Using multiple periodic terminal distributions instead of one generic Gaussian helps diffusion models better understand and generate data with clear geometric structure, like points on manifolds or faces with consistent features.
This paper introduces PTL-Diffusion, a diffusion model that uses multiple structured terminal distributions instead of a single Gaussian. By embedding periodic structure directly into the forward noising process, the model better captures data that lies on low-dimensional manifolds, improving how well generated samples match the underlying geometric structure of the data.