Ideogram v4

Ideogram's design-native image model, built for precise control over text, layout, and color in every output.

Overview

Ideogram v4 treats typography, colors and layout as inputs you control. It reads structured JSON prompts — describing every element, its position, and its color — and renders them.

The model was trained exclusively on structured captions, so it expects them: prompts written as prose underperform, and the more relationships you pin down, the more grounded the output.

Strengths for marketers

• In-image text: Best-in-class typography — multi-line, multi-font lettering rendered cleanly, with each text block placed exactly where you want it.

• Layout control: Place any element by bounding box. Titles, objects, and text land where you put them, not where the model guesses.

• Color palette conditioning: Lock exact brand colors with hex codes — up to 16 per image, 5 per element — instead of describing them in words.

• Detail & realism: Photoreal output with fine texture and accurate lighting alongside the design control.

Ideal use cases

• Posters & print visuals: Anything where the text is a strong part of the design.

• Editorial & layout-driven content: Multi-element compositions where placement and hierarchy carry the message.

Weaknesses

• Needs structured prompts — plain text prompts underperform. Results come from JSON with explicit elements, colors, and boxes.

• Reference images are style inspiration — Ideogram reads refs for look and mood, not for exact reproduction. For precise product imagery or character fidelity, route to a dedicated model.

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How to use effectively

Key principles

Ideogram v4 rewards structure over prose. Describe the image as data, not a sentence:

• Prompt in JSON. Wrap every request in the schema: a high_level_description, a style_description block, and a compositional_deconstruction listing each element. The model validates against this format before rendering.

• Type your text. A text element carries the literal string to render plus a separate desc for its styling. This is the mechanism behind multi-line, multi-font in-image text.

• Place elements with bounding boxes. Give any element a bbox as [y_min, x_min, y_max, x_max] in 0–1000 normalized coordinates, origin at the top-left. Boxes are honored precisely.

• Set color with hex codes. Use color_palette arrays — up to 16 hex values per image, 5 per element — to steer dominant colors directly rather than through descriptive language.

Example

json

{
  "high_level_description": "Retro square poster for a team tasting event, bold red type on salmon pink.",
  "style_description": {
    "aesthetics": "Retro, playful, warm.",
    "color_palette": ["#E8552D", "#F4B9A0"]
  },
  "compositional_deconstruction": {
    "background": "Solid salmon-pink ground with scattered confetti shapes.",
    "elements": [
      {
        "type": "text",
        "bbox": [70, 270, 470, 730],
        "text": "MANGO\nSAGO\nSOCIAL",
        "desc": "Bold blocky sans-serif, deep reddish-orange, stacked vertically, slightly distressed."
      },
      {
        "type": "obj",
        "desc": "Tall glass of mango sago dessert topped with cream, a whole mango beside it."
      }
    ]
  }
}