Image Composition is a known limitation of Diffusion Models. Through evaluation of our new benchmark MCC-250 we show that multimodal prompting leads to more compositional robustness as judged by humans.
-Evaluating the alignment of prompt embeddings as well as generated images across multiple languagese we show that well aligned embeddings enable the transfer of multiligualism to downstream tasks even for task-specific monolingual training data.