Diffusion models have achieved remarkable success in video generation; however, the high computational cost of the denoising process remains a major bottleneck. Existing approaches have shown promise in reducing the number of diffusion steps, but they often suffer from significant quality degradation when applied to video generation. We propose Guided Progressive Distillation (GPD), a framework that accelerates the diffusion process for fast and high-quality video generation. GPD introduces a novel training strategy in which a teacher model progressively guides a student model to operate with larger step sizes. The framework consists of two key components: (1) an online-generated training target that reduces optimization difficulty while improving computational efficiency, and (2) frequency-domain constraints in the latent space that promote the preservation of fine-grained details and temporal dynamics. Applied to the Wan2.1 model, GPD reduces the number of sampling steps from 48 to 6 while maintaining competitive visual quality on VBench. Compared with existing distillation methods, GPD demonstrates clear advantages in both pipeline simplicity and quality preservation.

翻译：扩散模型在视频生成领域取得了显著成功；然而，去噪过程的高计算成本仍然是主要瓶颈。现有方法在减少扩散步数方面展现出潜力，但在应用于视频生成时往往面临显著的画质下降。我们提出引导式渐进蒸馏（GPD），这是一个加速扩散过程以实现快速高质量视频生成的框架。GPD引入了一种新颖的训练策略，其中教师模型逐步指导学生模型以更大的步长运行。该框架包含两个关键组成部分：（1）在线生成训练目标，在提升计算效率的同时降低优化难度；（2）潜在空间中的频域约束，促进细粒度细节与时间动态特性的保持。在Wan2.1模型上的应用表明，GPD将采样步数从48步减少至6步，同时在VBench基准上保持具有竞争力的视觉质量。与现有蒸馏方法相比，GPD在流程简洁性与质量保持方面均展现出明显优势。