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 q...

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Bibliographic Details
Main Authors Gong, Jingzhi, Li, Sisi, d'Aloisio, Giordano, Ding, Zishuo, Ye, Yulong, Langdon, William B, Sarro, Federica
Format Journal Article
LanguageEnglish
Published 20.07.2024
Subjects
Computer Science - Artificial Intelligence
Computer Science - Computer Vision and Pattern Recognition
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Summary: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.
DOI:10.48550/arxiv.2407.14982