  Artificial intelligence is revolutionizing architecture through text-to-image
synthesis, converting textual descriptions into detailed visual
representations. We explore AI-assisted floor plan design, focusing on
technical background, practical methods, and future directions. Using tools
like, Stable Diffusion, AI leverages models such as Generative Adversarial
Networks and Variational Autoencoders to generate complex and functional
floorplans designs. We evaluates these AI models' effectiveness in generating
residential floor plans from text prompts. Through experiments with reference
images, text prompts, and sketches, we assess the strengths and limitations of
current text-to-image technology in architectural visualization. Architects can
use these AI tools to streamline design processes, create multiple design
options, and enhance creativity and collaboration. We highlight AI's potential
to drive smarter, more efficient floorplan design, contributing to ongoing
discussions on AI integration in the design profession and its future impact.
