AI image generators take a written description, called a prompt, and produce a brand new image that matches it, without copying any single existing picture directly. The results have improved dramatically over the past few years, moving from blurry, distorted shapes to highly detailed, coherent images.
The Basic Idea Behind Generation
Many popular image generators use a technique called diffusion. Training starts by taking real images and gradually adding random visual noise until the image is unrecognisable, while the model learns to reverse this process step by step. Once trained, the model can start from pure random noise guided by a text prompt and gradually remove noise in a way that steers the image toward matching the description, effectively 'sculpting' a picture out of static.
Writing Better Prompts
- Be specific about style, such as 'watercolour painting' or 'realistic photograph'.
- Mention lighting, mood, and composition rather than just the subject.
- Iterate: refine your prompt based on what the first result gets wrong.
AI image generators raise real questions around copyright and the use of artists' work in training data, which is an active area of ongoing legal and ethical discussion. Understanding both the creative potential and these open questions is useful before relying on the technology for commercial projects.