OmniMamba: Efficient and Unified Multimodal Understanding and Generation via State Space Models (2503.08686v1)

Abstract: 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. Code and models are released at https://github.com/hustvl/OmniMamba

• The paper introduces OmniMamba, a novel model leveraging state space models for highly efficient, unified multimodal understanding and generation.
• To achieve efficiency, OmniMamba uses techniques like decoupled vocabularies, task-specific LoRA, and a two-stage training strategy.
• Evaluations show OmniMamba achieves competitive results with significantly less data and offers substantial speed and memory efficiency gains.

An Analysis of OmniMamba: A Linear Architecture for Unified Multimodal Understanding and Generation

The OmniMamba model represents a significant advancement in the design and application of multimodal models, specifically focusing on understanding and generating text and visual data. Developed by leveraging the computational efficiency of state space models (SSMs), OmniMamba offers a substantial improvement over traditional Transformer-based models in terms of speed and resource consumption.

Overview of OmniMamba

OmniMamba is built upon the Mamba-2 architecture, which is known for its linear computational complexity. This foundation provides OmniMamba with significant advantages in inference speed and memory efficiency, making it particularly well-suited for applications requiring real-time interaction or operating on hardware with limited capabilities. The model diverges from previous approaches by integrating multimodal understanding and generation in a unified framework, achieving this through the next-token prediction paradigm. This allows the model to handle multimodal data more effectively than traditional designs that may separate these tasks.

Key Innovations

OmniMamba addresses two primary challenges in multimodal modeling: data inefficiency and high computational cost, often exacerbated by the reliance on large-scale datasets. It introduces several novel components to tackle these issues:

1. Decoupled Vocabularies: Unlike conventional models that utilize a unified vocabulary for different modalities, OmniMamba employs separate vocabularies for text and image modalities. This decoupling aids in efficiently learning modality-specific information without requiring extensive data to discern context.
2. Task-Specific Low-Rank Adaptation (LoRA): By using task-specific LoRA modules, OmniMamba efficiently fine-tunes individual components for specific tasks without requiring a complete model overhaul. This modular approach allows for a more focused and parameter-efficient adaptation.
3. Two-Stage Training Strategy: The model adopts a decoupling strategy in training. Initially, it independently optimizes task-specific modules and then conducts a unified fine-tuning to balance modality alignment and task-specific learning. This approach alleviates issues caused by imbalanced datasets in multimodal tasks.

Evaluation and Performance

OmniMamba's performance is evaluated against several benchmarks and models in the multimodal space. The model rivals and, in many cases, surpasses state-of-the-art models like JanusFlow and Show-o. It achieves competitive scores across various tasks with a mere 2M image-text pairs—a fraction compared to what analogous models require. Omnimamba also attains a 119.2 times speedup in sequence generation and reduces GPU memory usage by 63%, showcasing its efficiency in utilizing computational resources.

Implications and Future Prospects

Practically, the efficiency of OmniMamba implies broader accessibility and applicability, particularly in settings where computational resources are constrained, such as mobile devices or edge computing scenarios. Theoretically, the use of state space models in place of Transformers might inspire further exploration into alternative architectures that could rival or even surpass current deep learning paradigms in efficiency and performance.

The future of AI could see more models adopting OmniMamba's approach, particularly for applications in real-time systems or products operating under stringent processing requirements. Moreover, as a pioneer in employing SSMs for multimodal tasks, OmniMamba sets a precedent for future research aimed at optimizing AI models for both speed and resource efficiency.

In conclusion, OmniMamba illustrates a pivotal direction in AI research with its efficient design and successful application to multimodal understanding and generation tasks. Its innovations and strategic approach to model training and deployment highlight potential pathways to more effective AI applications in diverse domains.