Uniform Discrete Diffusion with Metric Path for Video Generation
Authors: name: Haoge Deng, name: Ting Pan, name: Fan Zhang, name: Yang Liu, name: Zhuoyan Luo, name: Yufeng Cui, name: Wenxuan Wang, name: Chunhua Shen, name: Shiguang Shan, name: Zhaoxiang Zhang, name: Xinlong Wang
Organization: Beijing Academy of Artificial Intelligence

Continuous-space video generation has advanced rapidly, while discrete
approaches lag behind due to error accumulation and long-context inconsistency.
In this work, we revisit discrete generative modeling and present Uniform
discRete diffuSion with metric pAth (URSA), a simple yet powerful framework
that bridges the gap with continuous approaches for the scalable video
generation. At its core, URSA formulates the video generation task as an
iterative global refinement of discrete spatiotemporal tokens. It integrates
two key designs: a Linearized Metric Path and a Resolution-dependent Timestep
Shifting mechanism. These designs enable URSA to scale efficiently to
high-resolution image synthesis and long-duration video generation, while
requiring significantly fewer inference steps. Additionally, we introduce an
asynchronous temporal fine-tuning strategy that unifies versatile tasks within
a single model, including interpolation and image-to-video generation.
Extensive experiments on challenging video and image generation benchmarks
demonstrate that URSA consistently outperforms existing discrete methods and
achieves performance comparable to state-of-the-art continuous diffusion
methods. Code and models are available at https://github.com/baaivision/URSA