The recent advancements in diffusion models have significantly pushed the boundaries of image synthesis and editing. A notable trend is the integration of semantic understanding and control into diffusion processes, enabling more precise and diverse image generation. Innovations such as diffusion-negative prompting and lifelong few-shot customization are bridging the semantic gap between human intent and machine understanding, leading to more intuitive and effective image synthesis. Additionally, the development of scalable, tokenization-free architectures is making diffusion models more feasible for on-device applications, addressing the computational challenges associated with traditional architectures. Image editing capabilities are also advancing rapidly, with models like SeedEdit and OmniEdit offering sophisticated, instruction-guided editing that balances fidelity to the original image with creative freedom. Notably, the introduction of training-free object insertion methods and flexible generative perception error models for autonomous driving further exemplifies the versatility and practical application of these models. These developments collectively indicate a shift towards more controllable, efficient, and contextually aware diffusion models, paving the way for broader real-world applications and deeper semantic interactions between humans and machines in image synthesis and editing tasks.