StarGAN v2 is a generative adversarial network that synthesizes diverse images across multiple visual domains from a single model. It learns bidirectional mappings between different style domains and produces varied outputs for the same input by sampling different latent codes. The official implementation includes pre-trained models and the AFHQ dataset for evaluation.