title: Scalable Autoregressive Image Generation with Mamba
summary: We introduce AiM, an autoregressive (AR) image generative model based on
  Mamba architecture. AiM employs Mamba, a novel state-space model characterized
  by its exceptional performance for long-sequence modeling with linear time
  complexity, to supplant the commonly utilized Transformers in AR image
  generation models, aiming to achieve both superior generation quality and
  enhanced inference speed. Unlike existing methods that adapt Mamba to handle
  two-dimensional signals via multi-directional scan, AiM directly utilizes the
  next-token prediction paradigm for autoregressive image generation. This
  approach circumvents the need for extensive modifications to enable Mamba to
  learn 2D spatial representations. By implementing straightforward yet
  strategically targeted modifications for visual generative tasks, we preserve
  Mamba's core structure, fully exploiting its efficient long-sequence modeling
  capabilities and scalability. We provide AiM models in various scales, with
  parameter counts ranging from 148M to 1.3B. On the ImageNet1K 256*256
  benchmark, our best AiM model achieves a FID of 2.21, surpassing all existing
  AR models of comparable parameter counts and demonstrating significant
  competitiveness against diffusion models, with 2 to 10 times faster inference
  speed. Code is available at https://github.com/hp-l33/AiM
paper_authors._id: 66c7f717c5c61e4baa81307e
paper_authors.name: Haopeng Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-26T07:15:40.855Z
paper_authors._id: 64e86fbd0c2413c3571ef7a6
paper_authors.avatarUrl: /avatars/960e17ba6a0d03fbd4700cd198adf5af.svg
paper_authors.fullname: Haopeng Li
paper_authors.type: user
paper_authors.user: hp-l33
paper_authors._id: 66c7f717c5c61e4baa81307f
paper_authors.name: Jinyue Yang
paper_authors._id: 66c7f717c5c61e4baa813080
paper_authors.name: Kexin Wang
paper_authors._id: 66c7f717c5c61e4baa813081
paper_authors.name: Xuerui Qiu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-18T09:37:08.473Z
paper_authors._id: 66d98c2bc9758ee602cb2e22
paper_authors.avatarUrl: /avatars/cea03f263d5ed292c4e558871112c61c.svg
paper_authors.fullname: Qiu
paper_authors.type: user
paper_authors.user: Xuerui123
paper_authors._id: 66c7f717c5c61e4baa813082
paper_authors.name: Yuhong Chou
paper_authors._id: 66c7f717c5c61e4baa813083
paper_authors.name: Xin Li
paper_authors._id: 66c7f717c5c61e4baa813084
paper_authors.name: Guoqi Li
paper_title: Scalable Autoregressive Image Generation with Mamba
paper_summary: We introduce AiM, an autoregressive (AR) image generative model based on
  Mamba architecture. AiM employs Mamba, a novel state-space model characterized
  by its exceptional performance for long-sequence modeling with linear time
  complexity, to supplant the commonly utilized Transformers in AR image
  generation models, aiming to achieve both superior generation quality and
  enhanced inference speed. Unlike existing methods that adapt Mamba to handle
  two-dimensional signals via multi-directional scan, AiM directly utilizes the
  next-token prediction paradigm for autoregressive image generation. This
  approach circumvents the need for extensive modifications to enable Mamba to
  learn 2D spatial representations. By implementing straightforward yet
  strategically targeted modifications for visual generative tasks, we preserve
  Mamba's core structure, fully exploiting its efficient long-sequence modeling
  capabilities and scalability. We provide AiM models in various scales, with
  parameter counts ranging from 148M to 1.3B. On the ImageNet1K 256*256
  benchmark, our best AiM model achieves a FID of 2.21, surpassing all existing
  AR models of comparable parameter counts and demonstrating significant
  competitiveness against diffusion models, with 2 to 10 times faster inference
  speed. Code is available at https://github.com/hp-l33/AiM
paper_ai_summary: AiM, an autoregressive image generative model using the Mamba state-space architecture, achieves superior quality and faster inference speed compared to existing AR and diffusion models.
paper_ai_keywords: autoregressive (AR) image generative model Mamba architecture state-space model long-sequence modeling linear time complexity Transformers next-token prediction paradigm visual generative tasks efficient long-sequence modeling scalability FID ImageNet1K diffusion models