Best Upscale Video GitHub Repositories for 2026

The open-source community has fundamentally democratized digital video restoration. For years, upscaling low-resolution footage, cleaning up compression artifacts, or restoring vintage home videos meant relying on expensive, proprietary software suites locked behind subscription paywalls. Today, exploring “upscale video GitHub” repositories grants creators, hobbyists, and developers access to studio-grade AI models for free.

Running entirely locally on your own GPU, these GitHub projects leverage advanced deep learning architectures—such as ESRGAN, Real-CUGAN, and temporal-consistent diffusion models—to reconstruct missing pixels, sharpen edges, and inject realistic textures into grainy or pixelated videos.

Top Open-Source Video Upscaling Repositories on GitHub

Because different projects cater to varying skill levels and visual styles, the GitHub ecosystem features several standout solutions:

1. Video2X (k4yt3x/video2x)

• Best For: Automated multi-engine processing pipelines.
• Overview: Video2X acts as a powerful orchestration wrapper. Instead of forcing you to manually split a video into thousands of individual frames, upscale them, and stitch them back together with audio, Video2X automates the entire FFmpeg pipeline. It supports various backends like Waifu2x and Real-ESRGAN.

2. QualityScaler & RealScaler (Djdefrag/QualityScaler)

• Best For: Clean Windows desktop applications for real-world footage.
• Overview: Built on advanced ESRGAN models, these tools are designed for users who want top-tier AI upscaling without wrestling with command-line arguments. They feature automatic image tiling (which prevents out-of-memory GPU crashes) and support multi-GPU setups.

3. Waifu2x Extension GUI (AaronFeng753/Waifu2x-Extension-GUI)

• Best For: Anime, 2D animation, and beginner-friendly desktop workflows.
• Overview: Originally famous for anime upscaling, this repository packages multiple engines (Waifu2x, Real-CUGAN, Anime4K, SRMD) into an intuitive drag-and-drop Windows application. It handles batch frame processing, denoising, and automatic audio retention seamlessly.

4. Upscale-A-Video & chaiNNer

• Best For: Cutting-edge research models and node-based visual scripting.
• Overview: For advanced creators, repositories like Upscale-A-Video introduce diffusion-based temporal consistency for real-world video super-resolution. Meanwhile, chaiNNer offers a visual node-based editor that allows you to chain together custom AI models, color correction nodes, and background removers into custom video pipelines.

Quick Comparison of GitHub Video Upscalers

| Tool Name | Primary Focus | Interface Type | Hardware Requirement |
| :— | :— | :— | :— |
| Video2X | Automated pipelines | CLI / GUI | Dedicated GPU recommended |
| QualityScaler | Real-world footage | Windows Desktop GUI | DirectX12 GPU (4GB+ VRAM) |
| Waifu2x Extension GUI | Anime & 2D content | Feature-rich GUI | Vulkan/CUDA compatible GPU |
| chaiNNer | Custom node workflows | Node-based GUI | Mid-to-high end GPU |

Getting Started: A Standard GitHub Python Workflow

If you choose a repository that requires manual setup rather than a packaged .exe installer, the typical installation workflow involves a few standard steps:

1. Clone the Repository:
   Open your terminal and clone the project using Git:

   git clone https://github.com/sczhou/Upscale-A-Video.git
   cd Upscale-A-Video
   

2. Set Up a Virtual Environment:
   Create an isolated Python environment to avoid dependency conflicts:

   conda create -n upscale-env python=3.10 -y
   conda activate upscale-env
   

3. Install Dependencies:
   Install PyTorch and the required libraries specified in the repository’s requirements.txt:

   pip install -r requirements.txt
   

4. Download Pre-trained Model Weights:
   AI models require large .pth or .safetensors weight files. Download these from the repository’s official release page or Google Drive link and place them in the designated weights/ or models/ directory.
5. Run Inference:
   Execute the inference script pointing toward your target video file:

   python inference.py -i input_video.mp4 -o output_directory/
   

Why Choose Open-Source Over Commercial Tools?

Diving into GitHub video upscaling projects offers unmatched benefits: absolute data privacy (all processing happens entirely offline on your local machine), zero recurring subscription fees, and total transparency over the algorithms being used. Whether you are reviving vintage family tapes or cleaning up compressed digital content, the open-source GitHub ecosystem puts Hollywood-grade AI enhancement directly into your hands.