Noise Scheduling

Noise scheduling is a crucial aspect of diffusion models, which generate data by reversing a diffusion process that gradually adds noise to an image or signal. Current research focuses on optimizing noise schedules for improved training efficiency and generation quality, exploring various schedules like cosine, polynomial, and those derived from the Ornstein-Uhlenbeck process, often within the context of specific model architectures. These improvements aim to enhance the fidelity and plausibility of generated outputs, particularly in challenging scenarios like low signal-to-noise ratios and high-resolution image generation. The resulting advancements have significant implications for various applications, including speech enhancement, image generation, and video generation.