HyperStyle addresses the trade-off between reconstruction fidelity and semantic editability in StyleGAN latent space inversion for real images. It offers a hypernetwork-based approach for near real-time inference, enabling accurate image representations in editable latent regions, benefiting researchers and practitioners in image editing and manipulation.

How It Works

HyperStyle employs a hypernetwork to learn weight modulations for a pre-trained StyleGAN generator, conditioned on a target image. This approach avoids the lengthy per-image fine-tuning of the generator itself, instead learning efficient weight adjustments. By carefully designing the hypernetwork, the parameter count is kept manageable while achieving reconstructions comparable to optimization-based methods, with the speed of encoder-based approaches.

Quick Start & Requirements

• Install: Use the provided environment/hyperstyle_env.yaml with Anaconda.
• Prerequisites: Linux or macOS, NVIDIA GPU with CUDA and CuDNN, Python 3.
• Models: Download pretrained HyperStyle models for faces, cars, and wild datasets, along with auxiliary models (e4e encoders, StyleGAN2 generators, loss computation models).
• Links:
  • Inference Notebook: notebooks/inference_playground.ipynb
  • Animation Notebook: notebooks/animation_playground.ipynb
  • Domain Adaptation Notebook: scripts/run_domain_adaptation.py

Highlighted Details

• Achieves reconstruction quality comparable to optimization techniques with encoder-based inference speeds.
• Enables editing of out-of-domain images not seen during training.
• Supports editing via StyleCLIP, InterFaceGAN, and GANSpace.
• Facilitates domain adaptation by applying learned weight offsets to fine-tuned generators.

Maintenance & Community

The project is the official implementation for a CVPR 2022 paper. It builds upon and credits several other open-source projects, including StyleGAN2, e4e, and StyleCLIP.

Licensing & Compatibility

The repository utilizes code and models with various licenses, including MIT, Apache 2.0, BSD 2-Clause, and an "Nvidia Source Code License-NC" for CUDA files within the StyleGAN2 ops directory. Compatibility for commercial use or closed-source linking should be carefully reviewed based on these individual licenses.

Limitations & Caveats

The "Nvidia Source Code License-NC" for StyleGAN2 CUDA ops may impose restrictions on commercial use. CPU inference is not inherently supported and may require modifications.