TokensGen: Harnessing Condensed Tokens for Long Video Generation
Authors: name: Wenqi Ouyang, name: Zeqi Xiao, name: Danni Yang, name: Yifan Zhou, name: Shuai Yang, name: Lei Yang, name: Jianlou Si, name: Xingang Pan

Generating consistent long videos is a complex challenge: while
diffusion-based generative models generate visually impressive short clips,
extending them to longer durations often leads to memory bottlenecks and
long-term inconsistency. In this paper, we propose TokensGen, a novel two-stage
framework that leverages condensed tokens to address these issues. Our method
decomposes long video generation into three core tasks: (1) inner-clip semantic
control, (2) long-term consistency control, and (3) inter-clip smooth
transition. First, we train To2V (Token-to-Video), a short video diffusion
model guided by text and video tokens, with a Video Tokenizer that condenses
short clips into semantically rich tokens. Second, we introduce T2To
(Text-to-Token), a video token diffusion transformer that generates all tokens
at once, ensuring global consistency across clips. Finally, during inference,
an adaptive FIFO-Diffusion strategy seamlessly connects adjacent clips,
reducing boundary artifacts and enhancing smooth transitions. Experimental
results demonstrate that our approach significantly enhances long-term temporal
and content coherence without incurring prohibitive computational overhead. By
leveraging condensed tokens and pre-trained short video models, our method
provides a scalable, modular solution for long video generation, opening new
possibilities for storytelling, cinematic production, and immersive
simulations. Please see our project page at
https://vicky0522.github.io/tokensgen-webpage/ .