Presenting AnyDoor, a cutting-edge diffusion-based image generator, designed to address the complex task of zero-shot customization with remarkable success. AnyDoor showcases exceptional capabilities in virtual try-on, multi-subject composition, and object moving without the need for extensive parameter tuning, making it highly versatile for various image generation and editing tasks.

At the core of AnyDoor's innovation lies a smart approach to zero-shot customization, achieved by complementing identity features with carefully designed detail features. These detail features maintain texture details while accommodating versatile local variations, ensuring seamless blending of objects with different scenes. The integration of knowledge from video datasets further enhances the model's generalizability and robustness, making it a powerful tool for real-world applications.

The methodology involves a sophisticated pipeline incorporating a segmentation module, ID extractor, high-pass filters, and a detail extractor, including a UNet encoder for hierarchical resolution detail maps. This comprehensive approach enables high controllability for editing specific local regions of the scene image, setting AnyDoor apart from previous works focused on text-guided local image editing.