FaceChain-Upload your own photos to generate your digital twin

Key Points: FaceChain, Photo to Digital Twins

Introduce FaceChain

FaceChain is a deep-learning toolchain powered by ModelScope that generates Digital Twins using deep learning. With just one portrait photo, you can create your own Digital Twin and generate personal portraits in various styles. It offers Python scripts or a Gradio interface for training and generating photos. Recent updates include pose control, robust face training, and support for different style models. FaceChain can be experienced through ModelScope Studio, HuggingFace Space, or Colab notebooks. It uses Stable Diffusion and LoRA models for personal portrait generation, and various other models for tasks like face detection, skin retouching, and more.

How it works

FaceChain: Upload your own photos to generate your digital twin (Digital-Twin), which has similar functions to the Miaoya Camera.

An individual’s physical features can be captured in one or more photos and turned into a digital twin, creating personal photos in different contexts (such as work photos).

FaceChain uses multiple models, including face detection models, image rotation models, human body analysis models, skin color modification models, etc.

Algorithm introduction:

Training phase: Input clear facial area images uploaded by users and output the Face LoRA model. Through a series of image processing and model fine-tuning, high-quality facial training images are obtained and a facial LoRA model is obtained.

Inference stage: Generate personal portrait images based on the facial LoRA model and style LoRA model, and then further improve the facial details of the portrait images.

You can train your digital twin model and generate photos through FaceChain’s Python script or the familiar Gradio interface, or you can experience FaceChain directly through ModelScope Studio.