Text-to-image is a generative AI technique that creates a new image from a written description, called a prompt. A text encoder converts the prompt into numbers that capture its meaning, and an image generator, usually a diffusion model, creates a picture that matches those numbers. It is the most widely used form of AI image generation.
This page explains how the technique works and how to control it. For the wider field of image tools and business use, see What is AI Image Generation?
Key takeaways
- A text-to-image system has three main parts: a text encoder, an image generator and an image decoder.
- The prompt steers the image at every generation step.
- Settings such as guidance scale, steps, seed and aspect ratio change the result.
- Specific, well-structured prompts produce more predictable images.
- Text-to-image became mainstream in 2022.
Text-to-image explained in simple terms
Imagine describing a picture to an artist over the phone. The artist first works out what you mean (the text encoder), then sketches rough shapes and refines them repeatedly, checking against your description each time (the diffusion model), and finally paints the full-resolution picture (the decoder). Text-to-image does all three in seconds.
How text-to-image works
- Text encoding: the prompt is split into tokens and turned into vectors by a text encoder, often trained on pairs of images and captions so it understands how words relate to visuals.
- Start from noise: the system creates random noise, usually in a compressed latent space.
- Guided denoising: over many steps, the diffusion model removes noise, conditioned on the prompt vectors through cross-attention, which links words to regions of the image.
- Classifier-free guidance: at each step the model compares predictions with and without the prompt, and pushes the image towards the prompt.
- Decoding: an autoencoder’s decoder turns the final latent into a full-resolution image.
Key settings
| Setting | What it controls | Practical tip |
|---|---|---|
| Guidance scale (CFG) | How strictly the image follows the prompt | Too high can look harsh or over-saturated |
| Steps | Number of denoising steps | More steps can add detail, with diminishing returns |
| Seed | The starting noise | Reuse a seed to reproduce or slightly vary an image |
| Aspect ratio / size | Image shape and resolution | Match the final use, such as a banner or square post |
| Negative prompt | What to avoid | “Blurry, extra fingers, watermark” |
| Sampler | The algorithm used for denoising | Some are faster, others more detailed |
A worked example: writing a better prompt
A weak prompt: “a cafe”.
A stronger prompt: “Interior of a small South Indian filter-coffee café in Pune, morning sunlight through tall windows, brass tumblers on wooden tables, warm tones, 35mm photography, shallow depth of field.”
The stronger prompt specifies subject, setting, details, lighting, mood and style. Each part gives the text encoder more signal, so the diffusion model has clearer guidance at every step. If the result is close but the tables are wrong, keep the same seed and change only the table details.
Prompt structure that works
- Subject: what is the main focus?
- Context: where, when, surrounding elements.
- Style: photo, illustration, 3D render, watercolour.
- Composition: close-up, wide shot, top-down.
- Lighting and colour: golden hour, studio lighting, pastel palette.
- Quality cues: detailed, high resolution, sharp focus.
Newer models understand natural, full-sentence descriptions well, so clear plain language often beats keyword lists.
Milestones
OpenAI’s DALL·E (2021) showed early text-to-image results. In 2022, DALL·E 2, Google’s Imagen, Midjourney and the open Stable Diffusion brought high-quality generation to the public. Since then, models have improved sharply at following complex prompts, rendering text and producing photorealistic images, and multimodal chat models can now generate and edit images within a conversation.
Limitations
- Long or complex prompts with many objects can be partly ignored.
- Spatial relations (“to the left of”), exact counts and text are common failure points.
- Results vary run to run unless the seed is fixed.
- Models reflect biases in their training images.
- Copyright and likeness rules apply to what you generate and publish.