Autoregressive Adversarial Post-Training for Real-Time Interactive Video
  Generation
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
684a79ca9b38e1e5a33a68bf
Shanchuan Lin
claimed_verified
2025-06-12T12:40:47.762Z
645863f7dc18eb1a9b5d29df
https://cdn-avatars.huggingface.co/v1/production/uploads/645863f7dc18eb1a9b5d29df/t49Nnyl4tbkUn7CmQqKZh.jpeg
Peter Lin
user
PeterL1n
684a79ca9b38e1e5a33a68c0
Ceyuan Yang
684a79ca9b38e1e5a33a68c1
Hao He
684a79ca9b38e1e5a33a68c2
Jianwen Jiang
684a79ca9b38e1e5a33a68c3
Yuxi Ren
684a79ca9b38e1e5a33a68c4
Xin Xia
684a79ca9b38e1e5a33a68c5
Yang Zhao
684a79ca9b38e1e5a33a68c6
Xuefeng Xiao
684a79ca9b38e1e5a33a68c7
Lu Jiang
Autoregressive Adversarial Post-Training for Real-Time Interactive Video
  Generation
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
Autoregressive adversarial post-training transforms a pre-trained latent video diffusion model into a real-time, interactive video generator with efficient one-step generation and reduced error accumulation.
autoregressive adversarial post-training latent video diffusion model single neural function evaluation KV cache student-forcing real-time video generation 24fps 736x416 resolution 1280x720 resolution H100 GPUs