Automatically synthesizing consistent models is a key prerequisite for many testing scenarios in autonomous driving or software tool validation where model-based systems engineering techniques are frequently used to ensure a designated coverage of critical cornercases. From a practical perspective, an inconsistent model is irrelevant as a test case (e.g. false positive), thus each synthetic model needs to simultaneously satisfy various structural and attribute well-formedness constraints. While different logic solvers or dedicated graph solvers have recently been developed, they fail to handle either structural or attribute constraints in a scalable way. In the current paper, we combine a structural graph solver that uses partial models with an SMT-solver to automatically derive models with which simultaneously fulfill structural and attribute constraints while key theoretical properties of model generation like completeness or diversity are still ensured. This necessitates a sophisticated bidirectional interaction between different solvers which carry out consistency checks, decision, unit propagation, concretization steps. We evaluate the scalability and diversity of our approach in the context of three complex case studies.