Arc2Face
| Property | Value |
|---|---|
| License | MIT |
| Paper | ArXiv Link |
| Language | English |
| Framework | Diffusers |
What is Arc2Face?
Arc2Face is a groundbreaking face generation model that can create diverse, identity-consistent photos of individuals using only their ArcFace ID-embedding. Built upon Stable Diffusion v1-5, it combines a finetuned CLIP ViT-L/14 encoder with a specialized UNet model to generate high-quality facial images while maintaining identity consistency.
Implementation Details
The model architecture consists of two main components: an encoder (finetuned CLIP ViT-L/14) that projects ID-embeddings to the CLIP latent space, and the arc2face UNet model that handles the actual image generation. The system is trained on a restored version of the WebFace42M database and further refined using FFHQ and CelebA-HQ datasets.
- Finetuned CLIP ViT-L/14 encoder for ID embedding projection
- Specialized UNet model for face generation
- Additional ControlNet model for pose control
- Built on Stable Diffusion v1-5 architecture
Core Capabilities
- Generate diverse facial images from ID embeddings
- Maintain identity consistency across generations
- Control pose through ControlNet integration
- Support for frontal hemisphere poses
- Single-person image generation
Frequently Asked Questions
Q: What makes this model unique?
Arc2Face stands out for its ability to generate face images using only ArcFace ID-embeddings, maintaining identity consistency while allowing for pose control and diverse representations. Its architecture combines state-of-the-art components from CLIP and Stable Diffusion.
Q: What are the recommended use cases?
The model is ideal for applications requiring identity-preserved face generation, such as avatar creation, face-based authentication testing, and facial analysis research. However, it's limited to single-person images and frontal hemisphere poses.
