ZigMa: A DiT-style Zigzag Mamba Diffusion Model
<|reference_start|>ZigMa: A DiT-style Zigzag Mamba Diffusion Model: The diffusion model has long been plagued by scalability and quadratic complexity issues, especially within transformer-based structures. In this study, we aim to leverage the long sequence modeling capability of a State-Space Model called Mamba to extend its applicability to visual data generation. Firstly, we identify a critical oversight in most current Mamba-based vision methods, namely the lack of consideration for spatial continuity in the scan scheme of Mamba. Secondly, building upon this insight, we introduce a simple, plug-and-play, zero-parameter method named Zigzag Mamba, which outperforms Mamba-based baselines and demonstrates improved speed and memory utilization compared to transformer-based baselines. Lastly, we integrate Zigzag Mamba with the Stochastic Interpolant framework to investigate the scalability of the model on large-resolution visual datasets, such as FacesHQ $1024\times 1024$ and UCF101, MultiModal-CelebA-HQ, and MS COCO $256\times 256$ . Code will be released at https://taohu.me/zigma/<|reference_end|>
@article{hu2024zigma:,
    title={ZigMa: A DiT-style Zigzag Mamba Diffusion Model},
    author={Vincent Tao Hu, Stefan Andreas Baumann, Ming Gui, Olga Grebenkova,
  Pingchuan Ma, Johannes Fischer, Bj"orn Ommer},
    journal={arXiv preprint arXiv:2403.13802},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13802},
    primaryClass={cs.CV cs.AI cs.CL cs.LG}
}