Recent advancements in unified multimodal understanding and visual generation (or multimodal generation) models have been hindered by their quadratic computational complexity and dependence on large-scale training data. We present OmniMamba, the first linear-architecture-based multimodal generation model that generates both text and images through a unified next-token prediction paradigm. The model fully leverages Mamba-2's high computational and memory efficiency, extending its capabilities from text generation to multimodal generation. To address the data inefficiency of existing unified models, we propose two key innovations: (1) decoupled vocabularies to guide modality-specific generation, and (2) task-specific LoRA for parameter-efficient adaptation. Furthermore, we introduce a decoupled two-stage training strategy to mitigate data imbalance between two tasks. Equipped with these techniques, OmniMamba achieves competitive performance with JanusFlow while surpassing Show-o across benchmarks, despite being trained on merely 2M image-text pairs, which is 1,000 times fewer than Show-o. Notably, OmniMamba stands out with outstanding inference efficiency, achieving up to a 119.2$\times$ speedup and 63% GPU memory reduction for long-sequence generation compared to Transformer-based counterparts.
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1@misc{zou2025omnimambaefficientunifiedmultimodal,
2 title={OmniMamba: Efficient and Unified Multimodal Understanding and Generation via State Space Models},
3 author={Jialv Zou and Bencheng Liao and Qian Zhang and Wenyu Liu and Xinggang Wang},
4 year={2025},
5 eprint={2503.08686},
6 archivePrefix={arXiv},
7 primaryClass={cs.CV},
8 url={https://arxiv.org/abs/2503.08686},
9}
