让视频生成模型更快更省显存，能在单卡上生成超高清视频。
DC-VideoGen: Efficient Video Generation with Deep Compression Video Autoencoder
- 用新型分块时序压缩自编码器，实现32倍空间、4倍时间压缩。
- 仅需10天GPU训练即可加速14.8倍，支持单卡生成2160x3840视频。
- 适配任何预训练扩散模型，适合追求高效部署的开发者。
我们提出DC-VideoGen，一种面向预训练视频扩散模型的后训练加速框架，通过轻量微调将其适配至深度压缩潜在空间，显著提升效率。该框架基于两大创新：(i) 采用新颖的分块因果时序设计的深度压缩视频自编码器，在保持重建质量与长视频泛化能力的前提下，实现32倍/64倍空间压缩和4倍时间压缩；(ii) 提出AE-Adapt-V鲁棒适配策略，可快速稳定地将预训练模型迁移至新潜空间。以预训练的Wan-2.1-14B模型为例，使用NVIDIA H100 GPU仅需10个GPU日即可完成适配。加速后的模型推理延迟较原始版本降低最高达14.8倍，且无需牺牲画质，进一步支持单卡生成2160x3840分辨率视频。代码已开源：https://github.com/dc-ai-projects/DC-VideoGen。
原文摘要 · Abstract (English)
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.
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