Email this article 
(Universitas Pendidikan Ganesha, Indonesia)(2) Ketut Agustini
(Universitas Pendidikan Ganesha, Indonesia)(3) I Gde Wawan Sudatha
(Universitas Pendidikan Ganesha, Indonesia)(4) Gede Rasben Dantes
(Universitas Pendidikan Ganesha, Indonesia)(5) I Gusti Putu Suharta
(Universitas Pendidikan Ganesha, Indonesia)(6) I Kadek Suartama
(Universitas Pendidikan Ganesha, Indonesia)*corresponding author
AbstractThe use of artificial intelligence image generators in creating digital illustration is experiencing rapid development as an alternative to support the work of designers in various fields, especially visual communication design. Despite its sophistication, we must consider some aspects of the human side in order to maximize the design aesthetic value. This research aims to find out what applications are used in the AI image generator, as well as to identify the results of digital illustrations using the AI image generator. The results of this research show that the image generation diffusion model with various techniques and variations developed can produce excellent and unique text-based quality and control. However, humans are still the best pilots for determining text-based commands that can produce and function the greatest result.
KeywordsAI Image Generator; Digital Illustration; Visual Communication Design;
|
DOIhttps://doi.org/10.29099/ijair.v8i1.1.1235 |
Article metrics10.29099/ijair.v8i1.1.1235 Abstract views : 232 | PDF views : 36 |
Cite |
Full Text Download
|
References
I. M. M. Yusa, Y. Yu, and T. Sovhyra, “Reflections on the Use of Artificial Intelligence in Works of Art,” Jadam, vol. 2, no. 2, pp. 152–167, 2022, doi: 10.58982/jadam.v2i2.334.
J. Chi, “The Evolutionary Impact of Artificial Intelligence on Contemporary Artistic Practices,” Commun. Humanit. Res., vol. 35, no. 1, pp. 6–11, 2024, doi: 10.54254/2753-7064/35/20240006.
D. Ashton, “‘People Don’t Buy Art, They Buy Artists’: Robot Artists – Work, Identity, and Expertise,” Converg. Int. J. Res. Into New Media Technol., vol. 30, no. 2, pp. 790–806, 2024, doi: 10.1177/13548565231220310.
Y. Wang, “Illustration Art Based on Visual Communication in Digital Context,” Mob. Inf. Syst., vol. 2022, no. 1, p. 7364003, Jan. 2022, doi: https://doi.org/10.1155/2022/7364003.
M. Mujahid, M. J. Hafiz, and A. M. Sari, “Media Desain Komunikasi Visual Sebagai Penunjang Promosi Pada SMK Yuppentek 7 Tangerang,” Mavib J., vol. 3, no. 1, pp. 43–52, 2022, doi: 10.33050/mavib.v3i1.1572.
A. Bakar, “Komunikasi Visual Penjenamaan Dalam Upaya Membangun Citra Visual Identitas Baru Kota Palembang,” Besaung J. Seni Desain Dan Budaya, vol. 8, no. 2, pp. 151–161, 2023, doi: 10.36982/jsdb.v8i2.2970.
F. Mutaqin, “Penerapan Smart Communication Bot Dengan Model Chatgpt Dalam Proses Pemilihan Dan Penempatan Objek Dalam Desain Poster Komersil,” J. Dasarupa Desain Dan Seni Rupa, vol. 5, no. 3, pp. 1–9, 2024, doi: 10.52005/dasarrupa.v5i3.169.
L.-Y. Chiou, P.-K. Hung, R.-H. Liang, and C.-T. Wang, “Designing with AI: An Exploration of Co-Ideation with Image Generators,” in 2023 ACM designing interactive systems conference, 2023, pp. 1941–1954. doi: 10.1145/3563657.3596001.
A. Nichol et al., “Glide: Towards photorealistic image generation and editing with text-guided diffusion models,” arXiv Prepr. arXiv2112.10741, 2021, doi: 10.48550/arXiv.2112.10741.
G. Kwon and J. C. Ye, “Clipstyler: Image style transfer with a single text condition,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 18041–18050. doi: 10.1109/CVPR52688.2022.01753.
V. Liu and L. B. Chilton, “Design guidelines for prompt engineering text-to-image generative models,” in 2022 CHI conference on human factors in computing systems, 2022, pp. 1–23.
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hierarchical Text-Conditional Image Generation with CLIP Latents,” arXiv Prepr. arXiv2204.06125, 2022.
J. Yu et al., “Scaling autoregressive models for content-rich text-to-image generation,” arXiv Prepr. arXiv2206.10789, vol. 2, no. 3, p. 5, 2022.
J. Oppenlaender, “The creativity of text-to-image generation,” in 25th international academic mindtrek conference, 2022, pp. 192–202. doi: 10.1145/3569219.3569352.
C. Saharia et al., “Photorealistic text-to-image diffusion models with deep language understanding,” in Advances in neural information processing systems, 2022, vol. 35. [Online]. Available: https://api.semanticscholar.org/CorpusID:248986576
N. Ruiz, Y. Li, V. Jampani, Y. Pritch, M. Rubinstein, and K. Aberman, “Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 22500–22510. doi: 10.48550/arXiv.2208.12242.
N. Kumari, B. Zhang, R. Zhang, E. Shechtman, and J.-Y. Zhu, “Multi-Concept Customization of Text-to-Image Diffusion,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 1931–1941. doi: 10.48550/arXiv.2212.04488.
N. Tumanyan, M. Geyer, S. Bagon, and T. Dekel, “Plug-and-play diffusion features for text-driven image-to-image translation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 1921–1930. doi: 10.48550/arXiv.2211.12572.
L. Zhang, A. Rao, and M. Agrawala, “Adding conditional control to text-to-image diffusion models,” in IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 3836–3847. doi: 10.48550/arXiv.2302.05543.
H. Vartiainen and M. Tedre, “Using arti?cial intelligence in craft education: crafting with text-to-image generative models,” Digit. Creat., vol. 34, no. 1, pp. 1–21, 2023.
N. Dehouche and K. Dehouche, “What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education,” Heliyon, vol. 9, no. 6, pp. 1–12, 2023, doi: https://doi.org/10.1016/j.heliyon.2023.e16757.
G. P. J. C. Noel, “Evaluating AI?powered text?to?image generators for anatomical illustration: A comparative study,” Anat. Sci. Educ., 2023.
H. Vartiainen, M. Tedre, and I. Jormanainen, “Co-creating digital art with generative AI in K-9 education: Socio-material insights,” Int. J. Educ. through art, vol. 19, no. 3, pp. 405–423, 2023.
G. Fallacara, M. P. Fanti, F. Parisi, N. Parisi, and V. Sangiorgio, “AI-driven image generation for enhancing design in digital fabrication: urban furnishings in historic city centres,” 2023.
S. Lee et al., “Diffusion explainer: Visual explanation for text-to-image stable diffusion,” arXiv Prepr. arXiv2305.03509, 2023, doi: 10.48550/arXiv.2305.03509.
J. Shi, W. Xiong, Z. Lin, and H. J. Jung, “Instantbooth: Personalized text-to-image generation without test-time finetuning,” in EEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 8543–8552.
N. Ruiz et al., “HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2024, pp. 6527–6536. doi: 10.48550/arXiv.2307.06949.
C. Mou et al., “T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models,” in AAAI Conference on Artificial Intelligence, Mar. 2024, vol. 38, no. 5, pp. 4296–4304. doi: 10.1609/aaai.v38i5.28226.
M. Liu, L. J. Zhang, and C. Biebricher, “Investigating students’ cognitive processes in generative AI-assisted digital multimodal composing and traditional writing,” Comput. Educ., vol. 211, p. 104977, 2024, doi: https://doi.org/10.1016/j.compedu.2023.104977.
T. Sandoval-Martin and E. Martínez-Sanzo, “Perpetuation of Gender Bias in Visual Representation of Professions in the Generative AI Tools DALL· E and Bing Image Creator,” Soc. Sci., vol. 13, no. 5, p. 250, 2024, doi: 10.3390/socsci13050250.
G. Alkhateeb, J. Storie, and M. Külvik, “Post-Conflict Urban Landscape Storytelling: Two Approaches to Contemporary Virtual Visualisation of Oral Narratives,” Land, vol. 13, no. 4. p. 406, 2024. doi: 10.3390/land13040406.
A. Tahri, M. Beroho, I. Boulahfa, and K. Aboumaria, “AI for awareness: Involvement in climate change in the Tangier-Tetouan-Al Hoceima region (Morocco),” E3S Web Conf., vol. 502, 2024, [Online]. Available: https://doi.org/10.1051/e3sconf/202450201007
A. Ghazvineh, “An inter-semiotic analysis of ideational meaning in text-prompted AI-generated images,” Lang. Semiot. Stud., vol. 10, no. 1, pp. 17–42, 2024, doi: doi:10.1515/lass-2023-0030.
P. Avhad, “WordCanvas: Text-to-Image Generation,” Int. J. Sci. Res. Eng. Manag., vol. 08, pp. 1–5, May 2024, doi: 10.55041/IJSREM32152.
X. Zhang et al., “Compositional Inversion for Stable Diffusion Models,” in AAAI Conference on Artificial Intelligence, Mar. 2024, vol. 38, no. 7 SE-AAAI Technical Track on Computer Vision VI, pp. 7350–7358. doi: 10.1609/aaai.v38i7.28565.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
________________________________________________________
The International Journal of Artificial Intelligence Research
Organized by: Departemen Teknik Informatika
Published by: STMIK Dharma Wacana
Jl. Kenanga No.03 Mulyojati 16C Metro Barat Kota Metro Lampung
Email: jurnal.ijair@gmail.com
This work is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.