This study investigates the effectiveness of Text-to-image (T2I) models in assisting industrial designers with the visualization of product concepts. Using action research and a reflection-in-action, this research conducts a series of experiments utilizing Stable Diffusion for generating chair concepts. The research findings are positioned within an existing framework of computational tools usage, and assess the quality of the generated images in supporting industrial design activities during the define and develop phases of the design process. While T2I models provide competent support to ambiguity and sketch improvement, their efficacy in generating coherent solutions for further development is limited. This study provides guidelines for the effective application of T2I models in industrial design practice. It discusses the T2I models’ suitability for specific design activities, addresses associated risks, and proposes a taxonomy of prompt modifiers that ensures a closer match between input prompts and generated product concepts.

Designing with words: exploring the integration of text-to-image models in industrial design

Qi Ai
2024-01-01

Abstract

This study investigates the effectiveness of Text-to-image (T2I) models in assisting industrial designers with the visualization of product concepts. Using action research and a reflection-in-action, this research conducts a series of experiments utilizing Stable Diffusion for generating chair concepts. The research findings are positioned within an existing framework of computational tools usage, and assess the quality of the generated images in supporting industrial design activities during the define and develop phases of the design process. While T2I models provide competent support to ambiguity and sketch improvement, their efficacy in generating coherent solutions for further development is limited. This study provides guidelines for the effective application of T2I models in industrial design practice. It discusses the T2I models’ suitability for specific design activities, addresses associated risks, and proposes a taxonomy of prompt modifiers that ensures a closer match between input prompts and generated product concepts.
2024
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1298066
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