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Outdoor Christmas Decoration from Input Image

Outdoor Christmas Decoration from Input Image
πŸ“ Prompt Template
[AI Instruction: Exterior Theme Expansion]
Analyze the provided **[Input Image]**.
Theme Extraction: Identify the main subject, color palette, and specific motifs (e.g., stars, candy canes, specific characters, elegant gold style, cute pop style, etc.) from the [Input Image].
Scale Up: Expand these elements into large-scale outdoor Christmas decorations and lighting installations.
[Scene and Setting]
A festive landscape photo taken at night with a wide-angle view of a house's front yard lavishly decorated for Christmas. The ground and roof are covered with fresh snow.
[Main Features (Based on Input Image)]

Large-Scale Installation: Convert the main subject of the [Input Image] into a large, illuminated outdoor decoration placed in the center of the yard (e.g., a giant glowing wireframe object, inflatable decoration, a large wooden cutout illuminated by spotlights, etc.).
[Auxiliary Decorations (Matching Input Style)]
Lighting: The color of the LED string lights covering trees, shrubs, and the exterior walls of the house is determined based on the main colors of the [Input Image].
Accents: Garlands on porch railings, door wreaths, and path markers use motifs and colors directly extracted from the [Input Image] (e.g., if the input image has a red ribbon, a giant weatherproof red ribbon is used outdoors).
[Atmosphere]
Magically glowing and impressive. The entire scene has a unified feel because all elements are based on the single style guide of the [Input Image].
πŸ’‘ About This Prompt

A structured prompt for the Lovart model (using Nano Banana Pro) designed to transform an input image into a large-scale, illuminated outdoor Christmas decoration scene. It analyzes the input image's theme, color palette, and motifs to create a cohesive, magical, and impressive holiday display.

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Lovart 公式 (γƒ©γƒ–γ‚’γƒΌγƒˆ)
@lovart公式(γƒ©γƒ–γ‚’γƒΌγƒˆ)
Metadata
Published Jun 12, 2026
Model
Nano Banana Pro 10 cr/run
Category
Abstract & Concept
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