/Fusion-Vision

Empowering artists with the power of StyleGAN2

Primary LanguageJupyter NotebookApache License 2.0Apache-2.0

Fusion Vision

Python 3.6 PyTorch 1.6

Fusion Vision is an end-to-end application that focuses on providing creative control over the generative process of StyleGAN2. This application helps artists focus on their creations rather than getting familiar with code (Creative coders, links to colab notebooks below)

Features

  • Generate images using 10+ models including
    • human and anime faces
    • abstract and modern art
    • trypophobia and microscopic imgs
    • imagenet and wildlife dataset
    • cats, horses, cars and churches
  • Fine-grained mixing of multiple seeds
  • Control hand-crafted features (like haircolor, age, gender in faces) for each model
  • Generate interpolating animations between images or animations with features controlled by audio

Background

Generative Adversarial Networs (GANs) can create images that are ORIGINAL in the true sense, but are hard to control. Using a mathematical technique called Principle Component Analysis, one can find such controls. Fusion Vision gives you those fine-grained controls over a StyleGAN2 model.

Inspiration

This artwork by Mario Klingemann inspired me to take up this project. (Play the video at 0.25x for some nightmare fuel).

Usage Instructions

For Artists

Yet to come

For Creative Coders

Stay tuned for notebooks

Using a custom trained model

If you've trained a StyleGAN2 model using the official NVIDIA code, convert your weights using this colab notebook

weights_tf_to_pt.ipynb Open In Colab

If you've trained your model using Kim Seonghyeon's code, you can skip the conversion.

Use the following notebook to do PCA on your model. Use the interactive widget in the notebook to fine-tune your components and save them

explore_latent_space.ipynb Open In Colab

Deploy to Openshift Online

Credits

License

The code of this repository is released under the Apache 2.0 license.