diffusionmodels

Diffusion models are a class of generative models in machine learning that have gained significant attention for their ability to produce high-quality synthetic data, particularly images. They operate by gradually adding noise to data over a series of steps, effectively destroying the original information. Then, in a reverse process, the model learns to denoise this corrupted data, progressively reconstructing the original structure. This denoising process is trained using a neural network, typically a U-Net architecture, to predict the noise added at each step.

The core idea behind diffusion models is inspired by non-equilibrium thermodynamics. They can be understood as a forward diffusion process that transforms a data distribution into a simple prior distribution, such as a Gaussian. The generative process then involves learning the reverse of this diffusion process, which allows sampling from the prior and transforming it back into the data distribution. This iterative refinement allows for the generation of increasingly realistic samples.

Key advantages of diffusion models include their stable training and their capacity to generate diverse and high-fidelity samples, often surpassing other generative models like Generative Adversarial Networks (GANs) in certain tasks. However, diffusion models can be computationally expensive during sampling due to the many steps required for the reverse diffusion process. Research is ongoing to improve their efficiency and explore their applications beyond image generation, including audio synthesis, video generation, and even drug discovery.