AI generates images using advanced algorithms, typically based on models like Generative Adversarial Networks (GANs) or diffusion models. In GANs, two neural networks—a generator and a discriminator—work together. The generator creates images from random data, while the discriminator evaluates them against real images, pushing the generator to improve until the output is convincingly realistic. Diffusion models, on the other hand, start with random noise and gradually refine it into a coherent image by learning patterns from vast datasets.
The process begins with training on millions of images, where the AI learns features like shapes, colors, and textures. When you give it a prompt—like “a cat in a spacesuit”—it translates the text into a mathematical representation and generates an image that matches. Tools like DALL·E, Midjourney, or Stable Diffusion excel at this, producing everything from photorealistic scenes to abstract art.
