Diffusion C L I P Celeb A_ H Q

Introduction

DiffusionCLIP is a diffusion model designed for robust image manipulation, particularly focusing on human faces. The model excels in image reconstruction, manipulation, and style transfer due to its superior inversion capabilities, outperforming traditional GAN-based models.

Architecture

The DiffusionCLIP model leverages text-guided diffusion processes to manipulate images effectively. It uses the CelebA-HQ dataset for training, which provides high-quality images of human faces, allowing for detailed and precise adjustments. The model integrates an identity-preserving loss function to maintain the facial identity during manipulations.

Training

This model is trained on the CelebA-HQ dataset, which is known for its high-resolution facial images. The training process involves the use of a pretrained IR-SE50 model to ensure that the identity of the human faces is preserved throughout the manipulation process.

Guide: Running Locally

1. Clone the Repository:

   git clone https://github.com/gwang-kim/DiffusionCLIP.git
   cd DiffusionCLIP
   

2. Install Dependencies: Ensure you have PyTorch installed. Install additional dependencies as specified in the repository's requirements file.

3. Download Pretrained Models:

   • Obtain the IR-SE50 model from TreB1eN's repository.

4. Run the Model: Follow the instructions in the repository to execute the model for image manipulation tasks.

5. Suggested Cloud GPUs: For efficient processing, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

The code and model are available under the terms specified in the DiffusionCLIP GitHub repository. Ensure compliance with the license before using the model for commercial purposes.