Diffusion model
Also called diffusion, stable diffusion, image generation model
A type of AI that generates images or video by starting from random noise and gradually refining it into a picture.
A diffusion model generates images, and increasingly video and audio, through a step-by-step denoising process. During training, the model sees real images with increasing amounts of random noise added, and learns to predict and remove that noise. To generate, it starts from pure noise and removes it step by step, guided by your text prompt, until a clear image emerges.
Diffusion models power many well-known image tools. Text guidance usually comes from a text encoder that turns your prompt into a representation the image model can follow, which is why concrete visual words such as subject, lighting, lens, composition and style work better than abstract ones. Settings you may see include steps (how many refinement passes), guidance scale (how strictly to follow the prompt) and seed (the starting noise, which makes results reproducible).
Some newer image systems are built into multimodal language models rather than being separate diffusion models, and handle text in images and complex instructions better. But diffusion remains the dominant approach for image and video generation.
Example
A prompt for "a red bicycle leaning against a blue door, morning light, 35mm photo" starts as static. Over 30 steps the shapes of a door and a bicycle appear, then colour, then fine details like spokes and wood grain.
Common mistake
Writing image prompts like chat messages: "Could you please make me a nice picture of a cosy cafe?" Diffusion models respond to visual descriptions, not politeness or abstract adjectives. Describe what the camera would see, and use a negative prompt for things to avoid.