Goku: Flow Based Video Generative Foundation Models

Shoufa Chen, ChongjianGE, Yuqi Zhang, Yida Zhang, Fengda Zhu, Hanish, Hongxiang Hao, Hui Wu, Zhichao Lai, Yifei Hu, Ting-Che Lin, Shilong Zhang, Fu Li, Chuan Li, Xing Wang, Yanghua Peng, peizesun, Ping Luo, Yi Jiang, Zehuan Yuan, Bingyue Peng, liuxiaobing

Goku: Flow Based Video Generative Foundation Models: 106 upvotes on Hugging Face Daily Papers, #2 of 27 papers on 2025-02-10. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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