How Does Artbreeder Work?

Artbreeder is one of the most popular tools when it comes to artificial intelligence portrait and landscape creation. The main goal that Artbreeder aims to accomplish is to provide users with the ability to combine a multitude of images to create new ones without putting in a lot of effort.

This platform directly relies on AI to develop photorealistic portraits of characters, as well. As such, Artbreeder has been the go-to tool in the eyes of many when it comes to AI tools and AI-generated artworks; it’s also a way through which you can learn how AI is used to create faces. In this article, we will explore what Artbreeder is, how it works, and how you can use it to your benefit.

What Is Artbreeder?

Artbreeder is an AI algorithm and tool that enables the creation of portrait and landscape images–it can also act as an AI face creator. This is a unique platform because it employs AI to create portraits of characters that are photorealistic, and offers the ability to alter each image through a simple interface that functions through sliders.

What this means is that each user of Artbreeder is provided with the opportunity to alter the character's face, change the age, skin tone, emotions, and other attributes to bring it as close as possible to their desired outcome.

Artbreeder and Its Functionality

Artbreeder promises users the ability to create any image or a collage of shapes and images. There's a feature available through the service known as a Splicer, which lets users create images by mixing them together and editing them.

Artbreeder runs on multiple components, which, when combined together, deliver a high level of functionality.  

  • Artbreeder has an image generation model, an image discriminator model, and a loss function that can define how well the generator imitated the real data.
  • The model also leverages multiple competing neural networks which work together to generate original pieces of art based on models.
  • One neural network gets to create the pieces, while another gets to determine if the image is real or fake. This way, the content gets categorised by classes, which are created using training data and images from different sources.
  • As a means of ensuring that there is good quality output, any Artbreeder user can set criteria for the generator and discriminator models to follow, after which they will be enabled to create results that feature those properties.
  • This model is trained with thousands of images, all of which were at one point utilised to train it. Whenever it generates images based on input content, Artbreeder can apply additional multi-scale transformations which have been composed by global average pooling as well as local deformations.

Generative adversarial networks (GANs) are a popular mechanism through which content gets synthesized. They are composed of two neural networks—a generator and a discriminator—which get pinned against one another.

In Conclusion

Alongside allowing users to create newly-generated images, Artbreeder also has numerous sliders through which users can further personalize their artwork. You can also learn how to make a person with This Person Does Not Exist to get another perspective on the procedure.