# SDIT: Scalable and Diverse Cross-domain Image Translation Source: [SDIT: Scalable and Diverse Cross-domain Image Translation]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:44:30.600Z Authors: Yaxing Wang, Abel Gonzalez-Garcia, Joost van de Weijer, Luis Herranz Published: 2019-08-19T15:33:44.000Z ## Abstract Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Multi-style Image-to-Image Translation on CelebA-HQ (test)](): Multi-style Image-to-Image Translation; CelebA-HQ (test); Realism (FID) \[Reference-guided\]: 33.12. ## Original sources and implementation [Original paper]() [PDF]()