## 3 Methods  
Gen-AI produces new data or content, including text, images, or 3D designs, by identifying and learning patterns from existing datasets using text-to-image, image-to-text, or image-to-image generation techniques. DGMs utilize deep learning algorithms to produce high-dimensional, complex, unique, and realistic data using a fundamental probability distribution from the existing dataset. In DGM models, compared to GAN and transformer models, VAE generates diverse and interpretable outputs. It learns from a structured latent space, making it ideal by integrating randomness for exploring data variations and performing tasks like anomaly detection or controlled design generation. Unlike GANs, which are challenging to train due to adversarial competition and mode collapse, VAE are simpler to optimize and ensure consistent results. Compared to GPT models, VAE handles a wider range of data types, including text, images, and numerical data. While GPTs lack latent space interpretability, VAE offers better control over generative processes, making them versatile for multi-domain applications. In this study, the VAE model is adopted to generate a design of a fumigation service robot for urban landscape terrains. This study uses Python v3.9.19 in the Anaconda environment and interactive LLM models like ChatGPT/Ollama to develop and integrate the model. Python is the primary programming language for implementing machine learning algorithms, data processing, and model optimization. ChatGPT/Ollama is integrated to facilitate interactive design generation, allowing real-time feedback and the customization of parameters through natural language conversations. PyTorch (an open-source machine learning library with encoders/decoders, optimizers, and backpropagation) served as the primary framework for building the MMF-VAE neural networks. In the VAE model, after receiving the input prompts from the user, the model processes the input prompts through a series of steps: tokenization and self-attention vectors to effectively understand the input data from the user, produce meaningful data in latent space, and generate a desired output. Tokenization breaks down the input data (image or text or numerical data) into smaller, manageable, and more understandable units known as tokens, enabling the model for faster and easier processing. During self-attention vectors, the model focuses on understanding each token's importance, relevance, and interdependency with the other tokens. Then, the tokens are processed through three segments, query, key, and value to understand the significance of each token. The query segment analyzes the token and then searches the input data for appropriate information. The key segment contains the reference tokens and matches its tokens with query tokens to determine their relevance. Then, the value segment is associated with storing information about each token and provides the data to generate targeted output based on the token weights. In VAE design generation, tokenization and self-attention vectors enhance the encoder's understanding of the complex inputs and generate accurate outputs from the decoder, resulting in structured, high-quality outputs tailored to specific design requirements. The VAE model primarily employs unimodal and multimodal approaches, as illustrated in Figure 1. A unimodal VAE processes a singular data modality, such as text, image, or numerical data. This approach compresses the input data of any type into a latent space while reconstructing the same type of data with variations from this representation. Unimodal VAE is easy to train, has a simpler architecture, and achieves high performance but fails in adaptability due to restricted diverse data type inputs. A multimodal VAE processes multiple data modalities simultaneously, such as the combination of input prompts of text, images, and numerical data. This approach encodes the inputs into a common latent space.