An AI that makes a picture by starting from random noise and cleaning it up step by step.
A diffusion model doesn't draw a picture in one go. It starts from pure static, like a TV with no signal. Then, over and over, it asks: which bits of noise should I remove to get closer to the description? After many small steps, a clear image appears.
Training works the other way round. The model sees real photos with more and more noise mixed in, and practises guessing exactly which noise was added so it can take it away. Many well-known image generators since about 2022 use this idea, and it is used to make video too.
Think of it like this
Think of staring at clouds. Once you decide one looks like a dog, you start to see the ears and the tail. A diffusion model does something similar with random dots: guided by your words, it nudges them step by step until the dog is really there.
For example
Type "a cat on a windowsill on a snowy night" and the model works its way from static to that scene over a series of steps.
Records about this
Source-checked moments from AI history.
Sparked the discourse that video generation models are world simulators
Made a high-quality generative model that runs on consumer GPUs open-weight, sparking both the LoRA and ControlNet ecosystem and the copyright debate
Diffusion in latent space rather than pixel space sharply cut compute; the direct basis of Stable Diffusion