Warlock-Studio

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.

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📥 Download Installer

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🆕 New in v6.0 — Process Chaining

• 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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🖼️ Interface Capture

Capture.webm

🔍 Quality Comparison

WsvideovsGit3.webm

WsvideovsGit.webm

WsvideovsGit2.webm

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✨ Key Features

• 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.

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🖥️ System Requirements

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.

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🤝 Contributions

We welcome contributions from the community.

📧 Contact: [email protected]

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📜 License & Credits

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

📊 Integrated Technologies & Licenses

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