MaskMamba: A Hybrid Mamba-Transformer Model for Masked Image Generation
Keywords: Masked Image Modeling, Bidirectional Mamba, In-context Condition, Non-autoregressive image synthesis
Abstract: Image generation models have encountered challenges related to scalability and quadratic complexity, primarily due to the reliance on Transformer-based backbones. In this study, we introduce MaskMamba, a novel hybrid model that integrates Mamba and Transformer architectures, utilizing Masked Image Modeling for non-autoregressive image synthesis. We meticulously redesign the bidirectional Mamba architecture by implementing two key modifications: (1) replacing causal convolutions with standard convolutions to better capture global context, and (2) utilizing concatenation instead of multiplication, which significantly boosts performance while accelerating inference speed. Additionally, we explore various hybrid schemes of MaskMamba, including both serial and grouped parallel arrangements. Furthermore, we incorporate an in-context condition that allows our model to perform both class-to-image and text-to-image generation tasks. Our MaskMamba outperforms Mamba-based and Transformer-based models in generation quality. Notably, it achieves a remarkable 54.44\% improvement in inference speed at a resolution of $2048\times 2048$ over Transformer.
Primary Area: generative models
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Submission Number: 6933
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