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Large-scale Text-to-Image Generation Models for Visual Artists’ Creative Works
Open accessAbstract
Large-scale Text-to-image Generation Models (LTGMs) (e.g., DALL-E), self-supervised deep learning models trained on a huge dataset, have demonstrated the capacity for generating high-quality open-domain images from multi-modal input. Although they can even produce anthropomorphized versions of objects and animals, combine irrelevant concepts in reasonable ways, and give variation to any user-provided images, we witnessed such rapid technological advancement left many visual artists disoriented in leveraging LTGMs more actively in their creative works. Our goal in this work is to understand how visual artists would adopt LTGMs to support their creative works. To this end, we conducted an interview study as well as a systematic literature review of 72 system/application papers for a thorough examination. A total of 28 visual artists covering 35 distinct visual art domains acknowledged LTGMs’ versatile roles with high usability to support creative works in automating the creation process (i.e., automation), expanding their ideas (i.e., exploration), and facilitating or arbitrating in communication (i.e., mediation). We conclude by providing four design guidelines that future researchers can refer to in making intelligent user interfaces using LTGMs.
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“People with art skills seem to have more concerns about AI-generated images [15, 36].”
From Sketchar: Supporting Character Design and Illustration Prototyping Using Generative AI (2024) · cites this paper as [36]
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“These works high-light the potential of multimodal learning in capturing complex relationships between different types of data, leading to richer and more accurate representations.”
BackgroundCreativeConnect: Supporting Reference Recombination for Graphic Design Ideation with Generative AI
Choi, D., Hong, S., Park, J., Chung, J. J. Y., & Kim, J. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642794 · Cited by 124
Self-citationPromptCharm: Text-to-Image Generation through Multi-modal Prompting and Refinement
Wang, Z., Huang, Y., Song, D., Ma, L., & Zhang, T. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642803 · Cited by 115
“As a complementary study to the prior research, Ko et al. conducted an interview study with 28 visual artists to help the research community understand the potential and design guidelines of using large-scale text-to-image models for visual art creations [31].”
BackgroundMethodResultInfluentialMcIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102
“…including images, audio, and video, by engaging in the generation of artistic content, encompassing applications in idea generation, storytelling, poetry, music composition, visual arts, and creative writing, and has resulted in commercial success like MidJourney and DALL-E [210], [211], [212].”
BackgroundIs It AI or Is It Me? Understanding Users’ Prompt Journey with Text-to-Image Generative AI Tools
Goloujeh, A. M., Sullivan, A., & Magerko, B. S. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642861 · Cited by 122
Metadata
- Authors
- Ko, H.-K., Park, G., Jeon, H., Jo, J., Kim, J., & Seo, J.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Seoul National University (KR)
- Sungkyunkwan University (KR)
- Venue
- Proceedings of the 28th International Conference on Intelligent User Interfaces
- Publisher
- ACM
- Published
- 2023-03-27
- Pages
- 919-933
- OpenAlex
- W4321275178
- Cited by
- 189
- References
- 71
- Citations by year
- 2026: 43 · 2025: 70 · 2024: 61 · 2023: 15
- FWCI
- 55.32
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM International Conference on Intelligent User Interfaces (IUI) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- National Research Foundation of Korea (NRF-2019R1A2C2089062,NRF-2019R1A2C1088900)
- Information & communications Technology Planning & Evaluation(IITP) (2019-0-00075,2019-0-00421)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Digital Media and Visual Art
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Art, Technology, and Culture
- Keywords
- text-to-image generation, visual artists, intelligent user interfaces, design guidelines, DALL-E
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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