The recent success of text-to-image synthesis has taken the world by storm and captured the
general public's imagination. From a technical standpoint, it also marked a drastic change in …

Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted
significant attention due to their ability to generate high-quality synthetic images. In this work …

While generative models produce high-quality images of concepts learned from a large-
scale database, a user often wishes to synthesize instantiations of their own concepts (for …

Diffusion models are powerful, but they require a lot of time and data to train. We propose
Patch Diffusion, a generic patch-wise training framework, to significantly reduce the training …

Text-to-image synthesis has recently seen significant progress thanks to large pretrained
language models, large-scale training data, and the introduction of scalable model families …

Large-scale text-to-image diffusion models can generate high-fidelity images with powerful
compositional ability. However, these models are typically trained on an enormous amount …

We present rectified flow, a surprisingly simple approach to learning (neural) ordinary
differential equation (ODE) models to transport between two empirically observed …

Dataset distillation is the task of synthesizing a small dataset such that a model trained on
the synthetic set will match the test accuracy of the model trained on the full dataset. In this …