title: ZigMa: Zigzag Mamba Diffusion Model
summary: 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_authors._id: 65fbb705a12d7b72306afaef
paper_authors.name: Vincent Tao Hu
paper_authors.status: admin_assigned
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paper_authors.fullname: Vincent Tao Hu
paper_authors.type: user
paper_authors.user: taohu
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paper_authors.name: Stefan Andreas Baumann
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paper_authors.fullname: Stefan Baumann
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paper_authors.user: stefan-baumann
paper_authors._id: 65fbb705a12d7b72306afaf1
paper_authors.name: Ming Gui
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paper_authors.fullname: Ming Gui
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paper_authors.user: mgui
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paper_authors.name: Olga Grebenkova
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paper_authors.fullname: Olga Grebenkova
paper_authors.type: user
paper_authors.user: magistrkoljan
paper_authors._id: 65fbb705a12d7b72306afaf3
paper_authors.name: Pingchuan Ma
paper_authors._id: 65fbb705a12d7b72306afaf4
paper_authors.name: Johannes Fischer
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-22T09:32:46.591Z
paper_authors._id: 63cea5dd257cc48a04f8dc21
paper_authors.avatarUrl: /avatars/db697e0954ff5be392b8ab588df2f0c4.svg
paper_authors.fullname: J. Schusterbauer
paper_authors.type: user
paper_authors.user: joh-schb
paper_authors._id: 65fbb705a12d7b72306afaf5
paper_authors.name: Bjorn Ommer
paper_title: ZigMa: Zigzag Mamba Diffusion Model
paper_summary: 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_ai_summary: Zigzag Mamba, an improvement over Mamba, enhances visual data generation with better spatial continuity, speed, and memory usage, and is scalable with Stochastic Interpolant on large-resolution datasets.
paper_ai_keywords: diffusion model scalability quadratic complexity State-Space Model Mamba spatial continuity scan scheme Zigzag Mamba zero-parameter method Stochastic Interpolant large-resolution visual datasets FacesHQ UCF101 MultiModal-CelebA-HQ MS COCO