Flux: Open-source text-to-image model with 12B parameters

Original Article ↗ Hacker News Discussion ↗

Model variants, licensing & availability

  • Three variants discussed:
    • FLUX.1 [schnell]: 4‑step, Apache 2.0, open weights, “fast” but slightly lower quality.
    • FLUX.1 [dev]: open weights with non‑commercial license, guidance‑distilled.
    • FLUX.1 [pro]: highest quality, API-only, closed weights.
  • Confusion and criticism around calling “dev” open source given usage restrictions; several argue “open weights” is more accurate.
  • Some uncertainty over what “guidance-distilled” means and how exactly dev/pro differ in practice.

Image quality, prompt adherence & comparisons

  • Many commenters find quality “remarkably good,” some saying dev/pro rival or exceed Midjourney 6.x and SD3, especially for photorealism and text-in-image.
  • Schnell is praised for speed and surprisingly good text rendering; also reveals watermarks/logos from training data more clearly.
  • Others note weak adherence in official examples (beach, cooking), missing requested elements, and vague “artsy” prompt wording.
  • Flux often fails at complex compositional prompts, spatial relations, negation (“no X”), engineering diagrams, precise layouts, and specific stylistic requests (e.g., certain fine‑art painters).

Hardware, local use & tooling

  • Official guidance: 12B parameters, ~24–33 GB VRAM typical; A100 not strictly required.
  • Reports of workable setups: 24–32 GB gaming cards, 32 GB V100, Jetson AGX Orin (slow), and even 8–12 GB VRAM with heavy offloading (very slow).
  • Mixed results on Apple Silicon due to bfloat16/MPS issues.
  • Popular frontends: ComfyUI, StableSwarmUI, Automatic1111; schnell/dev already integrated.

Censorship, NSFW & bias

  • Hosted endpoints apply NSFW filters; sometimes return black images. This is attributed to post‑inference classifiers, not the core model, but shows the model can generate NSFW internally.
  • Noted political bias: generic prompts like “a president” yield similar-looking specific figures.
  • Some users explicitly seek uncensored local use and expect fast NSFW fine‑tunes.

Data, IP & “open source” debate

  • Strong debate over whether models are copyrightable, whether licenses on weights are enforceable, and if models are derivative works of training data.
  • Concerns about learned logos/watermarks suggesting copyrighted sources; others argue training likely uses publicly visible, already‑quoted material.
  • Broader argument over misuse of the term “open source” for models without training data and with restrictive terms.

fal.ai UX, pricing & positioning

  • fal.ai clarifies it did not build Flux, only hosts optimized inference.
  • Mixed feedback: initial no‑login access later gated; GitHub-only login; prompts lost on sign-in; “low balance” emails despite free credits; unclear free-tier limits.