Show-o Turbo: Towards Accelerated Unified Multimodal Understanding and
  Generation
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.
67aaea0a0acaa007694aed73
Chenkai Xu
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2025-02-11T07:54:28.861Z
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xuchenkai
user
UnhurriedDawn
67aaea0a0acaa007694aed74
Xu Wang
claimed_verified
2025-02-11T07:54:26.432Z
6644548a3a16452261cdb173
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wangxu
user
asunalove
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Zhenyi Liao
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Yishun Li
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Tianqi Hou
67aaea0a0acaa007694aed78
Zhijie Deng
claimed_verified
2025-02-11T07:54:24.089Z
64bba541da140e461924dfed
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zhijie deng
user
zhijie3
Show-o Turbo: Towards Accelerated Unified Multimodal Understanding and
  Generation
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.
Show-o Turbo enhances multimodal generation efficiency by applying consistency distillation and trajectory segmentation for unified denoising of text and images.
denoising inference Show-o text-to-image generation image-to-text generation parallel decoding consistency distillation trajectory segmentation curriculum learning GenEval score classifier-free guidance