The recent developments in the field of generative models have significantly advanced the capabilities and evaluation methodologies of these models. A notable trend is the shift towards more efficient and user-tailored evaluation frameworks, which aim to reduce computational costs while enhancing the interpretability and applicability of evaluation results. Innovations in benchmarking libraries and metrics have also been introduced to address the growing complexity and diversity of generative tasks, particularly in text-to-image and time series domains. Additionally, there is a strong emphasis on developing comprehensive benchmarks that simulate real-world professional design scenarios, pushing the boundaries of generative models' versatility and applicability. Furthermore, the field is witnessing advancements in model identification and training monitoring, which promise to streamline the selection and optimization processes for generative models. These developments collectively highlight a move towards more intelligent, efficient, and user-centric generative model solutions.