Caviar and Gold Fine-Dining Overhead — ChaosRealmsAI · MIT

Caviar and Gold Fine-Dining Overhead — ChaosRealmsAI · MIT

原文

GPT Image 2 · en

Scene:
A square fine-dining hero shot photographed from directly overhead on a deep black marble table with soft veining. A single matte white porcelain plate sits slightly off center with generous negative space around it, lit by a precise top light with a faint 45 degree fill from the upper left. The atmosphere is quiet, premium, and editorial, like a Michelin restaurant campaign image made for a luxury magazine spread.

Subject:
At the center of the plate, build a small architectural tasting course around a quenelle of chilled creme fraiche, a restrained mound of glossy black caviar, tiny cubes of translucent kohlrabi, a few gold-leaf accents, and delicate edible flower petals in pale violet and white. Add a thin ring of clear herb oil and several micro dots of dark reduction so the dish reads as intentional constellation-like plating rather than random garnish. The portion should feel expensive and precise, not abundant.

Important details:
Preserve true culinary realism: caviar beads must look separate and hydrated, creme fraiche should hold a soft spooned texture, porcelain should remain clean, and sauce dots must have crisp edges. Keep the garnish logic sparse and balanced, with every component readable at a glance. The visual language is minimalist French fine dining with immaculate spacing, premium ingredient knowledge, and commercial food-photography polish.

Use case:
Michelin Plate Hero Shot single study 1/8, focused on a luxury caviar plate with disciplined top-down composition.

Constraints:
No readable text, no watermark, no logos, preserve exact 1:1 aspect ratio, no cutlery, no hands, no table clutter, no oversized portions, no messy sauce smears, no impossible garnish placements, no extra plates.

使い方

  1. パラメータを含む元のプロンプト全体をコピーします。
  2. 被写体、文字、構図を自分の要件に置き換えます。
  3. 対応する外部ツールで使用し、利用条件と結果を確認します。

開始前の確認

  • 参考画像の要否を確認してください。互換モデルの表示は、そのモデルでの生成実績を意味しません。
  • 名前、数値、画像内の文字を確認します。モデルの版で結果は変わります。

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