GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation
Jingzhi Gong, Sisi Li, Giordano d'Aloisio, Zishuo Ding, Yulong Ye, William B. Langdon, Federica Sarro
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/gjz78910/greenstableyoloOfficialIn paperpytorch★ 1
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.