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

Diffusion Models (DMs) have demonstrated state-of-the-art performance in content
generation without requiring adversarial training. These models are trained using a two-step …

Three billion years of evolution has produced a tremendous diversity of protein molecules,
but the full potential of proteins is likely to be much greater. Accessing this potential has …

Controlling the behavior of language models (LMs) without re-training is a major open
problem in natural language generation. While recent works have demonstrated successes …

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 …

To address the challenges of digital intelligence in the digital economy, artificial intelligence-
generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace …

We present a unified transformer, ie, Show-o, that unifies multimodal understanding and
generation. Unlike fully autoregressive models, Show-o unifies autoregressive and …

Diffusion models have achieved great success in modeling continuous data modalities such
as images, audio, and video, but have seen limited use in discrete domains such as …

We provide the first complete continuous time framework for denoising diffusion models of
discrete data. This is achieved by formulating the forward noising process and …

Current machine learning models for vision are often highly specialized and limited to a
single modality and task. In contrast, recent large language models exhibit a wide range of …