This study addresses the technical bottlenecks of generative AI in architectural style control by proposing a nodular workflow construction method based on ComfyUI (A graphical user interface for working with the Stable Diffusion model, simplifying the management of image generation parameters.), aiming to achieve precise and controllable generation of functionalist architectural renderings. Through deconstructing the technical characteristics of the Stable Diffusion (A generative AI model based on diffusion processes that transforms noise into images through iterative noise removal.) model, neural network components such as ControlNet (A neural network architecture used for precise control of image generation via additional input data.) edge constraints and LoRA(Low-Rank Adaptation. A method for fine - tuning neural networks using low - rank matrices, enabling modification of large models with minimal computational costs.) module enhancements are encapsulated into visual nodes, establishing a three-phase generation path of "case analysis - parameter encoding - dynamic adjustment". Experiments involving 10 classical functionalist architectural cases employed orthogonal experimental methods to validate node combination strategies, revealing that the optimal workflow incorporating MLSD (Multi-Level Semantic Diffusion. An algorithm that combines semantic segmentation and diffusion models to generate structurally consistent images.) straight-line detection and LoRA prefabricated component reinforcement significantly improves architectural style transfer effectiveness. The research demonstrates: 1) The nodular system overcomes the "black box" limitations of traditional AI tools by exposing latent space(A multi - dimensional space where neural networks encode semantic features of data.) parameters, enabling architects to directionally configure professional elements; 2) Workflow templates support rapid recombination within 4 nodes, enhancing cross-project adaptability while further compressing single-image generation time; 3) Strict architectural typology matching (e.g., residential-to-residential, office-to-office) is critical for successful style transfer, as typological deviations cause structural logic error rates to surge. This research holds significant implications in architectural design by leveraging ComfyUI to develop workflows that transform how architects visualize and communicate ideas, thereby improving project outcomes. It demonstrates practical applications of this technology, proving its potential to accelerate design processes and expand architects' creative possibilities.

Keywords: comfyui, functionalist architecture, style transfer, node-based workflow, artificial intelligence, architectural design, generative design