Exploration of Small and Medium Scale Landscape Architecture Spatial Layout Scheme Generation Based on Stable Diffusion Models
Artificial intelligence algorithms how to effectively extract landscape garden design features and apply them to the actual planning and design is a problem worth exploring,the existing research often focuses on the use of generative adversarial network to build generative design technology process,and in the research process also reflects the algorithm applied to the generation of design issues,and for the use of Stable Diffusion Models applied to the generation of landscape garden programs is relatively little research.Stable Diffusion Models applied to the generation of landscape architecture programs is relatively little research.In view of this,this paper establishes a dataset for deep learning with specially labeled planar plan labels,adopts the Stable Diffusion Models algorithm,generates landscape garden design schemes through algorithm training,and further evaluates the scheme generation results of the model in terms of the feature extraction ability of the generated scheme for the labeling,the reasonableness of the scheme,etc.,to explore whether the training model has the potential to be applied to the scheme design of small and medium scales of space.We also evaluate the results of the model in terms of its ability to extract labeled features and its reasonableness to explore whether the training model has the potential to be applied to the design of small and medium scale spaces.
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