A diffusion model is a type of model that learns to generate new data by simulating a random process in reverse.In AI, diffusion models work by first understanding how a certain type of data, like images, could have been generated as the result of a random process. It starts with some random data and gradually refines it until it looks like the desired output. This learning process happens in reverse when the model generates new data. Starting with a real piece of data, like a photo, it applies the random process to slowly transform that data back into randomness. This series of transformations is recorded and then played in reverse to generate new, similar data. This kind of model can be very effective for tasks like creating realistic images or enhancing the quality of existing ones.
To encapsulate, a diffusion model in AI is a tool that learns to create new data by reverse-engineering a random process, resulting in realistic and high-quality outputs.