eprintid: 10192852
rev_number: 15
eprint_status: archive
userid: 699
dir: disk0/10/19/28/52
datestamp: 2024-05-30 12:23:31
lastmod: 2024-11-04 14:45:24
status_changed: 2024-05-30 12:23:31
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Gong, Jingzhi
creators_name: Li, Sisi
creators_name: D’Aloisio, Giordano
creators_name: Ding, Zishuo
creators_name: Yulong, Ye
creators_name: Langdon, William B
creators_name: Sarro, Federica
title: GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: SBSE; ANN; GenAI; 
Text2Image; Stable Diffusion; 
Yolo
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
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.
date: 2024-07-02
date_type: published
publisher: Springer
official_url: https://doi.org/10.1007/978-3-031-64573-0_7
full_text_type: other
language: eng
verified: verified_manual
elements_id: 2279057
doi: 10.1007/978-3-031-64573-0_7
isbn_13: 978-3-031-64572-3
lyricists_name: Sarro, Federica
lyricists_id: FSSAR72
actors_name: Sarro, Federica
actors_id: FSSAR72
actors_role: owner
full_text_status: restricted
pres_type: paper
series: Lecture Notes in Computer Science
publication: International Symposium on Search Based Software Engineering
volume: 14767
place_of_pub: Cham, Switzerland
pagerange: 70-76
event_title: 16th International Symposium, SSBSE 2024
issn: 0302-9743
book_title: Search-Based Software Engineering: SSBSE 2024
editors_name: Jahangirova, Gunel
editors_name: Khomh, Foutse
citation:        Gong, Jingzhi;    Li, Sisi;    D’Aloisio, Giordano;    Ding, Zishuo;    Yulong, Ye;    Langdon, William B;    Sarro, Federica;      (2024)    GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation.                     In: Jahangirova, Gunel and Khomh, Foutse, (eds.) Search-Based Software Engineering: SSBSE 2024.  (pp. pp. 70-76).  Springer: Cham, Switzerland.      
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10192852/1/GreenStableYolo.pdf