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Görsel tutarlılıktan yaratıcı sapmaya: Zaha Hadid mimarlığında yapay zekâ ile görsel tamamlama çalışması

Yıl 2025, Sayı: Yapay zekâ ve sanat özel sayısı, 29 - 59, 22.10.2025

Pınar Çalışır Adem

https://doi.org/10.46372/arts.1744268

Öz

Yapay zekâ araçları, mimari tasarımda biçimsel spekülasyon ve hayal gücünü destekleyen araçlar olarak öne çıkmaktadır. Fakat, mevcut algoritmalar, mimari formdaki anlamsal ilişkileri tam olarak kavrayamadıkları için üretimlerinde biçimsel tutarsızlıklar ortaya çıkabilmektedir. Bu çalışma, görsel tamamlama (inpainting) araçlarının farklı prompt ve maskeleme stratejileri aracılığıyla eksik mimari imgeleri ne ölçüde aslına uygun biçimde tamamlayabildiğini ve hangi noktada yaratıcı sapmalar ürettiğini sorgulamaktadır. Örneklem, mimari literatürde sıkça yer alan ve biçimsel dili açısından bütüncül, güçlü bir kimlik sergileyen on Zaha Hadid Mimarlık yapısından oluşmaktadır. Cephe görselleri farklı oranlarda eksiltilmiş ve FLUX-1 adlı Stable Diffusion tabanlı görsel tamamlama aracıyla tamamlanmıştır. Sonuçlar, biçimsel süreklilik ve mimari dil açısından niteliksel olarak ve Yapısal Benzerlik İndeksi (SSIM) ile niceliksel olarak değerlendirilmiştir. Bulgular, eksiltme oranı arttıkça yapay zekâ aracının yorum gücünün belirginleştiğini göstermektedir.

Anahtar Kelimeler

yapay zeka destekli tasarım algoritmik yaratıcılık mimari görsel üretimi görsel tamamlama

Proje Numarası

-

Kaynakça

  • Arielli, E. (2024a). Even an AI could do that. In E. Arielli & L. Manovich (Eds.), Artificial aesthetics: Generative AI, art and visual media (pp. 8–24). Retrieved from https://manovich.net/index.php/projects/artificial-aesthetics
  • Arielli, E. (2024b). Human perception and the artificial gaze. In E. Arielli & L. Manovich (Eds.), Artificial aesthetics: Generative AI, art and visual media (pp. 95–117). Retrieved from https://manovich.net/index.php/projects/artificial-aesthetics
  • As, I., Pal, S., & Basu, P. (2018). Artificial intelligence in architecture: Generating conceptual design via deep learning. International journal of architectural computing, 16(4), 306–327. https://doi.org/10.1177/1478077118800982
  • Boden, M. A. (2010). Creativity and art: Three roads to surprise. Oxford University Press.
  • Bohm, D. (1996). On creativity. Routledge.
  • Carpo, M. (2017). The second digital turn: Design beyond intelligence. MIT Press.
  • Chen, J., Shao, Z., & Hu, B. (2023). Generating interior design from text: A new diffusion model-based method for efficient creative design. Buildings, 13, 1861. https://doi.org/10.3390/buildings13071861
  • Dilaveroglu, B. (2024). The architecture of visual narrative: Can text-to-image algorithms enhance the power of stylistic narrative for architecture. International journal of architectural computing, 22(3), 432–457. https://doi.org/10.1177/14780771241234449
  • Enjellina, E., Beyan, E. V. P., & Rossy, A. G. C. (2023). Review of AI image generator: Influences, challenges, and future prospects for architectural field. Journal of artificial intelligence in architecture, 2, 53–65. https://doi.org/10.24002/jarina.v2i1.6662
  • Foote, K. D. (2022). The history of machine learning and its convergent trajectory towards AI. In S. Carta (Ed.), Machine learning and the city: Applications in architecture and urban design, (pp. 129–142). Wiley. https://doi.org/10.1002/9781119815075.ch9
  • Fogel, D. B. (2022). Defining artificial intelligence. In S. Carta (Ed.), Machine learning and the city: Applications in architecture and urban design, (pp. 91–120). Wiley. https://doi.org/10.1002/9781119815075.ch7
  • Hanafy, N. O. (2023). Retracted: Artificial intelligence’s effects on design process creativity: A study on used A.I. text-to-image in architecture. Journal of building engineering, 80. https://doi.org/10.1016/j.jobe.2023.107999
  • Hugging Face. (n.d.). Using ControlNet with diffusers [Documentation]. Hugging Face. Retrieved August 5, 2025, from https://huggingface.co/docs/diffusers/using-diffusers/controlnet
  • Kim, J. Y., & Park, S. J. (2025). AI-driven biophilic façade design for senior multi-family housing using LoRA and stable diffusion. Buildings, 15(9), 1546. https://doi.org/10.3390/buildings15091546
  • Koestler, A. (1964). The act of creation. Hutchinson.
  • Kuang, Z., Zhang, J., Li, Y., et al. (2025). Preserving architectural heritage in urban renewal: A stable diffusion model framework for automated historical facade generation. npj heritage science, 13, 256. https://doi.org/10.1038/s40494-025-01826-4
  • Leach, N. (2022). In the mirror of AI: What is creativity? ARIN, 1, 15. https://doi.org/10.1007/s44223-022-00012-x
  • Li, P., Li, B., & Li, Z. (2024). Sketch-to-architecture: Generative AI-aided architectural design. arXiv. Retrieved from https://arxiv.org/abs/2403.20186
  • Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., & Van Gool, L. (2022). RePaint: Inpainting using denoising diffusion probabilistic models. arXiv. Retrieved from https://arxiv.org/abs/2201.09865
  • Manovich, L. (2019). Defining AI arts: Three proposals. In AI and dialog of cultures, Exhibition Catalog, Hermitage Museum, Saint-Petersburg, Russia. Retrieved from https://manovich.net/index.php/projects/defining-ai-arts-three-proposals
  • Ma, H., & Zheng, H. (2024). Text semantics to image generation: A method of building facades design based on stable diffusion model. In C. Yan, H. Chai, T. Sun, & P. F. Yuan (Eds.), Phygital intelligence. CDRF 2023. Computational design and robotic fabrication (pp. 24–35). Springer. https://doi.org/10.1007/978-981-99-8405-3_3
  • Nilsson, J., & Akenine-Möller, T. (2020). Understanding SSIM. arXiv. Retrieved from https://arxiv.org/abs/2006.13846
  • Picon, A. (2025). Artificial intelligence and architectural intention. Technology|architecture + design, 9(1), 6–9. https://doi.org/10.1080/24751448.2025.2465063
  • Ploennings, J., & Berger, M. (2023). AI art in architecture. AI in civil engineering, 2(8). https://doi.org/10.1007/s43503-023-00018-y
  • Rashid, M. (2025). Architect, AI and the maximiser scenario. AI & society, 40, 241–243. https://doi.org/10.1007/s00146-023-01848-1
  • Shi, Y., & Wang, C. (2025). Optimizing Ionic style facade creation by integrating shape grammars into stable diffusion. In H. Chai, D. W. N. Bao, Z. Guo, & P. F. Yuan (Eds.), Symbiotic intelligence. CDRF 2024. Computational design and robotic fabrication (pp. 297–306). Springer. https://doi.org/10.1007/978-981-96-3433-0_26
  • Sukkar, A. W., Fareed, M. W., Yahia, M. W., Mushtaha, E., & De Giosa, S. L. (2024a). Artificial Intelligence Islamic Architecture (AIIA): What is Islamic architecture in the age of artificial intelligence? Buildings, 14, 781. https://doi.org/10.3390/buildings14030781
  • Sukkar, A. W., Fareed, M. W., Yahia, M. W., Abdalla, S. B., Ibrahim, I., & Senjab, K. A. K. (2024b). Analytical evaluation of Midjourney architectural virtual lab: Defining major current limits in AI-generated representations of Islamic architectural heritage. Buildings, 14, 786. https://doi.org/10.3390/buildings14030786
  • Wang, Z., & Bovik, A. C. (2002). A universal image quality index. IEEE Signal Processing Letters, 9, 81–84. https://doi.org/10.1109/97.995823
  • Vela, L., Fuentes-Hurtado, F., & Colomer, A. (2023). Improving the quality of image generation in art with top-k training and cyclic generative methods. Scientific Reports, 13, 17764. https://doi.org/10.1038/s41598-023-44289-y
  • Zhang, J., Huang, Y., Li, Z., Li, Y., Yu, Z., & Li, M. (2024). Development of a method for commercial style transfer of historical architectural facades based on stable diffusion models. Journal of Imaging, 10, 165. https://doi.org/10.3390/jimaging10070165
  • Zhang, Z., Fort, J. M., & Giménez Mateu, L. (2023). Exploring the potential of artificial intelligence as a tool for architectural design: A perception study using Gaudí’s works. Buildings, 13, 1863. https://doi.org/10.3390/buildings13071863
  • Zylinska, J. (2020). AI art: Machine visions and warped dreams. Open Humanities Press. alimama-creative. (2023). FLUX-Controlnet-Inpainting [Computer software]. GitHub. Retrieved October 5, 2025, from https://github.com/alimama-creative/FLUX-Controlnet-Inpainting

From visual consistency to creative deviation: A study of image completion with AI in Zaha Hadid architecture

Yıl 2025, Sayı: Yapay zekâ ve sanat özel sayısı, 29 - 59, 22.10.2025

Pınar Çalışır Adem

https://doi.org/10.46372/arts.1744268

Öz

AI tools have emerged as instruments that support formal speculation and imagination in architectural design. However, current algorithms may fail to fully comprehend the semantic relationships within architectural form which leads to inconsistencies in their outcomes. This study investigates to what extent inpainting tools, through different prompt and masking strategies, can complete masked architectural images in a manner consistent to the original and when they begin to produce creative deviations. The sample consists of ten buildings by Zaha Hadid Architects, widely recognized in architectural literature for their coherent and distinctive formal language. Facade images were partially masked at different ratios and completed using FLUX-1, an inpainting tool based on Stable Diffusion. The results were evaluated qualitatively in terms of formal continuity and architectural language and quantitatively using the Structural Similarity Index (SSIM). Findings reveal that as the masking ratio increases, the interpretive capacity of the AI tool becomes more pronounced.

Anahtar Kelimeler

architectural design artificial intelligence algorithmic creativity architectural image inpainting

Etik Beyan

kapsam dışıdır

Destekleyen Kurum

-

Proje Numarası

-

Teşekkür

-

Kaynakça

  • Arielli, E. (2024a). Even an AI could do that. In E. Arielli & L. Manovich (Eds.), Artificial aesthetics: Generative AI, art and visual media (pp. 8–24). Retrieved from https://manovich.net/index.php/projects/artificial-aesthetics
  • Arielli, E. (2024b). Human perception and the artificial gaze. In E. Arielli & L. Manovich (Eds.), Artificial aesthetics: Generative AI, art and visual media (pp. 95–117). Retrieved from https://manovich.net/index.php/projects/artificial-aesthetics
  • As, I., Pal, S., & Basu, P. (2018). Artificial intelligence in architecture: Generating conceptual design via deep learning. International journal of architectural computing, 16(4), 306–327. https://doi.org/10.1177/1478077118800982
  • Boden, M. A. (2010). Creativity and art: Three roads to surprise. Oxford University Press.
  • Bohm, D. (1996). On creativity. Routledge.
  • Carpo, M. (2017). The second digital turn: Design beyond intelligence. MIT Press.
  • Chen, J., Shao, Z., & Hu, B. (2023). Generating interior design from text: A new diffusion model-based method for efficient creative design. Buildings, 13, 1861. https://doi.org/10.3390/buildings13071861
  • Dilaveroglu, B. (2024). The architecture of visual narrative: Can text-to-image algorithms enhance the power of stylistic narrative for architecture. International journal of architectural computing, 22(3), 432–457. https://doi.org/10.1177/14780771241234449
  • Enjellina, E., Beyan, E. V. P., & Rossy, A. G. C. (2023). Review of AI image generator: Influences, challenges, and future prospects for architectural field. Journal of artificial intelligence in architecture, 2, 53–65. https://doi.org/10.24002/jarina.v2i1.6662
  • Foote, K. D. (2022). The history of machine learning and its convergent trajectory towards AI. In S. Carta (Ed.), Machine learning and the city: Applications in architecture and urban design, (pp. 129–142). Wiley. https://doi.org/10.1002/9781119815075.ch9
  • Fogel, D. B. (2022). Defining artificial intelligence. In S. Carta (Ed.), Machine learning and the city: Applications in architecture and urban design, (pp. 91–120). Wiley. https://doi.org/10.1002/9781119815075.ch7
  • Hanafy, N. O. (2023). Retracted: Artificial intelligence’s effects on design process creativity: A study on used A.I. text-to-image in architecture. Journal of building engineering, 80. https://doi.org/10.1016/j.jobe.2023.107999
  • Hugging Face. (n.d.). Using ControlNet with diffusers [Documentation]. Hugging Face. Retrieved August 5, 2025, from https://huggingface.co/docs/diffusers/using-diffusers/controlnet
  • Kim, J. Y., & Park, S. J. (2025). AI-driven biophilic façade design for senior multi-family housing using LoRA and stable diffusion. Buildings, 15(9), 1546. https://doi.org/10.3390/buildings15091546
  • Koestler, A. (1964). The act of creation. Hutchinson.
  • Kuang, Z., Zhang, J., Li, Y., et al. (2025). Preserving architectural heritage in urban renewal: A stable diffusion model framework for automated historical facade generation. npj heritage science, 13, 256. https://doi.org/10.1038/s40494-025-01826-4
  • Leach, N. (2022). In the mirror of AI: What is creativity? ARIN, 1, 15. https://doi.org/10.1007/s44223-022-00012-x
  • Li, P., Li, B., & Li, Z. (2024). Sketch-to-architecture: Generative AI-aided architectural design. arXiv. Retrieved from https://arxiv.org/abs/2403.20186
  • Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., & Van Gool, L. (2022). RePaint: Inpainting using denoising diffusion probabilistic models. arXiv. Retrieved from https://arxiv.org/abs/2201.09865
  • Manovich, L. (2019). Defining AI arts: Three proposals. In AI and dialog of cultures, Exhibition Catalog, Hermitage Museum, Saint-Petersburg, Russia. Retrieved from https://manovich.net/index.php/projects/defining-ai-arts-three-proposals
  • Ma, H., & Zheng, H. (2024). Text semantics to image generation: A method of building facades design based on stable diffusion model. In C. Yan, H. Chai, T. Sun, & P. F. Yuan (Eds.), Phygital intelligence. CDRF 2023. Computational design and robotic fabrication (pp. 24–35). Springer. https://doi.org/10.1007/978-981-99-8405-3_3
  • Nilsson, J., & Akenine-Möller, T. (2020). Understanding SSIM. arXiv. Retrieved from https://arxiv.org/abs/2006.13846
  • Picon, A. (2025). Artificial intelligence and architectural intention. Technology|architecture + design, 9(1), 6–9. https://doi.org/10.1080/24751448.2025.2465063
  • Ploennings, J., & Berger, M. (2023). AI art in architecture. AI in civil engineering, 2(8). https://doi.org/10.1007/s43503-023-00018-y
  • Rashid, M. (2025). Architect, AI and the maximiser scenario. AI & society, 40, 241–243. https://doi.org/10.1007/s00146-023-01848-1
  • Shi, Y., & Wang, C. (2025). Optimizing Ionic style facade creation by integrating shape grammars into stable diffusion. In H. Chai, D. W. N. Bao, Z. Guo, & P. F. Yuan (Eds.), Symbiotic intelligence. CDRF 2024. Computational design and robotic fabrication (pp. 297–306). Springer. https://doi.org/10.1007/978-981-96-3433-0_26
  • Sukkar, A. W., Fareed, M. W., Yahia, M. W., Mushtaha, E., & De Giosa, S. L. (2024a). Artificial Intelligence Islamic Architecture (AIIA): What is Islamic architecture in the age of artificial intelligence? Buildings, 14, 781. https://doi.org/10.3390/buildings14030781
  • Sukkar, A. W., Fareed, M. W., Yahia, M. W., Abdalla, S. B., Ibrahim, I., & Senjab, K. A. K. (2024b). Analytical evaluation of Midjourney architectural virtual lab: Defining major current limits in AI-generated representations of Islamic architectural heritage. Buildings, 14, 786. https://doi.org/10.3390/buildings14030786
  • Wang, Z., & Bovik, A. C. (2002). A universal image quality index. IEEE Signal Processing Letters, 9, 81–84. https://doi.org/10.1109/97.995823
  • Vela, L., Fuentes-Hurtado, F., & Colomer, A. (2023). Improving the quality of image generation in art with top-k training and cyclic generative methods. Scientific Reports, 13, 17764. https://doi.org/10.1038/s41598-023-44289-y
  • Zhang, J., Huang, Y., Li, Z., Li, Y., Yu, Z., & Li, M. (2024). Development of a method for commercial style transfer of historical architectural facades based on stable diffusion models. Journal of Imaging, 10, 165. https://doi.org/10.3390/jimaging10070165
  • Zhang, Z., Fort, J. M., & Giménez Mateu, L. (2023). Exploring the potential of artificial intelligence as a tool for architectural design: A perception study using Gaudí’s works. Buildings, 13, 1863. https://doi.org/10.3390/buildings13071863
  • Zylinska, J. (2020). AI art: Machine visions and warped dreams. Open Humanities Press. alimama-creative. (2023). FLUX-Controlnet-Inpainting [Computer software]. GitHub. Retrieved October 5, 2025, from https://github.com/alimama-creative/FLUX-Controlnet-Inpainting
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Sanat ve Kültür Politikası
Bölüm Makale
Yazarlar

Pınar Çalışır Adem 0000-0001-6139-6289

Proje Numarası -
Yayımlanma Tarihi 22 Ekim 2025
Gönderilme Tarihi 16 Temmuz 2025
Kabul Tarihi 7 Ekim 2025
Yayımlandığı Sayı Yıl 2025 Sayı: Yapay zekâ ve sanat özel sayısı

Kaynak Göster

APA Çalışır Adem, P. (2025). From visual consistency to creative deviation: A study of image completion with AI in Zaha Hadid architecture. ARTS: Artuklu Sanat ve Beşeri Bilimler Dergisi(Yapay zekâ ve sanat özel sayısı), 29-59. https://doi.org/10.46372/arts.1744268
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