This paper introduces a generative diffusion model to discover new periodic orbits in 2D Navier-Stokes equations, even when trained on non-periodic turbulent data. By modifying the model and enforcing symmetries, it generated plausible candidates which were then refined into 111 previously unknown exact solutions, highlighting generative AI's role as a complementary tool for exploring complex solution spaces.