Best Prompts for Text-to-Image Models and How to Find Them
Open accessAbstract
Advancements in text-guided diffusion models have allowed for the creation of visually appealing images similar to those created by professional artists. The effectiveness of these models depends on the composition of the textual description, known as the prompt, and its accompanying keywords. Evaluating aesthetics computationally is difficult, so human input is necessary to determine the ideal prompt formulation and keyword combination. In this study, we propose a human-in-the-loop method for discovering the most effective combination of prompt keywords using a genetic algorithm. Our approach demonstrates how this can lead to an improvement in the visual appeal of images generated from the same description.
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AI media generation models have been trained on datasets that reflect historical erasure and bias.
“AI media generation, specifically image generation models that translate free-form text prompts into visuals [87, 94, 95], have been trained on datasets that reflect this historical erasure and bias.”
From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [87]
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- Authors
- Pavlichenko, N., & Ustalov, D.
- Affiliations
- Toloka, Belgrade, Serbia
- Venue
- Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2023-07-18
- Pages
- 2067-2071
- OpenAlex
- W4379466097
- Cited by
- 71
- References
- 12
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 33 · 2023: 15
- FWCI
- 7.61
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Digital Media and Visual Art
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- text-to-image generation, prompt engineering, diffusion models, human-in-the-loop evaluation, genetic algorithm, image aesthetics, visual appeal
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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