Subject-driven personalized text-to-image systems commonly generate multiple candidates but return a single output, yet the selection stage is rarely evaluated as an explicit system component. Identity-only selection may overemphasize reference similarity at the expense of prompt alignment and output diversity. We propose distribution-calibrated candidate selection (DCAL), a training-free sequential selector for a predefined ordered prompt set that jointly scores calibrated identity, text alignment, and cross-prompt novelty. All selectors operate on identical pre-generated K=4 candidate pools for all 30 DreamBooth subjects using a fixed LoRA-adapted generator. Relative to the fixed-seed Random-of-4 control, DCAL yields higher 1-LPIPS, CLIP-I, DINO-I, and CLIP-T for 22, 25, 28, and 22 of the 30 subjects, respectively. Compared with identity-only selection, DCAL improves CLIP-T (0.3264 versus 0.3179), CLIP diversity (0.2444 versus 0.2368), and DINO diversity (0.3286 versus 0.3170), while CLIP-I and DINO-I decrease from 0.8250 to 0.8204 and from 0.7560 to 0.7453. Compared with identity-text scoring, prompt-wise novelty calibration increases CLIP-T from 0.3253 to 0.3264 and CLIP diversity from 0.2366 to 0.2444. We further formalize finite-pool Pareto and perturbation-stability properties and analyze inference complexity.

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