Autolume

Autolume-Live is a cross-platform tool for real-time visual performances using StyleGAN.

For detailed documentation and demos look at the Metacreation Lab Website.

Downloads

Latest release:

• Autolume 2.16.2 for Windows

Examples

For examples of artworks created with Autolume see: https://www.metacreation.net/artworks

Development instructions

We recommend uv for managing python venv (installation link)

Dependencies

Windows

• CUDA (download link)
  • Minimum components: CUDA Development + CUDA Runtime
• Microsoft C++ Build Tools (download link)
  • Minimum components: Desktop development with C++
• FFmpeg: winget install Gyan.FFmpeg

Linux (Ubuntu 24+)

• CUDA (download link)
  • Minimum components: CUDA Development + CUDA Runtime
• FFmpeg and PortAudio: sudo apt install portaudio19-dev ffmpeg

macOS

• FFmpeg and PortAudio: brew install portaudio ffmpeg
• Command Line Tools for Xcode: xcode-select --install

Initial setup

Create the Python environment:

uv sync

Running the program

Start the program

uv run main.py

Create a release

Run the cross-platform release script on the OS you want to build for (PyInstaller cannot cross-compile — a Windows build must run on Windows, etc.):

uv run release.py

ffmpeg/ffprobe are downloaded and bundled into the release automatically via ffmpeg-downloader.

• Windows / Linux: output is the dist/Autolume/ folder.
• macOS: output is dist/Autolume.app. It is unsigned, so the first launch needs right-click → Open.

Building documentation

uv run zensical build

The documentation will be output to site.

To serve the documentation locally:

uv run zensical serve

The documentation will be served at http://127.0.0.1:8000/.

Credits

• Philippe Pasquier: Director of Metacreation Lab for Creative AI, Principal Investigator
• Arshia Sobhan: Project management
• Lionel Ringenbach: Software development
• Michael Tjokrowardojo: Software development
• Jonas Kraasch: Project first-phase development
• Arthur Deleu: Project development
• Mahshid Jabari: DevOps
• Peter Chen: Contributions to the super-resolution module
• Manuel Bolduc: User testing
• Ahmed Abuzuraiq: Research associate

Acknowledgment

StyleGAN3: https://github.com/NVlabs/stylegan3

GAN compression: https://github.com/lychenyoko/content-aware-gan-compression

GANSpace: https://github.com/harskish/ganspace

License

Some of the dependencies fall under the Nvidia Source Code License.

Project status

This code base is still open to changes and bugs might still appear. Please create issues and let us know so we can polish it for the final release!

As a dedicated research team behind Autolume, we emphasize that our system is a tool for exploration and innovation without direct support.