Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+tags:
+- diffusion
+- latent-diffusion
+- pytorch
+- text-to-image
+- image-generation
+---
+
+# 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](https://github.com/Won-Seong/simple-latent-diffusion-model).
+
+## 🚀 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:
+
+| Dataset | Type | Description |
+| :--- | :--- | :--- |
+| **CIFAR-10** | Unconditional | 32x32 image generation based on CIFAR-10 classes. |
+| **CelebA** | Unconditional | Human face generation trained on the CelebA dataset. |
+| **Asian Composite** | Text-to-Image (T2I) | CLIP-based conditional generation using the Asian Composite Dataset. |
+
+---
+
+## 🛠 How to Use
+If you want to experiment with these models and generate your own images, we provide a hands-on example notebook.
+
+1.  Open the **`cifar10_example.ipynb`** file provided in this repository.
+2.  Follow the step-by-step instructions to load the configurations and model weights.
+3.  Run the cells to start the sampling process and generate images.
+
+---
+
+## 🔗 References
+* **GitHub Repository**: [Won-Seong/simple-latent-diffusion-model](https://github.com/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 (PyTorch, Diffusers, Transformers, etc.) as specified in the GitHub repository's requirements.*