SoundWeaver: Compositional Warm-Starting for Text-to-Audio Diffusion Serving
Text-to-audio (T2A) diffusion models generate high-quality audio but require tens of neural function evaluations (NFEs), resulting in substantial inference latency. Existing acceleration methods primarily optimize each generation trajectory independently, even when reusable acoustic structure exists in prior examples. We introduce SoundWeaver, the first training-free framework for compositional cross example acoustic reuse. Rather than generating from pure noise, SoundWeaver constructs a prompt-conditioned acoustic prior from segments distributed across cached audios and warm-starts diffusion from an intermediate noise level. We introduce an Acoustic Composition Graph (ACG), which uses ne...
| Authors | Ayush Barik, Sofia Stoica, Nikhil Sarda et al. |
|---|---|
| Primary category | cs.SD |
| Published | 9 March 2026 |
| Last updated at source | 3 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.