Efficient Architecture Optimization for Conditional Diffusion Models via Evolutionary Algorithms
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This project is a provincial-level “National Undergraduate Innovation Training Program” project, positioned at the intersection of generative AI and design science. Starting from real-world application scenarios, it systematically examines the deep bottlenecks of existing conditional diffusion models in terms of control precision and generation quality, and conducts a systematic exploration spanning the entire pipeline from theoretical modeling, algorithm design, and experimental validation to engineering implementation and user deployment. The cumulative outputs include high-level conference papers, national invention patents, software copyrights, and a multi-agent AIGC system for design — forming a complete closed loop of “problem-driven → methodological innovation → outcome transformation → applied verification.” The system is currently open to real users, having accumulated genuine user feedback and practical experience, providing technical support and a practical paradigm for building efficient and controllable intelligent design capabilities.
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