Past talk · AI/ML Seminar Series
Conjugate Integrators for Fast Sampling in Diffusion Models
PhD Student, Department of Computer Science, University of California, Irvine
- Date & time
- Monday, October 14, 2024 · 1:00 PM
- Location
- Donald Bren Hall 4011
Abstract
Diffusion models exhibit excellent sample quality across multiple-generation tasks. However, their inference process is iterative and often requires hundreds of function evaluations. Moreover, it is unclear if existing methods for accelerating diffusion model sampling can generalize well across different types of diffusion processes. In the first part of my talk, I will introduce Conjugate Integrators, which project unconditional diffusion dynamics to an alternate space that is more amenable to faster sampling. In the second part of my talk, I will extend the idea of Conjugate Integrators from unconditional sampling to conditional diffusion sampling in the context of solving inverse problems. Empirically, on challenging inverse problems like 4x super-resolution on the ImageNet-256 dataset, conditional Conjugate Integrators can generate high-quality samples in as few as 5 conditional sampling steps.
About the speaker
Kushagra is a third-year PhD student in Computer Science at UCI, advised by Prof. Stephan Mandt. Previously, he completed his bachelor's and master's degrees in Computer Science from the Indian Institute of Technology. He is broadly interested in the efficient design and inference in deep generative models with a current focus on iterative refinement models like Diffusion Models and Stochastic Interpolants.