diffuusionmallinnus

Diffusion models are a class of generative models that have gained significant attention in recent years for their ability to produce high-quality synthetic data, particularly images. The core idea behind diffusion models is to progressively add noise to data until it becomes pure noise, and then learn to reverse this process, gradually denoising the data to generate new samples. This process can be understood as a forward diffusion process, which is typically a fixed Markov chain, and a reverse diffusion process, which is learned by a neural network.

The forward diffusion process gradually adds Gaussian noise to an input data sample over a series of time steps. At each step, a small amount of noise is added, transforming the original data into a more noisy version. This continues until, after many steps, the data is indistinguishable from pure noise. The reverse diffusion process aims to learn the inverse of this noise addition. A neural network, often a U-Net architecture, is trained to predict the noise that was added at each step of the forward process, or equivalently, to predict the less noisy version of the data. By iteratively applying this learned denoising step, starting from random noise, the model can generate new data samples that resemble the training data.

The training objective of diffusion models typically involves minimizing the difference between the predicted noise and the actual noise added during the forward process. Once trained, the model can be used for sampling by starting with random noise and repeatedly applying the learned reverse diffusion steps. The quality of generated samples is often very high, surpassing previous generative models like Generative Adversarial Networks (GANs) in certain benchmarks. Diffusion models have found applications in various fields, including image generation, audio synthesis, text-to-image generation, and even in scientific domains like molecular design.