Fluid antenna systems (FAS) have emerged as a promising technology for next-generation wireless systems. However, practical multiuser multiple-input multiple-output FAS (MIMO-FAS) faces two inherently coupled challenges: acquiring accurate high-dimensional channel state information (CSI) from limited RF chains and solving the combinatorial port selection problem, where the effectiveness of the latter highly depends on the result of the former. In this paper, we propose a unified two-stage diffusion framework that formulates the joint task as a maximum-a-posteriori (MAP) inference problem equivalent to maximizing the posterior expected utility of the port selection, and approximates it through two sequential stages. For Stage I, we employ a continuous flow-based diffusion model as a powerful implicit prior for 2D FAS channels and perform approximate posterior sampling via a parallel guided generation scheme, enabling accurate multiuser channel recovery even under severely low sub-sampling ratios. For Stage II, we train a discrete diffusion model on optimal labels computed by exhaustive search to propose a pool of promising port selection candidates. The final selection is then determined by maximizing the empirical posterior expected utility over this pool, followed by a lightweight local refinement. Extensive simulations demonstrate that the proposed framework simultaneously achieves accurate channel estimation and delivers over 90% of the deployed rate of the computationally expensive exhaustive search over the estimated channel, while substantially improving the minimum achievable rate relative to practical cascaded schemes.

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