Paris Double Exposure Editorial Portrait

Paris Double Exposure Editorial Portrait

原始文档

GPT Image 2 · en

Using the provided reference image as the strict identity source, transform the person into a luxury metropolitan editorial poster while preserving the exact facial identity, facial proportions, eyes, smile, hairstyle, and natural likeness. Change the outfit from the sports jersey into a plain oversized crewneck sweatshirt, and make the person a relaxed half-body portrait facing slightly to the viewer’s right with a soft confident smile.

Style and treatment: Create a refined monochrome black-and-white fashion editorial look with subtle dark green accents, soft studio lighting, high-end magazine composition, clean gray background, and a polished cinematic finish.

Main visual effect: Add a double-exposure Paris cityscape blended across the sweatshirt and lower torso, including modern high-rise buildings, dense Paris rooftops, and the Eiffel Tower visible on the right side of the chest area. Keep the cityscape grayscale and integrated naturally into the fabric, fading at the edges.

Layout additions: Add exactly 3 text groups on the left side: 1) small uppercase text at the top left reading {argument name="top label text" default="METROPOLITAN IDENTITY"}, 2) large vertical word reading {argument name="city title" default="PARIS"}, 3) small uppercase text near the lower left reading {argument name="series label" default="LUXURY EDITORIAL SERIES"}. Add exactly 2 vertical color swatches near the upper left: one dark green rectangle above one light gray rectangle. Add one thin vertical divider line beside the lower-left series label.

Constraints: Do not keep the original football jersey, logos, number, or blue color. Do not change the person’s identity. Avoid extra text, watermarks, or additional graphic elements. Maintain a premium minimalist editorial poster aesthetic.

使用方法

  1. 复制完整原始提示词,并保留其中的参数。
  2. 按需求替换主体、文字和构图要求。
  3. 在兼容的外部工具中使用,核对当前使用条件并检查结果。

开始前需了解

  • 先确认是否需要参考图。标注兼容模型,不代表该示例由这个模型生成。
  • 核对名称、数字与画面文字;不同模型版本的结果可能不同。

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