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参数化视错觉海报

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参数化视错觉海报 1
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参数化视错觉海报

一种独特的结构化提示词,用于通过参数化逻辑风格生成双重解读的负空间海报。

Category
Charts & Infographics
Model
GPT Image 2
Creator
Gadgetify
Source language
en
Views0
Source ID
23636
Published
Jun 1, 2026
Use in StudioOpen source

Full prompt

2x2 grid, do this for ai inferred figures and colors: python_scene_graph :: parametric_optical_illusion class variables: topic = "{argument name="topic" default="[topic]"}" duality = "infer opposing or layered meanings" primary_silhouette = "infer dominant readable outer shape" hidden_image = "infer secondary image from negative space" symbols = "infer minimal supporting symbols" palette = "infer 2-4 colors from mood" style = "minimalist negative-space poster" class composition: first_read = variables.primary_silhouette second_read = variables.hidden_image rule = "both images must share edges and contours" density = "low detail, high clarity" class shapelogic: black_shapes = "define primary silhouette" white_or_color_voids = "define hidden image" intersections = "designed so both readings remain intentional" class render: camera = "flat poster view" texture = "subtle paper grain optional" lighting = "none; graphic design clarity" output = "clean optical metaphor poster" def generate(): return "render {argument name="topic" default="[topic]"} as a dual-read negative-space image with no hard-coded symbols."
Translations

参数化视错觉海报

en

2x2 grid, do this for ai inferred figures and colors: python_scene_graph :: parametric_optical_illusion class variables: topic = "{argument name="topic" default="[topic]"}" duality = "infer opposing or layered meanings" primary_silhouette = "infer dominant readable outer shape" hidden_image = "infer secondary image from negative space" symbols = "infer minimal supporting symbols" palette = "infer 2-4 colors from mood" style = "minimalist negative-space poster" class composition: first_read = variables.primary_silhouette second_read = variables.hidden_image rule = "both images must share edges and contours" density = "low detail, high clarity" class shapelogic: black_shapes = "define primary silhouette" white_or_color_voids = "define hidden image" intersections = "designed so both readings remain intentional" class render: camera = "flat poster view" texture = "subtle paper grain optional" lighting = "none; graphic design clarity" output = "clean optical metaphor poster" def generate(): return "render {argument name="topic" default="[topic]"} as a dual-read negative-space image with no hard-coded symbols."

参数化视错觉海报

zh-CN

2x2 网格,针对 AI 推断的图形和颜色执行以下操作:python_scene_graph :: parametric_optical_illusion 类变量:topic = "{argument name="topic" default="[主题]"}" duality = "推断对立或分层的含义" primary_silhouette = "推断主导且可读的外部轮廓" hidden_image = "从负空间中推断次要图像" symbols = "推断极简的辅助符号" palette = "根据情绪推断 2-4 种颜色" style = "极简主义负空间海报" 类组合:first_read = variables.primary_silhouette second_read = variables.hidden_image rule = "两幅图像必须共享边缘和轮廓" density = "低细节,高清晰度" 类形状逻辑:black_shapes = "定义主要轮廓" white_or_color_voids = "定义隐藏图像" intersections = "设计使两种解读都保持意图明确" 类渲染:camera = "平面海报视图" texture = "可选细微纸张纹理" lighting = "无;平面设计清晰度" output = "简洁的视错觉隐喻海报" def generate(): return "将 {argument name="topic" default="[主题]"} 渲染为双重解读的负空间图像,且不包含硬编码符号。"

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