3 citations · 4 across the 11 of their papers we have counts for
4 papers · 1 filter
Presto! Distilling Steps and Layers for Accelerating Music Generation
Zachary Novack, Ge Zhu, Jonah Casebeer +3
Despite advances in diffusion-based text-to-music (TTM) methods, efficient, high-quality generation remains a challenge. We introduce Presto!, an approach to inference acceleration…
DITTO-2: Distilled Diffusion Inference-Time T-Optimization for Music Generation
Zachary Novack, Julian McAuley, Taylor Berg-Kirkpatrick +1
Controllable music generation methods are critical for human-centered AI-based music creation, but are currently limited by speed, quality, and control design trade-offs. Diffusion…
MusicHiFi: Fast High-Fidelity Stereo Vocoding
Ge Zhu, Juan-Pablo Caceres, Zhiyao Duan +1
Diffusion-based audio and music generation models commonly perform generation by constructing an image representation of audio (e.g., a mel-spectrogram) and then convert it to audi…
DITTO: Diffusion Inference-Time T-Optimization for Music Generation
Zachary Novack, Julian McAuley, Taylor Berg-Kirkpatrick +1
We propose Diffusion Inference-Time T-Optimization (DITTO), a general-purpose frame-work for controlling pre-trained text-to-music diffusion models at inference-time via optimizing…