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Image & Video AI

Enhancr

Enhancr is an Electron desktop application for AI-powered video frame interpolation, upscaling and restoration. It combines these stages in a graphical workflow with preview, trimming and batch-queue features, and supports inference through engines including TensorRT and NCNN.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Enhancr — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Enhancr when you want to enhance existing video footage through a desktop interface, especially when you need to combine enhancement stages or process clips in a batch queue.

Who it is for

  • Video editors and enthusiasts who want a desktop GUI for frame interpolation or video upscaling.
  • Users seeking to chain interpolation, upscaling and restoration, or process multiple clips in a queue.

Documented uses

  • Interpolate frames in existing footage.
  • Upscale video using built-in inference options or custom ESRGAN models.
  • Trim clips, preview processing and queue multiple videos.
  • Combine interpolation, upscaling and restoration stages.
Before you choose

Requirements & limits

The README describes a free version in release 0.9.9 and recommends the Patreon installer for the latest build and dependencies. The repository does not provide Pro builds or an embeddable Python environment.

  • The README lists Windows 10 as the minimum operating system and says Windows is the current stability focus; Linux and macOS support is described as planned for the 1.0 update.
  • NCNN inference requires a Vulkan-capable graphics processor under the listed minimum requirements; DirectML requires DirectX 12 capability.
  • TensorRT is for NVIDIA GPUs and requires building an engine on the machine used for inference. The README specifies at least a Pascal-generation GPU, CUDA 12.0 and driver version 525 or newer for TensorRT 8.6 and later.
  • The README recommends 16 GB RAM and a quad-core Intel Kaby Lake or AMD Ryzen processor or newer; these are listed as recommended rather than minimum requirements.
  • The app is built with Node.js and Electron; the public repository lists the GPL-3.0 license.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Enhancr on Windows

Set up Enhancr using its documented Windows installer.

Before you start

  • Windows 10 or Windows 11; Windows 10 is listed as the minimum.
  • A dual-core CPU with Hyperthreading enabled.
  • A Vulkan-capable graphics processor for NCNN inference or a DirectX 12-capable graphics processor for DirectML inference.

Steps

  1. Download the Enhancr 0.9.9 free installer from the linked release URL, or use the Patreon installer recommended by the project for the most recent version and dependencies.

  2. Run the downloaded installer to install the application.

Expected outcome

Enhancr is installed as a desktop GUI for video frame interpolation and upscaling.

Guide limitations

  • The README's listed minimum requirements are for Windows; it says stable, fully functioning support is focused on Windows, with Linux and macOS support planned for the 1.0 update.
  • TensorRT inference requires an NVIDIA GPU; the README states that TensorRT 8.6 dropped support for 2nd-generation Kepler and Maxwell GPUs, and specifies at least a Pascal GPU, CUDA 12.0, and driver version 525.xx or newer.
  • Builds and an embeddable Python environment for the Pro version are not provided through the repository.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
15 October 2026
Original website title
mafiosnik777/enhancr · Official homepage title · retained in its source language
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