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3D城市立體模型預覽圖
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3D城市立體模型地圖

一個複雜的參數化場景圖提示,用於產生高端 3D 城市地形圖,並將宏偉的字體融入建築之中。

這是一個其他靈感的GPT Image 2範例。請使用下方可直接使用的提示資訊產生類似的視覺素材,並在重複使用前查看Awesome GPT Image 2 Prompts署名和商業用途授權資訊。

需要完整的提示符號集?請使用 其他靈感 主題中心提供更多相關範例,或打開 GPT Image 2 提示庫 有關完整範例索引、可重複使用結構和來源歸屬的資訊。
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提示詞

可直接複製的提示

PYTHON_SCENE_GRAPH :: PARAMETRIC_CITY_RELIEF class Variables: city = "{argument name="city" default="[CITY]"}" city_name_text = "{argument name="city name" default="literal city name from input"}" region_context = "infer country, terrain, climate, culture, urban identity" topography = "infer mountains, rivers, coastlines, plains, islands, deserts, hills" urban_grid = "infer district density, roads, transit corridors, urban pattern" landmarks = "infer landmark_set(city)" signature_core = "infer most symbolic central landmark or public space" style = "luxury 3D cartographic city model" class TerrainSlab: form = "thick raised cutout map base" surface = Variables.topography edges = "engraved title panel, legend, compass, scale, inset regional map" material = "matte stone/plaster/cartographic model material" class CityTypography: text = Variables. city_name_text form = "monumental 3D letters" function = "each letter is an inhabitable building mass" placement = "integrated into city map, not floating" rule = "word must remain readable from aerial view" class UrbanLayer: roads = Variables.urban_grid districts = "infer neighborhoods and density zones" landmarks = Variables.landmarks core = Variables.signature_core labels = "derive place labels from city geography" class Atmosphere: camera = "elevated three-quarter macro" lighting = "soft premium studio daylight" details = "vehicles, clouds, aircraft, trees, people only if appropriate" def render(): return """ Render {argument name="target city" default="[CITY]"} as a raised terrain-map diorama where the city name becomes monumental architecture, surrounded by inferred geography, landmarks, labels, roads, and atlas-style cartographic details. """

提示變數

提示符號中可編輯的參數佔位符及其預設值。

3
多變的
city
預設
[CITY]
多變的
city name
預設
literal city name from input
多變的
target city
預設
[CITY]

Best for

  • - 其他靈感 visual exploration
  • - Image generation and reference-image edits
  • - Reusable briefs that keep source attribution visible

Change these parts

  • - Subject, product, character, or scene
  • - Aspect ratio, camera, lighting, and background
  • - Brand, text, color, and output constraints

Recommended model and settings

  • - Model: gpt-image-2
  • - Input mode: 文生圖
  • - Aspect ratio: source
  • - Commercial status: review original source

How to use this prompt

  1. 1. Copy the prompt and preserve its structure for the first test.
  2. 2. Replace the subject, context, and publishing channel.
  3. 3. Change one camera, lighting, or style variable per iteration.
  4. 4. Inspect the output, source, text, and rights before reuse.

Limitations, source, and reuse cautions

  • - Generated output is an editable draft, not factual, legal, or rights evidence.
  • - Review the linked source, people, brands, logos, text, and third-party media before commercial use.
內容來自Awesome GPT Image 2 Prompts 。必須註明出處。商業用途狀態為allowed ;付費使用前請查看來源版權資訊。

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預覽案例後,請謹慎使用此提示。

  1. 1.複製提示或直接在 Dovoo 中使用生成按鈕開啟提示。
  2. 2.根據您的實際使用情況調整變數、寬高比和參考影像。
  3. 3.發布或付費使用前,請確認版權、署名要求以及品牌或肖像權風險。