Fast Text-to-Audio Generation with Adversarial Post-Training

• URL: http://arxiv.org/abs/2505.08175v3
• Date: Tue, 20 May 2025 02:54:49 GMT
• Title: Fast Text-to-Audio Generation with Adversarial Post-Training
• Authors: Zachary Novack, Zach Evans, Zack Zukowski, Josiah Taylor, CJ Carr, Julian Parker, Adnan Al-Sinan, Gian Marco Iodice, Julian McAuley, Taylor Berg-Kirkpatrick, Jordi Pons,
• Abstract summary: Text-to-audio systems are slow at inference time, making their latency unpractical for many creative applications.<n>We present Adversarial Relativistic-Contrastive (ARC) post-training, the first adversarial acceleration algorithm for diffusion/flow models not based on distillation.
• Score: 39.000388217500785
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 $\approx$12s of 44.1kHz stereo audio in $\approx$75ms on an H100, and $\approx$7s on a mobile edge-device, the fastest text-to-audio model to our knowledge.

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