@inproceedings{discovery10192852,
            year = {2024},
          editor = {Gunel Jahangirova and Foutse Khomh},
         journal = {International Symposium on Search Based Software Engineering},
           title = {GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation},
       booktitle = {Search-Based Software Engineering: SSBSE 2024},
           pages = {70--76},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
       publisher = {Springer},
          series = {Lecture Notes in Computer Science},
          volume = {14767},
         address = {Cham, Switzerland},
           month = {July},
        abstract = {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.},
          author = {Gong, Jingzhi and Li, Sisi and D'Aloisio, Giordano and Ding, Zishuo and Yulong, Ye and Langdon, William B and Sarro, Federica},
            issn = {0302-9743},
             url = {https://doi.org/10.1007/978-3-031-64573-0\%5f7},
        keywords = {SBSE; ANN; GenAI; 
Text2Image; Stable Diffusion; 
Yolo}
}