Morse: Fast Sampling for Accelerating Diffusion Models Universally

Chao Li, Jiawei Fan, Anbang Yao

Keywords: Diffusion models, image generation, text-to-image generation, model acceleration

TL;DR: In this paper, we present a new and universal framework, called Morse, to accelerate diffusion models.

Abstract: In this paper, we present Morse, a simple and universal framework for accelerating diffusion models. The key insight of Morse is to reformulate the iterative generation (from noise to data) process via taking advantage of fast jump sampling and adaptive residual feedback strategies. Specifically, Morse involves two models called Dash and Dot that interact with each other. The Dash model is just the pre-trained diffusion model of any type, but operates in a jump sampling regime, creating sufficient space for sampling efficiency improvement. The Dot model is significantly faster than the Dash model, which is learnt to generate residual feedback conditioned on the observations at the current jump sampling point on the trajectory of the Dash model, lifting the noise estimate to easily match the next-step estimate of the Dash model without jump sampling. By chaining the outputs of the Dash and Dot models run in a time-interleaved fashion, Morse exhibits the merit of flexibly attaining desired image generation performance while improving overall runtime efficiency. With our proposed weight sharing strategy between the Dash and Dot models, Morse is efficient for training and inference. We validate the efficacy of our method under a variety of experimental setups. Our method shows an average speedup of 1.78× to 3.31× over a wide range of sampling step budgets relative to baseline diffusion models. Furthermore, we show that our method can be also generalized to improve the Latent Consistency Model (LCM-SDXL, which is already accelerated with consistency distillation technique) tailored for few-step text-to-image synthesis. The code will be made publicly available.

Primary Area: generative models

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Submission Number: 5952