2024/11/26 by Indranil Halder, Halder, Indranil
Computer Science · Physics and Astronomy · Social Sciences · #Cellular Automata and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Language and cultural evolution #Machine Learning (cs.LG) #Opinion Dynamics and Social Influence
paper · pdf · doi:10.48550/arxiv.2411.17807
openalex publication_date 2024/11/26 · openalex created_date 2024/12/05 · openalex updated_date 2026/07/28
We develop an analytically tractable single-step diffusion model based on a linear denoiser and present an explicit formula for the Kullback-Leibler divergence between the generated and sampling distribution, taken to be isotropic Gaussian, showing the effect of finite diffusion time and noise scale. Our study further reveals that the monotonic fall phase of Kullback-Leibler divergence begins when the training dataset size reaches the dimension of the data points. Finally, for large-scale practical diffusion models, we explain why a higher number of diffusion steps enhances production quality based on the theoretical arguments presented before.