Abstract: Diffusion models have demonstrated promising results in text-to-audio(TTA) generation tasks.However,their practical usability is limited by slow sampling speeds,which restricts their applicability in high-throughput scenarios.To address this issue,progressive distillation methods have been applied to effectively create more streamlined and efficient models.Nevertheless,these methods encounter issues of unbalanced weights at both high and low noise levels,potentially impacting the quality of gene-rated samples.In this paper,we propose a method to balanced SNR-aware,which is an enhanced loss-weighting mechanism for diffusion distillation and employs a balanced approach to weight the loss for both high and low noise levels.We evaluate the proposed method on the AudioCaps dataset,the experimental results showing superior performance during the reverse diffusion process compared to previous distillation methods with the same number of sampling steps.Furthermore,the BSA method allows for a significant reduction in sampling steps from 200 to 25,with minimal performance degradation compared to the original teacher models.

Key words: Text-to-Audio, Progressive distillation, Latent diffusion, Accelerated sampling