FreeSonic: Training-Free Temporal-Aware Decoupled Attention for Precise Audio Editing
Text-to-audio (TTA) generation has made significant strides, yet achieving precise and consistent audio editing remains a major challenge. However, existing methods struggle to balance temporal consistency with background preservation. In this paper, we propose FreeSonic, a training-free framework leveraging the state-of-the-art Rectified Flow-based TangoFlux model. FreeSonic utilizes an optimized inversion-reverse process and joint text-audio attention maps for precise target segment extraction. For content editing, a novel scheduled attention decoupling confines modifications to target regions while preserving original acoustic context. Furthermore, task-oriented noise injection enhance...
| Authors | Yuxuan Jiang, Mingyang Han, Yusheng Dai et al. |
|---|---|
| Primary category | cs.SD |
| Published | 13 June 2026 |
| Last updated at source | 13 June 2026 |
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