Our Divide & Bind can significantly improve a pretrained text-to-image model, faithfully generate multiple objects based on detailed textual description. Compared to prior state-of-the-art semantic nursing technique for text-to-image synthesis, Attend & Excite, our approach exhibits superior alignment with the input prompt and maintain a higher level of realism.

We perform latent optimization on-the-fly based on the attention maps, without fine-tuning the pretrained text-to-image model. We propose two novel loss terms: (1) Total-variation based attendance loss (2) Jensen–Shannon divergence based binding loss.

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