Goku: Flow Based Video Generative Foundation Models
Authors: name: Shoufa Chen; name: Chongjian Ge; name: Yuqi Zhang; name: Yida Zhang; name: Fengda Zhu; name: Hao Yang; name: Hongxiang Hao; name: Hui Wu; name: Zhichao Lai; name: Yifei Hu; name: Ting-Che Lin; name: Shilong Zhang; name: Fu Li; name: Chuan Li; name: Xing Wang; name: Yanghua Peng; name: Peize Sun; name: Ping Luo; name: Yi Jiang; name: Zehuan Yuan; name: Bingyue Peng; name: Xiaobing Liu
Published: 2025-02-07 13:03:55

Abstract: This paper introduces Goku, a state-of-the-art family of joint
image-and-video generation models leveraging rectified flow Transformers to
achieve industry-leading performance. We detail the foundational elements
enabling high-quality visual generation, including the data curation pipeline,
model architecture design, flow formulation, and advanced infrastructure for
efficient and robust large-scale training. The Goku models demonstrate superior
performance in both qualitative and quantitative evaluations, setting new
benchmarks across major tasks. Specifically, Goku achieves 0.76 on GenEval and
83.65 on DPG-Bench for text-to-image generation, and 84.85 on VBench for
text-to-video tasks. We believe that this work provides valuable insights and
practical advancements for the research community in developing joint
image-and-video generation models.