Launching today
Ideogram 4.0
Generate design-ready image with open weight, layout control
33 followers
Generate design-ready image with open weight, layout control
33 followers
Ideogram 4.0 is an open-weight text-to-image model trained from scratch, with bounding-box layout control, multilingual text rendering, and native 2K output. For developers and enterprises building on visual AI.
Ideogram 4.0 is an open-weight text-to-image model trained from scratch on structured JSON captions, built specifically for design-oriented output including typography, logos, posters, and brand visuals.
Proprietary models have held the lead on layout fidelity and accurate text rendering. Open alternatives have been usable for general photorealism but fall apart when a design needs copy to land in the right place, in the right font, at the right size. Ideogram solves this at the training level, pairing bounding-box coordinates with per-element descriptions so the model learns spatial structure rather than guessing at it.
Here is what that translates to in practice:
Explicit bounding-box layout control via JSON prompts, so every text region and object lands where the brief says it should
Multilingual text rendering across signage, logos, and multi-line typographic layouts, at native 2K resolution
Hex color palette conditioning for brand color control directly in the prompt
Self-hostable with fine-tuning support on proprietary data, and a commercial license that scales by deployment size
Hosted API access from $0.03/image with no subscription required
If you are an ML engineer evaluating open-weight image models for a production pipeline, or a creative technologist who needs design output that actually handles typography without manual cleanup, this is worth a serious look.
Download the weights on HuggingFace or try the model live at ideogram.ai.
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@rohanrecommends Training on bounding-box coordinates rather than letting the model guess at layout is really nice. But the hex palette conditioning is a great feature too. Brand colour control in the prompt is something I'm seeing as a real requirement in today's branding. Question on the bounding box - how tight does the bounding-box adherence hold when you push it. Does a dense, multi-text-region layout stay faithful, or does it start drifting the way the proprietary models do past a certain complexity?