GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation
<|reference_start|>GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation: 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.<|reference_end|>
@article{gong2024greenstableyolo:,
    title={GreenStableYolo: Optimizing Inference Time and Image Quality of
  Text-to-Image Generation},
    author={Jingzhi Gong, Sisi Li, Giordano d'Aloisio, Zishuo Ding, Yulong Ye,
  William B. Langdon, Federica Sarro},
    journal={arXiv preprint arXiv:2407.14982},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14982},
    primaryClass={cs.CV cs.AI}
}