Efficient Text-to-Audio Generation via Pruning

arXiv cs.AI · 18 hours ago

Researchers applied model pruning to AudioLDM, a text-to-audio diffusion model, to reduce computational requirements. The pruning reduced U-Net parameters by 83% and multiply-accumulate operations by 39% while maintaining or improving generation quality after lightweight finetuning. The pruned model initially lost ability to generate certain sounds like gunshots and sirens, but these capabilities were mostly recovered through finetuning.