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-<precision>_r32-FLUX.2-klein-9B-Nunchaku.safetensors; use nunchaku.utils.get_precision() for <precision> (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-{precision}_r32-FLUX.2-klein-9B-Nunchaku.safetensors
MCO = enable-model-cpu-offload
SCO = enable-sequential-cpu-offload

Image editing (1024x1024)

Setup	Mode	Peak VRAM	Throughput	Time to image	Throughput change vs. base	VRAM change vs. base
`Base`	`CUDA`	OOM	-	-	Baseline unavailable	Baseline unavailable
`Base`	`MCO`	20.18 GB	0.62 it/s	12 s	Baseline	Baseline
`Base`	`SCO`	2.55 GB	0.48 it/s	16 s	Baseline	Baseline
`Base + TE`	`CUDA`	25.60 GB	1.28 it/s	6 s	N/A (base OOM)	N/A
`Base + TE`	`MCO`	20.16 GB	0.83 it/s	9 s	+33.9%	-0.1%
`Base + TE`	`SCO`	6.08 GB	0.48 it/s	16 s	-0.5%	+138.4%
`Base + TR`	`CUDA`	24.51 GB	3.79 it/s	2 s	N/A (base OOM)	N/A
`Base + TR`	`MCO`	17.39 GB	1.08 it/s	7 s	+75.0%	-13.8%
`Base + TR`	`SCO`	4.35 GB	2.70 it/s	2 s	+461.6%	+70.6%
`Base + TR + TE`	`CUDA`	14.00 GB	3.81 it/s	2 s	N/A (base OOM)	N/A
`Base + TR + TE`	`MCO`	7.52 GB	1.88 it/s	4 s	+204.6%	-62.7%
`Base + TR + TE`	`SCO`	7.69 GB	2.68 it/s	2 s	+457.4%	+201.6%

Text-to-image (1024x1024)

Setup	Mode	Peak VRAM	Throughput	Time to image	Throughput change vs. base	VRAM change vs. base
`Base`	`CUDA`	OOM	-	-	Baseline unavailable	Baseline unavailable
`Base`	`MCO`	18.53 GB	0.83 it/s	9 s	Baseline	Baseline
`Base`	`SCO`	2.55 GB	0.62 it/s	12 s	Baseline	Baseline
`Base + TR + TE`	`CUDA`	15.21 GB	8.91 it/s	<1 s	N/A (base OOM)	N/A
`Base + TR + TE`	`MCO`	6.42 GB	2.60 it/s	3 s	+214.5%	-65.4%
`Base + TR + TE`	`SCO`	7.72 GB	3.00 it/s	2 s	+383.0%	+202.7%

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.

Option 1 (for users comfortable with coding)

Engine: vitoom-nunchaku — community-maintained Nunchaku build with FLUX.2 Klein and Qwen3 text encoder support
Framework: Diffusers with Flux2KleinPipeline support (official examples install from source):

pip install "git+https://github.com/huggingface/diffusers.git"

Install vitoom-nunchaku (recommended)

Upstream Nunchaku has not merged FLUX.2 Klein support for a long time (PR #926 still pending). Do not copy patch files manually. Install the prebuilt wheel from tonera/vitoom-nunchaku that matches your platform, Python, and CUDA.

Example (x86_64, Python 3.11, CUDA 13.0):

bash

pip install torch==2.11.* torchvision==0.26.* torchaudio==2.11.* \
  --index-url https://download.pytorch.org/whl/cu130

hf download tonera/vitoom-nunchaku \
  nunchaku-1.3.0.dev20260622+cu13.0torch2.11-cp311-cp311-linux_x86_64.whl \
  --local-dir ./wheels

pip install ./wheels/nunchaku-1.3.0.dev20260622+cu13.0torch2.11-cp311-cp311-linux_x86_64.whl

For cu128, cp310, or ARM64 aarch64 wheels, see the vitoom-nunchaku README.

Optional quantized text encoder: tonera/Qwen3-text-Nunchaku (svdq-int4-Qwen3-text-Nunchaku.safetensors).

Verify:

python -c "import nunchaku; from nunchaku import NunchakuFlux2Transformer2DModel; print(nunchaku.__version__)"

Minimal example (image-to-image + quantized Transformer)

Assumes vitoom-nunchaku is installed and weights are available locally or at tonera/FLUX.2-klein-9B-Nunchaku; set REPO to your directory or Hugging Face model id.

python

import torch
from diffusers import Flux2KleinPipeline
from diffusers.utils import load_image

from nunchaku import NunchakuFlux2Transformer2DModel
from nunchaku.utils import get_precision

REPO = "tonera/FLUX.2-klein-9B-Nunchaku"  # or local absolute path
NAME = "FLUX.2-klein-9B-Nunchaku"

transformer = NunchakuFlux2Transformer2DModel.from_pretrained(
    f"{REPO}/svdq-{get_precision()}_r32-{NAME}.safetensors",
    torch_dtype=torch.bfloat16,
)
pipe = Flux2KleinPipeline.from_pretrained(
    REPO, torch_dtype=torch.bfloat16, transformer=transformer
)

pipe.to("cuda")
# transformer.set_offload(
#     True, use_pin_memory=False, num_blocks_on_gpu=1
# )
# pipeline._exclude_from_cpu_offload.append("transformer")
# pipeline.enable_sequential_cpu_offload()

ref = load_image("https://example.com/your_ref.png").convert("RGB")
image = pipe(
    prompt="Describe your edit in English…",
    image=ref,
    guidance_scale=1.0,  # matches official Klein examples; tune if needed
    num_inference_steps=4,  # common for the distilled model; see Diffusers docs otherwise
    generator=torch.Generator("cpu").manual_seed(1),
).images[0]
image.save("flux2_klein_nunchaku.png")

For text-to-image, omit image (behavior per current Diffusers Flux2KleinPipeline docs). Use pipe.enable_model_cpu_offload() or similar if VRAM is tight.

If the steps above feel too difficult, install the vitoom platform instead (see below).

Option 2 (recommended: vitoom)

We recommend deploying via vitoom: it ships a complete vitoom-nunchaku runtime, Web UI, multi-reference editing, and LoRA management—no manual wheel install or file copying. See docker-usage-en.md.

bash

git clone https://github.com/tonera/vitoom.git
cd vitoom
python scripts/setup_vitoom.py
python scripts/load_vitoom_images.py --components backend,visual
docker compose up -d backend
docker compose -f docker-compose.inference.release.yml --profile visual up -d

In the Web UI: Models → download and activate tonera/FLUX.2-klein-9B-Nunchaku → run in Image workspace.

LoRA

FLUX.2 Klein 9B supports LoRA on the quantized Transformer when using vitoom-nunchaku (wheel or vitoom Visual image)—no manual file copying required:

python

lora_path = "/path/to/your_lora.safetensors"
transformer.update_lora_params(lora_path)
transformer.set_lora_strength(0.8)

License and compliance

These quantized weights are derived from FLUX.2-klein-9B. Use is subject to the FLUX Non-Commercial License and Black Forest Labs’ acceptable use policy; confirm commercial rights separately.

The model card YAML uses license: other because Hugging Face’s allowed license keys do not include the FLUX Non-Commercial License; binding terms are those linked above, not the generic other label alone.