JuyeopDang/simple-latent-diffusion-models

Simple Latent Diffusion Model (LDM)

This repository contains the pre-trained weights and configuration files for the Simple Latent Diffusion Model project.

For the full source code, detailed explanations, and implementation logic, please visit the original GitHub repository.

🚀 Model Description

This project implements a Latent Diffusion Model (LDM) from scratch. The repository includes:

• —Custom-trained VAE: For compressing images into a latent space.
• —Diffusion Model: A U-Net based architecture for the reverse diffusion process.
• —CLIP Weights: Integrated for text-guided image generation.

📂 Available Models & Checkpoints

The repository provides weights for three different datasets, covering both unconditional and conditional generation tasks:

🛠 How to Use

If you want to experiment with these models and generate your own images, we provide a hands-on example notebook.

1. 1.Open the `cifar10_example.ipynb` file provided in this repository.
2. 2.Follow the step-by-step instructions to load the configurations and model weights.
3. 3.Run the cells to start the sampling process and generate images.

🔗 References

• —GitHub Repository: Won-Seong/simple-latent-diffusion-model
• —Contact: For detailed code logic or issues, please refer to the GitHub documentation or open an issue there.

Note: Ensure you have the necessary dependencies installed as specified in the GitHub repository's requirements.