TADA! Tuning Audio Diffusion Models through Activation Steering

Title:TADA! Tuning Audio Diffusion Models through Activation Steering

Abstract:Audio diffusion models can synthesize high-fidelity music from text, yet achieving fine-grained control over specific musical attributes remains challenging, as their internal mechanisms for representing high-level concepts are poorly understood. In this work, we use activation patching to demonstrate that recent audio diffusion architectures exhibit a semantic bottleneck, where a small, shared subset of consecutive attention layers controls distinct musical concepts, such as the presence of specific instruments, vocals, or genres. Building on this, we systematically evaluate a broad spectrum of steering paradigms, comparing activation steering against prompt-level, score-space, and weight-space interventions, analyzing the interaction between the steering mechanism and the intervention site. Our new benchmark, supported by an extensive user study, demonstrates that localized activation steering establishes a new state-of-the-art in audio concept modulation.

Sound (cs.SD); Machine Learning (cs.LG)

arXiv:2602.11910 [cs.SD]

(or arXiv:2602.11910v3 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2602.11910 Focus to learn more arXiv-issued DOI via DataCite

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