Food & Drink

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Use Cases

Suitable for Food & Drink AI image generation workflows, with reusable structure for fast iteration.
Best for stable, style-consistent outputs in iterative generation.

Notes

Replace placeholders (such as [OBJECT] and {variable}) with concrete subjects and contexts.
Keep core constraints first (composition, lighting, lens, materials), then add style modifiers gradually.
If results become noisy, reduce prompt density and reintroduce key elements step by step.

#Food & Drink

Author@GdgtifySource URLhttps://x.com/Gdgtify/status/2004986136335110526

Prompt Template

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Inputs: Ingredients = [User Choice, e.g., Chicken, Bacon]

Step 1: Global Analysis
Analyze the ingredients.
Select 4 Distinct Countries with a famous dish using these ingredients  

Goal: A "Culinary Atlas" Grid showing 4 different cultural variations of the ingredients.

Layout Rule:
Output Format: A 2x2 Grid.
Content per Panel: Each of the 4 panels must show a Single Open Book Spread for that specific country.

Inside Each Panel (The Book Spread Rule):
Left Page (The History): 2D Vintage Sepia Sketches. A visual timeline or illustrated recipe of the dish (e.g., "1850 -> 1920 -> Today"). Flat, ink-drawn style.
Right Page (The Reality): 3D Pop-up Diorama. The finished, steaming meal emerges physically from the page. A tiny miniature chef from that country is standing on the page next to the massive food.
Consistency: All 4 books should look like part of the same series, but with different cultural contents.

Lighting: Cinematic top-down lighting, highlighting the depth difference between the flat left page and the 3D right page.
Output: 2x2 Grid, High Definition, Macro Detail.
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Build Image with Prompt

Sample Images