The authors propose Mine‑Sample‑Render (MSR), a test‑time procedure that decouples diversity generation from the internal sampling of a generative model by turning the model into a conditional renderer. First, they mine an interpretable categorical schema from a corpus of real‑world artifacts, extracting attribute‑level statistics that reflect the true distribution of configurations in the data. Second, they sample attribute configurations directly from these corpus‑derived statistics, thereby externalizing the diversity source. Third, they feed each sampled configuration into a pretrained generative model, which is used solely to render the conditioned output without influencing the sampling process. The method is evaluated across five heterogeneous domains—weather report generation, user simulation, webpage design, text‑to‑image synthesis, and agent‑generated 3D assets—where baseline models tend to produce mode‑collapsed or low‑variety samples despite quality guarantees. Experiments demonstrate that MSR‑guided generation yields outputs that align more closely with the empirical corpus distributions and exhibit substantially higher diversity, as measured by distributional similarity and variety metrics, while preserving the perceptual quality of the base generator. The approach thus offers a plug‑and‑compatible way to improve diversity at inference time without retraining or altering the underlying generative architecture.