--- base_model: - black-forest-labs/FLUX.2-dev pipeline_tag: text-to-image license: other tags: - quantized - mxfp4 - autoround - diffusion - text-to-image - autoquant-agent --- # FLUX.2-dev-MXFP4-RTN-AutoRound ## Model Details This is a **MXFP4** (4-bit micro-scaling) quantization of [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev), a flagship text-to-image diffusion model. Generated by [AutoRound](https://github.com/intel/auto-round) with RTN (round-to-nearest, iters=0). - **Base model:** [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev) - **Quantization:** MXFP4 (W4A4), group_size=32 - **Method:** AutoRound RTN - **Model size:** ~62 GB (vs ~110 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, 28 steps, guidance 3.5 ## Evaluation Evaluated with vllm-omni diffusion harness (28 steps, guidance 3.5, 1024×1024, seed 42). | Benchmark | BF16 Baseline | MXFP4 Quantized | |---|---|---| | DrawBench CLIP | 32.48 | 32.44 | | DrawBench CLIP-IQA | 71.35 | 71.01 | | DrawBench ImageReward | 1.15 | 1.11 | | GenEval | 0.844 | 0.835 | MXFP4 quantization is nearly lossless vs the BF16 baseline (GenEval 0.835 vs 0.844, CLIP 32.44 vs 32.48). ## Usage ```python from vllm_omni.entrypoints.omni import Omni from vllm_omni.inputs.data import OmniDiffusionSamplingParams omni = Omni(model="INCModel3/FLUX.2-dev-MXFP4-RTN-AutoRound", mode="text-to-image") params = OmniDiffusionSamplingParams( height=1024, width=1024, seed=42, guidance_scale=3.5, num_inference_steps=28, 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-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev). _Produced with [autoquant-agent](https://github.com/) — agent-driven quantize + evaluate + self-heal._