection, without cloud costs or privacy concerns.

Want more stories worth your time?

Add us to your Google favorites. We cover the tech stories, AI developments, and open-source projects that are easy to miss in the noise.

Add as a preferred source on Google

Don’t miss any Tech Story

Subscribe To Firethering NewsLetter

You Can Unsubscribe Anytime! Read more in our privacy policy

YOU MAY ALSO LIKE
LLM Wiki Desktop

LLM Wiki: An AI Knowledge Base That Builds Itself

0
Most AI tools can answer questions about your documents. But there's a problem with the usual approach. You add a bunch of files, ask a question and the AI searches through them to find an answer. Ask something else later and it does much of the same work again. LLM Wiki on the other hand, uses an LLM to build a persistent, structured wiki from them. Add a collection of documents and LLM Wiki analyzes them, creates pages for important entities and concepts, connects related information and keeps the knowledge base updated as you add more sources. That means your knowledge doesn't just sit inside a folder waiting to be searched. It gradually becomes an interconnected collection of information that you can browse, search and ask questions about.
hister search engine

Hister: Your Own Private Search Engine for Web Pages and Files

0
You know that page you read three months ago and somehow can’t find again? Hister is built for exactly that problem. It turns the web pages you visit and the files you keep into your own searchable index, so you can search the actual content instead of trying to remember a title, URL, or where you saved it.
Atomic Chat App

Atomic Chat: Run Open-Weight LLMs Locally on Windows, macOS & Linux

0
Want to run an AI model locally, but still use it with the tools you already have? Atomic Chat makes that possible. It lets you run open-weight LLMs from Hugging Face on your own computer, then exposes them through an OpenAI-compatible API so coding agents, CLIs, IDE plugins and other apps can use your local models too. You can run models such as Llama, Gemma, Qwen, Mistral and Phi, use Atomic Chat as a regular AI chat app, or connect it to tools such as OpenCode, Goose and Kilo Code. Your local conversations and API keys can stay on your machine, while cloud providers such as OpenAI, Anthropic, Mistral and Groq are available when you need them. Under the hood, it also supports multiple inference engines and performance features such as speculative decoding, Flash Attention and TurboQuant on supported models and hardware. So you're getting more than a local chatbot. Atomic Chat can act as the local AI layer behind the rest of your setup.