Low-Resource Guidance for Controllable Latent Audio Diffusion
Generative audio requires fine-grained controllable outputs, yet most existing methods require model retraining on specific controls or inference-time controls (\textit{e.g.}, guidance) that can also be…
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Key Takeaways
- May affect how AI can be used.
- By examining the bottlenecks of existing guidance-based controls, in particular their high cost-per-step due to decoder backpropagation, arXiv cs.AI introduces a guidance-based approach through…
What It Means
Context
By examining the bottlenecks of existing guidance-based controls, in particular their high cost-per-step due to decoder backpropagation, arXiv cs.AI introduces a guidance-based approach through selective TFG and Latent-Control Heads (LatCHs), which enables controlling latent audio diffusion models with low computational overhead. LatCHs operate directly in latent space, avoiding the expensive decoder step, and requiring minimal training resources (7M parameters and $\approx$ 4 hours of training). Experiments with Stable Audio Open demonstrate effective control over intensity, pitch, and beats (and a combination of those) while maintaining generation quality. arXiv cs.AI's method balances precision and audio fidelity with far lower computational costs than standard end-to-end guidance. Demo examples can be found at https://zacharynovack.github.io/latch/latch.html.
For builders
By examining the bottlenecks of existing guidance-based controls, in particular their high cost-per-step due to decoder backpropagation, arXiv cs.AI introduces a guidance-based approach through…
For Builders
By examining the bottlenecks of existing guidance-based controls, in particular their high cost-per-step due to decoder backpropagation, arXiv cs.AI introduces a guidance-based approach through…