Low-Resource Guidance for Controllable Latent Audio Diffusion

2026-03-04

AI Summary

Existing audio generation methods often rely on model retraining or computationally expensive inference-time guidance to achieve fine-grained control, struggling to balance precision and efficiency. This work proposes a low-resource latent-space guidance approach that enables precise, multi-dimensional control over attributes such as intensity, pitch, and tempo directly within the latent variable space of a diffusion model. By integrating a selective Targeted Feature Guidance (TFG) strategy with lightweight Latent Control Heads (LatCHs), the method achieves high-quality audio synthesis with only 7M additional parameters and approximately four hours of training on the Stable Audio Open model. The approach significantly reduces computational overhead while outperforming conventional end-to-end guidance techniques in both controllability and generation quality.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

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 computationally demanding. By examining the bottlenecks of existing guidance-based controls, in particular their high cost-per-step due to decoder backpropagation, we introduce 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. Our 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.

Problem

• controllable audio generation
• low-resource guidance
• latent diffusion models
• computational efficiency
• audio attributes control

Innovation

• Latent-Control Heads
• Low-Resource Guidance
• Audio Diffusion
• Selective TFG
• Controllable Generation