FLUX.2-klein-9B-MXFP8-RTN-AutoRound
Model Details
This is a MXFP8 (8-bit micro-scaling) quantization of black-forest-labs/FLUX.2-klein-9B, a lightweight distilled text-to-image model. Generated by AutoRound with RTN (round-to-nearest, iters=0).
- Base model: black-forest-labs/FLUX.2-klein-9B
- Quantization: MXFP8 (W8A8), group_size=32
- Method: AutoRound RTN
- Model size: ~10 GB (vs ~18 GB bf16)
Quantization Details
- Scheme: MXFP8 (data_type=mx_fp, bits=8, act_bits=8)
- Group size: 32
- Export format: auto_round (vllm-omni compatible)
- Calibration: coco2014, 4 steps, guidance 1.0
Evaluation
Evaluated with vllm-omni diffusion harness (4 steps, guidance 1.0, 1024×1024, seed 42).
| Benchmark | BF16 Baseline | MXFP8 Quantized |
|---|---|---|
| DrawBench CLIP | 32.81 | 32.93 |
| DrawBench CLIP-IQA | 69.64 | 69.63 |
| DrawBench ImageReward | 1.23 | 1.21 |
| GenEval | 0.856 | 0.853 |
MXFP8 quantization is essentially lossless vs the BF16 baseline (GenEval 0.853 vs 0.856, CLIP 32.93 vs 32.81).
Usage
from vllm_omni.entrypoints.omni import Omni
from vllm_omni.inputs.data import OmniDiffusionSamplingParams
omni = Omni(model="INCModel3/FLUX.2-klein-9B-MXFP8-RTN-AutoRound", mode="text-to-image")
params = OmniDiffusionSamplingParams(
height=1024, width=1024, seed=42,
guidance_scale=1.0, num_inference_steps=4, num_outputs_per_prompt=1,
)
out = omni.generate("a red bench in a park", sampling_params_list=[params])
License
Please follow the license of the original model black-forest-labs/FLUX.2-klein-9B.
Produced with autoquant-agent — agent-driven quantize + evaluate + self-heal.