  Diffusion distillation methods aim to compress the diffusion models into
efficient one-step generators while trying to preserve quality. Among them,
Distribution Matching Distillation (DMD) offers a suitable framework for
training general-form one-step generators, applicable beyond unconditional
generation. In this work, we introduce its modification, called Regularized
Distribution Matching Distillation, applicable to unpaired image-to-image (I2I)
problems. We demonstrate its empirical performance in application to several
translation tasks, including 2D examples and I2I between different image
datasets, where it performs on par or better than multi-step diffusion
baselines.
