GauGAN is a generative adversarial network (GAN) for creating synthetic images from textual descriptions, based on the StackGAN++ architecture. It uses a natural language processing (NLP) model to generate realistic images from textual descriptions and can be used to create photo-realistic images of people, animals, scenery, objects, etc.
The GauGAN system was developed by researchers at NVIDIA Corporation and is available online as a free beta. It has been used to create synthetic images of various scenes and objects, including faces, animals, cars, and buildings.
To use GauGAN, users first need to input a text description of the scene or object they want to generate an image of. The system will then generate a synthetic image based on the description. Users can then edit the generated image, if desired, to create a more realistic image.
GauGAN is still in beta and is not yet perfect. However, it has shown great promise and could become a valuable tool for artists, designers, and others who want to create realistic images from textual descriptions.
The company has released an updated version of the software, called GauGAN2. Like its predecessor, GauGAN2 can create realistic images from segmentation maps, which are labeled sketches that depict the layout of a scene.
However, the new version is much more efficient, thanks to many improvements in the underlying algorithms. For example, GauGAN2 can generate richer textures by using a larger training dataset. The result is an impressive piece of software that can create realistic images from nothing more than a few simple lines.
With GauGAN2, artists can use text, a paintbrush, and paint bucket tools, or both methods to design their landscapes.
The style transfer algorithm allows you to apply filters to your creations, so you can change a daytime scene to sunset, or a photorealistic image to a painting. You can even upload your own filters to layer onto your masterpieces, or upload custom segmentation maps and landscape images as a foundation for your artwork.