$\textit{Jump Your Steps}$: Optimizing Sampling Schedule of Discrete Diffusion Models
- URL: http://arxiv.org/abs/2410.07761v1
- Date: Thu, 10 Oct 2024 09:44:25 GMT
- Title: $\textit{Jump Your Steps}$: Optimizing Sampling Schedule of Discrete Diffusion Models
- Authors: Yong-Hyun Park, Chieh-Hsin Lai, Satoshi Hayakawa, Yuhta Takida, Yuki Mitsufuji,
- Abstract summary: We present $textitJump Your Steps$ (JYS), a novel approach that optimize the allocation of discrete sampling timesteps by minimizing CDE without extra computational cost.
In experiments across image, music, and text generation, JYS significantly improves sampling quality.
- Score: 16.738569359216438
- License: http://creativecommons.org/licenses/by/4.0/
- 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 $\tau$-leaping accelerate this process, they introduce $\textit{Compounding Decoding Error}$ (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 $\textit{Jump Your Steps}$ (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 papers
- Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations [53.180374639531145]
Self-Refining Diffusion Samplers (SRDS) retain sample quality and can improve latency at the cost of additional parallel compute.
We take inspiration from the Parareal algorithm, a popular numerical method for parallel-in-time integration of differential equations.
arXiv Detail & Related papers (2024-12-11T11:08:09Z) - Score-based Generative Models with Adaptive Momentum [40.84399531998246]
We propose an adaptive momentum sampling method to accelerate the transforming process.
We show that our method can produce more faithful images/graphs in small sampling steps with 2 to 5 times speed up.
arXiv Detail & Related papers (2024-05-22T15:20:27Z) - On the Trajectory Regularity of ODE-based Diffusion Sampling [79.17334230868693]
Diffusion-based generative models use differential equations to establish a smooth connection between a complex data distribution and a tractable prior distribution.
In this paper, we identify several intriguing trajectory properties in the ODE-based sampling process of diffusion models.
arXiv Detail & Related papers (2024-05-18T15:59:41Z) - Align Your Steps: Optimizing Sampling Schedules in Diffusion Models [63.927438959502226]
Diffusion models (DMs) have established themselves as the state-of-the-art generative modeling approach in the visual domain and beyond.
A crucial drawback of DMs is their slow sampling speed, relying on many sequential function evaluations through large neural networks.
We propose a general and principled approach to optimizing the sampling schedules of DMs for high-quality outputs.
arXiv Detail & Related papers (2024-04-22T18:18:41Z) - Accelerating Diffusion Sampling with Optimized Time Steps [69.21208434350567]
Diffusion probabilistic models (DPMs) have shown remarkable performance in high-resolution image synthesis.
Their sampling efficiency is still to be desired due to the typically large number of sampling steps.
Recent advancements in high-order numerical ODE solvers for DPMs have enabled the generation of high-quality images with much fewer sampling steps.
arXiv Detail & Related papers (2024-02-27T10:13:30Z) - Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time [49.598085130313514]
We propose discrete non-Markov diffusion models (DNDM), which naturally induce the predetermined transition time set.
This enables a training-free sampling algorithm that significantly reduces the number of function evaluations.
We study the transition from finite to infinite step sampling, offering new insights into bridging the gap between discrete and continuous-time processes.
arXiv Detail & Related papers (2023-12-14T18:14:11Z) - Fast Inference in Denoising Diffusion Models via MMD Finetuning [23.779985842891705]
We present MMD-DDM, a novel method for fast sampling of diffusion models.
Our approach is based on the idea of using the Maximum Mean Discrepancy (MMD) to finetune the learned distribution with a given budget of timesteps.
Our findings show that the proposed method is able to produce high-quality samples in a fraction of the time required by widely-used diffusion models.
arXiv Detail & Related papers (2023-01-19T09:48:07Z) - Denoising Diffusion Implicit Models [117.03720513930335]
We present denoising diffusion implicit models (DDIMs) for iterative implicit probabilistic models with the same training procedure as DDPMs.
DDIMs can produce high quality samples $10 times$ to $50 times$ faster in terms of wall-clock time compared to DDPMs.
arXiv Detail & Related papers (2020-10-06T06:15:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.