Iván Eduardo Chavez Ayub
01b1a2f89c
Updated README.md to include new installer information, features, and system requirements for version 5.0.
7.7 KiB
7.7 KiB
Warlock-Studio is a unified, high-performance platform for upscaling, restoration, denoising, and frame interpolation.
Inspired by Djdefrag tools such as QualityScaler and FluidFrames.
📥 Download Installer
This installer was built using PyInstaller and Inno Setup.
By default, it includes DirectML support to ensure maximum compatibility with any graphics card.
Select your preferred mirror to download the latest version (v5.0):
|
Recommended Mirror |
Direct Release |
✨ What is New in v5.0
The v5.0 release represents a foundational transformation of the application:
- 🧩 Modular Architecture: The core has been re-engineered into specialized components (
core,preferences,console,drag_drop) for superior stability and fault isolation. - 🧠 NEO Engine: A new diagnostic subsystem that scans CPU topology, RAM, and GPU VRAM to automatically recommend optimal tiling and thread settings.
- 🖥️ Integrated Debug Console: A real-time GUI terminal that intercepts
stdoutandstderrwith syntax highlighting, allowing users to diagnose FFmpeg or ONNX issues instantly. - ⚡ Native CUDA & Failover: The backend now strictly prioritizes CUDA (NVIDIA Optimized) > DirectML > CPU, with corrected integer typing for device IDs.
- 💎 Lossless Pipeline: Deprecated
.jpgusage in favor of.pngfor intermediate frames to prevent compression artifacts (blurriness).
🖼️ Interface Previews
⚙️ Preferences
🔍 Quality Comparison
✨ 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.
- 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
| Component | Minimum Specification | Recommended Specification |
|---|---|---|
| OS | Windows 10 (64-bit) | Windows 11 (64-bit) |
| RAM | 8 GB | 16 GB+ (Recommended for 4K Video) |
| GPU | DirectX 12 Compatible | NVIDIA RTX 3000/4000 Series (for CUDA) |
| VRAM | 4 GB | 8 GB+ (NEO Engine auto-tunes limits) |
| Storage | HDD Space | NVMe SSD (Highly recommended for RIFE) |
🤝 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 & Credits
© 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 | Xintao Wang | GitHub |
| GFPGAN | Apache 2.0 | TencentARC | 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 | Phil Harvey | Official Site |
| Python | PSF License | Python Software Foundation | Official Site |
| PyInstaller | GPLv2+ | PyInstaller Team | GitHub |
| Inno Setup | Custom | Jordan Russell | Official Site |