Diffusion Transformer

Also known as DiT

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35 papers in the last four weeks, up 169% on the four weeks before. 0.3% of all new papers.

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Latest papers 297

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Fluid-DiT: Graph-Free Diffusion Transformers for Fluid Flow Simulations Learning
Aug 7, 2026Shentong Mo, Guolin KeDiffusion Transformer Generative Modeling
CMU · DP Tech
HRDiT: Training-Free High-Resolution Image Generation with Off-the-Shelf Diffusion Transformer Models
Aug 7, 2026Yu Xue, Haoxuan Qu, Zhuoling Li +5Diffusion Transformer T2I Diffusion Models
Lancaster University, Lancaster, UK · South China University of Technology, Guangzhou, China · NVIDIA AI Tech Centre, Singapore
ControlRef: Efficient Layout-Guided Multi-Instance Generation via Anchored 4D-RoPE
Aug 7, 2026Yunkai Yang, Yudong Zhang, Xinying Chen +7Rotary Positional Embeddings Diffusion Transformer
1Sun Yat-Sen University · 2Tsinghua University · 3Beijing Institute of Technology +1
Adaptive-WAM: Quality-Guided Early-Exit Planning from Intermediate Video-Diffusion Features
Aug 6, 2026Sining Ang, Yuguang Yang, Yan WangEfficient Inference Diffusion Transformer
Institute for AI Industry Research (AIR), Tsinghua University · Department of Automation, University of Science and Technology of China · School of Electronic Information Engineering, Beihang University
MAVISEG: Manifold Propagation and Visual Prototypes for Zero-Shot Open-Vocabulary Segmentation in Diffusion Transformers
Aug 6, 2026Rajatsubhra Chakraborty, Xujun Che, Ritabrata Chakraborty +2Image Segmentation Diffusion Transformer
University of North Carolina at Charlotte, Charlotte, NC, USA · Manipal University Jaipur, Jaipur, Rajasthan, India
Visual Anchoring in Diffusion: Multimodal Zero-Shot Skeleton Action Recognition
Aug 5, 2026Zehao Bao, Shujun Guo, Bruce X. B. YuSkeleton-Based Action Recognition Diffusion Transformer
The University of Hong Kong Hong Kong SAR, China · Zhejiang University Hangzhou, China
When does training on downscaled images yield the same gradients?
Aug 5, 2026Seunghyun JiDiffusion Transformer Diffusion Model Fine-Tuning
Independent researcher
BRACE: Taming Sharp Irregularities via Barycentric Rational Forecasting for Fast Diffusion Transformers Inference
Aug 4, 2026Jinlong Yang, Jinke Wu, Lizilin +1Diffusion Transformer Diffusion Model Caching
Sichuan University Chengdu, China · School of Artificial Intelligence, Sichuan University Chengdu, China
SPADE: An Input-Adaptive Sparse Attention Engine for Fast Video Diffusion Models Inference
Aug 4, 2026Shanghao Liu, Renze Chen, Size Zheng +4Video Diffusion Models Diffusion Transformer
Eric · Beihang University · Peking University
ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density
Aug 4, 2026Liang Shuang, Haocheng Wang, Jiayi Song +2Diffusion Transformer Self-Supervised Pre-Training
Fudan University, Shanghai, China · Shenzhen Loop Area Institute, Shenzhen, China · The Chinese University of Hong Kong, Hong Kong SAR, China
DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers
Aug 4, 2026Binglei Li, Mengping Yang, Zhiyu Tan +4Representation Learning Diffusion Transformer
Fudan University, Shanghai, China · Shanghai Academy of AI for Science, Shanghai, China · Shanghai Innovation Institute, Shanghai, China
CAPE-T2V: Captioner-Anchored Prompt Enhancement toward Two-Sided Conditioning Alignment in Text-to-Video Generation
Aug 4, 2026Yizhuo Jia, Jingyun Hua, Yuanxing ZhangDiffusion Transformer Text-to-Video Generation
1Fudan University · 2Kling Team
AURORA-LM: Autoencoding Unified Representation for Continuous-Latent Diffusion Language Modeling
Aug 3, 2026Jiajun Liang, Yucheng Liao, Yukang Cao +12Diffusion Transformer Blockwise Diffusion Language Models
PRLab, Nanjing University · S-Lab, Nanyang Technological University · Imperial College London
CMuon: Accelerating and Stabilizing Diffusion Transformer Training via Chunked Momentum Orthogonalization
Aug 3, 2026Chuyan Chen, Peng Sun, Kun YuanDiffusion Transformer Image Generation
1Peking University · 2Westlake University · 3Zhejiang University
SPARE: Structural Parameter-Free Affinity Regularization for Flow Matching
Aug 3, 2026Zong-Wei Hong, Jinglun Li, Shen Zhang +3Diffusion Model Alignment Flow Matching
JIIOV Technology
UDT: Reconciling U-Nets and Diffusion Transformers with Data-Adaptive Token Reduction
Aug 2, 2026Junno Yun, Yaşar Utku Alçalar, Mehmet AkçakayaToken Merging Diffusion Transformer
University of Minnesota
CopyCat: Improving Fine-Grained Subject Consistency in Subject-to-Image Models within Seconds
Aug 1, 2026Peng Zheng, Ruiqi Liu, Rui Ma +1Diffusion Transformer Subject-Driven Image Generation
School of Artificial Intelligence, Jilin University · 2Shanghai Innovation Institute · 4Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, China +1
DualDiT: A Conditional Dual-Output Diffusion Transformer for Joint OCT Image and Segmentation Mask Generation
Jul 31, 2026Fernando García-Torres, Rocío del Amor, Sandra Morales +4Synthetic Data Augmentation Diffusion Transformer
Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (HUMAN-tech), Universitat Politècnica de València, Camino de Vera, s/n, Valencia, 46022, Comunidad Valenciana, Spain · Artikode Intelligence S.L, Camino de Vera, s/n, Valencia, 46022, Comunidad Valenciana, Spain · Biomedical Technology Center of the Medical Faculty, University of Muenster, Mendelstraße 17, Münster, 48149, North Rhine-Westphalia, Germany +1
StraightDP: Geometry-Aware Differential Privacy for Rectified-Flow Transformers
Jul 31, 2026Xujun Che, Depeng Xu, Xintao WuDiffusion Transformer Differentially Private Stochastic Gradient Descent
Department of Cybersecurity, University of North Carolina at Charlotte, Charlotte, NC, USA · Department of Electrical Engineering and Computer Science, University of Arkansas, Fayetteville, AR, USA
Chimera: Designing and Chinchilla-Scaling Hybrid Visual Diffusion Transformers
Jul 30, 2026Chongjian Ge, Hanwen Jiang, Tianyu Wang +9Video Diffusion Models Diffusion Transformer
Adobe Research
MIND: Multimodal Intent-Driven Network via Diffusion Transformers for Medical Image Fusion
Jul 30, 2026Yunzhan Fu, Xiangyu Shen, Yifei Sun +3Multi-Source Image Fusion Diffusion Transformer
Transvascular Implantation Devices Research Institute, Zhejiang University, Hangzhou, China · Hangzhou Institute of Technology, Xidian University, Hangzhou, China · Zhejiang University, Hangzhou, China +3
ProgFormer: Hierarchical Voxel Diffusion Transformer for Longitudinal Brain MRI Prediction
Jul 30, 2026Dexuan Ding, Yuankai Qi, Luping Zhou +3Diffusion Transformer Longitudinal Medical Image Analysis
Macquarie University · University of Sydney · University of California Merced
Transformer Transformer: A Unified Model for Motion-Conditioned Robot Co-design
Jul 28, 2026Huy Ha, C. Karen Liu, Shuran SongRobot Morphology-Control Co-Design Diffusion Transformer
Stanford University · Columbia University
MMOE: Modernizing Diffusion Transformers with Efficient Expert Design
Jul 27, 2026Yanhao Jia, Jiepeng Wang, Haibin Huang +3Diffusion Transformer Diffusion Model Acceleration
College of Computing and Data Science, Nanyang Technological University, Singapore, 639798 · Institute of Artificial Intelligence of China Telecom (TeleAI), Shanghai, China, 200232
Catalyst Diffusion Transformer: Generative Inverse Design of Heterogeneous Catalysts
Jul 27, 2026Hayoung Doo, Dong Hyeon Mok, Seoin Back +1Diffusion Transformer Materials Science
Department of Chemical Engineering and Materials Science, Ewha Womans University, Seoul 03760, Republic of Korea · Department of Chemical Engineering, Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, 03760, Republic of Korea · Institute for Multiscale Matter and Systems (IMMS), Ewha Womans University, Seoul 03760, Republic of Korea +4
WorldDiT: A Unified Diffusion Architecture for World and Action Modeling
Jul 27, 2026Sen Wang, R. Gnana Praveen, Bidhan Roy +1Diffusion Transformer Diffusion Policy
Bagel Labs (bagel.com)
OmniCache: Multidimensional Hierarchical Feature Caching For Diffusion Models
Jul 26, 2026Zhaoyuan He, Muhammad Muaz, Lili QiuVideo Diffusion Models Diffusion Transformer
Department of Computer Science, The University of Texas at Austin, Austin, TX, USA
Twins: Learn to Predict Unified Representations with Focal Loss
Jul 24, 2026Kaixiong Gong, Xin Cai, Bin Lin +9Diffusion Transformer Unified Multimodal Models
The Chinese University of Hong Kong · Tencent, Hunyuan · City University of Hong Kong +2
TRaM-VSR: Importance-Aware Token Routing and Merging for One-Step Diffusion Video Super-Resolution
Jul 24, 2026Sicheng Gao, Zhuyun Zhou, Yixuan Liu +3Diffusion Transformer Video Super-Resolution
Advanced Micro Devices Inc. · Computer Vision Lab, CAIDAS & IFI, University of Würzburg
InnoText: A Unified Model for Visual Text Generation and Editing
Jul 24, 2026Haowei Liu, Runze He, Jian Lu +10Diffusion Transformer Diffusion-Based Image Generation
Shenzhen Campus of Sun Yat-Sen University · University of Chinese Academy of Sciences · Chongqing University of Post and Telecommunications +5

Aug 7, 2026, cs.LGJ/K move · Enter open · S save

Fluid-DiT: Graph-Free Diffusion Transformers for Fluid Flow Simulations Learning

Shentong Mo, Guolin Ke

CMU · DP Tech

Diffusion Transformer Generative Modeling Diffusion Models Computational Fluid Dynamics

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Simulating complex fluid flows requires capturing full equilibrium distributions rather than just mean trajectories, yet high-fidelity solvers remain computationally prohibitive. Recent advances, such as Diffusion Graph Networks (DGNs), have combined diffusion models with graph neural networks to sample equilibrium states directly from unstructured meshes, enabling distributional accuracy even from short simulations. However, graph-based diffusion approaches suffer from hand-crafted architectural constraints, limited receptive fields in message passing, and costly multi-scale designs, which restrict scalability to larger and more complex domains. We propose Fluid-DiT, a Graph-Free Diffusion Transformer that replaces graph message passing with attention-based denoising, eliminating explicit graph design while preserving the ability to model distributions of chaotic flows. Our framework introduces a latent-space formulation that disentangles geometric fidelity from distributional learning, reducing high-frequency artifacts and accelerating sampling. By leveraging the transformer's global receptive field, Fluid-DiT naturally captures both local flow structures and long-range correlations without requiring hierarchical graph coarsening. On canonical benchmarks including laminar cylinder wakes, ellipse-flow systems, and turbulent 3D wing experiments, Fluid-DiT consistently outperforms graph-based diffusion baselines in both sample quality and distributional accuracy, achieving higher

R^2

correlations and lower Wasserstein distances. Moreover, it generalizes robustly from short, incomplete trajectories to unseen Reynolds numbers and geometries, demonstrating strong scalability.

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Latest papers 297