AI art generators use machine learning algorithms to create art. These algorithms are trained on large datasets of images and videos to learn the underlying patterns and styles of different types of art. The algorithm then uses this knowledge to generate new, unique images and videos that mimic the style of the input data.
There are several types of AI art generators, each with its approach to creating art. One popular type is the style transfer algorithm, which takes an input image and applies the style of another image to it.
For example, a user could photograph a landscape and apply the style of Van Gogh's "Starry Night" to create a new image that combines the two.
Another type of AI art generator is the generative adversarial network (GAN). GANs consist of two neural networks: a generator and a discriminator. The generator creates images or videos while the discriminator evaluates how realistic they are.
The two networks are trained together, with the generator trying to create more realistic images and the discriminator trying to identify which images are real and which are generated. Over time, the generator becomes better at creating realistic images, and the discriminator becomes better at identifying them.
AI art generators also use other techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to create new and unique artworks. These algorithms are highly complex and require a great deal of computational power to operate, but they have led to some truly impressive results in the world of AI-generated art.
TLDR; AI art generators use machine learning algorithms to create art by learning the underlying patterns and styles of different types of art. They come in many different forms, each with its own approach to creating new and unique artworks.
While these algorithms are still relatively new, they have already shown incredible potential for pushing the boundaries of what is possible in art and technology.