InstantID: Zero-shot Identity-Preserving Generation in Seconds (2401.07519v2)

Abstract: There has been significant progress in personalized image synthesis with methods such as Textual Inversion, DreamBooth, and LoRA. Yet, their real-world applicability is hindered by high storage demands, lengthy fine-tuning processes, and the need for multiple reference images. Conversely, existing ID embedding-based methods, while requiring only a single forward inference, face challenges: they either necessitate extensive fine-tuning across numerous model parameters, lack compatibility with community pre-trained models, or fail to maintain high face fidelity. Addressing these limitations, we introduce InstantID, a powerful diffusion model-based solution. Our plug-and-play module adeptly handles image personalization in various styles using just a single facial image, while ensuring high fidelity. To achieve this, we design a novel IdentityNet by imposing strong semantic and weak spatial conditions, integrating facial and landmark images with textual prompts to steer the image generation. InstantID demonstrates exceptional performance and efficiency, proving highly beneficial in real-world applications where identity preservation is paramount. Moreover, our work seamlessly integrates with popular pre-trained text-to-image diffusion models like SD1.5 and SDXL, serving as an adaptable plugin. Our codes and pre-trained checkpoints will be available at https://github.com/InstantID/InstantID.

Introduction to InstantID

InstantID is a pioneering solution for personalized image synthesis, creating a significant impact in the field of text-to-image diffusion models. This efficient method originates from the need to generate customized images that preserve the detailed identity of human subjects with fidelity. While there have been remarkable strides in image generation technology, achieving this high standard of detail and fidelity, which surpass a simple text description, remains a challenge.

Breaking Through Limitations

Current approaches to personalized image synthesis fit into two broad categories: methods that require fine-tuning during testing and those that do not. Fine-tuning methods, despite their accuracy, prove resource-intensive, lengthy, and often need multiple reference images, which limits their practicality. On the other hand, fine-tuning-free methods lack the capability to create high-fidelity, customized images. InstantID confronts these limitations by offering a simple, plug-and-play module that efficiently handles image personalization. It calls upon a uniquely designed face encoder—IdentityNet—that incorporates a single facial image, coupled with landmark and textual prompts, to guide image generation with precision.

The Mechanics of InstantID

InstantID functions as a lightweight adapter, weaving its magic into pre-trained text-to-image diffusion models without the necessity for fine-tuning. It comprises an ID embedding protocol to capture robust semantic facial features and an Image Adapter that enables images to serve as prompts. These elements are key to maintaining high fidelity in generated images. Furthermore, InstantID's IdentityNet encodes detailed features from the reference facial image, adding weak spatial control to ensure the integrity of the identity. Even during the training phase, only the newly added modules of InstantID are optimized, keeping the parameters of the foundational diffusion model intact. This trait underlines InstantID's flexibility and cost-efficiency.

Implications of InstantID

The practical applications of InstantID are vast, including novel view synthesis, ID interpolation, and multi-ID and multi-style synthesis. It promises significant advantages for industries such as e-commerce, virtual try-ons, and AI portraits. Another remarkable aspect of InstantID is its compatibility with various pre-trained models, further showcasing its versatility. The method can integrate with models like SD1.5 and SDXL, offering a diverse range of applications without the need for additional resources.

To conclude, InstantID represents a leap forward in identity-preservation within the field of image generation. Its ability to preserve complex identity attributes in real-time, with the backing of existing diffusion models, sets a new standard in the field. Researchers have made InstantID’s code and pre-trained checkpoints accessible, paving the way for further innovation and exploration within the community. The journey of InstantID underscores the ongoing development in AI-driven image creation and the relentless pursuit of fidelity and efficiency in personalized image synthesis.