INCModel3/Z-Image-Turbo-MXFP4-RTN-AutoRound

Z-Image-Turbo-MXFP4-RTN-AutoRound

Model Details

This is a MXFP4 (4-bit micro-scaling) quantization of Tongyi-MAI/Z-Image-Turbo, a 6B S3-DiT distilled text-to-image model. Generated by AutoRound with RTN (round-to-nearest, iters=0).

• —Base model: Tongyi-MAI/Z-Image-Turbo
• —Quantization: MXFP4 (W4A4), group_size=32
• —Method: AutoRound RTN
• —Model size: ~11 GB (vs 31 GB bf16)

Quantization Details

• —Scheme: MXFP4 (datatype=mxfp, bits=4, act_bits=4)
• —Group size: 32
• —Export format: auto_round (vllm-omni compatible)
• —Calibration: coco2014, 8 steps, guidance 0.0
• —Ignored layers: adaLN_modulation (kept full precision)

Evaluation

Evaluated with vllm-omni diffusion harness (8 steps, guidance 0.0, 1024×1024, seed 42).

MXFP4 quantization is nearly lossless vs the BF16 baseline (GenEval 0.741 vs 0.757, CLIP 31.66 vs 31.73).

Usage

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

omni = Omni(model="INCModel3/Z-Image-Turbo-MXFP4-RTN-AutoRound", mode="text-to-image")
params = OmniDiffusionSamplingParams(
    height=1024, width=1024, seed=42,
    guidance_scale=0.0, num_inference_steps=8, 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 Tongyi-MAI/Z-Image-Turbo.

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