How do AI image generators work?

You've probably seen one in your feed. A cat in a spacesuit, lit like a movie poster, with fur so sharp you could count the hairs. Nobody painted it, and nobody took a photo. Someone typed a sentence into an artificial intelligence (AI) image generator. And most tools like that begin in a surprising place: a square of pure static, like an old television with no signal.

To get there, the model learns backward. During training it sees a huge number of real images, many with captions. Noise is added to each picture, a little at a time, until it's nothing but static. The model's job is to undo one small step of that: look at a noisy image and work out which noise to take away. After enough practice, it gets very good at cleaning up. Instead of keeping a scrapbook of those pictures to cut and paste from, it learns what pictures in general tend to look like.¹

Then you type a description. Your words are turned into numbers the model can use, and at every cleaning step those numbers nudge the result toward something that matches. Without them, the model would still pull a picture out of the static, but it could be anything. Your sentence works like a compass. "A cat in a spacesuit" pulls each step a little closer to fur, a helmet, and stars.¹

The picture doesn't appear all at once. It starts as random dots, then rough blobs of color, then shapes, then detail. Stable Diffusion, a popular open model, runs fifty cleaning steps by default in Hugging Face's widely used software, and newer methods can get a good result in fewer. Because the starting static is random, the same words can give you a different picture each time. That's why it's common to make several and pick the best.²

Light and texture are patterns these models have seen over and over. Golden-hour light. A watercolor wash. The grain of an old film photo. So asking for a style usually works well. The model doesn't need to understand what a sunset is to get its colors right. It has seen what tends to go with the word "sunset."

Exact details are harder. For years, image tools were known for extra fingers, garbled lettering on signs, and getting "exactly five apples" wrong. A pattern of fingers is easy to copy, but knowing there should be five is a different kind of problem. Newer tools have improved a lot. In March 2025, OpenAI said its new image model was much better at putting text into pictures, while small or dense text could still come out wrong. Slips still happen, so it pays to look closely.³

When you make one yourself, be specific: the subject, the setting, the light, and the style. Before you use a picture, zoom in. Check the hands, any writing, and anything that should be countable. What's the first thing you'll zoom in on the next time an amazing picture scrolls past?