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+# Stable Diffusion nano
+
+## Overview
+Stable Diffusion nano is a simplified implementation of latent diffusion models inspired by the short course [How Diffusion Models Work](https://learn.deeplearning.ai/courses/diffusion-models/) by deeplearning.ai. The repository aims to make the concepts and structure of diffusion models, particularly Figure 3 from the paper [High-Resolution Image Synthesis with Latent Diffusion Model](https://arxiv.org/abs/2112.10752), accessible to beginners.
+
+![Figure 3 from the LDM Paper](https://raw.githubusercontent.com/metamath1/stable-diffusion-nano/main/assets/ldm-figure3.png)
+
+The repository implements models proposed in two key papers:
+- [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
+- [High-Resolution Image Synthesis with Latent Diffusion Model](https://arxiv.org/abs/2112.10752)
+
+### Why This Repository?
+The official implementations of the above papers are often too complex for beginners. Stable Diffusion nano simplifies these concepts and presents them in an intuitive, beginner-friendly manner using Jupyter notebooks. Our goal is to provide a hands-on learning experience by focusing on essential components while avoiding unnecessary complexity.
+
+For those interested in deeper theoretical insights, refer to [A Gentle Introduction to Diffusion Model: Part 1 - DDPM](https://metamath1.github.io/blog/posts/diffusion/ddpm_part1.html).
+
+---
+
+## Notebooks
+
+This repository includes the following notebooks:
+
+### 1. `01.ddpm.ipynb`
+- **Description**: Implements the basic Denoising Diffusion Probabilistic Model (DDPM).
+- **Goal**: Understand the fundamental process of adding and removing noise to generate images from random noise.
+
+### 2. `02.vae_latent_2d.ipynb`
+- **Description**: Implements an image encoder-decoder (VAE) for converting pixel-space images into latent-space representations, a crucial step for Latent Diffusion Models.
+- **Goal**: Learn how to compress and reconstruct images using a Variational Autoencoder (VAE).
+
+### 3. `03.ldm_nano.ipynb`
+- **Description**: Simplifies the structure in Figure 3 of the LDM paper to create a basic latent diffusion model.
+- **Goal**: Implement a complete but simplified Latent Diffusion Model while maintaining the essential architecture and principles.
+
+---
+
+## Visual Representations
+
+| **Concatenate**                                   | **Multi Head Attention**                                     |
+|-----------------------------------------------------------|----------------------------------------------------------|
+| ![CONCAT VAE](assets/LDM_CONCAT_VAE_4/ani_CONCAT_w5.gif)  | ![QKV VAE](assets/LDM_QKV_VAE_4/ani_QKV_w5.gif)         |
+
+---
+
+## Dataset
+We use a custom dataset of 16x16 image sprites prepared from:
+- [FrootsnVeggies](https://zrghr.itch.io/froots-and-veggies-culinary-pixels)
+- [kyrise](https://kyrise.itch.io/)
+
+This dataset was utilized in the course [How Diffusion Models Work](https://learn.deeplearning.ai/courses/diffusion-models/). The small resolution ensures faster training and inference, making it suitable for educational purposes.
+
+---
+
+## Getting Started
+### Prerequisites
+- Python 3.8 or higher
+- Jupyter Notebook
+- PyTorch
+- torchvision
+- numpy
+- matplotlib
+- plotly
+
+### Installation
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/yourusername/stable-diffusion-nano.git
+   cd stable-diffusion-nano
+   ```
+2. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+### Running the Notebooks
+Open any notebook in Jupyter and run the cells sequentially. Start with `01.ddpm.ipynb` for the basics and progress to `03.ldm_nano.ipynb` for the complete model. You can also run the notebooks on Google Colab for free. Simply upload the desired notebook to Colab and ensure the necessary dependencies are installed.
+
+#### Hands-On Notebook
+
+| Chapter                          | Colab                                                                 |
+|----------------------------------|----------------------------------------------------------------------|
+| DDPM Notebook                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/metamath1/stable-diffusion-nano/blob/main/01.ddpm.ipynb) |
+| VAE Latent 2D Notebook          | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/metamath1/stable-diffusion-nano/blob/main/02.vae_latent_2d.ipynb) |
+| LDM Nano Notebook               | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/metamath1/stable-diffusion-nano/blob/main/03.ldm_nano.ipynb) |
+
+---
+
+## Contributions
+Contributions are welcome! If you find any issues or want to add new features, feel free to open an issue or submit a pull request.
+
+---
+
+## License
+This project is licensed under the MIT License. See the `LICENSE` file for details.
+
+---
+
+## Acknowledgements
+- [How Diffusion Models Work](https://learn.deeplearning.ai/courses/diffusion-models/) by deeplearning.ai for the inspiration.
+- Authors of [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) and [High-Resolution Image Synthesis with Latent Diffusion Model](https://arxiv.org/abs/2112.10752) for their groundbreaking work.
+- [FrootsnVeggies](https://zrghr.itch.io/froots-and-veggies-culinary-pixels) and [kyrise](https://kyrise.itch.io/) for the dataset.
+

requirements.txt

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+torch==2.4.1
+torchvision==0.19.1
+numpy==1.26.4
+matplotlib==3.9.2
+jupyterlab==4.2.5
+pandas==2.1.4
+plotly==5.24.1