7 papers
Normalizing Trajectory Models
Jiatao Gu, Tianrong Chen, Ying Shen +3
Diffusion-based models decompose sampling into many small Gaussian denoising steps -- an assumption that breaks down when generation is compressed to a few coarse transitions. Exis…
Normalizing Flows with Iterative Denoising
Tianrong Chen, Jiatao Gu, David Berthelot +2
Normalizing Flows (NFs) are a classical family of likelihood-based methods that have received revived attention. Recent efforts such as TARFlow have shown that NFs are capable of a…
STARFlow-V: End-to-End Video Generative Modeling with Normalizing Flows
Jiatao Gu, Ying Shen, Tianrong Chen +6
Normalizing flows (NFs) are end-to-end likelihood-based generative models for continuous data, and have recently regained attention with encouraging progress on image generation. Y…
TADA: Improved Diffusion Sampling with Training-free Augmented Dynamics
Tianrong Chen, Huangjie Zheng, David Berthelot +3
Diffusion models have demonstrated exceptional capabilities in generating high-fidelity images but typically suffer from inefficient sampling. Many solver designs and noise schedul…
Mechanisms of Projective Composition of Diffusion Models
Arwen Bradley, Preetum Nakkiran, David Berthelot +2
We study the theoretical foundations of composition in diffusion models, with a particular focus on out-of-distribution extrapolation and length-generalization. Prior work has show…
STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis
Jiatao Gu, Tianrong Chen, David Berthelot +7
We present STARFlow, a scalable generative model based on normalizing flows that achieves strong performance in high-resolution image synthesis. The core of STARFlow is Transformer…