Authors

Venue

Date

Share

Jump Your Steps: Optimizing Sampling Schedule of Discrete Diffusion Models

Yong-Hyun Park

Chieh-Hsin Lai

Satoshi Hayakawa

Yuhta Takida

Yuki Mitsufuji

ICLR-25

2025

Abstract

Diffusion models have seen notable success in continuous domains, leading to the development of discrete diffusion models (DDMs) for discrete variables. Despite recent advances, DDMs face the challenge of slow sampling speeds. While parallel sampling methods like
-leaping accelerate this process, they introduce Compounding Decoding Error (CDE), where discrepancies arise between the true distribution and the approximation from parallel token generation, leading to degraded sample quality. In this work, we present Jump Your Steps (JYS), a novel approach that optimizes the allocation of discrete sampling timesteps by minimizing CDE without extra computational cost. More precisely, we derive a practical upper bound on CDE and propose an efficient algorithm for searching for the optimal sampling schedule. Extensive experiments across image, music, and text generation show that JYS significantly improves sampling quality, establishing it as a versatile framework for enhancing DDM performance for fast sampling.

Related Publications

A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?

Interspeech, 2025
Yigitcan Özer, Woosung Choi, Joan Serrà, Mayank Kumar Singh*, Wei-Hsiang Liao, Yuki Mitsufuji

We introduce the Robust Audio Watermarking Benchmark (RAW-Bench), a benchmark for evaluating deep learning-based audio watermarking methods with standardized and systematic comparisons. To simulate real-world usage, we introduce a comprehensive audio attack pipeline with var…

Training Consistency Models with Variational Noise Coupling

ICML, 2025
Gianluigi Silvestri, Luca Ambrogioni, Chieh-Hsin Lai, Yuhta Takida, Yuki Mitsufuji

Consistency Training (CT) has recently emerged as a promising alternative to diffusion models, achieving competitive performance in image generation tasks. However, non-distillation consistency training often suffers from high variance and instability, and analyzing and impr…

Supervised Contrastive Learning from Weakly-labeled Audio Segments for Musical Version Matching

ICML, 2025
Joan Serrà, R. Oguz Araz, Dmitry Bogdanov, Yuki Mitsufuji

Detecting musical versions (different renditions of the same piece) is a challenging task with important applications. Because of the ground truth nature, existing approaches match musical versions at the track level (e.g., whole song). However, most applications require to …

  • HOME
  • Publications
  • Jump Your Steps: Optimizing Sampling Schedule of Discrete Diffusion Models

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.