The short version
An image generator does not search for a matching photo, and it does not cut and paste pieces of existing pictures. It builds a new image from scratch, usually starting with something that looks like random static and gradually shaping it into a picture that fits your written description. What it knows about how to do that comes from having studied a very large number of images paired with captions.
Learning from captioned images
Training begins with a huge collection of images, each with some text describing it, such as a caption or alt text. The system is shown many examples and slowly adjusts the numbers inside a neural network so it gets better at a particular task. This is the same general idea described in machine learning basics: a model improves by being corrected over and over on examples.
Two things have to be learned together:
- What things look like. Patterns such as the shape of a dog, the texture of wood, the way light falls on water or the style of a watercolor.
- How words relate to those patterns. Connecting the phrase “a red bicycle leaning on a brick wall” to the visual features it describes.
The quality and range of the training data matter a great deal. A system tends to be good at subjects it saw often and weak at rare ones, and its output reflects whatever biases the examples contained.
From words to numbers
Computers cannot work with a sentence directly, so the description is first converted into numbers. A separate component, trained on text and images, turns your wording into a list of numbers that captures its meaning in a form the image model can use. Similar descriptions end up with similar numbers. This is why “a puppy” and “a young dog” tend to give related results, while changing a single word like “red” to “blue” shifts the output in a specific way.
Starting from noise
Many popular systems use an approach commonly called diffusion. The idea is easiest to understand by thinking about training in reverse.
During training, the system takes a real image and gradually adds random noise to it, step by step, until only static remains. The network is taught to predict and remove the noise at each step. By practicing this on enormous numbers of images, it learns what a slightly cleaner version of any noisy picture ought to look like.
At creation time, the process runs the other way:
- Start with an image of pure random noise.
- Look at the number-form of the description.
- Remove a little noise in the direction that better matches the description.
- Repeat this many times, with the picture becoming clearer at each step.
- Convert the final result into a regular image you can view.
Because the starting noise is random, the same description can yield a different picture each time. Many tools let you fix a starting value, often called a seed, to get repeatable results.
Keeping the work manageable
Generating a large image pixel by pixel is expensive. A common shortcut is to do most of the work in a compressed internal representation, a smaller, more abstract version of the image, and then expand it to full size at the end. This is one reason ordinary computers with a suitable graphics card can run some image models, although training such a model from scratch requires far more computing power, drawing on the same kind of data center energy that other large AI systems use.
Steering the result
Several controls affect what you get.
- The wording of the description. Specific nouns, settings, lighting and style words tend to help. How phrasing changes results is the focus of AI prompts.
- A guidance setting. This controls how strictly the system follows the text versus giving itself freedom. Very high values can produce stiff or over-saturated images.
- Negative descriptions. Some tools let you list things to avoid.
- Starting images. You can begin from an existing picture or sketch rather than pure noise, so the result keeps its layout.
- Editing parts of an image. Tools can regenerate only a selected region, which is useful for removing an object or extending a canvas.
Why mistakes happen
Generated images often look convincing at first glance and strange on closer inspection.
- Hands and fingers have complicated, variable shapes, and the system learns an average impression rather than a hand’s anatomy.
- Text inside images is often garbled, because letters are precise shapes and the model learns them as visual texture rather than as spelling. Newer systems have improved at this, though problems remain.
- Counting and spatial relations such as “three apples to the left of a cup” can go wrong, since the model has no explicit list of objects to track.
- Physical consistency, such as reflections, shadows or the number of legs on a chair, can break down.
These errors follow from the method. The system produces what looks statistically plausible, not what has been checked against how the world works. This is a visual cousin of why chatbots sometimes make things up.
Related abilities
Image generation is distinct from image recognition, where software identifies what is in a photo. They use related techniques, but one produces pictures and the other labels them. Generators can also produce misleading images of real events or people, which is why methods for telling machine output from photographs are a growing concern, covered under AI detection. Questions about whether training on artists’ work is acceptable are discussed under AI copyright.
What to keep in mind
An image generator is a tool for making plausible pictures from descriptions. It is useful for concepts, illustrations and exploration, and it is unreliable for anything requiring exact details, accurate text or factual depiction of real events. Treat each result as a draft to inspect, and expect the capabilities and flaws to keep shifting as the technology develops.