# Video2x — ML-Powered Video Super Resolution and Frame Interpolation

> A machine learning framework for upscaling video resolution and interpolating frames using multiple backends.

## Install

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# Video2x — ML-Powered Video Super Resolution and Frame Interpolation

## Quick Use
```bash
pip install video2x
video2x -i input.mp4 -o output.mp4 -p realesrgan -s 2
```

## Introduction
Video2x is an open-source video super-resolution and frame interpolation framework. It wraps multiple upscaling backends such as Real-ESRGAN and RealCUGAN behind a unified CLI and library interface, letting you enhance video quality without manual per-frame processing.

## What Video2x Does
- Upscales video resolution by 2x, 3x, or 4x using neural networks
- Interpolates frames to increase FPS (e.g., 24 to 60)
- Supports multiple backends: Real-ESRGAN, RealCUGAN, and RIFE
- Processes videos with Vulkan GPU acceleration
- Preserves audio streams and subtitle tracks during processing

## Architecture Overview
Video2x splits input video into frames, processes each through the selected ML model using Vulkan compute shaders, then reassembles them with FFmpeg. The Vulkan-based pipeline (via ncnn) enables GPU acceleration on NVIDIA, AMD, and Intel GPUs without requiring CUDA.

## Self-Hosting & Configuration
- Install via pip or download prebuilt binaries from GitHub releases
- Requires FFmpeg in PATH for video muxing and demuxing
- GPU acceleration needs Vulkan-capable drivers
- Configuration options include scale factor, model selection, and thread count
- Docker images are available for containerized processing

## Key Features
- Cross-platform: runs on Windows, Linux, and macOS
- Vulkan compute backend works with any modern GPU vendor
- Lossless processing pipeline preserves original quality where possible
- Batch processing support for multiple files
- Frame interpolation with RIFE for smooth slow-motion effects

## Comparison with Similar Tools
- **Topaz Video AI** — proprietary with a subscription; Video2x is free and open source
- **Real-ESRGAN (standalone)** — image-only; Video2x handles full video pipelines
- **Anime4K** — real-time shader approach; Video2x applies offline ML models for higher quality
- **waifu2x** — image upscaler; Video2x extends the concept to video with frame management
- **FFmpeg filters** — built-in scaling is interpolation-based; Video2x uses neural networks

## FAQ
**Q: Does Video2x require an NVIDIA GPU?**
A: No. The Vulkan backend works with NVIDIA, AMD, and Intel GPUs.

**Q: How long does processing take?**
A: Speed depends on resolution, scale factor, and GPU. A 1080p-to-4K upscale typically processes at 1-5 FPS on modern GPUs.

**Q: Can I use Video2x for live streaming?**
A: No, it is designed for offline batch processing.

**Q: What video formats are supported?**
A: Any format FFmpeg can decode and encode, including MP4, MKV, AVI, and WebM.

## Sources
- https://github.com/k4yt3x/video2x
- https://video2x.org/

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Source: https://tokrepo.com/en/workflows/asset-72a06ce3
Author: AI Open Source