AnyDoor is a diffusion-based image generation model that seamlessly transports target objects to new scenes at user-specified locations. Our model requires only a single training, allowing it to be easily generalized to diverse object-scene combinations without parameter adjustment for each object. To comprehensively describe a specific object, we incorporate detailed features in addition to standard identity features. These carefully designed features preserve texture details while enabling a variety of local variations (like lighting, direction, and pose), ensuring the object blends harmoniously with different environments. We also introduce a method to leverage knowledge from video datasets, where various forms of the same object can be observed along a temporal axis, thereby enhancing the model's generalization capacity and robustness. Extensive experiments validate the superiority of our approach and its immense potential in practical applications like virtual try-on and object placement.