GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation

2024-07-20 · Jingzhi Gong, Sisi Li, Giordano d'Aloisio, Zishuo Ding, Yulong Ye, William B. Langdon, Federica Sarro

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.

📄 PDF Abstract BibTeX arXiv:2407.14982

Code (1)

gjz78910/greenstableyolo 공식 구현 pytorch

Tasks

GPUImage Generation Text to Image Generation Text-to-Image Generation

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