223 citations · 384 across the 15 of their papers we have counts for
15 papers
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…
A Variational Perspective on Solving Inverse Problems with Diffusion Models
Morteza Mardani, Jiaming Song, Jan Kautz +1
Diffusion models have emerged as a key pillar of foundation models in visual domains. One of their critical applications is to universally solve different downstream inverse tasks…
DiffCollage: Parallel Generation of Large Content with Diffusion Models
Qinsheng Zhang, Jiaming Song, Xun Huang +2
We present DiffCollage, a compositional diffusion model that can generate large content by leveraging diffusion models trained on generating pieces of the large content. Our approa…
Seer: Language Instructed Video Prediction with Latent Diffusion Models
Xianfan Gu, Chuan Wen, Weirui Ye +2
Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video prediction (TVP) is an essential…
Affordance Diffusion: Synthesizing Hand-Object Interactions
Yufei Ye, Xueting Li, Abhinav Gupta +5
Recent successes in image synthesis are powered by large-scale diffusion models. However, most methods are currently limited to either text- or image-conditioned generation for syn…
Score-based Diffusion Models in Function Space
Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista +10
Diffusion models have recently emerged as a powerful framework for generative modeling. They consist of a forward process that perturbs input data with Gaussian white noise and a r…
PhysDiff: Physics-Guided Human Motion Diffusion Model
Ye Yuan, Jiaming Song, Umar Iqbal +2
Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in…
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
Yogesh Balaji, Seungjun Nah, Xun Huang +10
Large-scale diffusion-based generative models have led to breakthroughs in text-conditioned high-resolution image synthesis. Starting from random noise, such text-to-image diffusio…
BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster
Jason Dai, Ding Ding, Dongjie Shi +13
Most AI projects start with a Python notebook running on a single laptop; however, one usually needs to go through a mountain of pains to scale it to handle larger dataset (for bot…
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems
Jiayu Chen, Yuanxin Zhang, Yuanfan Xu +5
We introduce a curriculum learning algorithm, Variational Automatic Curriculum Learning (VACL), for solving challenging goal-conditioned cooperative multi-agent reinforcement learn…
Pseudo-Spherical Contrastive Divergence
Lantao Yu, Jiaming Song, Yang Song +1
Energy-based models (EBMs) offer flexible distribution parametrization. However, due to the intractable partition function, they are typically trained via contrastive divergence fo…
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
Yusuke Tashiro, Jiaming Song, Yang Song +1
The imputation of missing values in time series has many applications in healthcare and finance. While autoregressive models are natural candidates for time series imputation, scor…
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation
Abhishek Sinha, Jiaming Song, Chenlin Meng +1
Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire. This paper describes…
IQ-Learn: Inverse soft-Q Learning for Imitation
Divyansh Garg, Shuvam Chakraborty, Chris Cundy +3
In many sequential decision-making problems (e.g., robotics control, game playing, sequential prediction), human or expert data is available containing useful information about the…
Adversarial Constraint Learning for Structured Prediction
Hongyu Ren, Russell Stewart, Jiaming Song +2
Constraint-based learning reduces the burden of collecting labels by having users specify general properties of structured outputs, such as constraints imposed by physical laws. We…