Final2x review
Open-source local upscaling with multiple models, custom model support, and CPU/GPU processing.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Final2x is an open-source image super-resolution application for enlarging and restoring images locally. It is aimed at people who want a desktop tool that runs across Windows, macOS, and Linux without depending on a hosted image-processing service. The product includes desktop builds, a Python CLI, and a desktop backend. Its core use case is AI-based image restoration, with selectable output image formats and support for both CPU and GPU processing.
Model flexibility is a central part of Final2x. The application supports multiple image-restoration models and allows custom models through Git URLs or local paths using the cccv backend in the v4 release. Optional tile processing can help shape how images are handled, while the output format can be selected for the resulting image. This makes Final2x a fit for users who want more control over restoration models and processing preferences than a single-purpose preset workflow provides. It also suits technical users who value an open-source project with both graphical and command-line access.
Platform coverage is broad, but the installation path differs by operating system. Windows and macOS users can download desktop releases, while Linux users can install the core package with Python and PyTorch. That gives Linux users a flexible route, though it involves a more technical setup. Final2x should appeal to users seeking local, open-source restoration across major desktop platforms, especially those who want custom model support or CPU/GPU choice. Users looking for a browser-first workflow or a less technical installation path may prefer a different type of image upscaling tool.
Final2x pros and cons
- Where it wins
- Free, open-source desktop software for Windows, macOS, and Linux
- Supports multiple restoration models and custom models
- Offers optional tiling plus CPU and GPU processing
- Where it doesn't
- Linux installation involves Python and PyTorch
- Custom models require Git URLs or local paths
- The workflow is focused on local desktop and CLI use
Final2x fact sheet, pricing and score →
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