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Warlock-Studio-Universal/README.md
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Inspired by Djdefrag tools such as QualityScaler and FluidFrames, Warlock-Studio provides a unified, high-performance platform for upscaling, restoration, denoising, and frame interpolation.


📥 Download Installer

You can download the installer from either option below:
Warlock-Studio on SourceForge Download from GitHub

🖼️ Interface Previews

interface Preferences.webm


🖼️ Quality Comparison

WsvideovsGit.webm

WsvideovsGit2.webm

Comparison


Update 5.0

  • Modular Architecture (v5.0): The code has been refactored into specialized components (core, preferences, console, drag_drop) for superior stability, fault isolation, and code maintainability.
  • NEO Engine: Advanced diagnostic subsystem that scans VRAM, RAM, and CPU topology to generate optimal configuration recommendations in real-time.
  • ⚙️ Centralized Preferences Panel: Provides a quick-access Settings Button that opens a comprehensive panel for system management:
    • UI Control: Customize the Theme (Light/Dark), Interface Scaling, and Window Opacity.
    • Hardware Configuration: Adjust the VRAM limit, select the preferred GPU device, and configure \texttt{tiling} parameters based on NEO Engine data.
    • Maintenance: Tools to clear the model cache, manage error logs, and perform online update checks.
  • Integrated Console: A GUI terminal that intercepts sys.stdout and sys.stderr to display inference logs, errors, and warnings with syntax highlighting for instant debugging.
  • Native Drag & Drop: Direct support for dragging and dropping image and video files from the operating system explorer.

Key Features

  • AI Upscaling & Restoration Utilize Real-ESRGAN, BSRGAN, and IRCNN models for denoising, super-resolution, and detail recovery.
  • Face Restoration (GFPGAN) Recover facial details from low-resolution or blurry images and video frames.
  • Frame Interpolation (RIFE) Smooth motion or generate slow-motion content with 2×, 4×, or 8× interpolation.
  • Advanced Hardware Acceleration Intelligent provider selection prioritizes CUDA, falls back to DirectML, and finally CPU for maximum compatibility and performance.
  • Batch Processing Process multiple media files simultaneously, saving time and effort.
  • Custom Workflows Fine-grained control over models, resolution, output formats, and quality parameters.
  • Open-Source & Extensible Fully MIT licensed, for contributors and developers.

🖥️ System Requirements

  • OS: Windows 11 or Windows 10 (64-bit)
  • RAM: 8GB (minimum) / 16GB+ (recommended for 4K Video)
  • GPU: DirectX 12 compatible graphics card. NVIDIA (for CUDA), AMD, or Intel GPU with up-to-date drivers.
  • VRAM: 4GB+ recommended. The NEO Engine automatically tunes limits on startup.
  • Storage: Sufficient free space for input and processed media. SSD highly recommended for video I/O.

🤝 Contributions

We welcome contributions from the community:

Fork the repository. Create a branch for your feature or bug fix. Submit a Pull Request with a detailed description and testing notes.

📧 Contact: negroayub97@gmail.com


📜 License

© 2025 Iván Eduardo Chavez Ayub Licensed under MIT. Additional terms and attributions are provided in NOTICE.md.

📊 Integrated Technologies & Licenses

Technology    License                Author / Maintainer                      Source                                                     
QualityScaler MIT                    Djdefrag GitHub       
FluidFrames    MIT                    Djdefrag GitHub         
Real-ESRGAN    BSD 3-Clause / Apache Xintao Wang                              GitHub           
GFPGAN        Apache 2.0            TencentARC / Xintao Wang                GitHub             
RIFE          Apache 2.0            hzwer                                    GitHub
BSRGAN        Apache 2.0            Kai Zhang                                GitHub                   
IRCNN          BSD / Mixed            Kai Zhang                                GitHub                   
ONNX Runtime  MIT                    Microsoft                                GitHub         
FFmpeg        LGPL / GPL            FFmpeg Team                              Official Site                       
ExifTool      Artistic License      Phil Harvey                              Official Site                     
Python        PSF License            Python Software Foundation              Official Site                   
PyInstaller    GPLv2+                PyInstaller Team                        GitHub       
Inno Setup    Custom                Jordan Russell                          Official Site