Employing a forward diffusion chain to gradually map the data to a noise distribution, diffusion-based generative models learn how to generate the data by inferring a reverse diffusion chain. However, this approach is slow and costly because it needs many forward and reverse steps. We propose a faster and cheaper approach that adds noise not until the data become pure random noise, but until they reach a hidden noisy data distribution that we can confidently learn. Then, we use fewer reverse steps to generate data by starting from this hidden distribution that is made similar to the noisy data. We reveal that the proposed model can be cast as an adversarial auto-encoder empowered by both the diffusion process and a learnable implicit prior. Experimental results show even with a significantly smaller number of reverse diffusion steps, the proposed truncated diffusion probabilistic models can provide consistent improvements over the non-truncated ones in terms of performance in both unconditional and text-guided image generations.

翻译：采用前向扩散链逐步将数据映射到噪声分布，基于扩散的生成模型通过推断反向扩散链来学习生成数据。然而，这种方法因需要大量前向和反向步骤而缓慢且成本高昂。我们提出一种更快、更经济的方案：不将噪声添加至数据变成纯随机噪声，而是添加至我们能够可靠学习的隐藏有噪数据分布。随后，从该与有噪数据相似的隐藏分布出发，使用更少的反向步骤生成数据。我们揭示所提模型可被构建为一种由扩散过程和可学习隐式先验共同赋能的对抗自编码器。实验结果表明，即使大幅减少反向扩散步骤数量，所提出的截断扩散概率模型在无条件生成和文本引导图像生成性能上均能持续优于非截断模型。