Dynamic Prompt Optimizing for Text-to-Image Generation
Wenyi Mo Tianyu Zhang Yalong Bai Bing Su Ji-Rong Wen Qing Yang
Text-to-image generative models, specifically those based on diffusion models
like Imagen and Stable Diffusion, have made substantial advancements. Recently,
there has been a surge of interest in the delicate refinement of text prompts.
Users assign weights or alter the injection time steps of certain words in the
text prompts to improve the quality of generated images. However, the success
of fine-control prompts depends on the accuracy of the text prompts and the
careful selection of weights and time steps, which requires significant manual
intervention. To address this, we introduce the \textbf{P}rompt
\textbf{A}uto-\textbf{E}diting (PAE) method. Besides refining the original
prompts for image generation, we further employ an online reinforcement
learning strategy to explore the weights and injection time steps of each word,
leading to the dynamic fine-control prompts. The reward function during
training encourages the model to consider aesthetic score, semantic
consistency, and user preferences. Experimental results demonstrate that our
proposed method effectively improves the original prompts, generating visually
more appealing images while maintaining semantic alignment. Code is available
at https://github.com/Mowenyii/PAE.