activity
20242026
collaborators

8 papers

cs.CV2026

Equilibrium Forcing: Adaptive Video Generation Without Noise Conditioning

Hansen Jin Lillemark, Alex Rojas, Zachary Novack +5

Standard autoregressive video generation algorithms based on Diffusion and Flow Matching rely on rigid training objectives and static sampling schedules, limiting inference procedu…

cs.LG2025

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…

stat.ML2025

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…

cs.LG2025

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…

cs.LG2025

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…

stat.ML2024

Faster Sampling via Stochastic Gradient Proximal Sampler

Xunpeng Huang, Difan Zou, Yi-An Ma +2

Stochastic gradients have been widely integrated into Langevin-based methods to improve their scalability and efficiency in solving large-scale sampling problems. However, the prox…

stat.ML2024

Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference

Xunpeng Huang, Difan Zou, Hanze Dong +3

To generate data from trained diffusion models, most inference algorithms, such as DDPM, DDIM, and other variants, rely on discretizing the reverse SDEs or their equivalent ODEs. I…

stat.ML2024

Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo

Xunpeng Huang, Difan Zou, Hanze Dong +2

To sample from a general target distribution beyond the isoperimetric condition, Huang et al. (2023) proposed to perform sampling through reverse diffusion, g…