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