Pu L I D

Introduction

PuLID, short for Pure and Lightning ID Customization via Contrastive Alignment, is a project by Zinan Guo, Yanze Wu, and colleagues at ByteDance Inc. The model focuses on ID customization using contrastive alignment techniques and has been accepted for presentation at NeurIPS 2024.

Architecture

PuLID leverages contrastive alignment to achieve its ID customization capabilities. The model is designed to be lightweight and efficient, making it suitable for various applications. Specific details on its architecture can be found in the associated documentation for the PuLID-FLUX and PuLID for SDXL models.

Training

The model has undergone multiple releases, with updates incorporating feedback and improvements. Key releases include:

• PuLID-FLUX-v0.9.0 and v0.9.1, offering enhanced capabilities.
• PuLID-v1.1, which provides advancements over its predecessor, PuLID-v1.

Training datasets and methods are documented in the related arXiv paper and project documentation.

Guide: Running Locally

1. Clone the Repository: Start by cloning the PuLID repository from Hugging Face.
2. Install Dependencies: Ensure all required packages and libraries are installed.
3. Download Model Files: Obtain the desired model version, such as pulid_v1.1.safetensors or pulid_flux_v0.9.1.safetensors.
4. Run the Model: Execute the model using your local environment. Adjust configurations as necessary for your specific use case.

For optimal performance, consider using cloud GPUs such as those offered by AWS, Google Cloud Platform, or Microsoft Azure. These platforms provide scalable resources suitable for running intensive machine learning models.

License

PuLID is licensed under the Apache-2.0 License, allowing for wide usage and modification while maintaining compliance with its terms.