Abstract art created by AI art generators is often generated using generative models that have been trained on abstract art styles or techniques. These models learn to create new abstract art by analyzing large datasets of existing abstract art and identifying patterns and styles.

One approach to generating abstract art is through the use of generative adversarial networks (GANs). GANs can generate abstract images by training a generator network to produce new images that look similar to the training dataset. The generator is fed a random noise vector and produces an image that is then evaluated by a discriminator network to determine if it is real or fake. Over time, the generator learns to produce increasingly realistic images.

Another approach to generating abstract art is through the use of variational autoencoders (VAEs). VAEs learn to encode images into a lower-dimensional space, where they can be manipulated to produce new images that retain the same abstract style or patterns.

Once the generative model has been trained, it can be used to create new abstract art by sampling from the learned distribution of abstract art. The artist can then adjust various parameters of the generative model to generate different styles and forms of abstract art. Some AI art generators also allow the artist to specify a specific style or theme, such as color scheme, texture, or pattern.

The resulting abstract art created by AI art generators can be quite varied, ranging from colorful, chaotic images to more structured, geometric patterns. The AI-generated abstract art often has a unique, otherworldly quality that is both fascinating and beautiful.