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LatestBlack Forest Labs4.0
Black Forest LabsText → Image

Ideogram 4.0 arrives as an open-weight image model

A 9.3-billion-parameter text-to-image model lands on GitHub with downloadable weights and code.

Jun 3, 2026
NotableOther

Ideogram 4.0 has been released as an open-weight text-to-image model, with weights and accompanying code published on GitHub. At 9.3 billion parameters, it sits in the range that can run on well-equipped consumer and prosumer hardware, putting a capable image generator directly in the hands of developers and researchers rather than behind an API.

The model is a dense (non-mixture-of-experts) architecture focused squarely on turning text prompts into images. The release was surfaced through a Show HN post, the customary route for projects looking to reach an early technical audience that will kick the tires and report back.

Why it matters

Open weights change the calculus for anyone building on top of an image model. Instead of renting access, teams can inspect, fine-tune, and self-host:

  • Local and on-premise deployment without per-call costs
  • Fine-tuning for specific styles, brands, or domains
  • Reproducible research and auditing of model behavior

One caveat worth flagging: the license is listed as "other," so prospective users should read the terms before assuming standard permissive or commercial rights. As always with a fresh release, real-world quality and prompt adherence will become clearer as the community puts it through its paces.

Sources

    OlderMiniMax Releases M3, a Multimodal MoE ModelMiniMax · Vision-Language · 5 months agoNewerBoson AI's Higgs Audio v3 Offers Expressive, Multilingual TTSBosonai · Text → Speech · 5 months ago

    Get the model


    Specs

    Parameters9.3B
    LicenseOTHER
    Downloads72.1K
    Likes860

    Can you run it?

    Runs on a laptop — about 10.3 GB at FP8.

    • BF16 (as published)

      24 GB GPU (RTX 3090 / 4090) · 32 GB Mac

      19.6 GB
    • FP8

      12 GB GPU (RTX 3060 / 4070) · 16 GB Mac

      10.3 GB
    Your machine
    • Precision not published; assuming 16-bit (BF16) weights.
    • Parameter count is an editorial estimate — no weights metadata on Hugging Face.
    • Covers the published checkpoint; text encoders and VAE loaded alongside may add a few GB (many runtimes offload them to CPU).

    Estimated from the parameter count. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


    Modalities

    Text → Image
    3 versions — view changelog

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