  When models, e.g., for semantic segmentation, are applied to images that are
vastly different from training data, the performance will drop significantly.
Domain adaptation methods try to overcome this issue, but need samples from the
target domain. However, this might not always be feasible for various reasons
and therefore domain generalization methods are useful as they do not require
any target data. We present a new diffusion-based domain extension (DIDEX)
method and employ a diffusion model to generate a pseudo-target domain with
diverse text prompts. In contrast to existing methods, this allows to control
the style and content of the generated images and to introduce a high
diversity. In a second step, we train a generalizing model by adapting towards
this pseudo-target domain. We outperform previous approaches by a large margin
across various datasets and architectures without using any real data. For the
generalization from GTA5, we improve state-of-the-art mIoU performance by 3.8%
absolute on average and for SYNTHIA by 11.8% absolute, marking a big step for
the generalization performance on these benchmarks. Code is available at
https://github.com/JNiemeijer/DIDEX
