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Resilience assessment in smart grids is often limited by the small number of hand-crafted scenarios. Generative AI can create diverse, realistic synthetic stress events, providing a more rigorous benchmark. This article presents a generative scenario benchmark that combines synthetic cyber-attacks and extreme weather events with a standardized set of DER recovery actions to evaluate grid resilience. The benchmark is designed for comparative testing of cyber-physical distribution-grid controllers. The benchmark uses generative models to produce a large library of attack and weather scenarios. DER recovery actions are catalogued, and several baseline controllers are evaluated using standardized resilience metrics on a distribution feeder. The benchmark reveals distinct performance profiles: reinforcement learning excels in speed, robust optimization improves worst-case protection, and rule-based control provides simple but conservative responses. Three tables summarize the attack types, recovery actions, and comparative performance. The proposed benchmark offers a scalable, reproducible, and diverse testbed for developing and comparing next-generation resilient grid controllers. It supports transparent evaluation under coupled cyber-physical stress rather than isolated contingency cases.