TokensGen：利用壓縮令牌實現長視頻生成

TokensGen: Harnessing Condensed Tokens for Long Video Generation

July 21, 2025

作者: Wenqi Ouyang, Zeqi Xiao, Danni Yang, Yifan Zhou, Shuai Yang, Lei Yang, Jianlou Si, Xingang Pan

cs.AI

摘要

生成连贯的长视频是一项复杂的挑战：尽管基于扩散的生成模型能够生成视觉上令人印象深刻的短片，但将其扩展至更长时长时，常会遇到内存瓶颈和长期一致性问题。本文提出了一种新颖的两阶段框架——TokensGen，通过利用压缩的标记来解决这些问题。我们的方法将长视频生成分解为三个核心任务：(1) 片段内语义控制，(2) 长期一致性控制，以及(3) 片段间平滑过渡。首先，我们训练了To2V（Token-to-Video），这是一个由文本和视频标记引导的短片扩散模型，并配备了一个视频标记器，将短片压缩为富含语义的标记。其次，我们引入了T2To（Text-to-Token），这是一个视频标记扩散变换器，能够一次性生成所有标记，确保跨片段的全局一致性。最后，在推理阶段，采用自适应FIFO-Diffusion策略无缝连接相邻片段，减少边界伪影并增强过渡的平滑性。实验结果表明，我们的方法在不引入过高计算开销的情况下，显著提升了长期时间和内容的一致性。通过利用压缩标记和预训练的短片模型，我们的方法为长视频生成提供了一个可扩展、模块化的解决方案，为叙事、电影制作和沉浸式模拟开辟了新的可能性。详情请访问我们的项目页面：https://vicky0522.github.io/tokensgen-webpage/。

English

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/ .