Model Card (SVDQuant / Nunchaku) Language : English 中文 Model and upstream Quantized weights repo : tonera/FLUX.2 klein 9B Nunchaku Official full precision source : black forest labs/FLUX.2 klein 9B Quantized Transformer in this repo : svdq r32 FLUX.2 klein 9B Nunchaku.safetensors ; use nunchaku.utils.get precision() for (commonly fp4 or int4 ) so the file name matches your GPU and Nunchaku build Diffusers bundle (VAE, text encoder, etc.): same Hugging Face repo root; use the same from pretrained path when loading the pipeline 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. Quantization quality (excerpt from this repo) Metric Mean Median p50 p90 PSNR 17.56 17.52 20.62 SSIM 0.735 0.741 0.837 LPIPS 0.212 0.194 0.300 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. Performance benchmarks (RTX 5090 32GB, 8 steps, guidance scale = 1.0) Base = black forest labs/FLUX.2 klein 9B TE = svdq int4 Qwen3 text Nunchaku.safetensors TR = svdq {prec…
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