Text-to-image

You type "a watercolour of a seaside café at sunrise" and the tool returns one or more images that match. Many text-to-image tools are built on diffusion models, and many chat assistants now include image generation alongside text. Some tools also let you edit a photo you upload by describing the change.

A generated image is not a photograph of anything real. It may look like a photo, but no scene, person or product was captured. That distinction matters for businesses, because showing a generated image as if it were your real premises, team or work misleads customers, even when nobody intended to deceive. Labelling AI-made images where they could be mistaken for real ones is good practice, and some platforms, advertising policies and laws require it in certain cases.

How it works

The model is trained on a very large collection of images paired with text descriptions, learning how words relate to shapes, colours, styles and compositions. When you enter a prompt, a text encoder turns it into numbers that capture its meaning. The image generator then builds a picture guided by those numbers, typically by starting from random noise and refining it over many steps until it matches the description.

Because the process starts from randomness, the same prompt gives a different image each time. Most tools let you generate several variations, set the shape of the image, edit a part of it, or provide a reference image to guide style or layout. Results depend heavily on the prompt: subject, setting, style, lighting and viewpoint all change the outcome.

Text-to-image models still have typical weak points. Hands, legible text inside the image, exact counts of objects and precise brand details are improving but remain unreliable in many tools. The models also cannot know what your actual shop, product or staff look like unless you supply reference images, and even then they produce an approximation.

Example

A café owner needs artwork for a seasonal menu and a set of social posts. She asks a text-to-image tool for "flat illustration of autumn leaves and a steaming cup, warm orange and red, plain background, space for text on the left". After a dozen attempts and small changes to the prompt, she has a background that suits the menu design, and she adds the real menu text herself in her design software rather than relying on the AI to spell it.

For the posts showing her actual cakes, she uses her own photos instead. A generated cake would look appealing but would not be the cake customers receive, and a customer who notices the difference is more likely to distrust everything else on the page. She keeps the illustration prompts in a note so the next season's artwork stays in the same style, which is otherwise hard to repeat with a generative AI tool.

Why it matters for your business

Text-to-image tools can replace stock imagery and quick illustrations at low cost, which helps small teams produce backgrounds, concept sketches and decorative artwork. The risks are misleading customers with images of things that do not exist, generic results that look like everyone else's, and uncertain legal ground on copyright, which varies by country and by each tool's terms. Real photos of your real work remain more persuasive for anything a customer will buy.

Common mistakes

• Using generated images to show products, premises, staff or results that are not real.
• Relying on the tool to render text, logos or prices correctly inside the image.
• Prompting with a famous artist's name or a competitor's brand, which raises legal and reputational risk.
• Not checking the tool's terms for commercial use before using images in paid advertising.

Questions people ask

Can I use AI-generated images commercially?

Usually, depending on the tool's terms and your local law. Many providers allow commercial use of images you generate, but copyright protection for purely AI-made images is limited or unclear in several countries. Check the terms of the tool you use and avoid prompts that copy protected work.

Why does AI get text and hands wrong in images?

Because the model learns visual patterns rather than rules. Letters and fingers are small, highly variable details where a slight error is very noticeable, and training images often show them partly hidden. Some tools handle both better than others, but checking every image closely is still necessary.

Is text-to-image the same as a diffusion model?

No. Text-to-image describes the task: making a picture from a written prompt. A diffusion model is one technical method for doing it, and a very common one. Other methods exist, and diffusion models are also used for video, audio and image editing.