Diffusion Model

A diffusion model, in the context of generative AI, refers to a class of generative models that operate by iteratively applying diffusion processes to generate samples. These models are designed to model complex probability distributions and generate high-quality synthetic data samples.

Diffusion models are typically based on the concept of the Langevin diffusion process, which is a continuous-time stochastic process. The Langevin diffusion process describes the evolution of a particle's position over time based on a combination of a deterministic gradient term and a stochastic noise term. Diffusion models leverage this concept to generate samples by iteratively applying a series of diffusion steps.

Each step of the diffusion process involves two key operations: (1) a diffusion step that adds noise to the intermediate output, and (2) an inference step that estimates the original noise vector from the intermediate output. The diffusion step helps introduce randomness and diversify the samples, while the inference step assists in recovering the original noise distribution.

The diffusion process starts with a random noise vector or a given initial sample, often referred to as a "seed" or "base" sample. At each diffusion step, the model updates the sample by adding a controlled amount of noise. This noise is typically Gaussian, and its magnitude is adjusted during the diffusion steps. By carefully controlling the noise level, the model gradually transforms the initial sample towards a sample from the target distribution.

The diffusion process is reversible, meaning that it is possible to recover the initial sample from the final sample by applying the reverse diffusion steps. This property allows for both sample generation and likelihood estimation. To estimate the likelihood of a given sample, one can compute the probability density of the sample at each diffusion step and multiply them together.

Diffusion models have gained attention in the field of AI due to their unique properties and potential applications. Here are some perspectives on the application of diffusion models in AI:

1. Image Synthesis: Diffusion models have been applied to generate high-quality and realistic images. By iteratively diffusing noise, these models capture complex image distributions and produce visually appealing samples. Diffusion models can generate diverse images, allowing for creative exploration and synthesis of novel visual content.Image Editing and Manipulation: Diffusion models enable powerful image editing
2. Super-Resolution: Diffusion models have been applied to enhance the resolution of low-resolution images. By conditioning on a low-resolution image and iteratively diffusing and refining it, diffusion models can generate high-resolution versions that capture fine details and textures. Super-resolution using diffusion models has demonstrated superior performance compared to traditional interpolation methods.