InstantID is a novel method for zero-shot identity-preserving image generation, enabling high-fidelity customization using just one facial image. It addresses the limitations of existing methods by introducing a plug-and-play module that integrates strong semantic and weak spatial conditions to preserve facial identity. The method uses a novel IdentityNet to encode detailed facial features and a lightweight image adapter to support image prompts. InstantID is compatible with popular pre-trained text-to-image diffusion models like SD1.5 and SDXL, making it adaptable and efficient. It achieves state-of-the-art results with a single reference image, demonstrating high fidelity and flexibility. InstantID is free from the need for fine-tuning, making it highly practical for real-world applications. It outperforms existing methods in preserving identity while maintaining stylistic flexibility. The method is evaluated on various tasks, including novel view synthesis, identity interpolation, and multi-identity synthesis, showing its versatility and effectiveness. InstantID's efficiency and compatibility make it a promising solution for identity-preserving image generation.InstantID is a novel method for zero-shot identity-preserving image generation, enabling high-fidelity customization using just one facial image. It addresses the limitations of existing methods by introducing a plug-and-play module that integrates strong semantic and weak spatial conditions to preserve facial identity. The method uses a novel IdentityNet to encode detailed facial features and a lightweight image adapter to support image prompts. InstantID is compatible with popular pre-trained text-to-image diffusion models like SD1.5 and SDXL, making it adaptable and efficient. It achieves state-of-the-art results with a single reference image, demonstrating high fidelity and flexibility. InstantID is free from the need for fine-tuning, making it highly practical for real-world applications. It outperforms existing methods in preserving identity while maintaining stylistic flexibility. The method is evaluated on various tasks, including novel view synthesis, identity interpolation, and multi-identity synthesis, showing its versatility and effectiveness. InstantID's efficiency and compatibility make it a promising solution for identity-preserving image generation.