Ideogram 4（GitHub リポジトリ）

*Ideogram 4: Open image model at the forefront of design*

Ideogram 4 is Ideogram's first open-weight text-to-image model. It is a state-of-the-art foundation model trained from scratch — not a fine-tune of any existing model. It introduces a new structured JSON prompting interface, with best-in-class multilingual text rendering, deep language understanding, explicit bounding-box layout and color-palette controls, and native 2k resolution images. The easiest way to try the model is online at ideogram.ai.

We believe openness drives innovation, and we invite the research community to innovate with us on the forefront of visual intelligence.

Table of Contents

• News

• Model Zoo

• Performance

• Quick Start

• Model Summary

• Prompting Guide

• Documentation

• Citation

News

• [2026-06-03] Ideogram 4 released! Inference code and weights

are now public, and our technical blog post is live. See the

Quick Start section to generate your first image, or try the

model online at ideogram.ai.

Model Zoo

We plan to support more quantizations in the future.

Performance

We evaluate Ideogram 4 across third-party arenas and benchmarks, standard

open-source benchmarks, and our own internal human-preference benchmark. Across

all of them, **Ideogram 4 is the best open-weight image model by far, and sits

at the frontier of design.**

Design Arena

Design Arena is a third-party image Elo

leaderboard focused specifically on design-oriented generation. On the overall

board, Ideogram 4 is the top-ranked open-weight model, trailing only proprietary

GPT and Gemini models:

Filtered to open-weight models only, Ideogram 4 leads by a commanding margin,

well ahead of the next-best open model:

ContraLabs

ContraLabs ran a blind typography evaluation judged by

ten professional designers from Contra's top-earning talent. Ideogram 4 leads on

first-place win rate, picked as the best of four models 47.9% of the time

overall — well ahead of Gemini 3.1 Flash Image Preview (Nano Banana 2) at 30.0%,

FLUX.2 [max] (15.5%), and Grok Imagine 1.0 (15.0%):

It also wins on practical usability: asked "Would you use this in real client

work?", the same designers rated Ideogram 4 highest at 3.55 / 5 — significantly

above Nano Banana 2 (2.84), Grok Imagine 1.0 (2.61), and FLUX.2 [max] (2.49):

LMArena

On LMArena, a third-party text-to-image leaderboard that

measures general-purpose text-to-image use cases, Ideogram is the top-ranked

open-weight lab and a top-5 image generation lab overall — beaten only by giant

companies with vastly larger budgets and resources:

Ideogram internal eval

For our internal human-preference benchmark, focused on graphic design and

photography, we had graphic designers deeply familiar with professional design

work do the rating blind. Bradley-Terry scores rank Ideogram 4 #2 overall —

behind only GPT Image 2 medium — and the top open-weight model:

Open-source benchmarks

On standard open-source benchmarks measuring core capabilities — layout control

(7Bench), spatial reasoning and object fidelity (SpatialGenEval), text rendering

(X-Omni OCR), and prompt alignment (Prism) — Ideogram 4 closes the gap to the

leading closed-source models across every axis. On layout control (7Bench), it

is significantly better than all closed-source models:

At 9.3B parameters, Ideogram 4 delivers the best text rendering of any open-weight

release we benchmarked — ahead of much larger models like Qwen-Image (20B),

FLUX.2 [dev] (32B), and HunyuanImage 3.0 (80B MoE):

Quick Start

Install

pip install .

If you plan to modify the code, install in editable mode instead so changes

under src/ideogram4/ take effect without reinstalling:

pip install -e .

Model access

The model weights are gated on Hugging Face, so you must accept the gate and

authenticate before the code can download them — otherwise the download fails

with a 404 / GatedRepoError.

• Open the model page — ideogram-ai/ideogram-4-nf4

(or ideogram-ai/ideogram-4-fp8) — and click

Agree and access repository to accept the license gate.

• Create a Hugging Face access token at

huggingface.co/settings/tokens and log in so the

download is authenticated:

hf auth login

Alternatively, export the token directly: export HF_TOKEN="hf_...".

CLI

The plain --prompt is rewritten into the structured JSON caption the model

expects by a "magic prompt" LLM. By default this uses Ideogram's hosted

magic-prompt API, which is free and does the expansion server-side (no local

model or system prompt needed). It reads IDEOGRAM_API_KEY — get a key at

https://ideogram.ai/api/learn/:

python run_inference.py \
  --prompt "a ginger cat wearing a tiny wizard hat reading a spellbook" \
  --output out.png \
  --quantization "nf4" \
  --magic-prompt-key "$IDEOGRAM_API_KEY"

You can also run the expansion through your own LLM provider — one of our magic-prompt

system prompt is open source. See the

Prompting Guide for details.

For the highest-quality images, set --height 2048 --width 2048 and

--sampler-preset V4_QUALITY_48.

Safety screening with Hive

Prompt and output safety screening is performed via Hive.

Sign up and create a Text Moderation key and a Visual Content Moderation key,

then export them as HIVE_TEXT_MODERATION_KEY and HIVE_VISUAL_MODERATION_KEY

(or pass them via --hive-text-key / --hive-visual-key).

python run_inference.py \
  --prompt "an isometric illustration of a tiny city floating in the clouds" \
  --output out.png \
  --quantization "nf4" \
  --magic-prompt-key "$MAGIC_PROMPT_API_KEY" \
  --hive-text-key "$HIVE_TEXT_MODERATION_KEY" \
  --hive-visual-key "$HIVE_VISUAL_MODERATION_KEY"

For sampler presets, parameter reference, and optimization tips, see

docs/inference.md.

Model Summary

Ideogram 4 is a foundation model trained entirely from scratch, not a

fine-tune or distillation of any existing checkpoint. It is a flow-matching

text-to-image model built on a fully single-stream Diffusion Transformer

(DiT) architecture.

Architecture:

• Fully single-stream DiT. Text and image tokens are concatenated into one

unified sequence and processed through the same 34-layer transformer, with no

separate text or image branches. This enables deep cross-modal interaction at

every layer.

• Vision-language model as text encoder. Instead of a text-only encoder

like CLIP or T5, Ideogram 4 uses

Qwen3-VL-8B-Instruct,

a full vision-language model that provides far richer understanding of visual

concepts. Hidden states are extracted from 13 intermediate layers and

concatenated, giving the model multi-scale semantic features ranging from

surface-level token information to deep compositional understanding.

• Dual-branch classifier-free guidance. The conditional (positive) and

unconditional (negative) branches can be independently refined, enabling

separate control over prompt adherence and image quality.

• Flexible resolution. Native support for any resolution from 256 to 2048

(multiples of 16), with aspect ratios up to 6:1. A single model handles

everything from square thumbnails to ultrawide banners, with the noise

schedule auto-adjusting per resolution.

Key Capabilities:

• Extreme controllability. Ideogram 4 is trained on structured JSON

captions, giving users unprecedented control over composition, style,

lighting, color palette, typography, and spatial layout, all from a single

prompt.

• State-of-the-art text rendering. Ideogram 4 delivers best-in-class

in-image text generation (signage, logos, captions, watermarks, multi-line

text) with high fidelity directly from the prompt.

• Spatial layout control. Bounding-box coordinates in the prompt allow

explicit placement of subjects, text elements, and background regions.

• Color palette conditioning. Specify hex colors in the prompt to steer the

image's dominant color scheme.

For full architecture details, see

docs/model_architecture.md. For a walkthrough of

how the pipeline components fit together, see

docs/pipeline.md.

Prompting Guide

Ideogram 4 is trained exclusively on structured JSON captions. While

plain-text prompts work, you will get the best results by providing a JSON

object that follows our caption schema.

Key points:

• Use JSON prompts for maximum controllability — the model was trained on

them and understands the structure natively.

• Color palette conditioning — specify a colour_palette array of hex

colors in the style description to steer the image's color scheme.

• Aspect ratio flexibility — Ideogram 4 supports a wide range of aspect

ratios (any multiple-of-16 resolution from 256 to 2048 on each side). This

is a key advantage for practical use: portraits, landscapes, banners,

phone wallpapers, social media formats, etc.

• Bounding-box layout — specify bbox coordinates in the prompt to

explicitly place subjects, text elements, and background regions.

• Compositional control — use compositional_deconstruction with bounding

boxes and per-element descriptions for precise spatial layout.

Why JSON-only training? We train exclusively on JSON so that training

and inference share a single, common prompt format. The training captions themselves are deliberately

extremely descriptive: each JSON exhaustively describes everything in

the image to maximize training efficiency. The more

text-to-image relationships each caption pins down, the more grounded

supervision the model extracts from a single training pair, rather than

having to infer those relationships across many sparsely-captioned samples.

Why JSON at inference time? Because the model was trained on captions

that name every object explicitly, the most reliable way to get every

requested object rendered is to mirror that pattern. Plain-text prompts still work, but

won't perform as well since the model was only trained on structured JSON captions.

Don't want to write JSON by hand? That's what *magic prompt* is for: it uses

an LLM to expand a plain-text prompt into a full structured caption before

generation, so you get JSON-quality results from a casual prompt. It runs by

default in run_inference.py (see the CLI section).

See docs/prompting.md for a full guide.

Documentation

Citation

If you find the provided code or models useful for your research, consider citing them as:

@misc{ideogram-4-2026,
    author={Ideogram AI},
    title={{Ideogram 4}},
    year={2026},
    howpublished={\url{https://ideogram.ai/blog/ideogram-4.0/}},
}

We're Hiring!

We're looking for Research Scientists and Research Engineers to

work on next-generation generative models and the products built on top of

them. Interested candidates please apply https://jobs.ashbyhq.com/ideogram