Accelerating Diffusion-Based Denoising Model With Optimized Time Steps

Diffusion models achieve state-of-the-art performance in image denoising but are hindered by a slow, iterative sampling process. While advanced, training-free samplers and optimized schedules offer significant acceleration, their validation has been focused on image generation. Their applicability to pre-trained denoising models, which operate on conditional inputs, remains a critical, unverified gap. We address this gap by applying and validating a post-hoc timestep optimization that accelerates pre-trained denoising models without retraining. By minimizing the discretization error of the underlying diffusion ODE, this method finds a non-uniform optimal schedule for a given number of function evaluations (NFE). Our extensive experiments on diverse models for real-world, weather, and Gaussian noise demonstrate that the proposed optimized schedule effectively reduces the required NFE. We demonstrate that it is possible to reduce the number of sampling steps while significantly preserving, and often improving, restoration quality as measured by PSNR, SSIM, and LPIPS. This work presents and validates a practical, training-free strategy for substantially accelerating diffusion-based denoising, enhancing its viability for real-world applications. Our code is available at: https://github.com/cv-cho/Accelerating-Diffusion-Denoising-Models.