DC-VideoGen: Efficient Video Generation with Deep Compression Video
  Autoencoder
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.
68dc88d34159d1f2418f9a36
Junyu Chen
68dc88d34159d1f2418f9a37
Wenkun He
68dc88d34159d1f2418f9a38
Yuchao Gu
68dc88d34159d1f2418f9a39
Yuyang Zhao
68dc88d34159d1f2418f9a3a
Jincheng Yu
68dc88d34159d1f2418f9a3b
Junsong Chen
claimed_verified
2025-10-01T10:22:15.012Z
645b5b09bc7518912e1f9733
/avatars/4d35f728b41f93881a9b67c337f4d1df.svg
Chen
user
Lawrence-cj
68dc88d34159d1f2418f9a3c
Dongyun Zou
68dc88d34159d1f2418f9a3d
Yujun Lin
68dc88d34159d1f2418f9a3e
Zhekai Zhang
68dc88d34159d1f2418f9a3f
Muyang Li
claimed_verified
2025-10-01T10:22:18.394Z
63129589bbaa385279d1826e
https://cdn-avatars.huggingface.co/v1/production/uploads/63129589bbaa385279d1826e/0AUs3q4ngRZ-wXuY1jP9G.jpeg
Muyang Li
user
Lmxyy
68dc88d34159d1f2418f9a40
Haocheng Xi
68dc88d34159d1f2418f9a41
Ligeng Zhu
68dc88d34159d1f2418f9a42
Enze Xie
68dc88d34159d1f2418f9a43
Song Han
68dc88d34159d1f2418f9a44
Han Cai
DC-VideoGen: Efficient Video Generation with Deep Compression Video
  Autoencoder
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.
DC-VideoGen accelerates video generation by adapting pre-trained diffusion models to a deep compression latent space, reducing inference latency and enabling high-resolution video generation.
DC-VideoGen video diffusion model deep compression latent space lightweight fine-tuning Deep Compression Video Autoencoder chunk-causal temporal design AE-Adapt-V Wan-2.1-14B model inference latency high-resolution video generation