A Comprehensive Review on Noise Control of Diffusion Model
This is an incremental review that synthesizes existing knowledge on noise schedules for researchers in generative AI.
The paper reviews noise schedules in diffusion models, which are crucial for controlling noise injection to influence sampling and training quality, by examining various schedules and their features and performance.
Diffusion models have recently emerged as powerful generative frameworks for producing high-quality images. A pivotal component of these models is the noise schedule, which governs the rate of noise injection during the diffusion process. Since the noise schedule substantially influences sampling quality and training quality, understanding its design and implications is crucial. In this discussion, various noise schedules are examined, and their distinguishing features and performance characteristics are highlighted.