diffsions

Diffusions refers to a class of algorithms used in machine learning, particularly for generative modeling. These models learn to generate data, such as images or audio, by reversing a process of gradual noise addition. The core idea is to start with a simple, noisy distribution and learn a series of transformations that gradually denoise it, eventually arriving at a sample from the desired data distribution.

The process can be conceptualized in two main phases: a forward diffusion process and a reverse diffusion process. The forward process systematically adds Gaussian noise to the data over many discrete time steps, transforming the original data into pure noise. The reverse process, which is learned by the model, aims to undo this noise addition, starting from pure noise and progressively removing it to reconstruct a realistic data sample.

Deep neural networks, often U-Net architectures, are typically employed to learn the reverse diffusion process. At each step of the reverse process, the network predicts the noise that was added in the corresponding forward step, allowing for its subtraction to refine the sample. This iterative denoising approach has proven highly effective for generating high-quality and diverse data.

Diffusions models have gained significant traction due to their ability to produce state-of-the-art results in image generation, text-to-image synthesis, and other creative applications. They offer advantages in terms of sample quality and diversity compared to some earlier generative models. Research continues to explore their applications and improve their efficiency.