Higgsfield GPT Image 2.0

A prompt director for GPT Image 2.0. Converts plain-text concepts into production-ready prompts that route by output type: structured JSON for layout-dense images (UI mockups, infographics, character sheets, multi-panel posters), dense cinematic prose for single-subject scenes (portraits, photographs, landscapes), or auto-derive meta-prompts for theme-only concepts where the model self-generates the composition.

Translated from Adil Aliyev's gpt-image-2-director source corpus per the v3.7.13 / v3.7.15 translation precedent. Two companion satellites extend this sub-skill: static-ads-workflow.md covers the ad-recreation workflow that uses GPT Image 2.0 as its generation engine, and reference-sheet-workflow.md covers the Automatic Product Reference Sheet + Automatic Prompt Creator workflow (one product image → a multi-view identity-locked reference sheet for high-consistency generation).

1. What GPT Image 2.0 is

GPT Image 2.0 is an image-generation model with a distinct capability profile that shapes how its prompts should be written. Four properties drive format choice across the three prompt taxonomies in §§ 2–5 below:

Granular layout precision. GPT Image 2.0 honors granular layout instructions — top-left panel shows X, mid-right shows Y, N icons in a row labeled A/B/C — in a way other models don't reliably match. This is testable: run the same multi-region brief against comparable image models and observe the difference. It's also why the Format A JSON taxonomy works as well as it does: the model reads JSON region keys as layout intent.

Text rendering. Multi-line paragraphs, mixed scripts (CJK + Latin), small UI labels, numeric data in tables — all sharp and legible. This is one of the model's distinctive strengths over comparable image generators. Same testability boundary: a user can verify by running prompts with mixed scripts and small UI labels against comparable models and observing the difference. The implication for prompts: embed real text in quotation marks exactly as it should render; do not paraphrase.

Design and UI as sweet spot. Website landing pages, social-feed mockups, magazine covers, infographics, exploded product diagrams, exam-paper layouts — anything with real information density. Lean prompts into the strengths.

Cinematic photorealism is the weakness. Human faces often go plasticky on realism-flagged prompts. Lean into stylized, illustrated, or editorial aesthetics rather than hyperreal skin. When realism is requested, frame it as film photography (grain, flash, 35mm) rather than as "photorealistic" — film-photography language tends to produce the look users want without triggering the plasticky-skin failure mode. Cross-reference: vocab.md § Visual Style Vocabulary → Film Stock Emulation for the broader film-photography language family.