%B Search-Based Software Engineering: SSBSE 2024
%C Cham, Switzerland
%X Tuning the parameters and prompts for improving AI-based text-to-image generation has remained a substantial yet unaddressed challenge. Hence we introduce GreenStableYolo, which improves the parameters and prompts for Stable Diffusion to both reduce GPU inference time and increase image generation quality using NSGA-II and Yolo. Our experiments show that despite a relatively slight trade-off (18%) in image quality compared to StableYolo (which only considers image quality), GreenStableYolo achieves a substantial reduction in inference time (266% less) and a 526% higher hypervolume, thereby advancing the state-of-the-art for text-to-image generation.
%E Gunel Jahangirova
%E Foutse Khomh
%K SBSE; ANN; GenAI; 
Text2Image; Stable Diffusion; 
Yolo
%I Springer
%T GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation
%L discovery10192852
%P 70-76
%S Lecture Notes in Computer Science
%V 14767
%J International Symposium on Search Based Software Engineering
%A Jingzhi Gong
%A Sisi Li
%A Giordano D’Aloisio
%A Zishuo Ding
%A Ye Yulong
%A William B Langdon
%A Federica Sarro
%D 2024
%O This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.