DC-VideoGen: Efficient Video Generation with Deep Compression Video
  Autoencoder
Authors: name: Junyu Chen; name: Wenkun He; name: Yuchao Gu; name: Yuyang Zhao; name: Jincheng Yu; name: Junsong Chen; name: Dongyun Zou; name: Yujun Lin; name: Zhekai Zhang; name: Muyang Li; name: Haocheng Xi; name: Ligeng Zhu; name: Enze Xie; name: Song Han; name: Han Cai
Published: 2025-09-29 17:59:31

Abstract: We introduce DC-VideoGen, a post-training acceleration framework for
efficient video generation. DC-VideoGen can be applied to any pre-trained video
diffusion model, improving efficiency by adapting it to a deep compression
latent space with lightweight fine-tuning. The framework builds on two key
innovations: (i) a Deep Compression Video Autoencoder with a novel chunk-causal
temporal design that achieves 32x/64x spatial and 4x temporal compression while
preserving reconstruction quality and generalization to longer videos; and (ii)
AE-Adapt-V, a robust adaptation strategy that enables rapid and stable transfer
of pre-trained models into the new latent space. Adapting the pre-trained
Wan-2.1-14B model with DC-VideoGen requires only 10 GPU days on the NVIDIA H100
GPU. The accelerated models achieve up to 14.8x lower inference latency than
their base counterparts without compromising quality, and further enable
2160x3840 video generation on a single GPU. Code:
https://github.com/dc-ai-projects/DC-VideoGen.