Autoregressive Adversarial Post-Training for Real-Time Interactive Video
  Generation
Authors: name: Shanchuan Lin, name: Ceyuan Yang, name: Hao He, name: Jianwen Jiang, name: Yuxi Ren, name: Xin Xia, name: Yang Zhao, name: Xuefeng Xiao, name: Lu Jiang

Existing large-scale video generation models are computationally intensive,
preventing adoption in real-time and interactive applications. In this work, we
propose autoregressive adversarial post-training (AAPT) to transform a
pre-trained latent video diffusion model into a real-time, interactive video
generator. Our model autoregressively generates a latent frame at a time using
a single neural function evaluation (1NFE). The model can stream the result to
the user in real time and receive interactive responses as controls to generate
the next latent frame. Unlike existing approaches, our method explores
adversarial training as an effective paradigm for autoregressive generation.
This not only allows us to design an architecture that is more efficient for
one-step generation while fully utilizing the KV cache, but also enables
training the model in a student-forcing manner that proves to be effective in
reducing error accumulation during long video generation. Our experiments
demonstrate that our 8B model achieves real-time, 24fps, streaming video
generation at 736x416 resolution on a single H100, or 1280x720 on 8xH100 up to
a minute long (1440 frames). Visit our research website at
https://seaweed-apt.com/2