58 citations · 93 across the 10 of their papers we have counts for
6 papers · 1 filter
Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling
Kaiwen Zheng, Yongxin Chen, Hanzi Mao +3
Masked diffusion models (MDMs) have emerged as a popular research topic for generative modeling of discrete data, thanks to their superior performance over other discrete diffusion…
Improved Order Analysis and Design of Exponential Integrator for Diffusion Models Sampling
Qinsheng Zhang, Jiaming Song, Yongxin Chen
Efficient differential equation solvers have significantly reduced the sampling time of diffusion models (DMs) while retaining high sampling quality. Among these solvers, exponenti…
gDDIM: Generalized denoising diffusion implicit models
Qinsheng Zhang, Molei Tao, Yongxin Chen
Our goal is to extend the denoising diffusion implicit model (DDIM) to general diffusion models~(DMs) besides isotropic diffusions. Instead of constructing a non-Markov noising pro…
Fast Sampling of Diffusion Models with Exponential Integrator
Qinsheng Zhang, Yongxin Chen
The past few years have witnessed the great success of Diffusion models~(DMs) in generating high-fidelity samples in generative modeling tasks. A major limitation of the DM is its…
Path Integral Sampler: a stochastic control approach for sampling
Qinsheng Zhang, Yongxin Chen
We present Path Integral Sampler~(PIS), a novel algorithm to draw samples from unnormalized probability density functions. The PIS is built on the Schrödinger bridge problem which…
Diffusion Normalizing Flow
Qinsheng Zhang, Yongxin Chen
We present a novel generative modeling method called diffusion normalizing flow based on stochastic differential equations (SDEs). The algorithm consists of two neural SDEs: a forw…