Stable diffusion is a concept used in stochastic processes, which are mathematical models used to represent systems or phenomena that evolve over time in a way that involves randomness. In the context of machine learning and generative models, stable diffusion refers to a process where random noise is added to data in a controlled and consistent manner.

The term “stable” in stable diffusion indicates that the process is controlled and does not lead to extreme or chaotic outcomes. The noise is added in such a way that the overall system or data remains stable and does not explode or collapse.

In the context of generative models, a diffusion process can be used to transform simple random noise into complex patterns that resemble real data. The model learns to control the diffusion process in such a way that it can generate a wide variety of data samples, all of which are consistent with the training data.

Stable diffusion is a key component of some types of generative models, such as Diffusion Probabilistic Models and certain types of Generative Adversarial Networks (GANs). It allows these models to generate diverse and high-quality samples, making them powerful tools for tasks like image synthesis, text generation, and more.