Restore Damaged Old Portrait to Modern Photo AI Image Prompt
Use the uploaded image as the main restoration and identity reference. Convert this severely damaged {argument name="subject type" default="old portrait"} into a clean, fully restored, modern-looking photo while preserving the original appearance, pose, and composition. Restore all missing and damaged areas of the photograph, including torn sections, faded parts, scratches, stains, cracks, peeling emulsion, discoloration, and burned or washed-out regions. Reconstruct the {argument name="subjects" default="man and woman"} completely so both subjects appear whole, natural, and clearly visible. Keep the same arrangement: the woman standing in front holding a {argument name="prop" default="large bouquet of flowers"}, and the man standing slightly behind her. Preserve their facial features, expressions, body proportions, posture, and elegant formal clothing, but refine everything to look polished, realistic, and complete. Transform the image from an antique sepia damaged print into a high-quality modern portrait with: clean natural skin tones, realistic facial details, sharp focus, balanced lighting, soft studio background, improved clarity and texture, realistic modern photo finishing. The final image should feel like a professionally restored contemporary portrait, as if the same couple was photographed clearly with a modern camera, while still keeping the classic formal elegance of the original scene. Style: photorealistic, high detail, restored portrait, modern studio photography, clean, elegant, natural colors, sharp and realistic.
Content: Structure: uploaded image as restoration and identity reference; preserve appearance, pose, composition; repair damage and reconstruct subjects; fixed woman-front, man-behind, holding flowers layout; convert to modern studio portrait with photoreal, natural skin, sharp focus, balanced light, soft background. Includes subject type, subjects, prop parameters.
Pros: Clear restoration scope and preservation details; consistent style with strong identity and composition constraints.
Cons: Heavily depends on uploaded image quality; many constraints may limit generation freedom.
Reference image: Clearly relies on the uploaded image and reference image.
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