TY - GEN SN - 0302-9743 N2 - 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. EP - 76 Y1 - 2024/07/02/ N1 - This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. TI - GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation T3 - Lecture Notes in Computer Science UR - https://doi.org/10.1007/978-3-031-64573-0_7 A1 - Gong, Jingzhi A1 - Li, Sisi A1 - D?Aloisio, Giordano A1 - Ding, Zishuo A1 - Yulong, Ye A1 - Langdon, William B A1 - Sarro, Federica KW - SBSE; ANN; GenAI; Text2Image; Stable Diffusion; Yolo AV - restricted CY - Cham, Switzerland PB - Springer SP - 70 ID - discovery10192852 ER -