Accelerating Diffusion Models via Early Stop of the Diffusion Process

Understand

Denoising Diffusion Probabilistic Models (DDPMs) have achieved impressive performance on various generation tasks.

• By modeling the reverse process of gradually diffusing the data distribution into a Gaussian distribution, generating a sample in DDPMs can be regarded as iteratively denoising a randomly sampled Gaussian noise.
• However, in practice DDPMs often need hundreds even thousands of denoising steps to obtain a high-quality sample from the Gaussian noise, leading to extremely low inference efficiency.
• In this work, we propose a principled acceleration strategy, referred to as Early-Stopped DDPM (ES-DDPM), for DDPMs.