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Optimizing product visualization via advanced Stable Diffusion XL model and prompt engineering
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Autores
Orientador(es)
Resumo(s)
Recent advances in generative AI have shown promising potential in creative tasks, yet challenges
persist in adapting these technologies for specialized industrial applications. This research
introduces an AI-powered product design assistant that bridges abstract concepts and visual
representations, focusing on product design visualization. Through model implementation on
Gradio, comparative analysis, and a comprehensive prompt engineering framework, the system
leverages Stable Diffusion XL to generate industrial product visualizations, complemented by
product variations and a dynamic user preference analyzer. The system's structured database
supports preference integration and mood board functionality, enabling users to organize and
visualize their design roadmap. Results demonstrate improved image generation efficiency while
preserving industrial design specifications.
Descrição
Palavras-chave
Artificial Intelligence Text-to-image generation Stable Diffusion XL Product design User preference Prompt engineering Industrial design Computer vision Machine learning Human-computer interaction