  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.
