title: Show-o Turbo: Towards Accelerated Unified Multimodal Understanding and
    Generation
summary: There has been increasing research interest in building unified multimodal
  understanding and generation models, among which Show-o stands as a notable
  representative, demonstrating great promise for both text-to-image and
  image-to-text generation. The inference of Show-o involves progressively
  denoising image tokens and autoregressively decoding text tokens, and hence,
  unfortunately, suffers from inefficiency issues from both sides. This paper
  introduces Show-o Turbo to bridge the gap. We first identify a unified
  denoising perspective for the generation of images and text in Show-o based on
  the parallel decoding of text tokens. We then propose to extend consistency
  distillation (CD), a qualified approach for shortening the denoising process of
  diffusion models, to the multimodal denoising trajectories of Show-o. We
  introduce a trajectory segmentation strategy and a curriculum learning
  procedure to improve the training convergence. Empirically, in text-to-image
  generation, Show-o Turbo displays a GenEval score of 0.625 at 4 sampling steps
  without using classifier-free guidance (CFG), outperforming that of the
  original Show-o with 8 steps and CFG; in image-to-text generation, Show-o Turbo
  exhibits a 1.5x speedup without significantly sacrificing performance. The code
  is available at https://github.com/zhijie-group/Show-o-Turbo.
paper_authors._id: 67aaea0a0acaa007694aed73
paper_authors.name: Chenkai Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-11T07:54:28.861Z
paper_authors._id: 65708920806dee337da0eef5
paper_authors.avatarUrl: /avatars/945e328dedc8e1e3111f48c344ad5b03.svg
paper_authors.fullname: xuchenkai
paper_authors.type: user
paper_authors.user: UnhurriedDawn
paper_authors._id: 67aaea0a0acaa007694aed74
paper_authors.name: Xu Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-11T07:54:26.432Z
paper_authors._id: 6644548a3a16452261cdb173
paper_authors.avatarUrl: /avatars/4643db904204e3a60202a29e8c884139.svg
paper_authors.fullname: wangxu
paper_authors.type: user
paper_authors.user: asunalove
paper_authors._id: 67aaea0a0acaa007694aed75
paper_authors.name: Zhenyi Liao
paper_authors._id: 67aaea0a0acaa007694aed76
paper_authors.name: Yishun Li
paper_authors._id: 67aaea0a0acaa007694aed77
paper_authors.name: Tianqi Hou
paper_authors._id: 67aaea0a0acaa007694aed78
paper_authors.name: Zhijie Deng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-11T07:54:24.089Z
paper_authors._id: 64bba541da140e461924dfed
paper_authors.avatarUrl: /avatars/367993765b0ca3734b2b100db33ed787.svg
paper_authors.fullname: zhijie deng
paper_authors.type: user
paper_authors.user: zhijie3
paper_title: Show-o Turbo: Towards Accelerated Unified Multimodal Understanding and
    Generation
paper_summary: There has been increasing research interest in building unified multimodal
  understanding and generation models, among which Show-o stands as a notable
  representative, demonstrating great promise for both text-to-image and
  image-to-text generation. The inference of Show-o involves progressively
  denoising image tokens and autoregressively decoding text tokens, and hence,
  unfortunately, suffers from inefficiency issues from both sides. This paper
  introduces Show-o Turbo to bridge the gap. We first identify a unified
  denoising perspective for the generation of images and text in Show-o based on
  the parallel decoding of text tokens. We then propose to extend consistency
  distillation (CD), a qualified approach for shortening the denoising process of
  diffusion models, to the multimodal denoising trajectories of Show-o. We
  introduce a trajectory segmentation strategy and a curriculum learning
  procedure to improve the training convergence. Empirically, in text-to-image
  generation, Show-o Turbo displays a GenEval score of 0.625 at 4 sampling steps
  without using classifier-free guidance (CFG), outperforming that of the
  original Show-o with 8 steps and CFG; in image-to-text generation, Show-o Turbo
  exhibits a 1.5x speedup without significantly sacrificing performance. The code
  is available at https://github.com/zhijie-group/Show-o-Turbo.
paper_ai_keywords: multimodal Show-o text-to-image image-to-text denoising image tokens autoregressive decoding diffusion models consistency distillation (CD) trajectory segmentation curriculum learning GenEval classifier-free guidance (CFG)