SoundWeaver: Semantic Warm-Starting for Text-to-Audio Diffusion Serving

Ayush Barik, Sofia Stoica, Nikhil Sarda, Arnav Kethana, Abhinav Khanduja, Muchen Xu, Fan Lai

SoundWeaver: Semantic Warm-Starting for Text-to-Audio Diffusion Serving: 3 upvotes on Hugging Face Daily Papers, #35 of 48 papers on 2026-03-13. Day-by-day upvote history.

Text-to-audio diffusion models produce high-fidelity audio but require tens of function evaluations (NFEs), incurring multi-second latency and limited throughput. We present SoundWeaver, the first training-free, model-agnostic serving system that accelerates text-to-audio diffusion by warm-starting from semantically similar cached audio. SoundWeaver introduces three components: a Reference Selector that retrieves and temporally aligns cached candidates via semantic and duration-aware gating; a Skip Gater that dynamically determines the percentage of NFEs to skip; and a lightweight Cache Manager that maintains cache utility through quality-aware eviction and refinement. On real-world audio traces, SoundWeaver achieves 1.8--3.0 times latency reduction with a cache of only {sim}1K entries while preserving or improving perceptual quality.

Paper page on Hugging Face · arXiv

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