Show HN: Ideogram 4.0 – open-weight 9.3B text-to-image model

It's our new text-to-image model: a 9.3B single-stream diffusion transformer trained entirely from scratch.

We focused heavily on controllability through structured JSON prompts, with strong text rendering, spatial awareness through bounding box guidance, and color palette control.

It has the best text rendering of any open-weight model we've tested so far, and the NF4 quantized checkpoint runs on a single 24GB GPU.

For more technical details and examples see our blog post: https://ideogram.ai/blog/ideogram-4.0/

We will be happy to answer any questions :)

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spate141 20 hours ago | parent | next [-]

I made a detailed playground notebook to run on Nvidia GPU locally and a detailed article on setup.

https://snehal.ai/ideogram4-local-playground/

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Frannky a day ago | parent | prev | next [-]

What are the best alternatives for template + text rendering? I need Canva-level templating ability with the system matching font dimensions, positions, and the image without background.

I ended up creating an algo using color variance and auto-sizing, an LLM to select the fonts and text and nano banana. The thing I need is nano-banana-level images with Canva+human-level abilities automated.

It would be awesome to have a free LLM that can do that. Running on a 16GB RAM Mac. I have a lot of images and templates I can train on too. Not sure if I overengineered it, would love to have just one LLM that can do everything for free.

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vunderba 2 days ago | parent | prev | next [-]

Nice to see another locally hostable model! It’s going to take me a bit longer to add this model to the GenAI Showdown benchmark [1], since I’ll need to add a bit of customization so it produces highly optimized JSON-structured prompts.

It might be worth noting that fal.ai [2] (a fairly popular router in the generative AI space) doesn’t really mention or emphasize the JSON-structured prompt format, and seems to suggest it works just as well with natural language. It might be worth reaching out to them, at least to clarify this point and make things a bit clearer.

[1] - https://genai-showdown.specr.net

[2] - https://fal.ai/ideogram-4

	▲	pigcat a day ago \| parent [-]
		[1] Nice, let us know if you need a hand You can call our API to generate structured json from text using this endpoint (this endpoint is free of charge, but you need a free account to create an api key): https://developer.ideogram.ai/api-reference/api-reference/ma... [2] Fal runs the text_prompt (natural language) through the magic prompt system, so indeed results should be good there

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b3ing a day ago | parent | prev | next [-]

Will it work on Apple silicon machines? Maybe in the Draw Things application? Or is it all command line

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nuancebydefault 2 days ago | parent | prev | next [-]

The galery of generated images looks amazing, it's hard (but often possible) to spot inconsistencies in detailed images.

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tom-wal a day ago | parent | prev | next [-]

Thank you. I will test this on a Mac mini and come back here

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elpocko 2 days ago | parent | prev | next [-]

Non-commercial license, you should not call that "open-weight". Words have meaning.

And people are having a laugh at how censored the model is.

https://old.reddit.com/r/StableDiffusion/comments/1tvtu2u/id...

https://old.reddit.com/r/StableDiffusion/comments/1tvxhzv/id...

	▲	aaraujo002 2 days ago \| parent [-]
		The model should perform better with JSON prompts than with natural language prompts. The safety filter may also be triggered incorrectly by natural language prompts, we are aware of this and will make a future checkpoint update to improve it. See how to prompt the model here: https://github.com/ideogram-oss/ideogram4/blob/main/docs/pro... Disclaimer: I work at Ideogram.

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mahab 2 days ago | parent | prev [-]

Exciting!