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Synthesizing images with user-specified subjects has received growingattention due to its practical applications. Despite the recent success insingle subject customization, existing algorithms suffer from high trainingcost and low success rate along with increased number of subjects. Towardscontrollable image synthesis with multiple subjects as the constraints, thiswork studies how to efficiently represent a particular subject as well as howto appropriately compose different subjects. We find that the text embeddingregarding the subject token already serves as a simple yet effectiverepresentation that supports arbitrary combinations without any model tuning.Through learning a residual on top of the base embedding, we manage to robustlyshift the raw subject to the customized subject given various text conditions.We then propose to employ layout, a very abstract and easy-to-obtain prior, asthe spatial guidance for subject arrangement. By rectifying the activations inthe cross-attention map, the layout appoints and separates the location ofdifferent subjects in the image, significantly alleviating the interferenceacross them. Both qualitative and quantitative experimental results demonstrateour superiority over state-of-the-art alternatives under a variety of settingsfor multi-subject customization.

Cones 2: Customizable Image Synthesis with Multiple Subjects