ZigMa: A DiT-style Zigzag Mamba Diffusion Model

• URL: http://arxiv.org/abs/2403.13802v2
• Date: Mon, 1 Apr 2024 17:58:02 GMT
• Title: ZigMa: A DiT-style Zigzag Mamba Diffusion Model
• Authors: Vincent Tao Hu, Stefan Andreas Baumann, Ming Gui, Olga Grebenkova, Pingchuan Ma, Johannes Fischer, Björn Ommer,
• Abstract summary: We aim to leverage the long sequence modeling capability of a State-Space Model called Mamba to extend its applicability to visual data generation. We introduce a simple, plug-and-play, zero- parameter method named Zigzag Mamba, which outperforms Mamba-based baselines. We integrate Zigzag Mamba with Interpolant framework to investigate the scalability of the model on large-resolution visual datasets.
• Score: 23.581004543220622
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/

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