The September 29 2025 paper introduces DC-VideoGen, a new post-training framework designed to significantly accelerate video diffusion models and reduce their training costs. This system relies on two main innovations: the Deep Compression Video Autoencoder (DC-AE-V), which achieves high spatial and temporal compression using a novel chunk-causal temporal modeling approach to maintain reconstruction quality; and AE-Adapt-V, an efficient finetuning strategy using LoRA to adapt pre-trained models to the new latent space while preserving their original knowledge and semantics. Experimental results demonstrate that DC-VideoGen successfully accelerates inference speed by up to 14.8× for high-resolution videos and drastically reduces training expenses, all while maintaining or improving video generation quality across tasks like text-to-video and image-to-video generation. Source: https://arxiv.org/pdf/2509.25182