Denoising diffusion probabilistic models (DDPMs) (Ho et al. 2020) have shown
impressive results on image and waveform generation in continuous state spaces.
Here, we introduce Discrete Denoising Diffusion Probabilistic Models (D3PMs),
diffusion-like generative models for discrete data that generalize the
multinomial diffusion model of Hoogeboom et al. 2021, by going beyond
corruption processes with uniform transition probabilities. This includes
corruption with transition matrices that mimic Gaussian kernels in continuous
space, matrices based on nearest neighbors in embedding space, and matrices
that introduce absorbing states. The third allows us to draw a connection
between diffusion models and autoregressive and mask-based generative models.
We show that the choice of transition matrix is an important design decision
that leads to improved results in image and text domains. We also introduce a
new loss function that combines the variational lower bound with an auxiliary
cross entropy loss. For text, this model class achieves strong results on
character-level text generation while scaling to large vocabularies on LM1B. On
the image dataset CIFAR-10, our models approach the sample quality and exceed
the log-likelihood of the continuous-space DDPM model.

本文提出了离散去噪扩散概率模型（D3PM），用于离散数据的扩散式生成模型，包括了仿真高斯核、嵌入空间中基于最邻近、引入吸收状态等过渡矩阵。研究表明过渡矩阵的选择对图像和文本领域下的生成模型结果至关重要，且提出的新损失函数在字符级别文本生成上取得了很好的效果。