The development of video diffusion models unveils a significant challenge: the substantial computational demands. To mitigate this challenge, we note that the reverse process of diffusion exhibits an inherent entropy-reducing nature. Given the inter-frame redundancy in video modality, maintaining full frame rates in high-entropy stages is unnecessary. Based on this insight, we propose TPDiff, a unified framework to enhance training and inference efficiency. By dividing diffusion into several stages, our framework progressively increases frame rate along the diffusion process with only the last stage operating on full frame rate, thereby optimizing computational efficiency. To train the multi-stage diffusion model, we introduce a dedicated training framework: stage-wise diffusion. By solving the partitioned probability flow ordinary differential equations (ODE) of diffusion under aligned data and noise, our training strategy is applicable to various diffusion forms and further enhances training efficiency. Comprehensive experimental evaluations validate the generality of our method, demonstrating 50% reduction in training cost and 1.5x improvement in inference efficiency.

翻译：视频扩散模型的发展揭示了一个重大挑战：巨大的计算需求。为缓解这一挑战，我们注意到扩散的反向过程具有固有的熵减特性。考虑到视频模态中的帧间冗余性，在高熵阶段维持全帧率是不必要的。基于这一洞见，我们提出了TPDiff——一个提升训练与推理效率的统一框架。通过将扩散过程划分为多个阶段，我们的框架沿着扩散进程逐步提升帧率，仅最后阶段在全帧率下运行，从而优化计算效率。为训练多阶段扩散模型，我们提出了一种专用训练框架：分阶段扩散。通过在对齐的数据与噪声条件下求解扩散的分段概率流常微分方程（ODE），我们的训练策略可适用于多种扩散形式，并进一步提升训练效率。综合实验评估验证了本方法的普适性，实现了训练成本降低50%与推理效率提升1.5倍的显著效果。