What is StyleGAN? A type of Generative Adversarial Network (GAN) developed by NVIDIA that is primarily intended for high-quality image generation (Creative Commons BY-NC-SA 4.0: non-commercial)

StyleGAN is a type of generative adversarial network (GAN) developed by NVIDIA that is primarily intended for high-quality image generation. The features and mechanisms of StyleGAN are explained below.

summary

StyleGAN was announced by NVIDIA in 2019 and has since gained significant attention in the field of image generation. This model offers several improvements over traditional GANs, dramatically improving the quality and controllability of the generated images.

Main features

1. Style separation and maneuverability
   • It can independently manipulate elements of different styles (e.g., coarse structure or fine textures of images).
   • This allows you to adjust the “rough shape of the image” and “fine details” separately.
2. High-Quality Image Generation
   • It has the ability to generate high-resolution, realistic images, and the generated images are almost indistinguishable even to the human eye.
3. Introducing Style Maps
   • While traditional GANs feed random noise (latent vectors) directly into the generator, StyleGAN converts this into a style map and injects it step by step.
   • This mechanism allows for better control over the “hierarchical features” of the generated image, from rough shapes to fine details.
4. Smooth interpolation
   • The latent space is refined to achieve smooth interpolation (image change) between different images.

Architectural features

StyleGAN has introduced the following mechanisms.

1. Mapping Network

• A network that converts a latent vector (z) into a style space (w).
• This gives you more control over the features of the generated image.

2. AdaIN（Adaptive Instance Normalization）

• An important technique for reflecting style in image generation.
• Each generation layer reflects “style information” and flexibly adjusts the characteristics of the generated images.

3. Progressive Growing

• A method that starts learning at low resolution at the beginning of training and gradually evolves to high resolution.
• This allows for stable generation of high-resolution images.

Application Examples

1. Generate fake images
   • Generate high-resolution facial images, landscapes, art, and more.
2. Style Transformation
   • Transformation between different styles (e.g., making a realistic face anime-like).
3. Data Expansion
   • Generate synthetic data to compensate for the lack of training data.
4. Creative uses
   • Such as art production and character design.

Version evolution

• StyleGAN (2019) Early version introduces the innovation of style separation.
• StyleGAN2 (Late 2019) Improved style distortion and artifacts.
• StyleGAN3 (2021) Improves temporal consistency and continuity, and supports video generation.

Official Documentation and Resources

• Thesis:
  • StyleGAN Original Paper (arXiv)
  • StyleGAN2 Paper (arXiv)
  • StyleGAN3 Paper (arXiv)
• Code:
  • StyleGAN GitHub Repository

StyleGAN is a cutting-edge technology in image generation technology, particularly used in applications that require control and high-quality generation.

StyleGAN License

StyleGAN (as well as StyleGAN2 and StyleGAN3) is available in the official repository published by NVIDIA, and its license is specified as follows:

1. Dataset (FFHQ) License

• License Name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
• License Scope:
  • FFHQ dataset (images, JSON metadata, download scripts).
  • Non-commercial use only.
  • There are conditions for attribution, non-commercial, and shareAlike.
  • The same license must be applied to derivative works.
• You can find out more about it at the link below:
  • CC BY-NC-SA 4.0 License Details

2. StyleGAN Code License

• License Name: NVIDIA Software License
• Main conditions:
  1. The code can be used for academic research and non-commercial purposes.
  2. Permission from NVIDIA is required for commercial use.
  3. Copyright notation must be maintained.
  4. The software is provided “as is” without warranty of any kind.
  Notes:
  • The license is described in detail in the files includedLICENSE.txt in NVIDIA’s repository.
  • If you are considering commercial use, you should contact NVIDIA directly.
• StyleGAN repository:
  • StyleGAN GitHub (NVIDIA)
  • StyleGAN2 GitHub (NVIDIA)
  • StyleGAN3 GitHub (NVIDIA)

3. Notes

Commercial Use

• Both the StyleGAN code and the FFHQ dataset are intended for non-commercial use.
• If you want to use it commercially, you must obtain permission from NVIDIA.

FFHQ dataset image license

• The images themselves in the dataset must follow the license of the original Flickr image (e.g., CC BY 2.0, CC BY-NC 2.0, Public Domain, etc.), which applies separately.

4. Conclusion

• Code (StyleGAN, StyleGAN2, StyleGAN3): NVIDIA Software License
  • Non-commercial use is permitted, commercial use is required.
• Dataset (FFHQ): Creative Commons BY-NC-SA 4.0
  • Available for non-commercial purposes, requires the same license as attribution.