ZigMa: Zigzag Mamba Diffusion Model

Vincent Tao Hu, Stefan Baumann, Ming Gui, Olga Grebenkova, Pingchuan Ma, J. Schusterbauer, Bjorn Ommer

ZigMa: Zigzag Mamba Diffusion Model: 18 upvotes on Hugging Face Daily Papers, #9 of 18 papers on 2024-03-21. Day-by-day upvote history.

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 1024times 1024 and UCF101, MultiModal-CelebA-HQ, and MS COCO 256times 256. Code will be released at https://taohu.me/zigma/

Paper page on Hugging Face · arXiv

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