AIが動く。未来が更新される。FLUX.2-klein-9B-Nunchaku
FLUX.2 \[klein\] 9B is BFL’s distilled flow model, supporting text-to-image and multi-reference editing; hardware and licensing details are on the official model card. Also: mean rel_l2 ≈ 0.0717, mean cosine ≈ 1.0006 (raw value in `data.json` is 1.000562; may include floating-point error). For fuller notes and updates, see the Hugging Face model page. In practice, `Base + TR + TE` is the best-balanced option under `MCO`: for image editing, peak VRAM drops from 20.18 GB to 7.52 GB while throughput rises to about 3.05x the base model; for text-to-image, VRAM drops from 18.53 GB to 6.42 GB while throughput rises to about 3.15x. Under `SCO`, quantization still improves speed substantially, but peak VRAM does not necessarily keep decreasing because the base `SCO` path already minimizes VRAM aggressively.
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FLUX.2 \[klein\] 9B is BFL’s distilled flow model, supporting text-to-image and multi-reference editing; hardware and licensing details are on the official model card. Also: mean rel_l2 ≈ 0.0717, mean cosine ≈ 1.0006 (raw value in `data.json` is 1.000562; may include floating-point error). For fuller notes and updates, see the Hugging Face model page. In practice, `Base + TR + TE` is the best-balanced option under `MCO`: for image editing, peak VRAM drops from 20.18 GB to 7.52 GB while throughput rises to about 3.05x the base model; for text-to-image, VRAM drops from 18.53 GB to 6.42 GB while throughput rises to about 3.15x. Under `SCO`, quantization still improves speed substantially, but peak VRAM does not necessarily keep decreasing because the base `SCO` path already minimizes VRAM aggressively.