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The text-to-image synthesis by diffusion models has recently shown remarkableperformance in generating high-quality images. Although performs well forsimple texts, the models may get confused when faced with complex texts thatcontain multiple objects or spatial relationships. To get the desired images, afeasible way is to manually adjust the textual descriptions, i.e., narratingthe texts or adding some words, which is labor-consuming. In this paper, wepropose a framework to learn the proper textual descriptions for diffusionmodels through prompt learning. By utilizing the quality guidance and thesemantic guidance derived from the pre-trained diffusion model, our method caneffectively learn the prompts to improve the matches between the input text andthe generated images. Extensive experiments and analyses have validated theeffectiveness of the proposed method.

Seek for Incantations: Towards Accurate Text-to-Image Diffusion Synthesis through Prompt Engineering