Restore Family Photos Without Rewriting the Past: GPT Image 2.5 Guide

GPT Image 2.5 Old Photo Restoration is presented as a preservation-first workflow for repairing family and archival photographs while distinguishing supported cleanup from speculative reconstruction. It recommends saving untouched high-resolution masters, documenting known and unknown details, addressing one narrow issue at a time, and using separate prompts for scratches, fading, group photographs, missing regions, and optional colorization. The guidance emphasizes that improved sharpness, reconstructed facial features, and color choices may appear convincing without being historically accurate, so users should compare outputs against originals at equal scale, inspect every face and unchanged area, and reject edits that alter expressions, age cues, objects, framing, or unresolved damage. Colorized versions should remain clearly labeled as interpretations and be retained alongside accepted monochrome restorations. The workflow uses Atlas Cloud’s Sunburst Edit model with deliberate quality, dimensions, PNG output, and version records, while noting that no restoration generations, costs, or performance results were completed because access to the test playground required sign-in. It also advises checking privacy terms, recording quoted and settled charges, setting retry limits, considering manual retouching for precise repairs, and evaluating print readiness through actual pixel dimensions and proof prints.

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