Put the cover under the lamp. Each of the five things that made it work is a layer you can switch on: the words, the grid it sits on, the colours it uses, the subject, the light.
. No text at all — the yellowed grain and vintage feel directly trigger nostalgia and make people want to tap in for the story.
Posted by 私藏一寸月光 on Xiaohongshu, 7 May 2025. Shown with credit and a link back.
Keep all five layers and swap what’s under them: your shop, your budget, your city. You get four covers and the post text.
Verbatim. Copy it, or let Queenie swap in your own details.
Referencing the case study "Old photo of Mom holding me when she was young (AI-generated)" for its old-photo restoration approach, vintage texture, visual narrative and posting tone, generate a new set of retro old-photo image-text content. New topic: {enter the photo topic you want to restore, rebuild or recreate} Reference platform: Xiaohongshu Content direction: retro old-photo image-text Creative method: keep the original case's nostalgic emotion, film/photo-paper texture, human relationships, period scene feel and restoration constraints; if it is photo restoration, it must stay faithful to the original identity, facial features, composition and historical context, avoiding excessive beautification, redrawing, false historical information and a modern digital plastic look. Please output: 1. Cover title and subtitle; 2. 6-9 page image-text script, each page including visual description, main copy, era/texture keywords and a save prompt; 3. Visual prompts that can be used directly for image generation or photo restoration, explaining requirements for people, scene, lighting, film grain, damage repair and colour restoration; 4. Posting suggestions: title length, tags, image ratio and platform tone. Constraints: do not replicate the original author, original people or brand logos; do not fabricate historical facts; when involving photos of real people, prioritise identity consistency and do not apply age reduction, beautification, face slimming or facial feature rewriting.
