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 (data_type=mx_fp, 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).

Benchmark	BF16 Baseline	MXFP4 Quantized
DrawBench CLIP	31.73	31.66
DrawBench CLIP-IQA	70.57	68.52
DrawBench ImageReward	1.00	0.91
GenEval	0.757	0.741

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 — agent-driven quantize + evaluate + self-heal.