Diffusion Noise

Diffusion noise, the random input used in diffusion models for generating data, is a critical factor influencing the quality and efficiency of these generative models. Current research focuses on optimizing noise selection and scheduling, including methods to improve training speed and the fidelity of generated outputs, and exploring how noise characteristics can be leveraged as priors for tasks like motion generation and image manipulation. These advancements are significant because they directly impact the performance and capabilities of diffusion models across diverse applications, from image synthesis and editing to reinforcement learning.