StableIdentity is a latest advancement based on large pre-trained text-to-image models, capable of achieving high-quality, human-centric generation. Unlike existing methods, StableIdentity ensures the stable retention of identity and flexible editability, even when training is conducted using only one facial image of each subject. It utilizes a facial encoder and identity prior to encode the input face, then projects the facial representation into an editable prior space. By combining identity priors and editability priors, the learned identity can be injected into various contexts. Additionally, StableIdentity incorporates a masked two-stage diffusion loss to enhance pixel-level perception of the input face and maintain the diversity of generation. Extensive experiments demonstrate that StableIdentity outperforms previous customization methods. The learned identity can also be flexibly combined with existing modules like ControlNet. Notably, we are the first to directly inject identities learned from a single image into video/3D generation without fine-tuning. We believe that StableIdentity is an important step towards unifying image, video, and 3D customization generation models.