Chongxuan Li's Homepage

Selected Publications Full Publications

Diffusion Language Models (LLaDA series)

Large Language Diffusion Models

Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, Chongxuan Li†

Preprint, 2025 [Paper] [Code] [Demo]
LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning

Zebin You, Shen Nie, Xiaolu Zhang, Jun Hu, Jun Zhou, Zhiwu Lu, Ji-Rong Wen, Chongxuan Li†

Preprint, 2025 [Paper] [Code] [Demo]
LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models

Fengqi Zhu, Rongzhen Wang, Shen Nie, Xiaolu Zhang, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, Chongxuan Li†

Preprint, 2025

3D world models

FlexWorld: Progressively Expanding 3D Scenes for Flexiable-View Synthesis

Luxi Chen, Zihan Zhou, Min Zhao, Yikai Wang†, Ge Zhang, Wenhao Huang, Hao Sun, Ji-Rong Wen, Chongxuan Li†

Preprint, 2025 [Paper] [Code] [Demo]
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation

Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li†, Hang Su, Jun Zhu†

Advances in Neural Information Processing Systems (NeurIPS), 2023 [Paper] [Code] [Demo]

Spotlight, Most Influential Papers in NeurIPS 2023 (11/3584)

Diffusion transformers

Scaling Diffusion Transformers Efficiently via µP

Chenyu Zheng, Xinyu Zhang,Rongzhen Wang, Wei Huang, Zhi Tian, Weilin Huang, Jun Zhu, Chongxuan Li†

Preprint, 2025 [Paper] [Code]
RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers

Min Zhao, Guande He, Yixiao Chen, Hongzhou Zhu, Chongxuan Li†, Jun Zhu†

International Conference on Machine Learning (ICML), 2025 [Paper] [Code] [Demo]
All are Worth Words: a ViT Backbone for Diffusion Models

Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li†, Hang Su, Jun Zhu†

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023 [Paper] [Code]

Fast sampling for diffusion models

Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models

Fan Bao, Chongxuan Li†, Jun Zhu†, Bo Zhang

International Conference on Learning Representations (ICLR), 2022 [Paper] [Code]

Oral, Outstanding Paper Award (7/3391)
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps

Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu

Advances in Neural Information Processing Systems (NeurIPS), 2022 [Paper] [Code]

Oral, Most Influential Papers in NeurIPS 2022 (10/2905)

Preprint

Large Language Diffusion Models

Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, Chongxuan Li†

[Paper] [Code] [Demo]
FlexWorld: Progressively Expanding 3D Scenes for Flexiable-View Synthesis

Luxi Chen, Zihan Zhou, Min Zhao, Yikai Wang†, Ge Zhang, Wenhao Huang, Hao Sun, Ji-Rong Wen, Chongxuan Li†

[Paper] [Code] [Demo]
Concat-ID: Towards Universal Identity-Preserving Video Synthesis

Yong Zhong, Zhuoyi Yang, Jiayan Teng, Xiaotao Gu, Chongxuan Li†

[Paper] [Code] [Demo]
Real-time Identity Defenses against Malicious Personalization of Diffusion Models

HanZhong Guo, Shen Nie, Chao Du, Tianyu Pang, Hao Sun†, Chongxuan Li†

[Paper] [Code]
MicroDreamer: Zero-shot 3D Generation in ~20 Seconds by Score-based Iterative Reconstruction

Luxi Chen, Zhengyi Wang, Zihan Zhou, Tingting Gao, Hang Su, Jun Zhu, Chongxuan Li†

[Paper] [Code]

2025

RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers

Min Zhao, Guande He, Yixiao Chen, Hongzhou Zhu, Chongxuan Li†, Jun Zhu†

International Conference on Machine Learning (ICML), 2025
[Paper] [Code] [Demo]
Effective and Efficient Masked Image Generation Models

Zebin You, Jingyang Ou, Xiaolu Zhang, Jun Hu, Jun Zhou, Chongxuan Li†

International Conference on Machine Learning (ICML), 2025
[Paper] [Code]
A Theory for Conditional Generative Modeling on Multiple Data Sources

Rongzhen Wang, Yan Zhang, Chenyu Zheng, Chongxuan Li†, Guoqiang Wu†

International Conference on Machine Learning (ICML), 2025
[Paper] [Code]
Are Image Distributions Indistinguishable to Humans Indistinguishable to Classifiers?

Zebin You, Xinyu Zhang, Hanzhong Guo, Jingdong Wang, Chongxuan Li†

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2025
[Paper]
Scaling up Masked Diffusion Models on Text

Shen Nie, Fengqi Zhu, Chao Du, Tianyu Pang, Qian Liu, Guangtao Zeng, Min Lin, Chongxuan Li†

International Conference on Learning Representations (ICLR), 2025
[Paper] [Code]
Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data

Jingyang Ou, Shen Nie, Kaiwen Xue, Fengqi Zhu, Jiacheng Sun, Zhenguo Li, Chongxuan Li†

International Conference on Learning Representations (ICLR), 2025
[Paper] [Code]
Improving Long-Text Alignment for Text-to-Image Diffusion Models

Luping Liu, Chao Du, Tianyu Pang, Zehan Wang, Chongxuan Li, Dong Xu

International Conference on Learning Representations (ICLR), 2025
[Paper] [Code]
On Memorization in Diffusion Models

Xiangming Gu, Chao Du, Tianyu Pang, Chongxuan Li, Min Lin, Ye Wang

Transactions on Machine Learning Research (TMLR), 2025
[Paper] [Code]
ControlVideo: Conditional Control for Text-driven Video Editing and Beyond

Min Zhao, Rongzhen Wang, Fan Bao, Chongxuan Li†, Jun Zhu†

Science China Information Sciences (SCIS), 2025
[Paper] [Code]

2024

On Mesa-Optimization in Autoregressively Trained Transformers: Emergence and Capability

Chenyu Zheng, Wei Huang, Rongzhen Wang, Guoqiang Wu, Jun Zhu, Chongxuan Li†

Advances in Neural Information Processing Systems (NeurIPS), 2024
[Paper] [Code]
Lower Bounds of Uniform Stability in Gradient-Based Bilevel Algorithms for Hyperparameter Optimization

Rongzhen Wang, Chenyu Zheng, Guoqiang Wu, Xu Min, Xiaolu Zhang, Jun Zhou, Chongxuan Li†

Advances in Neural Information Processing Systems (NeurIPS), 2024
[Paper]
Identifying and Solving Conditional Image Leakage in Image-to-Video Generation

Min Zhao, Hongzhou Zhu, Chendong Xiang, Kaiwen Zheng, Chongxuan Li†, Jun Zhu†

Advances in Neural Information Processing Systems (NeurIPS), 2024
[Paper] [Code] [Demo]
Graph Diffusion Policy Optimization

Yijing Liu, Chao Du, Tianyu Pang, Chongxuan Li, Wei Chen, Min Lin

Advances in Neural Information Processing Systems (NeurIPS), 2024
[Paper] [Code]
PoseCrafter: One-Shot Personalized Video Synthesis Following Flexible Poses

Yong Zhong, Min Zhao, Zebin You, Xiaofeng Yu, Changwang Zhang, Chongxuan Li†

European Conference on Computer Vision (ECCV), 2024
[Paper] [Demo]
CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model

Zhengyi Wang, Yikai Wang, Yifei Chen, Chendong Xiang, Shuo Chen, Dajiang Yu, Chongxuan Li, Hang Su, Jun Zhu

European Conference on Computer Vision (ECCV), 2024
[Paper] [Project] [Code] [Demo]
Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations

Kaiwen Xue, Yuhao Zhou, Shen Nie, Xu Min, Xiaolu Zhang, Jun Zhou, Chongxuan Li†

International Conference on Machine Learning (ICML), 2024
[Paper] [Code]
EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction

Yang Zhang, Wenbing Huang, Zhewei Wei, Ye Yuan, Chongxuan Li

International Conference on Machine Learning (ICML), 2024

Oral
[Paper] [Code]
The Blessing of Randomness: SDE Beats ODE in General Diffusion-based Image Editing

Shen Nie, Hanzhong Allan Guo, Cheng Lu, Yuhao Zhou, Chenyu Zheng, Chongxuan Li†

International Conference on Learning Representations (ICLR), 2024
[Paper] [Code] [Demo]
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference

Siqi Kou, Lei Gan, Dequan Wang, Chongxuan Li†, Zhijie Deng†

International Conference on Learning Representations (ICLR), 2024
[Paper] [Code]

2023

ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation

Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li†, Hang Su, Jun Zhu†

Advances in Neural Information Processing Systems (NeurIPS), 2023

Spotlight

Most Influential Papers in NeurIPS 2023 (11/3584)
[Paper] [Code] [Demo]
Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels

Zebin You, Yong Zhong, Fan Bao, Jiacheng Sun, Chongxuan Li†, Jun Zhu

Advances in Neural Information Processing Systems (NeurIPS), 2023

Spotlight
[Paper] [Code] [Demo]
Toward Understanding Generative Data Augmentation

Chenyu Zheng, Guoqiang Wu, Chongxuan Li†

Advances in Neural Information Processing Systems (NeurIPS), 2023
[Paper] [Code]
Gaussian Mixture Solvers for Diffusion Models

Hanzhong Allan Guo, Cheng Lu, Fan Bao, Tianyu Pang, Shuicheng Yan, Chao Du†, Chongxuan Li†

Advances in Neural Information Processing Systems (NeurIPS), 2023
[Paper] [Code]
On Evaluating Adversarial Robustness of Large Vision-Language Models

Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Chongxuan Li, Ngni-Man Cheung, Min Lin

Advances in Neural Information Processing Systems (NeurIPS), 2023
[Paper] [Code]
Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning

Yudeng Lin, Qingtian Zhang, Bin Gao, Jianshi Tang, Peng Yao, Chongxuan Li, Shiyu Huang, Zhengwu Liu, Ying Zhou, Yuyi Liu, Wenqiang Zhang, Jun Zhu, He Qian, Huaqiang Wu

Nature Machine Intelligence (NMI), 2023
[Paper]
MissDiff: Training Diffusion Models on Tabular Data with Missing Values

Yidong Ouyang, Liyan Xie, Chongxuan Li, Guang Cheng

Structured Probabilistic Inference & Generative Modeling Workshop @ International Conference on Machine Learning (SPIGM@ICML), 2023
[Paper] [Long version]
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale

Fan Bao, Shen Nie, Kaiwen Xue, Chongxuan Li, Shi Pu, Yaole Wang, Gang Yue, Yue Cao, Hang Su, Jun Zhu

International Conference on Machine Learning (ICML), 2023
[Paper] [Code]
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications

Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li†, Jun Zhu

International Conference on Machine Learning (ICML), 2023
[Paper] [Code]
Towards Understanding Generalization of Macro-AUC in Multi-label Learning

Guoqiang Wu, Chongxuan Li, Yilong Yin

International Conference on Machine Learning (ICML), 2023
Exact Energy-Guided Diffusion Sampling via Contrastive Energy Prediction

Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, Jun Zhu

International Conference on Machine Learning (ICML), 2023
All are Worth Words: a ViT Backbone for Diffusion Models

Fan Bao, Shen Nie, Kaiwen Xue, Yue Cao, Chongxuan Li†, Hang Su, Jun Zhu†

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023
[Paper] [Code]
Equivariant Energy-Guided SDE for Inverse Molecular Design

Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li†, Jun Zhu†

International Conference on Learning Representations (ICLR), 2023
Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models

Yong Zhong, Hongtao Liu, Xiaodong Liu, Fan Bao, Weiran Shen†, Chongxuan Li†

International Conference on Learning Representations (ICLR), 2023

2022

Why Are Conditional Generative Models Better Than Unconditional Ones?

Fan Bao, Chongxuan Li†, Jiacheng Sun, Jun Zhu†

Score-based Model workshop @ Advances in Neural Information Processing Systems (SBM@NeurIPS), 2022
EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations

Min Zhao, Fan Bao, Chongxuan Li†, Jun Zhu†

Advances in Neural Information Processing Systems (NeurIPS), 2022
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps

Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu

Advances in Neural Information Processing Systems (NeurIPS), 2022

Oral

Most Influential Papers in NeurIPS 2022 (10/2905)
[Paper] [Code]
Fast Lossless Neural Compression with Integer-Only Discrete Flows

Siyu Wang, Jianfei Chen, Chongxuan Li, Jun Zhu, Bo Zhang

International Conference on Machine Learning (ICML), 2022
Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching

Cheng Lu, Kaiwen Zheng, Fan Bao, Chongxuan Li, Jianfei Chen, Jun Zhu

International Conference on Machine Learning (ICML), 2022
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models

Fan Bao, Chongxuan Li†, Jiacheng Sun, Jun Zhu†, Bo Zhang

International Conference on Machine Learning (ICML), 2022
[Code]
Memory Replay with Data Compression for Continual Learning

Liyuan Wang, Xingxing Zhang, Kuo Yang, Longhui Yu, Chongxuan Li†, Lanqing Hong, Shifeng Zhang, Zhenguo Li, Yi Zhong†, Jun Zhu†

International Conference on Learning Representations (ICLR), 2022
[Paper]
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models

Fan Bao, Chongxuan Li†, Jun Zhu†, Bo Zhang

International Conference on Learning Representations (ICLR), 2022

Oral, Outstanding Paper Award (7/3391)
[Paper] [Code]
Probabilistic Neural-Symbolic Models with Inductive Posterior Constraints

Ke Su, Hang Su, Chongxuan Li, Jun Zhu, Bo Zhang

IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022
Deep reinforcement learning with credit assignment for combinatorial optimization

Dong Yan, Jiayi Weng, Shiyu Huang, Chongxuan Li, Yichi Zhou, Hang Su, Jun Zhu

Pattern Recognition, 2022 (Runner-up Prize of IEEE VIZDoom RL Competition 2017)
[Paper]

2021

Triple Generative Adversarial Networks

Chongxuan Li, Kun Xu, Jun Zhu, Jiashuo Liu, Bo Zhang.

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
[Paper] [Code]
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization

Fan Bao*, Guoqiang Wu*, Chongxuan Li*, Jun Zhu, Bo Zhang

Advances in Neural Information Processing Systems (NeurIPS), 2021
[Paper]
Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization

Guoqiang Wu*, Chongxuan Li*, Kun Xu, Jun Zhu

Advances in Neural Information Processing Systems (NeurIPS), 2021
[Paper]
ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning

Liyuan Wang, Kuo Yang, Chongxuan Li†, Lanqing Hong, Zhenguo Li, Jun Zhu†

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2021
[Paper]
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models

Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong, Jun Zhu, Bo Zhang

International Conference on Machine Learning (ICML), 2021
[Paper] [Code]
Implicit Normalizing Flows

Cheng Lu, Jianfei Chen, Chongxuan Li, Qiuhao Wang, Jun Zhu

International Conference on Learning Representations (ICLR), 2021

Spotlight
[Paper] [Code]
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering

Tsung Wei Tsai, Chongxuan Li, Jun Zhu

International Conference on Learning Representations (ICLR), 2021
[Paper] [Code]

2020

Efficient Learning of Generative Models via Finite-Difference Score Matching

Tianyu Pang*, Kun Xu*, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu

Advances in Neural Information Processing Systems (NeurIPS), 2020
[Paper] [Code]
Bi-level Score Matching for Learning Energy-based Latent Variable Models

Fan Bao*, Chongxuan Li*, Hang Su, Jun Zhu, Bo Zhang

Advances in Neural Information Processing Systems (NeurIPS), 2020
[Paper] [Code]
Learning Implicit Generative Models by Teaching Density Estimators

Kun Xu*, Chao Du*, Chongxuan Li, Jun Zhu, Bo Zhang

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2020
[Paper] [Code]
Understanding and Stabilizing GANs' Training Dynamics with Control Theory

Kun Xu, Chongxuan Li, Huanshu Wei, Jun Zhu, Bo Zhang

International Conference on Machine Learning (ICML), 2020
[Paper] [Code]
To Relieve Your Headache of Training an MRF, Take AdVIL

Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu, Bo Zhang

International Conference on Learning Representations (ICLR), 2020
[Paper]

2019

Conditional Graphical Generative Adversarial Networks

Chongxuan Li, Jun Zhu, Bo Zhang

Journal of Software (in Chinese), 2019
Multi-objects Generation with Amortized Structural Regularization

Kun Xu, Chongxuan Li, Jun Zhu and Bo Zhang

Advances in Neural Information Processing Systems (NeurIPS), 2019
[Paper]

2018

Max-Margin Deep Generative Models for (Semi-)Supervised Learning

Chongxuan Li, Jun Zhu, Bo Zhang.

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018
[Paper] [Code]
Graphical Generative Adversarial Networks

Chongxuan Li, Max Welling, Jun Zhu and Bo Zhang

Advances in Neural Information Processing Systems (NeurIPS), 2018
[Paper] [Code]
Learning to Write Stylized Chinese Characters by Reading a Handful of Examples

Danyang Sun, Tongzheng Ren, Chongxuan Li, Jun Zhu, Hang Su, Bo Zhang

International Joint Conferences on Artificial Intelligence (IJCAI), 2018
[Paper]
Collaborative Filtering with User-Item Co-Autoregressive Models

Chao Du , Chongxuan Li, Yin Zheng, Jun Zhu, Bo Zhang

Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018
[Paper] [Code]

2017

Triple Generative Adversarial Nets

Chongxuan Li, Kun Xu, Jun Zhu, Bo Zhang

Advances in Neural Information Processing Systems (NeurIPS), 2017
[Paper] [Code]
Population Matching Discrepancy and Applications in Deep Learning

Jianfei Chen, Chongxuan Li, Yizhong Ru, Bo Zhang

Advances in Neural Information Processing Systems (NeurIPS), 2017
[Paper]

2016

Towards Better Analysis of Deep Convolutional Neural Networks

Mengchen Liu, Jiaxin Shi, Zhen Li, Chongxuan Li, Jun Zhu, Shixia Liu

IEEE VAST 2016, TVCG track, 2016
[Paper] [Demo]
Learning to Generate with Memory

Chongxuan Li, Jun Zhu, Bo Zhang

International Conference on Machine Learning (ICML), 2016
[Paper] [Code]

2015

Max-Margin Deep Generative Models

Chongxuan Li, Jun Zhu, Tianlin Shi, Bo Zhang

Advances in Neural Information Processing Systems (NeurIPS), 2015
[Paper] [Code]

Chongxuan Li（李崇轩）

Selected Publications Full Publications

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