aaronfeng753/waifu2x-extension-gui

Video, Image and GIF upscale/enlarge(Super-Resolution) and Video frame interpolation. Achieved with Waifu2x, Real-ESRGAN, Real-CUGAN, RTX Video Super...

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Summary Information

Updated 17 minutes ago
Added to GitGenius on September 3rd, 2026
Created on January 20th, 2020
Open Issues & Pull Requests: 93 (+0)
GitHub issues: Enabled
Number of forks: 1,051
Total Stargazers: 16,978 (+1)
Total Subscribers: 171 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 0.1 hours
Mean response time: 36.5 days
90th percentile: 67.7 days
Tracked items: 80

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Work labelled "BUG Report" is answered fastest, typically in under an hour, while "Feature request" waits about 3 days. Only 13% of issues opened in the past year have been closed. Three people close 97% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 31
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 564 days
Stale 30+ days: 31
Stale 90+ days: 27

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • Feature request (28)
  • BUG Report (27)
  • Fixed (9)
  • enhancement (6)
  • bug (4)
  • invalid (4)
  • Not a bug (3)
  • Not enough info (2)

Most active issues this week

No issue events were indexed in the last 7 days.

Detailed Description

Waifu2x-Extension-GUI is a desktop application for upscaling and frame-interpolating images, GIFs, and videos using machine learning models.

The tool addresses the problem of low-resolution multimedia by applying deep convolutional neural networks to enlarge and enhance visual content. It bundles multiple specialized engines—including Waifu2x, Real-ESRGAN, Real-CUGAN, Anime4K, RIFE, and others—each optimized for different content types and quality targets. Users select their input files and desired processing parameters, and the software applies the chosen model to produce enlarged or interpolated output. The tool can also analyze video frames to optimize quality and processing speed, and supports multi-GPU acceleration across AMD, Nvidia, and Intel hardware.

The project suits Windows users who need to upscale anime, artwork, photographs, or video content without specialized machine learning knowledge. It is particularly valuable for those working with lower-resolution source material who want straightforward batch processing with minimal configuration. The bundled preset configurations allow one-click application of recommended settings, though advanced users can fine-tune thread counts, engine-specific parameters, and processing pipelines. The tool's support for simultaneous processing of multiple file types and its multi-GPU capability distinguish it from simpler single-purpose upscalers.

Development activity shows consistent maintenance with regular updates and ongoing compatibility testing across different GPU vendors. The project maintains documentation in multiple languages and provides detailed changelog records of modifications. The maintainer actively gathers user feedback through a Patreon channel and continues to expand engine support and feature capabilities based on community needs.