A complex, multi-part prompt designed for double inference, generating a miniature architectural cityscape diorama constructed entirely from edible ingredients based on the national gastronomy of a specified location. It uses culinary textures to replace traditional building materials and specifies tilt-shift photography style. (free to use) — Free AI image template on PicX Studio.
Anchor: {argument name="country city name" default="[Country/City Name]"}::4 Morphology: Miniature architectural cityscape, skyline constructed entirely from edible ingredients, tiny skyscrapers and houses mimicking {argument name="country city architecture" default="[Country/City]"} architectural styles but formed from food, culinary structural engineering, hyper-detailed diorama::3 Material Physics: {argument name="country city gastronomy" default="[Country/City]"} national gastronomy as construction material, native cuisine textures replacing concrete and glass, masonry made of regional staples, savory and sweet ingredient structural integrity, authentic local food palette::3 Illumination: Soft focus macro lighting, ambient occlusion, mouth-watering highlights::2 Render Stack: Tilt-shift photography, macro lens, f/2.8, 8k, scale model aesthetic, unreal engine 5 render::1 Negative: [Real concrete, real metal, plastic, inedible, humans, cutlery, plates, blurry]:: -1
Use Edible Cityscape Diorama (Double Inference) to plan an AI image around nano-banana-2 generation. Match the preview's composition first, then personalize the subject, setting, and mood for your own project.
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Create the image, compare the result with the preview, and refine one variable at a time.
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Yes. You can change the subject, setting, lighting, mood, format, and available model controls before creating your image.
This template is free to open and customize. Generation usage is shown in PicX Studio before you create.
Start with nano-banana-2, which is the model associated with this template. You can compare another compatible model for a different finish.
A continuous mix — similar concepts first, woven with same-model prompts and fresh picks from the catalog.
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