Fast Text-to-Audio Generation with Adversarial Post-Training
Authors: name: Zachary Novack, name: Zach Evans, name: Zack Zukowski, name: Josiah Taylor, name: CJ Carr, name: Julian Parker, name: Adnan Al-Sinan, name: Gian Marco Iodice, name: Julian McAuley, name: Taylor Berg-Kirkpatrick, name: Jordi Pons

Text-to-audio systems, while increasingly performant, are slow at inference
time, thus making their latency unpractical for many creative applications. We
present Adversarial Relativistic-Contrastive (ARC) post-training, the first
adversarial acceleration algorithm for diffusion/flow models not based on
distillation. While past adversarial post-training methods have struggled to
compare against their expensive distillation counterparts, ARC post-training is
a simple procedure that (1) extends a recent relativistic adversarial
formulation to diffusion/flow post-training and (2) combines it with a novel
contrastive discriminator objective to encourage better prompt adherence. We
pair ARC post-training with a number optimizations to Stable Audio Open and
build a model capable of generating approx12s of 44.1kHz stereo audio in
approx75ms on an H100, and approx7s on a mobile edge-device, the fastest
text-to-audio model to our knowledge.