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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.14982 |
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| _version_ | 1866929429332099072 |
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| author | Gong, Jingzhi Li, Sisi d'Aloisio, Giordano Ding, Zishuo Ye, Yulong Langdon, William B. Sarro, Federica |
| author_facet | Gong, Jingzhi Li, Sisi d'Aloisio, Giordano Ding, Zishuo Ye, Yulong Langdon, William B. Sarro, Federica |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14982 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation Gong, Jingzhi Li, Sisi d'Aloisio, Giordano Ding, Zishuo Ye, Yulong Langdon, William B. Sarro, Federica Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2407.14982 |