How do AI image generators work?

Last updated: July 2026

You type "a fox reading a newspaper in a Parisian café, oil painting" and seconds later there it is, an image that never existed before. No artist drew it. No photo was taken. The method behind this is genuinely surprising: the AI builds the picture by removing noise from static.

Tools like DALL-E, Midjourney, and Stable Diffusion have made image generation feel routine, but the way they work is one of the more counterintuitive ideas in modern AI. The short version: they start with a screen of random visual noise and gradually clean it up into a picture, steered the whole way by the meaning of your words. Let's unpack how that's even possible.

The core trick: learning to remove noise

Almost all modern image generators are built on a technique called diffusion.¹ The idea is easier to understand backwards, so start with the training process.

Take a clear photograph. Add a little random speckled noise to it. Add a bit more, then more, over many steps, until the image is completely buried under static and you can't tell what it originally was. That part is easy; anyone can add noise to a picture. The clever move is to train an AI to reverse it: to look at a noisy image and predict what the slightly cleaner version looked like.²

Show a model millions of images at every stage of being noised up, and it gets very good at this prediction. Once trained, you can hand it a screen of pure random noise and ask it to "denoise" that, and step by step it will conjure a clean, detailed image that was never photographed at all.¹

How your words steer the picture

Denoising random static would just give you random images. What makes the model produce the specific thing you asked for is that the cleanup is guided by your prompt at every step.

First, the text has to be turned into something the model can use. Your sentence is converted into a numerical representation of its meaning, often using a system called CLIP, which was trained to connect images and text.³ That representation acts like a target. As the model removes noise, it's continually nudged toward an image that matches the meaning of your words, so "Parisian café" pulls the emerging picture toward café-shaped things and warm light rather than, say, a snowy mountain.⁴

There's a nice detail from the research here. Studies of how these models work suggest the overall shape of the image is largely decided in the early steps of denoising, with the fine details filled in later, a "first shape, then details" pattern.⁵ The prompt does most of its steering early, setting up the composition, and the model handles the texture and detail afterwards.

Working in "latent space" to save effort

One more idea makes this practical rather than impossibly slow. Doing all this denoising directly on millions of individual pixels would take enormous computing power. So systems like Stable Diffusion do the work in a compressed form called latent space.⁶

You can think of latent space as a kind of shorthand for images: a much smaller representation that still captures the important features, like a rough sketch that holds the essence without every detail. The model does its noisy-to-clean work in this efficient shorthand, then expands the result back into a full-resolution picture at the end. It's the difference between planning a painting as a quick thumbnail versus agonising over every brushstroke from the start.

Why AI images sometimes look "off"

Understanding the method explains the classic AI image glitches, the six-fingered hands, the melted text, the jewellery that merges into skin. The model has learned what things tend to look like from its training images, but it has no real understanding of anatomy, physics, or how objects work. It knows hands are usually pinkish shapes with several finger-like protrusions near the end of an arm, but not the hard rule that there should be exactly five.

So it generates what's visually plausible rather than what's physically correct, and when plausibility and reality drift apart, you get the tell-tale weirdness. Newer models have largely fixed the famous hand problem by training on more and better data, but the underlying limitation, plausibility without understanding, is why subtle errors persist. If you're interested in spotting AI images, we've written a practical guide to telling whether an image is AI-generated.

The short version

An AI image generator makes a picture by starting from random noise and removing it step by step, guided at every step by a numerical version of your prompt, until a clear image emerges that matches your words. It learned to do this by watching millions of images get turned into noise and teaching itself to run the process backwards. To make it fast, it works in a compressed shorthand of the image rather than on raw pixels.

It's a strange way to make art, sculpting a picture out of static, but it's become one of the defining technologies of the era. And it's the same core idea, extended across time, that lets AI generate video too, which is a story we tell in how AI generates video.

Sources

1. Onyx, "How Text-to-Image AI Models Work" — onyxgs.com
2. "From Creation to Curriculum: generative AI in Arts Universities," arXiv:2412.16531 (2024) — arxiv.org
3. "Prompting the E-Brushes: Users as Authors in Generative AI," arXiv:2406.11844 (2024) — arxiv.org
4. Onyx, op. cit. (on prompt guidance during denoising).
5. "Towards Understanding the Working Mechanism of Text-to-Image Diffusion Model," arXiv:2405.15330 (2024) — arxiv.org
6. "Prompting the E-Brushes," op. cit. (on latent space in Stable Diffusion).