To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

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 …

Large language models (LLMs) are increasingly deployed as the backend for various
applications, including code completion tools and AI-powered search engines. Unlike …

Abstract Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding
excessive compute demands by training a diffusion model in a compressed lower …

Score distillation sampling (SDS) has shown great promise in text-to-3D generation by
distilling pretrained large-scale text-to-image diffusion models, but suffers from over …

Abstract We introduce Adversarial Diffusion Distillation (ADD), a novel training approach that
efficiently samples large-scale foundational image diffusion models in just 1–4 steps while …

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 …

Finding correspondences between images is a fundamental problem in computer vision. In
this paper, we show that correspondence emerges in image diffusion models without any …

The most advanced text-to-image (T2I) models require significant training costs (eg, millions
of GPU hours), seriously hindering the fundamental innovation for the AIGC community …

Abstract We present Visual AutoRegressive modeling (VAR), a new generation paradigm
that redefines the autoregressive learning on images as coarse-to-fine" next-scale …