Ideogram

Ideogram AI Generator

Current Version: V4 · Last Updated: June 2026 Legacy versions available via the left-hand panel.

Quick Overview

Introduction

Ideogram is a text-to-image model developed by Ideogram, Inc., best known for being one of the most reliable tools at rendering accurate, readable text within generated images. As of V4, it's also the company's first open-weight release — meaning businesses and researchers can download the model, fine-tune it on their own data, and run it on their own servers.

V4 is built from scratch, not an update to V3. The most notable technical change is a new text processing system that uses a full vision-language model instead of simpler text encoders. In practice, this means better understanding of complex descriptions and more accurate rendering of text across languages, including non-Latin scripts. V4 also adds native 2K resolution output, transparent background generation, and a way to specify exactly where elements should appear in the image.

This model is a strong fit for designers, marketing teams, and anyone creating text-heavy graphics like logos, posters, or ad creatives. It's less suited to freeform creative work where you want the model to interpret loosely-worded prompts freely.

What's New in V4

• Open weights with a commercial license — first version you can download, fine-tune, and deploy on your own servers

• Native 2K resolution — generates up to 2048×2048, with flexible aspect ratios up to 6:1

• Transparent backgrounds — output assets ready for compositing, no manual cutout needed

• Layout control via bounding boxes — tell the model where to place text or objects using coordinate inputs

• Optional JSON prompting — for users who want precise, repeatable layouts; natural language still works fine

• Multilingual text rendering — as of V4, readable in-image text now works for non-Latin scripts too

• Upgraded text understanding — uses a vision-language model (Qwen3-VL-8B) instead of a simpler text encoder, giving better comprehension of complex prompts

The jump from V3 to V4 is meaningful for professional use. Transparent backgrounds and layout control are genuinely new capabilities. For casual creative use, V3 and V4 produce similar quality — the new features matter most when you need precise, production-ready output.

Core Features

Native 2K Output

V4 generates at up to 2048×2048 natively, with no upscaling step needed. Textures, lighting, and fine detail hold well at full resolution, making outputs usable for print and high-resolution display directly.

Transparent Backgrounds

As of V4, you can generate images with transparent backgrounds. Product shots, icons, and graphic elements come out ready to drop into other designs without manual cutting.

Natural Language and JSON Prompting

V4 responds well to regular text prompts — describe what you want in plain language and it works. For users who need tighter control over layout and colors, there's also an optional JSON format where you can specify exact positions, hex color palettes, and per-element descriptions. You don't need to use JSON to get good results.

Prompt Guide

V4 works well with natural language prompts — you don't need to learn any special format to get good results. That said, a few habits make a noticeable difference.

Writing Effective Natural Language Prompts

Be specific about what you want to see: lighting, surface, mood, and composition. "product photo of a glass perfume bottle, soft studio lighting, white background, shallow depth of field" produces more consistent results than "product photo". Design vocabulary terms (editorial, minimalist, matte, high-contrast) work well. Vague mood words like "beautiful" or "amazing" add little.

For text in images, spell out exactly what you want: "a poster with the headline 'Summer Sale' in bold sans-serif at the top". The more specific you are about placement and style, the better.

Using the JSON Format (Optional — for Advanced Control)

If you need precise, repeatable layouts — like placing a headline at an exact position or matching a brand's hex colors — V4 supports a structured JSON prompt format. Here's a simple example:

json

{
  "style_description": "Minimalist product poster, clean white background, soft shadow",
  "elements": [
    {
      "description": "A glass bottle of perfume, amber liquid, gold cap",
      "bounding_box": [0.3, 0.1, 0.7, 0.75]
    },
    {
      "text": "LUMIÈRE",
      "font_style": "serif, light weight",
      "bounding_box": [0.25, 0.8, 0.75, 0.92]
    }
  ],
  "color_palette": ["#F5F0E8", "#C9A96E", "#2C2C2C"]
}

Bounding box coordinates are [x_min, y_min, x_max, y_max] as a fraction of the image size (0 to 1). Keep element regions from overlapping for cleaner results.

Choosing the Right Speed Mode

Turbo is great for exploring ideas quickly. Switch to Balanced or Quality once you have a direction you like — they run more generation steps and produce noticeably sharper detail, especially for text and fine textures.

Version History