The integration of generative artificial intelligence (AI) into sustainable fashion presents a transformative approach to ethical design and environmentally conscious production. This research explores the application of generative adversarial networks (GANs) and diffusion models in automating the creation of fashion designs that prioritize material efficiency, biodegradability, and minimal waste. By leveraging datasets of sustainable textiles, zero-waste patterns, and life-cycle assessments, the system generates innovative garment concepts aligned with circular economy principles. The AI framework incorporates environmental constraints and ethical sourcing parameters into the design process, enabling rapid prototyping of low-impact fashion lines. Furthermore, this approach facilitates personalized eco-fashion and reduces reliance on resource-intensive sampling and physical iterations. The study demonstrates the potential of generative AI to accelerate the transition toward a more responsible fashion industry by enhancing creativity, reducing carbon footprints, and promoting scalable sustainability solutions from design to distribution.
Wai Yie Leong (Mon,) studied this question.
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