  Textual style transfer is the task of transforming stylistic properties of
text while preserving meaning. Target "styles" can be defined in numerous ways,
ranging from single attributes (e.g, formality) to authorship (e.g,
Shakespeare). Previous unsupervised style-transfer approaches generally rely on
significant amounts of labeled data for only a fixed set of styles or require
large language models. In contrast, we introduce a novel diffusion-based
framework for general-purpose style transfer that can be flexibly adapted to
arbitrary target styles at inference time. Our parameter-efficient approach,
ParaGuide, leverages paraphrase-conditioned diffusion models alongside
gradient-based guidance from both off-the-shelf classifiers and strong existing
style embedders to transform the style of text while preserving semantic
information. We validate the method on the Enron Email Corpus, with both human
and automatic evaluations, and find that it outperforms strong baselines on
formality, sentiment, and even authorship style transfer.
