---
base_model:
- black-forest-labs/FLUX.2-klein-9B
pipeline_tag: text-to-image
license: other
tags:
- quantized
- mxfp4
- autoround
- diffusion
- text-to-image
- autoquant-agent
---

# FLUX.2-klein-9B-MXFP4-RTN-AutoRound

## Model Details

This is a **MXFP4** (4-bit micro-scaling) quantization of [black-forest-labs/FLUX.2-klein-9B](https://huggingface.co/black-forest-labs/FLUX.2-klein-9B), a lightweight distilled text-to-image model. Generated by [AutoRound](https://github.com/intel/auto-round) with RTN (round-to-nearest, iters=0).

- **Base model:** [black-forest-labs/FLUX.2-klein-9B](https://huggingface.co/black-forest-labs/FLUX.2-klein-9B)
- **Quantization:** MXFP4 (W4A4), group_size=32
- **Method:** AutoRound RTN
- **Model size:** ~7 GB (vs ~18 GB bf16)

## Quantization Details

- **Scheme:** MXFP4 (data_type=mx_fp, bits=4, act_bits=4)
- **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 | MXFP4 Quantized |
|---|---|---|
| DrawBench CLIP | 32.81 | 32.77 |
| DrawBench CLIP-IQA | 69.64 | 68.81 |
| DrawBench ImageReward | 1.23 | 1.18 |
| GenEval | 0.856 | 0.845 |

MXFP4 quantization shows moderate degradation vs the BF16 baseline (GenEval 0.845 vs 0.856, ImageReward 1.18 vs 1.23). MXFP8 is recommended for higher fidelity.

## Usage

```python
from vllm_omni.entrypoints.omni import Omni
from vllm_omni.inputs.data import OmniDiffusionSamplingParams

omni = Omni(model="INCModel3/FLUX.2-klein-9B-MXFP4-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](https://huggingface.co/black-forest-labs/FLUX.2-klein-9B).

_Produced with [autoquant-agent](https://github.com/) — agent-driven quantize + evaluate + self-heal._