290 citations · 981 across the 34 of their papers we have counts for
10 papers · 1 filter
Why Are Conditional Generative Models Better Than Unconditional Ones?
Fan Bao, Chongxuan Li, Jiacheng Sun +1
Extensive empirical evidence demonstrates that conditional generative models are easier to train and perform better than unconditional ones by exploiting the labels of data. So do…
DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models
Cheng Lu, Yuhao Zhou, Fan Bao +3
Diffusion probabilistic models (DPMs) have achieved impressive success in high-resolution image synthesis, especially in recent large-scale text-to-image generation applications. A…
Equivariant Energy-Guided SDE for Inverse Molecular Design
Fan Bao, Min Zhao, Zhongkai Hao +3
Inverse molecular design is critical in material science and drug discovery, where the generated molecules should satisfy certain desirable properties. In this paper, we propose eq…
All are Worth Words: A ViT Backbone for Diffusion Models
Fan Bao, Shen Nie, Kaiwen Xue +4
Vision transformers (ViT) have shown promise in various vision tasks while the U-Net based on a convolutional neural network (CNN) remains dominant in diffusion models. We design a…
Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models
Yong Zhong, Hongtao Liu, Xiaodong Liu +3
Deep generative models (DGMs) are data-eager because learning a complex model on limited data suffers from a large variance and easily overfits. Inspired by the classical perspecti…
EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations
Min Zhao, Fan Bao, Chongxuan Li +1
Score-based diffusion models (SBDMs) have achieved the SOTA FID results in unpaired image-to-image translation (I2I). However, we notice that existing methods totally ignore the tr…
Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score Matching
Cheng Lu, Kaiwen Zheng, Fan Bao +3
Score-based generative models have excellent performance in terms of generation quality and likelihood. They model the data distribution by matching a parameterized score network w…
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models
Fan Bao, Chongxuan Li, Jiacheng Sun +2
Diffusion probabilistic models (DPMs) are a class of powerful deep generative models (DGMs). Despite their success, the iterative generation process over the full timesteps is much…
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
Cheng Lu, Yuhao Zhou, Fan Bao +3
Diffusion probabilistic models (DPMs) are emerging powerful generative models. Despite their high-quality generation performance, DPMs still suffer from their slow sampling as they…
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models
Fan Bao, Chongxuan Li, Jun Zhu +1
Diffusion probabilistic models (DPMs) represent a class of powerful generative models. Despite their success, the inference of DPMs is expensive since it generally needs to iterate…