Ideogram 4 Skill

Text-to-image generation with Ideogram 4 (9.3B, open-weight, released June 2026). Its superpower is best-in-class in-image text rendering — it beats much larger models (FLUX.2 dev 32B, Qwen-Image 20B, Hunyuan 80B) at rendering legible signage, logos, captions, and multi-line text — plus exact color-palette and bounding-box control.

That advantage is locked behind a structured JSON caption format. A plain-text prompt gets you FLUX-level results and misses the entire point of using this model. This skill teaches Claude to act as the "magic prompt" expander — turning a user's casual request into the JSON caption Ideogram 4 was trained on.

Backend: The toolkit uses Ideogram's hosted v4 API (not self-hosted weights). The API accepts a structured json_prompt, so everything this skill teaches applies directly — Claude builds the caption, the tool posts it as json_prompt. Paid API plans include a commercial license, which the self-hostable weights (non-commercial) do not — that's why we use the API. Cost is ~$0.03/image (turbo) to ~$0.09/image (quality).

When to Use This Skill

Reach for Ideogram 4 (over FLUX.2) when the image needs:

• Legible on-image text — title cards, thumbnails, lower-thirds backgrounds, signage, logos, quote cards, CTAs with a headline baked in
• Exact brand colors — hex color-palette conditioning, per-element
• Controlled layout — bounding boxes place text/objects in specific regions
• Multilingual text in the image

Use FLUX.2 instead when: the image has no critical text, you need commercial-licensed output, or you just want a fast atmospheric background. FLUX takes plain natural-language prompts; Ideogram wants JSON. See tools/flux2.py.

The One Thing to Get Right

Always emit a structured JSON caption, not a plain sentence. The model is trained exclusively on JSON captions that name every element explicitly. Claude is a better expander than Ideogram's free hosted magic-prompt (their own docs note the shipped one "is not the same used in production"), so build the caption yourself using this skill rather than passing raw text.

Minimal valid caption:

{"high_level_description":"A sailboat at sunset on calm water.","style_description":{"aesthetics":"serene, warm, golden hour","lighting":"golden hour backlighting","photo":"wide angle, f/8","medium":"photograph","color_palette":["#FF6B35","#F7C59F","#004E89"]},"compositional_deconstruction":{"background":"Calm ocean at low horizon with orange-pink sky.","elements":[{"type":"obj","desc":"White triangular sail silhouetted against the setting sun."}]}}

Full schema, strict key-ordering rules, and the bbox coordinate system are in prompting.md. Worked title-card / thumbnail / quote-card examples are in examples.md.

Quick Reference — tools/ideogram4.py

Thin wrapper over Ideogram's hosted v4 API. Needs IDEOGRAM_API_KEY in .env (key from developer.ideogram.ai). --json posts the caption as the API's json_prompt field (no server-side magic prompt — Claude is the expander); --prompt posts text_prompt.

# Hand-authored JSON caption (the recommended path for text/layout) — Claude writes caption.json
python3 tools/ideogram4.py --json caption.json --output title.png

# Caption from stdin (Claude can pipe it directly)
cat caption.json | python3 tools/ideogram4.py --json - --output title.png

# Plain prompt — Ideogram's server-side magic prompt expands it (weaker; prefer --json)
python3 tools/ideogram4.py --prompt "Title card: 'AI ENGINEERING REVIEW' bold white on dark" --output title.png

# Inject brand hex colors into the caption's palette (JSON mode)
python3 tools/ideogram4.py --json caption.json --brand digital-samba --output cta.png

# Quality tier + resolution
python3 tools/ideogram4.py --json caption.json --speed QUALITY --resolution 2048x2048 --output slide.png

Key Files

• prompting.md — full JSON schema, strict key ordering, bbox coordinate system, palette rules
• examples.md — worked captions for title cards, thumbnails, quote cards, brand CTAs

Video Production Fit

Ideogram 4's niche in the toolkit is slides and thumbnails with baked-in text, where FLUX and LTX-2 fail (both render garbled text). Natural pairings:

Then feed the still into Remotion (<OffthreadVideo>/Img) or animate it with tools/ltx2.py --input.