Show-o Turbo: Towards Accelerated Unified Multimodal Understanding and
  Generation
Authors: name: Chenkai Xu, name: Xu Wang, name: Zhenyi Liao, name: Yishun Li, name: Tianqi Hou, name: Zhijie Deng

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.