SoundWeaver: Semantic Warm-Starting for Text-to-Audio Diffusion Serving
Authors: name: Ayush Barik, name: Sofia Stoica, name: Nikhil Sarda, name: Arnav Kethana, name: Abhinav Khanduja, name: Muchen Xu, name: Fan Lai
Organization: University of Illinois at Urbana-Champaign

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.