  Diffusion probabilistic models have shown great success in generating
high-quality images controllably, and researchers have tried to utilize this
controllability into text generation domain. Previous works on diffusion-based
language models have shown that they can be trained without external knowledge
(such as pre-trained weights) and still achieve stable performance and
controllability. In this paper, we trained a diffusion-based model on StylePTB
dataset, the standard benchmark for fine-grained text style transfers. The
tasks in StylePTB requires much more refined control over the output text
compared to tasks evaluated in previous works, and our model was able to
achieve state-of-the-art performance on StylePTB on both individual and
compositional transfers. Moreover, our model, trained on limited data from
StylePTB without external knowledge, outperforms previous works that utilized
pretrained weights, embeddings, and external grammar parsers, and this may
indicate that diffusion-based language models have great potential under
low-resource settings.
