The recent advancements in diffusion-based generative models have significantly pushed the boundaries of image editing and generation. A common theme across several papers is the focus on addressing unintended consequences and enhancing control over the generative process. Innovations in attribute leakage mitigation, concept erasure, and moderation of generalization in generative models are particularly prominent. These developments aim to balance the flexibility and power of diffusion models with the need for precise control and ethical considerations. Notably, methods for precise, fast, and low-cost concept erasure, as well as strategies to moderate the generalization of score-based models, are advancing the field by providing more robust and ethical generative solutions. Additionally, efforts to mitigate NSFW content generation and enhance model security through novel attack strategies and defense mechanisms are crucial for maintaining the integrity and safety of these models in real-world applications. These advancements collectively underscore a shift towards more controlled, secure, and ethically sound generative AI systems.