Low-Resource Guidance for Controllable Latent Audio Diffusion
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of a low-resource, inference-time control framework for latent audio diffusion models, which effectively balances control precision, audio fidelity, and runtime performance. The methodology and results presented are significant advancements in the field of controllable audio generation, showcasing the potential for efficient and high-quality audio synthesis.
Methodology
The paper introduces a novel approach to controllable audio generation through the use of Latent-Control Heads (LatCHs) and selective Training-Free Guidance (TFG). By operating directly in latent space, the proposed method significantly reduces computational overhead associated with traditional end-to-end guidance methods. The methodology is well-structured, with clear explanations of how LatCHs function and the rationale behind selective TFG. The authors provide a solid theoretical foundation, linking their work to existing literature while clearly delineating their contributions.
Results & evaluation
The experiments are comprehensive, utilizing the Stable Audio Open (SAO) dataset and comparing the proposed methods against established baselines, including end-to-end guidance and readouts. The evaluation metrics are well-defined, including both qualitative assessments (mean opinion scores) and quantitative metrics (FDopenl3, KLpasst, and CLAP). The results demonstrate that LatCHs outperform traditional methods in terms of both audio quality and computational efficiency, which is a significant achievement in the field of audio generation.
Reproducibility
The paper provides sufficient details regarding the experimental setup, including hyperparameters and training procedures for LatCHs. However, the lack of a publicly available code repository may hinder full reproducibility. The authors do mention the datasets used, which aids in replicating the experiments, but the absence of a project URL limits access to the implementation.
Limitations
One limitation is the potential challenge in generalizing the method to more complex audio generation tasks beyond the evaluated controls (intensity, pitch, and beats). Additionally, the reliance on specific feature extractors may limit the applicability of the approach to other audio domains. The authors also note that controls with greater variability, such as pitch, pose challenges, indicating room for improvement in handling such cases.
Broader impact
The proposed framework has significant implications for the field of generative audio, particularly in applications requiring real-time audio manipulation and control. The ability to generate high-quality audio with low computational costs can benefit various industries, including music production, gaming, and virtual reality. Furthermore, the approach could pave the way for more accessible audio generation tools for creators without extensive computational resources. The main contribution of this paper is the introduction of a low-resource, inference-time control framework for latent audio diffusion models, which effectively balances control precision, audio fidelity, and runtime performance. The methodology and results presented are significant advancements in the field of controllable audio generation, showcasing the potential for efficient and high-quality audio synthesis.
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