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This article proposes a method for transferring knowledge of semantic segmentation from a labeled source domain to an unlabeled target domain without using the source-domain data. Such a problem is called source-data-free domain adaptation, in which a pre-trained source-domain model and the unlabeled target-domain data are used to transfer the label knowledge across the domains. Like most previous methods, our method uses pseudo labels for distilling and transferring the source-domain knowledge. On the basis of the pseudo-label learning, our method improves the domain adaptation performance in two innovative ways: 1) reducing the domain differences by source-data-free style transfer and 2) exploring the style diversity within the target domain by style modification. To this end, we introduce two additional modules: 1) an inter-domain style transfer module which aligns the feature statistics of the source and target domains before producing the pseudo labels thereby improving the pseudo labels' accuracy, and 2) an intra-domain style modification module which modifies the image styles within the target domain for learning intra-domain style-invariant features. Our method with the two modules outperforms previous source-data-free domain adaptation methods in two commonly used benchmarks. Moreover, our method is well compatible with the previous methods for further improvement.