8 papers
VASR: Variance-Aware Systematic Resampling for Reward-Guided Diffusion
Shivanshu Shekhar, Sagnik Mukherjee, Jia Yi Zhang +1
Sequential Monte Carlo (SMC) samplers for reward-guided diffusion models often suffer from rapid lineage collapse: a few high-reward particles dominate the population within a hand…
On the -Free Inference Complexity of Absorbing Discrete Diffusion
Xunpeng Huang, Yingyu Lin, Nishant Jain +4
Absorbing discrete diffusion has emerged as a dominant framework for discrete data generation. However, a significant disparity remains between its empirical success and theoretica…
A Sharp KL-Convergence Analysis for Diffusion Models under Minimal Assumptions
Nishant Jain, Tong Zhang
Diffusion-based generative models have emerged as highly effective methods for synthesizing high-quality samples. Recent works have focused on analyzing the convergence of their ge…
Almost Linear Convergence under Minimal Score Assumptions: Quantized Transition Diffusion
Xunpeng Huang, Yingyu Lin, Nikki Lijing Kuang +4
Continuous diffusion models have demonstrated remarkable performance in data generation across various domains, yet their efficiency remains constrained by two critical limitations…
Multi-Step Consistency Models: Fast Generation with Theoretical Guarantees
Nishant Jain, Xunpeng Huang, Yian Ma +1
Consistency models have recently emerged as a compelling alternative to traditional SDE-based diffusion models. They offer a significant acceleration in generation by producing hig…
Capturing Conditional Dependence via Auto-regressive Diffusion Models
Xunpeng Huang, Yujin Han, Difan Zou +2
Diffusion models have demonstrated appealing performance in both image and video generation. However, many works discover that they struggle to capture important, high-level relati…