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MXAttention framework optimizes MXFP4 attention for video generation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed MXAttention, a novel data-free post-training quantization framework designed to optimize MXFP4 attention in diffusion-based video generation models. This framework addresses numerical issues like clipping-underflow trade-offs and normalization errors that typically degrade generation quality. MXAttention incorporates Universal Optimal Scaling (UOS) for distribution-independent scaling and Pre-Normalization Quantization (PNQ) to maintain normalization accuracy. Experiments demonstrate that MXAttention significantly reduces the quality gap between MXFP4 and FP16 formats, achieving near FP16 generation quality with minimal overhead and competitive performance against NVFP4 baselines. AI

IMPACT Improves efficiency and quality in diffusion-based video generation models by optimizing attention mechanisms.

RANK_REASON Research paper detailing a new technical framework for optimizing AI model performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

MXAttention framework optimizes MXFP4 attention for video generation

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Research paper detailing a new technical framework for optimizing AI model performance. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jianlin Yu, Jing Lin, Linghui Kong, Aiyue Chen, Weiyi Sun, Chenyu Zeng, Wangli Lan, Jinxi Li, Zhuo Zheng, Ziyang Yue, Danning Ke, Fei Yi, Tianchi Hu, Yuan Ding, Yiwu Yao, Junsong Wang ·

    MXAttention: Data-Free Optimal Scaling and Pre-Normalization Quantization for MXFP4 Attention

    arXiv:2607.24377v1 Announce Type: cross Abstract: The quadratic cost of attention is a major bottleneck in diffusion-based video generation models. MXFP4 attention provides a promising path toward efficient inference, but direct MXFP4 quantization often degrades generation qualit…

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