The recent advancements in the field of image processing and video generation are marked by a significant shift towards more efficient and controllable models. Researchers are increasingly focusing on developing methods that not only enhance the quality of outputs but also reduce computational overhead. Transformer-based models, which have shown remarkable performance in various tasks, are being optimized for memory efficiency and computational cost. Innovations such as adaptive token routing and distillation-based training processes are being employed to make these models more practical for high-resolution image processing and real-time applications. Additionally, there is a growing emphasis on integrating physical models and constraints into learning frameworks to improve the accuracy and robustness of tasks like dehazing and depth estimation. In video generation, the incorporation of cinematic language and optical controls is paving the way for more sophisticated and user-controllable video synthesis. These developments collectively indicate a move towards more efficient, controllable, and context-aware solutions in the field.