标题： 音频扩散变换器的训练后量化 显示英文标题

标题： Post-Training Quantization for Audio Diffusion Transformers

摘要： 扩散变换器（DiTs）能够实现高质量的音频合成，但通常计算量大且需要大量存储，这限制了它们的实际部署。 在本文中，我们对音频DiTs的训练后量化（PTQ）技术进行了全面评估，分析了静态和动态量化方案之间的权衡。 我们探索了两个实用的扩展：（1）一种去噪时间步 aware 的平滑方法，该方法根据每个输入通道和时间步调整量化尺度以减轻激活异常值，以及（2）一种基于奇异值分解（SVD）的轻量级低秩适配器（LoRA）分支，用于补偿残差权重误差。 使用Stable Audio Open我们对W8A8和W4A8配置进行了基准测试，涵盖了客观指标和人类感知评分。 我们的结果表明，动态量化即使在较低精度下也能保持保真度，而静态方法在较低延迟下仍具有竞争力。 总体而言，我们的研究结果表明，低精度DiTs可以在减少内存使用高达79%的同时保留高保真生成。 显示英文摘要

摘要： Diffusion Transformers (DiTs) enable high-quality audio synthesis but are often computationally intensive and require substantial storage, which limits their practical deployment. In this paper, we present a comprehensive evaluation of post-training quantization (PTQ) techniques for audio DiTs, analyzing the trade-offs between static and dynamic quantization schemes. We explore two practical extensions (1) a denoising-timestep-aware smoothing method that adapts quantization scales per-input-channel and timestep to mitigate activation outliers, and (2) a lightweight low-rank adapter (LoRA)-based branch derived from singular value decomposition (SVD) to compensate for residual weight errors. Using Stable Audio Open we benchmark W8A8 and W4A8 configurations across objective metrics and human perceptual ratings. Our results show that dynamic quantization preserves fidelity even at lower precision, while static methods remain competitive with lower latency. Overall, our findings show that low-precision DiTs can retain high-fidelity generation while reducing memory usage by up to 79%.