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Maps music and video into the latent space of StyleGAN networks.

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GANce - gance

Sample network output

Maps music and video into the latent space of StyleGAN (mostly focused on StyleGAN2) networks.

See for the development story, hardware configuration, future plans etc.


The following is a snippet from the music video for 'buzzz' by Won Pound from his self-titled album Won Pound released by Minaret Records:


Because of GitHub's video size limitations, this clip has been compressed. The complete, album-length music video for Won Pound is available in 4k on YouTube here. The 'buzzz' section of the video starts here.

The video was entirely synthesized with GANce, here's the command to produce NOVA, for example:

python projection-file-blend \
  --wav './gance/assets/audio/masters/15 - NOVA (end 2) (pass 2).wav' \
  --output-path ./nova.mp4 \
  --debug-path ./nova_debug.mp4 \
  --debug-side-length 1000 \
  --debug-window 200 \
  --run-config ./nova_config.json \
  --log log.txt \
  --projection-file-path ./gance/assets/projection_files/resumed_prod_nova_3-1.hdf5 \
  --networks-json ./prod_networks.json \
  --output-fps 60 \
  --output-side-length 2160 \
  --alpha 0.25 \
  --fft-roll-enabled \
  --fft-amplitude-range -5 5 \
  --blend-depth 12 \
  --phash-distance 25 \
  --bbox-distance 50 \
  --track-length 5


The functionality available in the libraries are exposed through a series of command line interfaces. The following is a listing of the commands that are stable and mostly usable at this point.

Script Description Scan directories of networks for pickled networks, for each of these networks, load them, and feed in a vector to make sure they are still functional (no bit rot). Feed inputs (music, videos) into a network and record the output. Also tools to visualize these vectors against the network outputs. Tools to prepare images for styleGAN training. Tools to project video into the latent space of a given network, and also visualize the results. Resulting projection files are consumed primarily in Given some styleGAN networks, load each network and synthesize a number of images with them. Interesting vectors can be reused with other networks.

Getting Started

Python Dependencies

See the requirements directory for required Python modules for building, testing, developing etc. They can all be installed in a virtual environment using the follow commands:

python3 -m venv venv
source venv/bin/activate
pip install -r ./requirements/dev.txt -r ./requirements/prod.txt -r ./requirements/test.txt

There's also a bin script to do this:


Developer Guide

The following is documentation for developers that would like to contribute to GANce.

Type System Limitations

A common idiom found in this application is the following:

# Shape (1, Any, Any)
CompleteLatentsType = NewType("CompleteLatentsType", "np.ndarray[np.float32]")  # type: ignore

Where a NewType is created based on an np.ndarray. This doesn't really do anything outside of making the programs a bit more simple to understand to the reader. The following would pass mypy:

CompleteLatentsType("oh no!")

This is because:

  • We're stuck on numpy==1.16.4 because of the tensorflow dependency of stylegan2.
  • np.ndarray types are Any as far as mypy is concerned in this version.
  • When you NewType("MyType", Any), it stops mypy from being able to identify type mismatches.

Read more here: python/mypy#6701

We could resolve this if we take the time to verify that it's okay to upgrade the numpy version.


This project uses pytest to manage and run unit tests. Unit tests located in the test directory are automatically run during the CI build. You can run them manually with:


Local Linting

There are a few linters/code checks included with this project to speed up the development process:

  • Black - An automatic code formatter, never think about python style again.
  • Isort - Automatically organizes imports in your modules.
  • Pylint - Check your code against many of the python style guide rules.
  • Mypy - Check your code to make sure it is properly typed.

You can run these tools automatically in check mode, meaning you will get an error if any of them would not pass with:


Or actually automatically apply the fixes with:


There are also scripts in ./tools/ that include run/check for each individual tool.

Using pre-commit

First you need to init the repo as a git repo with:

git init

Then you can set up the git hook scripts with:

pre-commit install

By default:

  • black
  • pylint
  • isort
  • mypy

Are all run in apply-mode and must pass in order to actually make the commit.

Also by default, pytest needs to pass before you can push.

If you'd like skip these checks you can commit with:

git commit --no-verify

If you'd like to quickly run these pre-commit checks on all files (not just the staged ones) you can run:

pre-commit run --all-files