The field of diffusion models for video generation is rapidly advancing, with a focus on improving efficiency and reducing computational costs. Recent developments have led to the creation of novel frameworks and techniques that enable faster and more efficient video generation, such as SlimDiff, DriftLite, and QuantSparse. These innovations have achieved significant reductions in parameters, latency, and computational requirements, making diffusion models more practical for real-world applications. Noteworthy papers include SlimDiff, which introduced a training-free, activation-guided structural compression framework, and DriftLite, which proposed a lightweight, training-free particle-based approach for inference-time scaling of diffusion models. Other notable works, such as NeRV-Diffusion and SANA-Video, have demonstrated superior video generation quality and efficiency, while DC-Gen and DC-VideoGen have shown promising results in post-training acceleration and deep compression of latent spaces. Overall, the field is moving towards more efficient, scalable, and high-quality video generation capabilities.