The integration of diffusion models with attention mechanisms has emerged as a pivotal trend in advancing video processing and generation. These models are being repurposed to tackle complex tasks such as amodal segmentation, video relighting, and motion intensity modulation, showcasing a shift towards more sophisticated and versatile video processing techniques. Notable advancements include the use of diffusion models in video summarization and restoration, addressing long-standing challenges in video quality and content retention. Frameworks like MotionFlow and MotionShop, which leverage attention-driven motion transfer and mixture of score guidance, respectively, are setting new benchmarks in the field. These innovations enhance the fidelity and versatility of video generation, paving the way for more creative and controlled video editing experiences. Additionally, the development of motion estimators and the decoupling of motion intensity modulation in image-to-video generation are significant steps towards more accurate and scalable video processing solutions. Overall, the field is witnessing a rapid evolution towards more nuanced and controllable video generation and processing, driven by advancements in diffusion models and attention-based techniques.