Cinematic Double Exposure Fashion Portrait

Cinematic Double Exposure Fashion Portrait

Original document

GPT Image 2 · en

A cinematic double-exposure fashion portrait featuring {argument name="subject" default="a woman"} with the uploaded face used 100% as reference, wearing stylish metallic aviator sunglasses. The composition artfully blends a large, semi-transparent close-up profile of her face on the left with her full-body figure standing confidently on the right. She wears {argument name="clothing" default="an open, light-grey textured corduroy shirt layered over a dark grey fitted t-shirt and dark denim jeans"}, posing naturally with her hands in her pockets. She has long, flowing dark hair, flawless skin, elegant feminine features, and a confident, sophisticated expression.

The background is a minimalist neutral studio setting, allowing the double-exposure composition to remain the visual focus. The color grading features a sophisticated {argument name="color palette" default="warm desaturated sepia and charcoal"} palette, enhanced by a soft golden-orange light-leak flare in the upper-right corner. Professional 85mm lens photography with high-contrast yet soft cinematic lighting accentuates the fabric textures, facial details, and layered portrait effect.

Ultra-realistic skin texture, luxury fashion-editorial aesthetic, premium magazine-cover quality, subtle film grain, cinematic depth, elegant shadows, refined color grading, modern retro-inspired styling, masterpiece composition, ultra-sharp focus, visually striking storytelling, high-end commercial fashion campaign look.

How to use

  1. Copy the complete original prompt, including any parameters.
  2. Replace the subject, text and composition with your own requirements.
  3. Use a compatible external tool; check its current access conditions and review the result.

Before you start

  • Check whether a reference image is required. A compatible model label does not mean this example was generated with that model.
  • Keep names, figures and visible text accurate; results can vary between model versions.

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