Best AI Video Upscaler GitHub in 2025

Name: Introducing HitPaw VikPea - Best AI Video Enhancer to Boost Video Quality (2025)
Uploaded: 2025-07-14T12:01:50Z
Duration: 1 min 9 s

"Looking to blow up some videos to higher resolutions, but don't have the budget to pay for any paid product, are there any open-source AI video upscalers?" - A question from Reddit.

When it comes to boosting video quality, not everyone wants—or needs—to invest in premium AI software. That’s why many creators, developers, and hobbyists turn to GitHub in search of reliable, community-driven tools that can upscale and enhance videos for free.

This guide explores the best video upscalers on GitHub, breaking down their capabilities, practical strengths, common drawbacks, and the overall experience of setting them up.

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Open-source video upscalers are great, but let’s be honest: setting them up isn’t always fun. From Python environments and model downloads to command-line scripts and slow processing, the workflow can easily get overwhelming.

HitPaw VikPea solves this problem with a clean, beginner-friendly interface that delivers professional-level AI upscaling, color enhancement, deblurring, denoising, frame repair, and more—no coding, no setup pain. It brings the power of open-source models into a smooth UI that anyone can use. It offers free download and trial to see a side-by-side comparison.

Part 1. What is Video Upscaler GitHub

Video upscaling refers to the process of enhancing a video's resolution—typically from SD or HD to Full HD, 4K, or even 8K—using algorithms or AI-based technology. On GitHub, developers share open-source video enhancer GitHub projects that empower users to build and customize their own AI video upscalers.

These tools are often flexible, modifiable, and free to use, making them popular among tech-savvy users. With growing demand for 4K video upscaler GitHub solutions, more contributors are leveraging deep learning models to restore details and sharpen visuals. If you're looking to upscale video GitHub-style, here are the best tools available.

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Part 2. Best 6 Video Upscale AI GitHub Tools

1. QualityScaler

QualityScaler is one of the most frequently recommended video quality enhancer GitHub tools. Built on advanced ESRGAN models, it allows users to AI upscale video on GitHub with minimal configuration. As an open source video upscaler GitHub project, it focuses on clean results, consistent color retention, and improved texture sharpness.

Features:

User-friendly GUI.
Upscales both images and videos.
Overcomes GPU VRAM limitations with automatic image tiling and merging.
Option to resize before upscaling.
Multi-GPU support.
Supports a wide range of image and video formats.

System Requirements:

Operating System: Windows 11 or Windows 10.
RAM: 8GB or more.
GPU: DirectX12 compatible with 4GB VRAM or more.

Programming Language:
Quality Scaler is developed entirely in Python.

Pros

Easy to set up compared to other GitHub video enhancers
Excellent quality for anime and real-life videos
Lightweight and resource-friendly

Cons

Requires manual frame extraction for full video workflows
Limited GUI options
No built-in timeline editing

2. REAL Video Enhancer

System Requirements:: Windows, Linux, macOS (depending on Python setup)

REAL Video Enhancer is a rising AI video enhancer GitHub project built for clarity-focused upscaling. Based on REAL-CUGAN and Real-ESRGAN variations, it provides smooth, natural enhancements that work especially well with highly compressed sources. As an open source video upscaler GitHub option, it offers strong balance between quality, stability, and usability.

Features:

REAL-CUGAN & Real-ESRGAN model support
Noise reduction and compression cleanup
Batch frame enhancement
Works with anime + real-life footage

Pros

High-quality enhancement for compressed videos
Good preservation of edges and colors
Active community updates

Cons

Requires manual setup
No GUI
Slower than some alternatives

3. Waifu2x Extension GUI

Waifu2x Extension GUI is one of the most user-friendly AI video enhancer GitHub tools, especially famous among anime upscalers. As a fully packaged GitHub video enhancer, it includes multiple AI engines such as Waifu2x, Real-CUGAN, SRMD, and Anime4K. Its fast processing, GPU usage, and clean interface make it a top choice for creators who want reliable AI upscale video GitHub performance without complex setup.

Features:

Upscale and denoise images (png, jpg, webp, etc.).
Enhance animated images (gif, animated webp).
Improve video quality (mp4, webm, ogv, etc.).
Apply effects like speed or reverse.
Customize settings: noise, scale, mode, framerate, etc.
Concurrent upscaling for multiple images or frames.
Switch between waifu2x and Real-ESRGAN Anime upscalers.

System Requirements:

Installation: Download the installer from the releases tab.
Note: Installation under C:/Program Files may require administrative rights.
MacOS: users need to compile waifu2x-converter-cpp from source.

Programming Language:
The core of Waifu2x-GUI is developed using TypeScript (82.2%), with other components in Less, JavaScript, and HTML.

Pros

User-friendly interface
Excellent for anime and line-art content
No manual Python setup needed

Cons

Can consume significant VRAM
Output varies depending on model choice

4. SimpleMediaUpscalerLite

SimpleMediaUpscalerLite is a lightweight GitHub video enhancer designed for users who want a simple, straightforward AI upscale video GitHub workflow without dozens of configuration options. It is one of the easiest open source video upscaler GitHub tools for beginners, providing efficient upscaling while staying resource-friendly.

Features:

Upscale individual files or entire folders using various engines.
GUI file manager for input & output file selection.
Set custom scaling multipliers.
Supports images (.png, .jpg, .jpeg) and videos (.mp4, .mkv).
Multiple upscaling algorithms to choose from.
Plugin support to expand upscaling engine options.

System Requirements:

Officially supported on Windows 10, 11.
Unofficial support for Windows XP, Vista, 7, 8 via Python.
Linux distros with wine installed.

Programming Language:
The application is primarily developed in Python (57.1%), with components in Vue (24.8%), JavaScript (12.9%), Shell (4.4%), and HTML (0.8%).

Pros

Beginner-friendly
Low hardware requirements
Fast and efficient

Cons

Fewer customization options
Limited model selection
No GUI

5. Video2x

Supported platforms: Windows (fully supported), Linux (limited support)

Video2X is a widely known AI video enhancer GitHub solution that automates video upscaling using multiple backend engines like Waifu2x, Real-ESRGAN, Anime4K, and SRMD. As one of the most flexible GitHub video enhancer tools, it allows users to upscale full videos without manually managing frame sequences. The software is ideal for users who want an all-in-one AI upscale video GitHub interface with substantial backend customization.

Features:

Upscales video/GIF/image with lossless quality.
Supports multiple renowned upscaling algorithms.
Offers both GUI and CLI interfaces for user convenience.
Capable of upscaling demos like Spirited Away's movie trailer and various GIFs.
Provides a standard test clip for performance benchmarks.

Programming Language:
Video2X is predominantly developed in Python, accounting for 94.4% of the codebase, with other components in Dockerfile and Shell.

Pros

Supports many AI models
Good balance between quality and speed
Handles entire videos automatically

Cons

Setup can be lengthy due to multiple models
Some engines require GPU for acceptable speeds

6. RealScaler

RealScaler is a powerful open source video upscaler GitHub project based on Real-ESRGAN that focuses on delivering clean, artifact-free results. Many creators choose it when they need a fast and stable video quality enhancer GitHub option that performs well with real-world footage. The tool produces natural textures and reduces compression noise, making it ideal for upscaling 480p or 720p videos to HD or 4K.

Features:

User-friendly GUI for easy operation.
Capable of upscaling both images and videos.v
Features automatic image tiling and merging to bypass GPU VRAM limitations.
Provides an option to resize media before upscaling.
Multi-GPU support for enhanced performance.Multi-GPU support for enhanced performance.
Supports a variety of image formats (png, jpeg, bmp, webp, tif) and video formats (mp4, wemb, gif, mkv, flv, avi, mov, qt).

System Requirements:

Operating Systems: Windows 11 or Windows 10.
Minimum RAM: 8GB.
GPU: DirectX12 compatible with at least 4GB VRAM.

Programming Language:
RealScaler is entirely developed in Python, encompassing both the backend and frontend functionalities.

Pros

Excellent for real-world videos
High-quality output with minimal distortions
GPU-accelerated

Cons

No GUI
Requires separate frame extraction
Slow on CPUs

Part 3. Difficulties in Using Open-source Video Enhancer GitHub

Open-source AI video enhancer GitHub projects can be powerful, but they’re rarely beginner-friendly. Many users download these tools expecting a quick plug-and-play experience, only to find that AI upscale video GitHub workflows often involve technical setup, manual commands, and a lot of experimentation.

1. Complex Installation and Setup

Most GitHub video enhancers rely on Python, PyTorch, CUDA, and other frameworks that require manual installation. You may need to clone repositories, install dependencies, configure environments, or download model files before upscaling anything. For newcomers, this makes even the simplest video quality enhancer GitHub setup feel overwhelming.

2. Lack of a Unified Interface

Unlike commercial tools with polished GUIs, many GitHub projects provide only scripts or command-line interfaces. Users often have to extract frames with FFmpeg, upscale them manually, then reassemble the video afterward.

3. Limited Support and Documentation

Since most open-source video upscalers are community-driven projects or research demos, support is limited. Documentation may be outdated or overly technical, leaving beginners to navigate GitHub issues, Discord threads, or Reddit posts in hopes of finding answers.

Part 4. Best AI Alternative to Video Upscaler GitHub

While GitHub offers some powerful video enhancer and AI video upscaler tools, most require advanced technical skills: coding knowledge, Python environments, model configuration, and powerful GPUs. For users who want the same AI-powered video upscaling results without the complexity, HitPaw VikPea is the perfect solution.

With VikPea, you can upscale low-resolution footage to 1080p, 2K, 4K, or higher in just a few clicks. Beyond basic resolution enhancement, it intelligently removes compression artifacts, reduces noise, restores facial details, enhances colors, and sharpens textures across everything from old home videos to smartphone recordings. And if your footage suffers from stuttering or low frame rates, VikPea’s AI frame interpolation helps deliver smoother, more cinematic motion.

HitPaw VikPea Amidst GitHub Alternatives

Upscale videos to 8K with exceptional detail and clarity.
Mature AI designed for upscaling anime and human face videos.
Superior noise reduction for crystal-clear videos.
Instantly see the upscaled result before export.
Intuitive design for easy video enhancement.
Watermark-free previews, batch processing, and GPU acceleration for quick, quality results.

HitPaw VikPea vs. GitHub Tools

Feature	HitPaw VikPea	GitHub Tools
Coding Required
Installation Complexity	Low (Download & Install)	High (Python, CUDA, Models)
User Interface	Clean, beginner-friendly UI	Mostly CLI
Processing Speed	Optimized with GPU & Cloud Acceleration	Depends on setup
Format Support	Built-in format support	Manual via ffmpeg
Ideal For	Everyone, including beginners	Developers, tech users

How to Use HitPaw VikPea

Step 1. Download and install this program to computer. Launch it and choose Video Enhancer module from the side menu.Import your low-resolution video to the program.
Step 2. As your needs, choose the General Restoration Model, Portrait Model or Animation and others. Each one will upscale your video from different aspects.
Step 3. After making your selection, hit 'Preview' to instantly see a side-by-side before-and-after comparison.
Step 4.Once satisfied, 'Export' the masterpiece you made with AI upscale video GitHub alternative.

Part 5. FAQs of Video Upscaler GitHub

Q1. How does video2x work?

A1. Unlike traditional methods, Video 2x uses deep learning techniques to analyze and extrapolate missing details in low-resolution videos, resulting in improved visual fidelity. It utilizes state-of-the-art AI algorithms, such as convolutional neural networks (CNNs), which are trained on vast amounts of high-resolution video data. During the upscaling process, Video 2x analyzes each frame's content and generates high-resolution versions based on its learned knowledge.

Q2. How does waifu2x work?

A2. Waifu2x is an image scaling and noise reduction program specifically designed for anime-style art and other types of photos. The tool was inspired by the Super-Resolution Convolutional Neural Network (SRCNN). It primarily uses Nvidia CUDA for computing, but there are alternative implementations that support OpenCL and Vulkan. The name "waifu2x" combines the anime slang "waifu," meaning a female character one is attracted to, with "2x," indicating two-times magnification.

Q3. What is TTA upscale?

A3. TTA, or test-time augmentation, mode averages the upscaling results of eight augmented inputs. This mode can reduce several types of artifacts, enhancing the final image quality. However, it's worth noting that TTA mode is approximately 8 times slower than the non-TTA mode.

Bottom Line

Open-source video upscaler GitHub tools offer amazing potential for developers and enthusiasts. From Waifu2x-GUI to RealScaler, there's no shortage of innovation on GitHub. However, if you prefer a more accessible and powerful AI solution, HitPaw VikPea is the best non-GitHub alternative for upscaling videos to 2K, 4K, or even 8K without hassle. Whether you're improving old footage or enhancing modern clips, choosing the right GitHub video upscaler or its alternative makes all the difference.

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