Show-o2: Improved Native Unified Multimodal Models

• URL: http://arxiv.org/abs/2506.15564v2
• Date: Fri, 20 Jun 2025 08:39:17 GMT
• Title: Show-o2: Improved Native Unified Multimodal Models
• Authors: Jinheng Xie, Zhenheng Yang, Mike Zheng Shou,
• Abstract summary: Show-o2 is a native unified multimodal models that leverage autoregressive modeling and flow matching.<n>Built upon a 3D causal variational autoencoder space, unified visual representations are constructed through a dual-path of spatial (-temporal) fusion.
• Score: 21.78513101265258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents improved native unified multimodal models, \emph{i.e.,} Show-o2, that leverage autoregressive modeling and flow matching. Built upon a 3D causal variational autoencoder space, unified visual representations are constructed through a dual-path of spatial (-temporal) fusion, enabling scalability across image and video modalities while ensuring effective multimodal understanding and generation. Based on a language model, autoregressive modeling and flow matching are natively applied to the language head and flow head, respectively, to facilitate text token prediction and image/video generation. A two-stage training recipe is designed to effectively learn and scale to larger models. The resulting Show-o2 models demonstrate versatility in handling a wide range of multimodal understanding and generation tasks across diverse modalities, including text, images, and videos. Code and models are released at https://github.com/showlab/Show-o.

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