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  -