%0 Generic %A Gong, Jingzhi %A Li, Sisi %A D’Aloisio, Giordano %A Ding, Zishuo %A Yulong, Ye %A Langdon, William B %A Sarro, Federica %C Cham, Switzerland %D 2024 %E Jahangirova, Gunel %E Khomh, Foutse %F discovery:10192852 %I Springer %K SBSE; ANN; GenAI; Text2Image; Stable Diffusion; Yolo %P 70-76 %T GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation %U https://discovery.ucl.ac.uk/id/eprint/10192852/ %V 14767 %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. %Z This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.