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This is a Core AI conversion of FLUX.2 [klein] 9B by Black Forest Labs, under the FLUX Non-Commercial License (LICENSE.md): non-commercial, non-production use only. You must use content filters or review output before distributing it, and disclose AI generation where the law requires. By agreeing you accept that licence.

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FLUX.2 [klein] 9B β€” Core AI (float16)

Black Forest Labs' FLUX.2 [klein] 9B converted to a Core AI diffusion bundle, 1024 Γ— 1024, 4 steps β€” the "quality" option of the imagine tool. Text-to-image only, square only.

Licence β€” read this first

  • FLUX Non-Commercial License v2.1 (LICENSE.md): non-commercial and non-production use only. Rights come directly from Black Forest Labs to you, not from us.
  • Β§2(e): you must either use content filters to prevent unlawful or infringing output, or review output before distributing it β€” and disclose AI generation where the law requires. imagine writes the IPTC digital-source-type field (trainedAlgorithmicMedia) into every file.
  • BFL's Attribution Notice and our modification statement are in NOTICE. This is not an official Black Forest Labs product.
  • Components (all in BFL's release, all under this licence): the 9B transformer, the Qwen3-8B text encoder (Apache-2.0 upstream) and the FLUX.2 VAE.

Conversion

Apple's recipe, apple/coreai-models (BSD-3) at 52dbd93, float16, no quantization, --single-function, 1024: --experimental (9B is not in Apple's list), one component at a time so the 9B transformer export never held the text encoder (β‰ˆ14 GB peak).

Files

size
metadata.json, tokenizer/, vae_bn_mean.npy, vae_bn_var.npy small
TextEncoder.aimodel (Qwen3-8B, the layers FLUX uses) 11 GB
Transformer.aimodel 17 GB
VAEDecoder.aimodel 95 MB

Measured (macOS 27, M3 Max, 36 GB, GPU, 1024 Γ— 1024, imagine)

Measure Result
one picture, whole command, parts loaded one at a time (--low-memory) 99–137 s, median β‰ˆ110 s
peak memory footprint, one at a time 4.1 GB
all parts resident at once 176 s β€” slower, memory pressure on 36 GB
first run (compiles for this Mac's GPU, cached per program) 304 s
4B on the same prompts 44–53 s

Against 4B on the same 9 prompts and seeds: a tie on photos, products, landscapes and counting; 4B was better on an app icon; 9B was clearly better at longer text ("SOUND EFFECTS PACK" nearly right where 4B wrote gibberish). That is why it is offered, opt-in.

Use

imagine model accept quality         # once: shows these terms, records your acceptance
imagine --quality "a coffee shop sign that says FRESH BREAD DAILY" -o sign.png

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