Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- ylecun/mnist
+- uoft-cs/cifar10
+- uoft-cs/cifar100
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-to-image
+tags:
+- diffusion
+- unet
+- res
+---
+
+<a id="readme-top"></a>
+<!-- PROJECT SHIELDS -->
+
+<!-- PROJECT LOGO -->
+<br />
+<div align="center">
+  <a href="https://github.com/Yavuzhan-Baykara/Stable-Diffusion">
+    
+  </a>
+
+  <h3 align="center">Diffusion Model Sampler</h3>
+
+  <p align="center">
+    An implementation of a diffusion model sampler using a UNet transformer to generate handwritten digit samples.
+    <br />
+    <a href="https://github.com/Yavuzhan-Baykara/Stable-Diffusion"><strong>Explore the docs »</strong></a>
+    <br />
+    <br />
+    <a href="https://github.com/Yavuzhan-Baykara/Stable-Diffusion">View Demo</a>
+    ·
+    <a href="https://github.com/Yavuzhan-Baykara/Stable-Diffusion/issues/new?labels=bug&template=bug-report---.md">Report Bug</a>
+    ·
+    <a href="https://github.com/Yavuzhan-Baykara/Stable-Diffusion/issues/new?labels=enhancement&template=feature-request---.md">Request Feature</a>
+  </p>
+</div>
+
+<!-- TABLE OF CONTENTS -->
+<details>
+  <summary>Table of Contents</summary>
+  <ol>
+    <li>
+      <a href="#about-the-project">About The Project</a>
+      <ul>
+        <li><a href="#built-with">Built With</a></li>
+      </ul>
+    </li>
+    <li>
+      <a href="#getting-started">Getting Started</a>
+      <ul>
+        <li><a href="#prerequisites">Prerequisites</a></li>
+        <li><a href="#installation">Installation</a></li>
+      </ul>
+    </li>
+    <li><a href="#usage">Usage</a></li>
+    <li><a href="#results">Results</a></li>
+    <li><a href="#roadmap">Roadmap</a></li>
+    <li><a href="#contributing">Contributing</a></li>
+    <li><a href="#license">License</a></li>
+    <li><a href="#contact">Contact</a></li>
+    <li><a href="#acknowledgments">Acknowledgments</a></li>
+  </ol>
+</details>
+
+<!-- ABOUT THE PROJECT -->
+## About The Project
+
+Diffusion models have shown great promise in generating high-quality samples in various domains. In this project, we utilize a UNet transformer-based diffusion model to generate samples of handwritten digits. The process involves:
+1. Setting up the model and loading pre-trained weights.
+2. Generating samples for each digit.
+3. Creating a GIF to visualize the generated samples.
+
+<div align="center">
+  <img src="./digit_samples.gif" alt="MNIST GIF" width="200" height="200" style="display:inline-block;">
+  <img src="./digit_samples_cifar.gif" alt="CIFAR-10 GIF" width="200" height="200" style="display:inline-block;">
+</div>
+
+<p align="right">(<a href="#readme-top">back to top</a>)</p>
+
+### Built With
+
+#### AI and Machine Learning Libraries
+
+<div align="center">
+  <img src="https://icon.icepanel.io/Technology/svg/TensorFlow.svg" alt="Python" width="40" height="40" style="display:inline-block;">
+  <img src="https://icon.icepanel.io/Technology/svg/PyTorch.svg" alt="PyTorch" width="40" height="40" style="display:inline-block;">
+  <img src="https://icon.icepanel.io/Technology/svg/NumPy.svg" alt="NumPy" width="40" height="40" style="display:inline-block;">
+  <img src="https://icon.icepanel.io/Technology/svg/Matplotlib.svg" alt="Matplotlib" width="40" height="40" style="display:inline-block;">
+  <img src="https://img.shields.io/badge/Pillow-5A9?style=for-the-badge&logo=pillow&logoColor=white" alt="Pillow" width="40" height="40" style="display:inline-block;">
+</div>
+
+<p align="right">(<a href="#readme-top">back to top</a>)</p>
+
+<!-- GETTING STARTED -->
+## Getting Started
+
+To get a local copy up and running follow these simple example steps.
+
+### Prerequisites
+
+Ensure you have the following prerequisites installed:
+* Python 3.8 or higher
+* CUDA-enabled GPU (optional but recommended)
+* The following Python libraries:
+  - torch
+  - torchvision
+  - numpy
+  - Pillow
+  - matplotlib
+
+### Installation
+
+1. Clone the repository:
+    ```sh
+    git clone https://github.com/Yavuzhan-Baykara/Stable-Diffusion.git
+    cd Stable-Diffusion
+    ```
+2. Install the required Python libraries:
+    ```sh
+    pip install torch torchvision numpy Pillow matplotlib
+    ```
+
+<p align="right">(<a href="#readme-top">back to top</a>)</p>
+
+<!-- USAGE -->
+## Usage
+
+To train the UNet transformer with different datasets and samplers, use the following command:
+```sh
+python train.py <dataset> <sampler> <epoch> <batch_size>