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. The key idea is to stop the diffusion process early where only the few initial diffusing steps are considered and the reverse denoising process starts from a non-Gaussian distribution. By further adopting a powerful pre-trained generative model, such as GAN and VAE, in ES-DDPM, sampling from the target non-Gaussian distribution can be efficiently achieved by diffusing samples obtained from the pre-trained generative model. In this way, the number of required denoising steps is significantly reduced. In the meantime, the sample quality of ES-DDPM also improves substantially, outperforming both the vanilla DDPM and the adopted pre-trained generative model. On extensive experiments across CIFAR-10, CelebA, ImageNet, LSUN-Bedroom and LSUN-Cat, ES-DDPM obtains promising acceleration effect and performance improvement over representative baseline methods. Moreover, ES-DDPM also demonstrates several attractive properties, including being orthogonal to existing acceleration methods, as well as simultaneously enabling both global semantic and local pixel-level control in image generation.
翻译:DDPM在DDPM中生成一个样本,可以被看作是随机抽样的Gaussian噪音。但在实践中,DDPM通常需要数百甚至数千个脱色步骤才能从高斯噪音中获取高质量的样本,从而导致极低的推断效率。在这项工作中,我们提出了一个原则加速战略,称为将数据分布逐渐扩散到高斯分布的反向进程,在DDPM中生成一个样本,在DDPM中生成一个样本,可以在DDPM中进行迭代性地脱色。然而,DDPM通常需要数百甚至数千个脱色步骤,以便从高斯噪音中获取高质量的样本,例如GAN和VAE,在ES-DDM中,从目标非GAPM中采集的样本,在DDPMM(ES-NetPM (ES-DM ) (ES-Net-DM) (ESDR) (ES-DR) ) 之前的样本中进行早期改进,在高层次的SDRDD(L) 和整个亚氏基因-DDD(IM ) 模型中进行大幅改进。