Conformal Selective Generation: Regional Delivery with Proxy Risk Control for Image Inpainting

Image inpainting produces plausible completions, yet deployment requires deciding which generated regions to show. We introduce Conformal Selective Generation (CSG), a framework for regional selective release of a completion designated before scoring. CSG separates the deliverable from auxiliary samples used to estimate uncertainty. It applies a fixed regional score and calibrates a RELEASE/ABSTAIN threshold through conformal risk control across sources. A source loss with a fixed denominator measures released proxy errors and preserves dependence among regions and repeated instances from the same source. Under a frozen protocol and source exchangeability, calibration controls marginal expected proxy risk for any score fixed during development that induces nested release sets. Our primary score measures DINOv2 disagreement between the designated output and the auxiliary centroid. We also evaluate DINO, CLIP, LPIPS, and scores adapted to regional release under the same protocol. On a benchmark of natural images, the primary instantiation achieves 65.3% mean region coverage at mean proxy risk 0.098 across 200 paired source re-splits. Alternative scores yield different risk–coverage tradeoffs. Experiments across four restorers, auxiliary bank sizes, human judgments, perturbations, and mask geometries characterize utility and applicability limits. CSG provides a protocol for partial delivery under an explicit proxy risk budget, with the regional score as a separate modeling choice.