Title: Unified Single Transformer for Multimodal Video Understanding and Generation
Primary area: foundation or frontier models, including LLMs
Keywords: Mulitmodal, Large Language Model

Abstract: With the advancement of language models, unified multimodal understanding and generation have made significant strides, with model architectures evolving from separated components to unified single-model frameworks. This paper explores an efficient training paradigm to build a single transformer for unified multimodal understanding and generation. Specifically, we propose a multimodal warmup strategy utilizing prior knowledge to extend capabilities. To address cross-modal compatibility challenges, we introduce feature pre-scaling and multimodal AdaLN techniques. Integrating the proposed technologies, we present the UST, a new single multimodal transformer. With limited training costs, UST achieves competitive performance across multiple image and video understanding and generation benchmarks over advanced unified models. All codes will be made public.