Warlock Studio is a unified platform for upscaling, restoring, denoising, and interpolating frames in videos and images. It is inspired by and based on Djdefrag tools such as QualityScaler and FluidFrames.
This installer was built using PyInstaller and Inno Setup.
By default, it includes DirectML support to ensure maximum compatibility with any graphics card (NVIDIA/AMD/INTEL).
Select your preferred option to download the latest version (Direct Release/SourceForge):
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- Create multi-step pipelines; order steps to run sequentially per file.
- RIFE interpolation integrates as a chain step for video sources (graceful skip on images).
- Per-step model selection via a combobox fed by auto-discovered ONNX models in
AI-onnx/. - Automatic output routing: intermediate steps use temp folders; the final step writes to your chosen output path.
- Smart extension/codec correction by media type to prevent invalid outputs.
- Memory-safe execution with per-step VRAM tile sizing and cleanup between steps.
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- AI Upscaling & Restoration – Utilize Real-ESRGAN, BSRGAN, RealESRNet, RealESR_Animex4, 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.
- Process Chaining – Build sequential workflows by chaining steps. Mix upscaling, face restoration, and interpolation; each step’s output becomes the next step’s input automatically. Includes model auto-discovery, per-step GPU/codec settings, and smart validation (e.g., RIFE requires video).
- 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.
| Component | Minimum Specification | Recommended Specification |
|---|---|---|
| OS | Windows 10 (64-bit) | Windows 11 (64-bit) |
| RAM | 8 GB | 16 GB+ (Required for 4K & High-FPS Video) |
| GPU | DirectX 12 Compatible (DML) / NVIDIA GTX 10-Series | NVIDIA RTX 3060+ / AMD RX 6000+ |
| VRAM | 4 GB | 8 GB - 12 GB+ (For Stable Diffusion/Video Interpolation) |
| Storage | 2 GB available space | SSD (Critical for RIFE & Temp Video Processing) |
| Architecture | x64 | x64 (Native DirectML Support) |
Performance Tip: Given that Warlock Studio leverages DirectML for hardware acceleration, keeping your GPU drivers updated is essential for maximizing processing speed across NVIDIA, AMD, and Intel hardware.
We welcome contributions from the community.
📧 Contact: [email protected]
© 2025 Iván Eduardo Chavez Ayub
Licensed under MIT. Additional terms and attributions are provided in NOTICE.md.
| Technology / Model | License | Author / Maintainer | Source |
|---|---|---|---|
| Real-ESRGAN | BSD 3-Clause | Xintao Wang | GitHub |
| • RealESRGANx4 | BSD 3-Clause | Xintao Wang | Same as above |
| • RealESRNetx4 | BSD 3-Clause | Xintao Wang | Same as above |
| • RealESR_Gx4 | BSD 3-Clause | Xintao / Community | Same as above |
| • RealESR_Animex4 | BSD 3-Clause | Community | Same as above |
| BSRGAN | Apache 2.0 | Kai Zhang | GitHub |
| • BSRGANx4 | Apache 2.0 | Kai Zhang | Same as above |
| • BSRGANx2 | Apache 2.0 | Kai Zhang | Same as above |
| IRCNN | BSD / Mixed | Kai Zhang | GitHub |
| • IRCNN_Mx1 | BSD / Mixed | Kai Zhang | Same as above |
| • IRCNN_Lx1 | BSD / Mixed | Kai Zhang | Same as above |
| GFPGAN | Apache 2.0 | TencentARC | GitHub |
| RIFE | MIT | Hzwer / Megvii | GitHub |
| QualityScaler | MIT | Djdefrag | GitHub |
| FluidFrames | MIT | Djdefrag | GitHub |
| DirectML | MIT | Microsoft | GitHub |
| ONNX Runtime | MIT | Microsoft | GitHub |
| CustomTkinter | MIT | Tom Schimansky | GitHub |
| TkinterDnD2 | MIT | pmgagne | GitHub |
| OpenCV (cv2) | Apache 2.0 | OpenCV Team | Official Site |
| NumPy | BSD 3-Clause | NumPy Developers | Official Site |
| Pillow (PIL) | HPND | Python-Pillow Team | GitHub |
| MoviePy | MIT | Zulko | GitHub |
| FFmpeg | LGPL / GPL | FFmpeg Team | Official Site |
| ExifTool | Artistic | Phil Harvey | Official Site |
| Psutil | BSD 3-Clause | Giampaolo Rodola | GitHub |
| WMI | MIT | Tim Golden | GitHub |
| GPUtil | MIT | Anders Krogh | GitHub |
| Requests | Apache 2.0 | Kenneth Reitz | GitHub |
| Packaging | Apache 2.0 | PyPA | GitHub |
| Natsort | MIT | Seth M. Morton | GitHub |
| Python | PSF License | Python Software Foundation | Official Site |
| PyInstaller | GPLv2+ | PyInstaller Team | GitHub |
| Inno Setup | Custom | Jordan Russell | Official Site |