Higgsfield Gpt Image 2

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).

Sections 1–11 were written for GPT Image 2.0. GPT Image 2.5 — its surface, when to prefer it, and what is and is not documented about prompting it — is the next section.

GPT Image 2.5

New on Higgsfield in the 2026-09-26 catalog as gpt_image_2_5 — not yet field-rated. Facts below are [OFFICIAL — platform, snapshot 2026-09-26] (MCP and CLI agree on all of them) or [OFFICIAL — OpenAI docs] from the image generation guide and the Sunburst / Flare model pages (fetched 2026-09-26).

Surface

A bare call is flare / low / 1k — draft settings. OpenAI: "Use quality: "low" for quick drafts. For final assets, compare higher quality settings to find the right balance of detail, latency, and cost." State quality and resolution explicitly in every delivered settings header.

When to prefer 2.5 vs 2.0

Only platform and vendor facts — neither model has been field-rated against the other here, so no quality ranking is claimed.

Prefer GPT Image 2.5 when:

• The asset needs a transparent background — background: transparent. It is the first transparent-background route in the MCP catalog, and the only one both Higgsfield surfaces agree on. OpenAI's API needs PNG or WebP output for transparency; Higgsfield exposes no output-format parameter, so confirm the downloaded file actually carries alpha before building on it.
• A quality tier above high is wanted — xhigh / max. OpenAI: earlier GPT Image models "support quality settings up to high".
• The frame needs 27:16, 16:27, 9:8, or 8:9 — 2.5 has them, 2.0 does not (DTC Ads / ms_image is the other image model that does).
• The job is editing a reference precisely → variant: sunburst ("for workflows where editing precision matters most"). Fast everyday generation → variant: flare ("fast, high-quality everyday image generation").
• It is a new integration — OpenAI: "For new integrations, use one of the GPT Image 2.5 models."

Keep GPT Image 2.0 when:

• Continuing work already made on 2.0. The repo's field evidence — clothing and wardrobe on GPT Image 2 (../../image-models.md § Routing by Asset Class) and the Soul Cinema → GPT Image 2 refinement pipeline — was gathered on 2.0 and does not transfer to 2.5 automatically.
• A Higgsfield workflow names gpt_image_2 — e.g. the MCP's bundled thumbnail-generation workflow routes its 3D-logo step to gpt_image_2.
• You need CLI mask inpainting on 2.0 — mask / is_inpaint are CLI-listed only (next table).

CLI ↔ MCP disagreement on gpt_image_2

The two official Higgsfield surfaces describe gpt_image_2 differently on 2026-09-26. Recorded as a disagreement — neither surface is declared the winner:

Until one is verified on a real generation: set quality and resolution explicitly on 2.0, and do not promise background, masks, or auto / 4:5 / 5:4 on gpt_image_2 to an MCP user.

Prompting status on 2.5

The Format A / B / C taxonomy in §§ 2–9 was built and exercised on GPT Image 2.0. No GPT Image 2.5 prompting guide was found in OpenAI's image-generation guide or the Sunburst / Flare model pages (searched 2026-09-26), and the taxonomy has not been validated on 2.5 — the 2.5 prompting dialect is not yet documented here. If a user asks for a 2.5 prompt, the §§ 2–5 formats may be used only with an explicit note that they are 2.0 craft, unvalidated on 2.5. What OpenAI does state for its GPT Image models: complex prompts can take up to 2 minutes; text rendering "can still struggle with precise text placement and clarity"; and consistency for recurring characters or brand elements may drift across generations.

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.

2. Three prompt formats

Pick one based on the user's concept. If the concept fits multiple, pick the one best suited to the subject — don't hedge.

Each format has its own craft patterns in §§ 3–5 below. The routing decision is consolidated in § 6.

Tie-break

When in genuine doubt between A and B (e.g., "a character with some labels around them") — default to A. Layout precision is GPT Image 2.0's primary differentiator and prompts should reach for it.

3. Format A — Structured JSON

Write a single JSON object describing every visible region. GPT Image 2.0 reads this as a layout spec.

Core fields to reach for

• type — one-line description of what this image is ("infographic poster", "landing page mockup", "exploded view diagram", "anime character reference sheet", "social media app interface mockup")
• style — the visual style ("cute flat vector illustration, cozy, warm, soft shading", "clean high-tech 3D render, studio lighting, glowing accents", "GTA V cover art style, cel-shaded, thick black panel borders")
• subject or character — the main entity, with specific visual attributes
• layout — nested objects for regions: header, centerpiece, sections, footer, left_side, right_side, grid_panels, top_header, bottom_bar, etc. This is where precision matters most.
• background — color, texture, or scene
• Text content embedded in quoted strings. Keep real text if the user provided it — don't paraphrase. CJK and other non-Latin scripts stay in their original form.

Key patterns that make JSON prompts work

Count-and-label pattern. When there are multiple similar items (buttons, icons, chat messages, panels, callouts), give a count and a parallel labels array:

"messages": {
  "count": 7,
  "items": ["user1: hello", "user2: hi there", "..."]
}

Position-scoped regions. Explicitly name positions: top-left, top-center, mid-right, bottom-center-right. GPT Image 2.0 respects these.

Section objects with title, position, count, labels. For infographics with multiple zones:

{
  "title": "衣装・装備詳細",
  "position": "bottom-left",
  "count": 9,
  "labels": ["胸当て", "肩当て", "腕甲", "..."]
}

Templateable slots with {argument name="x" default="y"}. When a user explicitly wants a reusable template, slot notation like {argument name="city" default="Tokyo"} works. As a heuristic, don't add slots by default on one-off prompts — keep the prompt concrete. When slots are used, the default value should be a realistic value, not a placeholder string.

Inline typography callouts. When typography matters, include it inline: "title in large serif font", "11px Inter Regular", "Space Grotesk Bold Caps".

Worked example — minimal shape

{
  "type": "landing page mockup",
  "style": "clean modern e-commerce, soft pastel palette, generous whitespace",
  "layout": {
    "header": {
      "logo": "small black wordmark 'AURA'",
      "nav": ["Shop", "About", "Journal", "Contact"],
      "cart_icon": "top-right"
    },
    "hero": {
      "left_side": "large product photo of amber glass serum bottle on marble",
      "right_side": {
        "headline": "Skin, restored.",
        "subheadline": "A 7-day reset ritual. Clinically tested.",
        "cta_button": "black pill button 'Shop the ritual'"
      }
    },
    "below_hero": {
      "ingredient_grid": {"count": 4, "labels": ["Vitamin C", "Niacinamide", "Peptides", "Hyaluronic Acid"]}
    }
  }
}

For the ad-recreation variant of structured layout — fractional-coordinate zone notation, safe-zone discipline, brand-vs-structure separation, wireframe intermediation — see the companion static-ads-workflow.md § 3 (Layout zones + safe zones) and § 4 (Brand-vs-structure separation).

4. Format B — Dense cinematic prose

Write one continuous paragraph. Order the information roughly as: image type / medium → main subject with specific visual details → pose or action → background / setting → environmental details → lighting → color palette / film stock / texture → mood descriptor.

Roughly because GPT Image 2.0 handles slight ordering deviation gracefully — the order is a scaffolding, not a HARD RULE. The point is to put the most concrete visual anchors (medium, subject, props) early enough that the model commits to them before downstream details modify.

What makes prose prompts work

Specific over atmospheric. "White ribbed tank top and a loose beige knit cardigan slipping off one shoulder" beats "casual outfit". GPT Image 2.0 executes specificity better than it interprets mood.

Concrete props and objects. Reference exact things: "a white vintage Toyota Levin hatchback with red taillights", "an open notebook, a pen, and a pink flower on a desk". See vocab.md § Composition Vocabulary for the broader spatial-anchoring vocabulary that pairs with concrete-prop direction.

Camera and film language. "35mm film photograph", "direct camera flash", "low-angle dynamic perspective", "aerial drone shot", "shallow depth of field" — these actually steer the output toward the look named.

Embedded text in quotation marks. When text appears in the image, put it in quotes exactly as it should render: elegant vertical Japanese text that reads "都会の夜に溶けていく". This pattern leans into the text-rendering capability documented in § 1.

Avoid "photorealistic" when faces are in frame. Use "cinematic", "film photograph", "35mm", "editorial portrait" instead — these bias toward a look GPT Image 2.0 actually nails, rather than triggering its plasticky-skin failure mode. Cross-reference: § 1 capability framing + vocab.md § Visual Style Vocabulary → Film Stock Emulation.