This study explores the integration of text-to-image generative AI, particularly Stable Diffusion, in conjunction with ControlNet and LoRA models in conceptual landscape design. Traditional methods in landscape design are often time-consuming and limited by the designer’s individual creativity, also often lacking efficiency in the exploration of diverse design solutions. By leveraging AI tools, we demonstrate a workflow that efficiently generates detailed and visually coherent landscape designs, including natural parks, city plazas, and courtyard gardens. Through both qualitative and quantitative evaluations, our results indicate that fine-tuned models produce superior designs compared to non-fine-tuned models, maintaining spatial consistency, control over scale, and relevant landscape elements. This research advances the efficiency of conceptual design processes and underscores the potential of AI in enhancing creativity and innovation in landscape architecture.
BoudierMBeltránNMichelangeloA (2024) How to generate new versions of an original character? an application of LoRA and DreamBooth fine-tuning of diffusion models. https://hdl.handle.net/10230/68765.
7.
BuxtonB (2010) Sketching User Experiences: Getting the Design Right and the Right Design. Burlington, MA: Morgan Kaufmann.
8.
CaoYLiSLiuY, et al. (2023) A comprehensive survey of AI-generated content (AIGC): a History of generative AI from GAN to ChatGPT (arXiv:2303.04226). DOI: 10.48550/arXiv.2303.04226.
9.
ChenRZhaoJYaoX, et al. (2023) Generative design of outdoor green spaces based on generative adversarial networks. Buildings13(4): 1083. DOI: 10.3390/buildings13041083.
10.
CreswellAWhiteTDumoulinV, et al. (2018) Generative adversarial networks: an overview. IEEE Signal Processing Magazine35(1): 53–65. DOI: 10.1109/MSP.2017.2765202.
11.
DanhaiveRMuellerCT (2021) Design subspace learning: structural design space exploration using performance-conditioned generative modeling. Automation in Construction127: 103664. DOI: 10.1016/j.autcon.2021.103664.
12.
DeeC (2001) Form and Fabric in Landscape Architecture: A Visual Introduction. London: Taylor & Francis.
13.
DerevyankoNZalevskaO (2023) Comparative analysis of neural networks Midjourney, stable diffusion, and DALL-E and ways of their implementation in the educational process of students of design specialities. Scientific Bulletin of Mukachevo State University. Series “Pedagogy and Psychology9: 36. DOI: 10.52534/msu-pp3.2023.36.
14.
DuHLiZNiyatoD, et al. (2023, March 23). Diffusion-based reinforcement learning for edge-enabled AI-generated content services. https://arxiv.org/abs/2303.13052v3.
15.
FernbergPChamberlainB (2023) Artificial intelligence in landscape architecture: a literature review. Landscape Journal42(1): 13–35. DOI: 10.3368/lj.42.1.13.
16.
Fui-Hoon NahFZhengRCaiJ, et al. (2023) Generative AI and ChatGPT: applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research25(3): 277–304. DOI: 10.1080/15228053.2023.2233814.
17.
GonzalezRC (2018) Deep convolutional neural networks [lecture notes]. IEEE Signal Processing Magazine35(6): 79–87. DOI: 10.1109/MSP.2018.2842646.
HanZChenY (2024) Automatic generation of standard nursing unit floor plan in general hospital based on stable diffusion. Buildings14(9): 2601. DOI: 10.3390/buildings14092601.
21.
HanafyNO (2023) Artificial intelligence’s effects on design process creativity: “A study on used A.I. text-to-image in architecture”. Journal of Building Engineering80: 107999. DOI: 10.1016/j.jobe.2023.107999.
22.
HoldenR (1984) Basic elements of landscape architectural design. Landscape and Planning11(3): 260–261.
23.
HoşerMKöymenE (2023) Analysis of text-to-image artificial intelligence Systems in terms of contribution to interior coloring. Bilişim Teknolojileri Dergisi16(4): 275–283. DOI: 10.17671/gazibtd.1252993.
24.
HuangCZhangGYaoJ, et al. (2022) Accelerated environmental performance-driven urban design with generative adversarial network. Building and Environment224: 109575. DOI: 10.1016/j.buildenv.2022.109575.
25.
Huynh-ThuQGhanbariM (2008) Scope of validity of PSNR in image/video quality assessment. Electronics Letters44(13): 800–801.
26.
JiangFMaJWebsterCJ, et al. (2024) Automated site planning using CAIN-GAN model. Automation in Construction159: 105286. DOI: 10.1016/j.autcon.2024.105286.
KotturiYAndersonAFordG, et al. (2024) Deconstructing the veneer of simplicity: Co-designing introductory generative AI workshops with local entrepreneurs. In Proceedings of the CHI conference on human factors in computing systems, Honolulu, HI, May 11–16, 2024: 1–16. DOI: 10.1145/3613904.3642191.
30.
KulkarniAShivanandaAKulkarniA, et al. (2023) Diffusion model and generative AI for images. In: KulkarniAShivanandaAKulkarniAGudivadaD (eds) Applied Generative AI for Beginners: Practical Knowledge on Diffusion Models, ChatGPT, and Other LLMs. New York, NY: Apress, 155–177. DOI: 10.1007/978-1-4842-9994-4_8.
31.
KwonYWuEWuK, et al. (2023, October 2) DataInf: efficiently estimating data influence in LoRA-tuned LLMs and diffusion models. https://arxiv.org/abs/2310.00902v1.
MandapuramMGutlapalliSSBodepudiA, et al. (2018) Investigating the prospects of generative artificial intelligence. Asian Journal of Humanity, Art and Literature5(2): 2. DOI: 10.18034/ajhal.v5i2.659.
38.
NortonJNBrouwerAB (2009) Chapter 4—the planning, design and construction process. In: HesslerJRLehnerNDM (eds) Planning and Designing Research Animal Facilities. Cambridge, MA: Academic Press: 17–44. DOI: 10.1016/B978-0-12-369517-8.00004-9.
39.
OppenlaenderJ (2022) The creativity of text-to-image generation. In Proceedings of the 25th International Academic Mindtrek Conference: 192–202. DOI: 10.1145/3569219.3569352.
40.
PaananenVOppenlaenderJVisuriA. (2023). Using text-to-image generation for architectural design ideation (arXiv:2304.10182). arXiv. DOI: 10.48550/arXiv.2304.10182.
41.
PhillipsCJiaoJClubbE (2024) Testing the capability of AI art tools for urban design. IEEE Computer Graphics and Applications44(2): 37–45. DOI: 10.1109/MCG.2024.3356169.
42.
PressmanA (2012) Designing Architecture: The Elements of Process. London: Routledge.
43.
RaneN (2023) ChatGPT and similar generative artificial intelligence (AI) for smart industry: role, challenges and opportunities for industry 4.0, industry 5.0 and society 5.0. (SSRN Scholarly Paper 4603234) DOI: 10.2139/ssrn.4603234.
44.
ReidGW (2007) From Concept to Form in Landscape Design. Hoboken, NJ: John Wiley & Sons.
SalterAGannD (2003) Sources of ideas for innovation in engineering design. Research Policy32(8): 1309–1324. DOI: 10.1016/S0048-7333(02)00119-1.
47.
ShawSLYeXGoodchildM, et al. (2024) Human dynamics research in GIScience: challenges and opportunities. Computational Urban Science4(1): 31.
48.
SmithJSHsuY-CZhangL, et al. (2023, April 12). Continual diffusion: continual customization of text-to-image diffusion with C-LoRA. https://arxiv.org/abs/2304.06027v1.
49.
Souter-BrownG (2014) Landscape and Urban Design for Health and Well-Being: Using Healing, Sensory and Therapeutic Gardens. London: Routledge.
50.
StilesR (1994) Landscape theory: a missing link between landscape planning and landscape design?Landscape and Urban Planning30(3): 139–149. DOI: 10.1016/0169-2046(94)90053-1.
51.
StupariuM-SCushmanSAPleşoianuA-I, et al. (2022) Machine learning in landscape ecological analysis: a review of recent approaches. Landscape Ecology37(5): 1227–1250. DOI: 10.1007/s10980-021-01366-9.
52.
SunCQiuXXuY, et al. (2019) How to fine-tune BERT for text classification? In: SunMHuangXJiH, et al. (eds) Chinese Computational Linguistics. Berlin: Springer International Publishing: 194–206. DOI: 10.1007/978-3-030-32381-3_16.
ThompsonJWSorvigK (2000) Sustainable landscape construction: a guide to green building outdoors. https://trid.trb.org/view/680366.
55.
WanJShiH (2021) Research on urban renewal public space design based on convolutional neural network model. Security and Communication Networks2021: 1–9. DOI: 10.1155/2021/9504188.
56.
WangZBovikACSheikhHR, et al. (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing13(4): 600–612. DOI: 10.1109/TIP.2003.819861.
57.
WuJGanWChenZ, et al. (2023) AI-generated content (AIGC): a survey (arXiv:2304.06632). DOI: 10.48550/arXiv.2304.06632.
58.
XuLXiangliYRaoA, et al. (2021) BlockPlanner: city block generation with vectorized graph representation. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV): 5057–5066. DOI: 10.1109/ICCV48922.2021.00503.
59.
YangLZhangZSongY, et al. (2023) Diffusion models: a comprehensive survey of methods and applications. ACM Computing Surveys56(4): 105:1–105:39. DOI: 10.1145/3626235.
60.
YeXDuanLPengQ (2021) Spatiotemporal prediction of theft risk with deep inception-residual networks. Smart Cities4(1): 204–216.
61.
YeXDuJYeY (2022) MasterplanGAN: facilitating the smart rendering of urban master plans via generative adversarial networks. Environment and Planning B: Urban Analytics and City Science49(3): 794–814.
62.
YeXJamonnakSVan ZandtS, et al. (2024) Developing campus digital twin using interactive visual analytics approach. Frontiers of Urban and Rural Planning2(1): 9.
63.
YeeR (2012) Architectural Drawing: A Visual Compendium of Types and Methods. Hoboken, NJ: John Wiley & Sons.
64.
YelavichSAdamsB (2014) Design as Future-Making. London: Bloomsbury Publishing.
65.
ZhangLAgrawalaM (2023). Adding conditional control to text-to-image diffusion models (arXiv:2302.05543). DOI: 10.48550/arXiv.2302.05543.
66.
ZhangRIsolaPEfrosAA, et al. (2018) The unreasonable effectiveness of deep features as a perceptual metric. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition: 586–595. DOI: 10.1109/CVPR.2018.00068.
67.
ZhangCZhangCZhangM, et al. (2023) Text-to-image diffusion models in generative AI: a survey (arXiv:2303.07909). DOI: 10.48550/arXiv.2303.07909.
68.
ZhangLTianXZhangC, et al. (2024a) Aided design of bridge aesthetics based on stable diffusion fine-tuning. DOI: 10.48550/ARXIV.2409.15812.
69.
ZhangCWuQGambardellaCC, et al. (2024b) Taming stable diffusion for text to 360° panorama image generation. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): 6347–6357. DOI: 10.1109/CVPR52733.2024.00607.
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