Text-to-image AI generates a picture from a written description. You type “a lighthouse on a rocky coast during a storm, oil painting” and the model creates a new image that matches. Tools like ChatGPT, Gemini, Midjourney, Adobe Firefly and Stable Diffusion all work this way, trained on huge collections of images paired with captions.
How text-to-image models work
Training teaches the model the link between words and visual features: what “storm”, “lighthouse” and “oil painting” look like. When you enter a prompt, a text encoder converts it into numbers that capture its meaning. The image model then generates pixels that match those numbers.
Diffusion vs autoregressive models
Most image generators, including Stable Diffusion, Flux, Midjourney and Firefly, use diffusion. They start from random noise and remove it step by step, guided by the prompt, until an image appears. Some newer generators, including OpenAI’s image models inside ChatGPT, generate images more like a language model writes text, building them piece by piece with the full understanding of a chat model. That is one reason ChatGPT follows long, detailed instructions and renders text so well.
The main text-to-image tools
| Tool | Approach | Access |
|---|---|---|
| ChatGPT | OpenAI GPT image models, built into chat | Free tier and paid plans |
| Gemini | Google image models | Free tier and Google AI plans |
| Midjourney | Diffusion, artistic focus | Paid |
| Adobe Firefly | Diffusion, licensed training data | Free credits and Adobe plans |
| Stable Diffusion, Flux | Open-weight diffusion models | Free to run locally |
For a ranked comparison with prices, see the best AI image generators.
How to get better results
Be concrete about subject, setting, style, lighting and composition. Put the most important element first. Use art and photo vocabulary (“watercolor”, “macro shot”, “isometric”). Generate several variations and refine the best one instead of rewriting from scratch. Our AI image prompts guide has a full formula and examples, and you can reverse-engineer a style with image to prompt AI.
What text-to-image AI still struggles with
Recent models handle hands, faces and short text far better than in 2023, but problems remain: exact counts of many objects, complex spatial relationships, consistent characters across many images, long passages of text, and accurate diagrams or maps. Generated images can also reflect biases in training data, such as default assumptions about gender or ethnicity for professions.
Copyright and responsible use
The legal picture is still developing. Several lawsuits by artists and image libraries challenge how training data was collected. In the US, the Copyright Office has said that material generated entirely by AI, without meaningful human authorship, cannot be registered for copyright. Commercial use rights depend on each tool’s terms. Avoid prompts that copy a living artist’s specific work, real people’s likeness or trademarks, and label AI images where your audience would expect to know.
From images to video
The same ideas extend to video: text-to-video models generate short clips from prompts. See the best AI video generators for tools that do this.
Frequently asked questions
How does text-to-image AI work?
The model is trained on millions of images paired with descriptions. When you type a prompt, it converts the words into a numerical representation and generates an image that matches it, usually by gradually removing noise from a random starting image.
Who owns images made with text-to-image AI?
It depends on the tool's terms and your country's law. Most services let you use outputs, often commercially on paid plans, but in the US purely AI-generated images without meaningful human authorship are not eligible for copyright registration.
Why does AI draw hands and text badly?
Older models struggled because hands and letters have precise structure that varies a lot in training images. Recent models have improved greatly, but errors still appear in complex scenes.