How the f***k does Generative AI create images???

Generative AI has become increasingly popular in recent years, and it’s not hard to see why. One of the most impressive capabilities of generative AI is the ability to create images, from realistic photographs to abstract art. But how exactly does generative AI create images?

At its core, generative AI is based on a neural network architecture called a Generative Adversarial Network (GAN). This network consists of two parts: a generator and a discriminator. The generator is responsible for creating new images, while the discriminator evaluates the generated images and tries to determine if they are real or fake.

To create an image, the generator starts with a random vector, often called a latent vector, that represents a point in a high-dimensional space. The generator then uses a series of convolutional and deconvolutional layers to transform this latent vector into an image that is as realistic as possible. The output of the generator is then fed into the discriminator, along with real images from a training dataset.

The discriminator’s job is to determine whether each image it receives is real or fake. It does this by outputting a value between 0 and 1, where 0 represents a fake image and 1 represents a real image. The generator is trained to create images that are as realistic as possible, so it tries to fool the discriminator by creating images that are difficult to distinguish from real images.

This process of creating and evaluating images continues iteratively, with the generator getting better at creating realistic images and the discriminator getting better at identifying fake images. Over time, the generator learns to create images that are indistinguishable from real images, and the discriminator becomes less effective at identifying fake images.

One of the most impressive examples of generative AI creating images is StyleGAN, developed by Nvidia. StyleGAN uses a similar GAN architecture to create realistic human faces. By training on a dataset of real faces, StyleGAN is able to create new faces that are incredibly realistic, with details such as wrinkles, pores, and hair that look like they could be real.

Another example is DeepDream, a project by Google that uses a modified GAN architecture to create surreal, dreamlike images. DeepDream starts with an existing image and then uses the generator to enhance certain features of the image, such as edges or patterns. The result is a trippy, otherworldly image that looks like something out of a Salvador Dali painting.

In conclusion, generative AI creates images by using a GAN architecture, which consists of a generator and a discriminator. The generator creates new images from a random vector, and the discriminator evaluates the realism of these images. Through iterative training, the generator learns to create images that are indistinguishable from real images. Examples such as StyleGAN and DeepDream demonstrate the impressive capabilities of generative AI in creating realistic and surreal images.