InstantID is a novel diffusion model-based solution for zero-shot identity-preserving image generation. It addresses the limitations of existing methods, such as high storage demands, lengthy fine-tuning processes, and the need for multiple reference images, by using only a single facial image to generate images with high fidelity and style customization. The key contributions of InstantID include: 1. **Pluggability and Compatibility**: InstantID is designed to be compatible with pre-trained text-to-image diffusion models like SD1.5 and SDXL, making it easy to integrate into existing workflows. 2. **Tuning-Free**: The method requires no fine-tuning during inference, making it highly economical and practical for real-world applications. 3. **Superior Performance**: InstantID achieves state-of-the-art results with just one reference image, demonstrating high fidelity and flexibility in generating images with strong identity preservation. InstantID consists of three main components: - **ID Embedding**: Captures robust semantic face information. - **Image Adapter**: A lightweight module with decoupled cross-attention to support images as visual prompts. - **IdentityNet**: Encodes detailed features from the reference facial image with additional spatial control. The method is trained on large-scale datasets and evaluated on various tasks, including image-only generation, image + prompt generation, and compatibility with pre-trained spatial control models like ControlNet. Experimental results show that InstantID outperforms existing methods in terms of identity preservation, text control, and stylistic flexibility. The paper also explores several real-world applications, such as novel view synthesis, identity interpolation, and multi-identity synthesis, highlighting the versatility and effectiveness of InstantID.InstantID is a novel diffusion model-based solution for zero-shot identity-preserving image generation. It addresses the limitations of existing methods, such as high storage demands, lengthy fine-tuning processes, and the need for multiple reference images, by using only a single facial image to generate images with high fidelity and style customization. The key contributions of InstantID include: 1. **Pluggability and Compatibility**: InstantID is designed to be compatible with pre-trained text-to-image diffusion models like SD1.5 and SDXL, making it easy to integrate into existing workflows. 2. **Tuning-Free**: The method requires no fine-tuning during inference, making it highly economical and practical for real-world applications. 3. **Superior Performance**: InstantID achieves state-of-the-art results with just one reference image, demonstrating high fidelity and flexibility in generating images with strong identity preservation. InstantID consists of three main components: - **ID Embedding**: Captures robust semantic face information. - **Image Adapter**: A lightweight module with decoupled cross-attention to support images as visual prompts. - **IdentityNet**: Encodes detailed features from the reference facial image with additional spatial control. The method is trained on large-scale datasets and evaluated on various tasks, including image-only generation, image + prompt generation, and compatibility with pre-trained spatial control models like ControlNet. Experimental results show that InstantID outperforms existing methods in terms of identity preservation, text control, and stylistic flexibility. The paper also explores several real-world applications, such as novel view synthesis, identity interpolation, and multi-identity synthesis, highlighting the versatility and effectiveness of InstantID.