Recently, ChatGPT, along with DALL-E-2 and Codex, has been gaining significant attention
from society. As a result, many individuals have become interested in related resources and …

Denoising diffusion models represent a recent emerging topic in computer vision,
demonstrating remarkable results in the area of generative modeling. A diffusion model is a …

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 …

Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

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

Diffusion models have significantly advanced the fields of image, audio, and video
generation, but they depend on an iterative sampling process that causes slow generation …

Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

Offline reinforcement learning (RL), which aims to learn an optimal policy using a previously
collected static dataset, is an important paradigm of RL. Standard RL methods often perform …

Text-to-image diffusion models have demonstrated remarkable capabilities in transforming
text prompts into coherent images yet the computational cost of the multi-step inference …

Denoising diffusion models embody a type of generative artificial intelligence that can be
applied in computer vision, natural language processing and bioinformatics. In this Review …