Diffusion Models for Generative Modeling

Diffusion Models Overview

Diffusion models are generative models that learn to create new data by reversing a gradual noise-adding process. In the forward diffusion process, data is progressively corrupted with Gaussian noise until it resembles a simple distribution, typically a standard normal distribution. A neural network then learns the reverse process, removing noise step by step to reconstruct data similar to the training examples. This framework can be expressed through Markov chains, denoising diffusion probabilistic models, score-based models, or stochastic differential equations. Denoising Diffusion Probabilistic Models (DDPMs) train a network to predict the noise present in a partially corrupted sample. During generation, the model begins with random noise and repeatedly estimates and removes noise until a data sample is produced. Diffusion models are widely used in image generation, denoising, inpainting, super-resolution, and video generation, often with text encoders and cross-attention for text-conditioned outputs. They are also applied to text generation, summarization, sound synthesis, and reinforcement learning.

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Forward and Reverse Diffusion

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