MXAttention framework optimizes MXFP4 attention for video generation

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 →

• HunyuanVideo
• MindIE-SD
• MXAttention
• MXFP4
• NVFP4
• Pre-Normalization Quantization
• Universal Optimal Scaling
• VBench
• Wan2.2

• paper
• infra

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