Wear-Any-Way is a novel framework for virtual try-on that enables users to customize the wearing style of garments. Unlike previous methods, it supports both high-fidelity generation and precise manipulation of the wearing style. The framework uses a dual-branch pipeline, with a main U-Net for generating try-on results and a reference U-Net for extracting garment features. Sparse correspondence alignment is introduced to allow point-based control, enabling users to manipulate specific parts of the garment, such as rolling up sleeves or opening a coat. This approach achieves state-of-the-art performance in standard virtual try-on and supports various real-world scenarios, including model-to-model try-on and complex human poses. The framework also incorporates human pose control and training strategies like condition dropping and zero-initialization to enhance flexibility and robustness. Wear-Any-Way supports both click and drag interactions, allowing users to customize the wearing style dynamically. The method demonstrates superior quality and controllability, offering unprecedented customization in digital fashion experiences. It is evaluated on standard benchmarks and compared with existing methods, showing significant improvements in detail preservation and generation quality. The framework is designed to be a practical tool for e-commerce and has potential for future research in virtual try-on.Wear-Any-Way is a novel framework for virtual try-on that enables users to customize the wearing style of garments. Unlike previous methods, it supports both high-fidelity generation and precise manipulation of the wearing style. The framework uses a dual-branch pipeline, with a main U-Net for generating try-on results and a reference U-Net for extracting garment features. Sparse correspondence alignment is introduced to allow point-based control, enabling users to manipulate specific parts of the garment, such as rolling up sleeves or opening a coat. This approach achieves state-of-the-art performance in standard virtual try-on and supports various real-world scenarios, including model-to-model try-on and complex human poses. The framework also incorporates human pose control and training strategies like condition dropping and zero-initialization to enhance flexibility and robustness. Wear-Any-Way supports both click and drag interactions, allowing users to customize the wearing style dynamically. The method demonstrates superior quality and controllability, offering unprecedented customization in digital fashion experiences. It is evaluated on standard benchmarks and compared with existing methods, showing significant improvements in detail preservation and generation quality. The framework is designed to be a practical tool for e-commerce and has potential for future research in virtual try-on.