Diffusion Models

Diffusion models are a class of generative AI models used in machine learning to create high-quality synthetic data – such as images, audio or text – by reversing a gradual noise-adding process. Inspired by physical diffusion processes, these models start with random noise and iteratively refine it to produce structured outputs. During training, the model learns how data degrades over time, and during generation, it learns to reverse that process. Diffusion models have gained prominence for their ability to generate photorealistic images and complex content with fine-grained control, as seen in tools like DALL·E 2 and Stable Diffusion. They are known for their stability, flexibility and effectiveness in high-fidelity generative artificial intelligence applications.