Abstract

Latent Diffusion Models have recently emerged as the state-of-the-art approach for synthetic image generation. In the Web context, their adoption may significantly impact the way it is currently approached, from both sides of content generation and exploration. For example, future Web platforms may create alternative and personalised images for individual users or improve the accessibility for users with disabilities. However, due to the nascent stage of this research area, there remains a knowledge gap in effectively utilising these models, which can clutter the digital space with poor-quality AI-generated, thus diminishing the overall perceived impact and the user experience. To address this issue, we propose a novel methodology aimed at generating high-quality prompts with minimal user effort. In particular, we present BLACK (Background, Lighting, Amenities, Context, and Kinesis), a prompt generation model directly designed for achieving high-quality images satisfying a proposed set of five desiderata. Through concrete examples, we demonstrate the impact of the prompting model in improving the generation quality. As a second contribution, we publicly release a structured resource of prompts along with expected results.

Citation

F. Torrielli, “Paint it, BLACK: a novel methodology for prompting,” in Proceedings of the Workshop on GENerative, Explainable and Reasonable Artificial Learning co-located with the 15th Biannual Conference of the Italian SIGCHI Chapter (CHITALY 2023), Torino, Italy, September 20-22, 2023, F. Torrielli, L. D. Caro, and A. Rapp, Eds., ser. CEUR Workshop Proceedings, vol. 3571, CEUR-WS.org, 2023, pp. 3–11.

Citation

Federico Torrielli, “Paint it, BLACK: a Novel Methodology for Prompting,” in Proceedings of the Workshop on GENerative, Explainable and Reasonable Artificial Learning co-located with the 15th Biannual Conference of the Italian SIGCHI Chapter (CHITALY 2023), Torino, Italy, September 20-22, 2023, Federico Torrielli, Luigi Di Caro, and Amon Rapp, Eds., ser. CEUR Workshop Proceedings, vol. 3571, CEUR-WS.org, 2023, pp. 3–11.

@inproceedings{DBLP:conf/general/Torrielli23,
	title        = {Paint it, BLACK: a Novel Methodology for Prompting},
	author       = {Federico Torrielli},
	year         = 2023,
	booktitle    = {Proceedings of the Workshop on GENerative, Explainable and Reasonable Artificial Learning co-located with the 15th Biannual Conference of the Italian {SIGCHI} Chapter {(CHITALY} 2023), Torino, Italy, September 20-22, 2023},
	publisher    = {CEUR-WS.org},
	series       = {CEUR Workshop Proceedings},
	volume       = 3571,
	pages        = {3--11},
	url          = {https://ceur-ws.org/Vol-3571/short1.pdf},
	editor       = {Federico Torrielli and Luigi Di Caro and Amon Rapp},
	timestamp    = {Tue, 02 Jan 2024 17:44:44 +0100},
	biburl       = {https://dblp.org/rec/conf/general/Torrielli23.bib},
	bibsource    = {dblp computer science bibliography, https://dblp.org}
}