- name
- ideogram4
- description
- Prompting patterns for Ideogram 4 text-to-image — best-in-class in-image text rendering and exact color/layout control via structured JSON captions. Use when generating images that need legible on-image text (title cards, thumbnails, logos, signage, CTAs), precise brand colors, or controlled spatial layout. Triggers include title slide image, thumbnail with text, on-image text, legible text in image, brand color palette image, bounding-box layout, Ideogram.
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 asjson_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 ~$0.10/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: