The recent advancements in text-to-image diffusion models have significantly pushed the boundaries of generative capabilities, particularly in the areas of personalization, editing, and safety. Researchers are focusing on enhancing the controllability and precision of image generation, allowing for more nuanced and specific outputs. This trend is evident in the development of methods that enable fine-grained customization of visual concepts, as well as those that ensure the safety and ethical use of these powerful models. Additionally, there is a growing emphasis on the stability and fidelity of video generation, with approaches that mitigate unintended changes and improve the overall quality of edited videos. Notably, the field is also addressing the critical issue of preventing the relearning of unlearned concepts in diffusion models, ensuring that these models remain secure and free from misuse. Overall, the direction of the field is towards more sophisticated, controllable, and secure generative models that can be applied in a wide range of practical scenarios.