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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.
- +Attention Manipulation allows us to weight image and text tokens at inference time and guide their influence on the resulting generation.
+MultiFusion increases expresivness in composition through arbitrary and flexible promptin of image and text sequences.
+Negative prompting with images enables a more powerful supression than through text prompts.
+MultiFusion produces meaningful image variations without the need for inversion or renoising if the input image.
+