# Using Text-to-Image Generation for Architectural Design Ideation

arXiv: https://arxiv.org/abs/2304.10182 · HTML: https://mlsanity.com/paper/2304.10182/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 43 references that are in the map.
- First posted on arXiv 20 Apr 2023.

## Builds on (most cited first, 17 of 17)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale](https://mlsanity.com/paper/2211.03759/) (2022)
- [The Creativity of Text-to-Image Generation](https://mlsanity.com/paper/2206.02904/) (2022)
- [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://mlsanity.com/paper/2204.13988/) (2022)
- [Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering](https://mlsanity.com/paper/2303.13534/) (2023)
- [A Prompt Log Analysis of Text-to-Image Generation Systems](https://mlsanity.com/paper/2303.04587/) (2023)
- [The Prompt Artists](https://mlsanity.com/paper/2303.12253/) (2023)
- [The Data-Production Dispositif](https://mlsanity.com/paper/2205.11963/) (2022)
- [DALLE-URBAN: Capturing the urban design expertise of large text to image transformers](https://mlsanity.com/paper/2208.04139/) (2022)
- [A Word is Worth a Thousand Pictures: Prompts as AI Design Material](https://mlsanity.com/paper/2303.12647/) (2023)
- [Architext: Language-Driven Generative Architecture Design](https://mlsanity.com/paper/2303.07519/) (2023)

## Built on by (most cited first, 16 of 16)

- [Generative AI Models for Different Steps in Architectural Design: A Literature Review](https://mlsanity.com/paper/2404.01335/) (2024)
- [When Safety Collides: Resolving Multi-Category Harmful Conflicts in Text-to-Image Diffusion via Adaptive Safety Guidance](https://mlsanity.com/paper/2602.20880/) (2026)
- [POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generation](https://mlsanity.com/paper/2504.13392/) (2025)
- [Perceptions and Realities of Text-to-Image Generation](https://mlsanity.com/paper/2306.08363/) (2023)
- [SCAPE: Searching Conceptual Architecture Prompts using Evolution](https://mlsanity.com/paper/2402.00089/) (2024)
- [The Cultivated Practices of Text-to-Image Generation](https://mlsanity.com/paper/2306.11393/) (2023)
- [CAD-Prompted Generative Models: A Pathway to Feasible and Novel Engineering Designs](https://mlsanity.com/paper/2407.08675/) (2024)
- [Exploring Language Patterns of Prompts in Text-to-Image Generation and Their Impact on Visual Diversity](https://mlsanity.com/paper/2504.14125/) (2025)
- [Automatic Scene Generation: State-of-the-Art Techniques, Models, Datasets, Challenges, and Future Prospects](https://mlsanity.com/paper/2410.01816/) (2024)
- [Offline Evaluation of Set-Based Text-to-Image Generation](https://mlsanity.com/paper/2410.17331/) (2024)
- [RMLer: Synthesizing Novel Objects across Diverse Categories via Reinforcement Mixing Learning](https://mlsanity.com/paper/2512.19300/) (2025)
- [Investigating Social Bias in Narrative Image Generation](https://mlsanity.com/paper/2608.01780/) (2026)
- [Empowering Clients -- Transformation of Design Processes Due to Generative AI](https://mlsanity.com/paper/2411.15061/) (2024)
- [Generative Visual Communication in the Era of Vision-Language Models](https://mlsanity.com/paper/2411.18727/) (2024)
- [From Architectural Sketch to Conceptual Representation: Using Structure-Aware Diffusion Model to Generate Renderings of School Buildings](https://mlsanity.com/paper/2503.03090/) (2025)
- [ArchShapeNet:An Interpretable 3D-CNN Framework for Evaluating Architectural Shapes](https://mlsanity.com/paper/2506.14832/) (2025)

## Abstract (arXiv)

The recent progress of text-to-image generation has been recognized in architectural design. Our study is the first to investigate the potential of text-to-image generators in supporting creativity during the early stages of the architectural design process. We conducted a laboratory study with 17 architecture students, who developed a concept for a culture center using three popular text-to-image generators: Midjourney, Stable Diffusion, and DALL-E. Through standardized questionnaires and group interviews, we found that image generation could be a meaningful part of the design process when design constraints are carefully considered. Generative tools support serendipitous discovery of ideas and an imaginative mindset, enriching the design process. We identified several challenges of image generators and provided considerations for software development and educators to support creativity and emphasize designers' imaginative mindset. By understanding the limitations and potential of text-to-image generators, architects and designers can leverage this technology in their design process and education, facilitating innovation and effective communication of concepts.