The Recognizability of a Place Through Generative Representation of Intangible Qualities

The paper explores the use of AI-based generative image models to represent the intangible qualities of architectural spaces, such as atmosphere, perception, and emotional experience. Through a comparative experimentation involving multiple platforms (Midjourney, Stable Diffusion with ControlNet, Leonardo.Ai, and Veras), the study investigates how text-to-image and image-to-image processes can translate descriptive prompts and visual inputs into evocative representations. The methodology combines architectural survey outputs (point clouds, photographs, sketches, and watercolors) with textual prompts to guide image generation. Results highlight the varying capabilities of different models in balancing formal coherence and expressive interpretation, demonstrating the potential of AI as a complementary tool for communicating non-measurable aspects of space, while also identifying current limitations in geometric accuracy and semantic control.