We present a family of distributed algorithms for Critical Test Generation (CTG). CTG subsumes the problems of requirement falsification and scenario generation, two important methods for safety validation of cyber-physical systems. In this article, we explore different strategies to parallelize and distribute a CTG algorithm based on generative models for test generation. By leveraging the sampling flexibility of generative models, our designs scale from synchronous to fully asynchronous distribution while maintaining test diversity. To ensure practical deployment across environments, the solution also considers scalability, data security, and compatibility with different systems under test through an implementation-agnostic architecture, in which the PaaS and SaaS layers support seamless migration and hybrid extensions. Experiments across multiple systems and distribution scales show that all distributed CTG variants outperform the sequential baseline in both efficiency and effectiveness. Among them, the fully asynchronous variant delivers the best results, combining fast execution with broad falsification capability. These results highlight the practical and generalizable potential of distributed CTG algorithms for testing complex systems in large-scale settings.