SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
Fuente:
arXiv
Saved in:
| Main Authors: | Ma, Nanye, Goldstein, Mark, Albergo, Michael S., Boffi, Nicholas M., Vanden-Eijnden, Eric, Xie, Saining |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
How to build a consistency model: Learning flow maps via self-distillation
by: Boffi, Nicholas M., et al.
Published: (2025)
by: Boffi, Nicholas M., et al.
Published: (2025)
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
by: Albergo, Michael S., et al.
Published: (2023)
by: Albergo, Michael S., et al.
Published: (2023)
Diffusion Transformers with Representation Autoencoders
by: Zheng, Boyang, et al.
Published: (2025)
by: Zheng, Boyang, et al.
Published: (2025)
Flow map matching with stochastic interpolants: A mathematical framework for consistency models
by: Boffi, Nicholas M., et al.
Published: (2024)
by: Boffi, Nicholas M., et al.
Published: (2024)
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
by: Chen, Yifan, et al.
Published: (2024)
by: Chen, Yifan, et al.
Published: (2024)
Flow Map Distillation Without Data
by: Tong, Shangyuan, et al.
Published: (2025)
by: Tong, Shangyuan, et al.
Published: (2025)
Stochastic interpolants with data-dependent couplings
by: Albergo, Michael S., et al.
Published: (2023)
by: Albergo, Michael S., et al.
Published: (2023)
Dynamic Test-Time Compute Scaling in Control Policy: Difficulty-Aware Stochastic Interpolant Policy
by: Chun, Inkook, et al.
Published: (2025)
by: Chun, Inkook, et al.
Published: (2025)
Multitask Learning with Stochastic Interpolants
by: Negrel, Hugo, et al.
Published: (2025)
by: Negrel, Hugo, et al.
Published: (2025)
NETS: A Non-Equilibrium Transport Sampler
by: Albergo, Michael S., et al.
Published: (2024)
by: Albergo, Michael S., et al.
Published: (2024)
Deep learning probability flows and entropy production rates in active matter
by: Boffi, Nicholas M., et al.
Published: (2023)
by: Boffi, Nicholas M., et al.
Published: (2023)
Model-free learning of probability flows: Elucidating the nonequilibrium dynamics of flocking
by: Boffi, Nicholas M., et al.
Published: (2024)
by: Boffi, Nicholas M., et al.
Published: (2024)
Transition Matching Distillation for Fast Video Generation
by: Nie, Weili, et al.
Published: (2026)
by: Nie, Weili, et al.
Published: (2026)
Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders
by: Tong, Shengbang, et al.
Published: (2026)
by: Tong, Shengbang, et al.
Published: (2026)
X-SiT: Inherently Interpretable Surface Vision Transformers for Dementia Diagnosis
by: Bongratz, Fabian, et al.
Published: (2025)
by: Bongratz, Fabian, et al.
Published: (2025)
Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis
by: Tang, Bingda, et al.
Published: (2025)
by: Tang, Bingda, et al.
Published: (2025)
SiT-MLP: A Simple MLP with Point-wise Topology Feature Learning for Skeleton-based Action Recognition
by: Zhang, Shaojie, et al.
Published: (2023)
by: Zhang, Shaojie, et al.
Published: (2023)
Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps
by: Ma, Nanye, et al.
Published: (2025)
by: Ma, Nanye, et al.
Published: (2025)
Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think
by: Yu, Sihyun, et al.
Published: (2024)
by: Yu, Sihyun, et al.
Published: (2024)
Lipschitz-Guided Design of Interpolation Schedules in Generative Models
by: Chen, Yifan, et al.
Published: (2025)
by: Chen, Yifan, et al.
Published: (2025)
Test-time scaling of diffusions with flow maps
by: Sabour, Amirmojtaba, et al.
Published: (2025)
by: Sabour, Amirmojtaba, et al.
Published: (2025)
PISA Experiments: Exploring Physics Post-Training for Video Diffusion Models by Watching Stuff Drop
by: Li, Chenyu, et al.
Published: (2025)
by: Li, Chenyu, et al.
Published: (2025)
Scale-Adaptive Generative Flows for Multiscale Scientific Data
by: Chen, Yifan, et al.
Published: (2025)
by: Chen, Yifan, et al.
Published: (2025)
DiffusionGuard: A Robust Defense Against Malicious Diffusion-based Image Editing
by: Choi, June Suk, et al.
Published: (2024)
by: Choi, June Suk, et al.
Published: (2024)
Variational Optimality of Föllmer Processes in Generative Diffusions
by: Chen, Yifan, et al.
Published: (2026)
by: Chen, Yifan, et al.
Published: (2026)
ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer
by: Hu, Jinyi, et al.
Published: (2024)
by: Hu, Jinyi, et al.
Published: (2024)
Discrete Flow Maps
by: Potaptchik, Peter, et al.
Published: (2026)
by: Potaptchik, Peter, et al.
Published: (2026)
REPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion Transformers
by: Leng, Xingjian, et al.
Published: (2025)
by: Leng, Xingjian, et al.
Published: (2025)
Hierarchical Flow Diffusion for Efficient Frame Interpolation
by: Hai, Yang, et al.
Published: (2025)
by: Hai, Yang, et al.
Published: (2025)
Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs
by: Tong, Shengbang, et al.
Published: (2024)
by: Tong, Shengbang, et al.
Published: (2024)
Deconstructing Denoising Diffusion Models for Self-Supervised Learning
by: Chen, Xinlei, et al.
Published: (2024)
by: Chen, Xinlei, et al.
Published: (2024)
Video Interpolation with Diffusion Models
by: Jain, Siddhant, et al.
Published: (2024)
by: Jain, Siddhant, et al.
Published: (2024)
Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models
by: Ai, Xinyue, et al.
Published: (2025)
by: Ai, Xinyue, et al.
Published: (2025)
DINTR: Tracking via Diffusion-based Interpolation
by: Nguyen, Pha, et al.
Published: (2024)
by: Nguyen, Pha, et al.
Published: (2024)
High-Resolution Frame Interpolation with Patch-based Cascaded Diffusion
by: Hur, Junhwa, et al.
Published: (2024)
by: Hur, Junhwa, et al.
Published: (2024)
Analysis of Attention in Video Diffusion Transformers
by: Wen, Yuxin, et al.
Published: (2025)
by: Wen, Yuxin, et al.
Published: (2025)
ResDiT: Evoking the Intrinsic Resolution Scalability in Diffusion Transformers
by: Ma, Yiyang, et al.
Published: (2025)
by: Ma, Yiyang, et al.
Published: (2025)
On the Scalability of Diffusion-based Text-to-Image Generation
by: Li, Hao, et al.
Published: (2024)
by: Li, Hao, et al.
Published: (2024)
MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers
by: Ma, Haoyu, et al.
Published: (2023)
by: Ma, Haoyu, et al.
Published: (2023)
BlenderFusion: 3D-Grounded Visual Editing and Generative Compositing
by: Chen, Jiacheng, et al.
Published: (2025)
by: Chen, Jiacheng, et al.
Published: (2025)
Similar Items
-
How to build a consistency model: Learning flow maps via self-distillation
by: Boffi, Nicholas M., et al.
Published: (2025) -
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
by: Albergo, Michael S., et al.
Published: (2023) -
Diffusion Transformers with Representation Autoencoders
by: Zheng, Boyang, et al.
Published: (2025) -
Flow map matching with stochastic interpolants: A mathematical framework for consistency models
by: Boffi, Nicholas M., et al.
Published: (2024) -
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
by: Chen, Yifan, et al.
Published: (2024)