MultiBooth: Towards Generating All Your Concepts in an Image from Text

Understand

This paper introduces MultiBooth, a novel and efficient technique for multi-concept customization in image generation from text.

• Despite the significant advancements in customized generation methods, particularly with the success of diffusion models, existing methods often struggle with multi-concept scenarios due to low concept fidelity and high inference cost.
• MultiBooth addresses these issues by dividing the multi-concept generation process into two phases: a single-concept learning phase and a multi-concept integration phase.
• During the single-concept learning phase, we employ a multi-modal image encoder and an efficient concept encoding technique to learn a concise and discriminative representation for each concept.