%K SBSE; ANN; GenAI; Text2Image; Stable Diffusion; Yolo %I Springer %C Cham, Switzerland %B Search-Based Software Engineering: SSBSE 2024 %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 %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 %S Lecture Notes in Computer Science %O This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. %D 2024 %T GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation %P 70-76 %L discovery10192852