UniPortrait offers a unified framework for human image personalization, enabling high-fidelity identity preservation across single and multiple subjects. It supports extensive facial editing and free-form text descriptions without requiring predefined layouts, targeting researchers and developers in computer vision and generative AI.

How It Works

UniPortrait integrates ControlNet, IP-Adapter, and StyleAligned within a diffusion model architecture. This approach allows for flexible control over identity and style, leveraging pre-trained models for efficient and high-quality personalization. The framework's unification of single and multi-ID scenarios simplifies complex personalization tasks.

Quick Start & Requirements

• Install via pip install -r requirements.txt.
• Requires Git LFS for model downloads.
• Download models from HuggingFace (specific IP-Adapter models are noted as recommended for manual download).
• Run the Gradio app with python gradio_app.py.
• Project page and HuggingFace demo are available.

Highlighted Details

• Unified framework for single and multi-human image personalization.
• High-fidelity identity preservation.
• Extensive facial editability and free-form text description support.
• Integrates ControlNet, IP-Adapter, and StyleAligned.

Maintenance & Community

The project is actively developed, with recent releases including inference code, demos, and a technical report. Contact is available via email or GitHub issues.

Licensing & Compatibility

The repository's license is not explicitly stated in the README. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project is presented as a recent release with ongoing development, indicating potential for evolving features and stability. Specific model requirements and download instructions suggest a non-trivial setup process.