Fast Text-to-Audio Generation with Adversarial Post-Training
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.
6823efb1b220ff8b82381de5
Zachary Novack
claimed_verified
2025-05-15T10:32:43.346Z
643060c6cb3fe707b24c53a2
https://cdn-avatars.huggingface.co/v1/production/uploads/643060c6cb3fe707b24c53a2/MIoM9hrX0vV4XRyrm-4Kz.jpeg
Zachary Novack
user
ZacharyNovack
6823efb1b220ff8b82381de6
Zach Evans
6823efb1b220ff8b82381de7
Zack Zukowski
6823efb1b220ff8b82381de8
Josiah Taylor
6823efb1b220ff8b82381de9
CJ Carr
6823efb1b220ff8b82381dea
Julian Parker
6823efb1b220ff8b82381deb
Adnan Al-Sinan
6823efb1b220ff8b82381dec
Gian Marco Iodice
6823efb1b220ff8b82381ded
Julian McAuley
6823efb1b220ff8b82381dee
Taylor Berg-Kirkpatrick
6823efb1b220ff8b82381def
Jordi Pons
Fast Text-to-Audio Generation with Adversarial Post-Training
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.
Adversarial Relativistic-Contrastive (ARC) post-training optimizes diffusion/flow models for fast text-to-audio generation with minimal latency.
Adversarial Relativistic-Contrastive (ARC) post-training diffusion models flow models relativistic adversarial formulation contrastive discriminator prompt adherence Stable Audio Open