Social Twin
Social Twin Technology Explained
The Social Twin is built on three core technologies: LoRA fine-tuning for identity capture, identity locking for consistent output, and multi-modal generation across image, video, and voice.
LoRA Fine-Tuning
LoRA (Low-Rank Adaptation) is a lightweight method for fine-tuning large AI models on a small dataset. For Social Twin, we use LoRA to teach the base image model what a specific person looks like — face structure, skin tone, typical expressions, and style. Training requires 10–20 reference images and completes in minutes.
Identity Locking
Identity locking is the mechanism that attaches the LoRA model to every generation in the pipeline. Rather than relying on prompt descriptions alone, The Social Twin injects the identity token and LoRA weights into every image, video, and lipsync generation — ensuring the output always references the same trained identity.
Multi-Modal Generation
Social Twin identity extends across all content modalities. The same LoRA identity anchors image generation (FLUX 2 Dev, Atom V), video generation (Kling V3, Veo 3.1, Seedance 2.0), and lipsync/voice (Kling Lipsync, OmniHuman, ElevenLabs). One twin, every format.
Agentic Pipeline Integration
The Social Twin's agentic filmmaker automatically attaches twin profiles to the correct generation nodes in a production pipeline. When the Story Builder creates a shot list, it assigns the relevant character twin to each shot — requiring no manual prompt engineering from the filmmaker.
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