https://github.com/saadsalmanakram/diffusion-ss
Learn everything you need to know about diffusion...
https://github.com/saadsalmanakram/diffusion-ss
diffusion diffusion-model diffusion-models stable-diffusion
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Learn everything you need to know about diffusion...
- Host: GitHub
- URL: https://github.com/saadsalmanakram/diffusion-ss
- Owner: saadsalmanakram
- Created: 2024-08-29T10:29:23.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-28T05:33:35.000Z (8 months ago)
- Last Synced: 2025-01-28T06:25:55.223Z (8 months ago)
- Topics: diffusion, diffusion-model, diffusion-models, stable-diffusion
- Homepage:
- Size: 62.5 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
---
# 🌫️ Diffusion Masterclass – Understanding & Implementing Diffusion Models
## 📝 Introduction
**Diffusion models** are a class of generative models that have recently gained prominence in deep learning, particularly in **image synthesis, denoising, and probabilistic modeling**. These models iteratively **add noise to data** and then learn to reverse the process to generate realistic samples.
This repository serves as a **comprehensive guide** to mastering **Diffusion Models**, covering theoretical foundations, practical implementations, and applications in AI.
📌 **Understand the mathematics behind diffusion models**
📌 **Implement diffusion models from scratch using PyTorch**
📌 **Explore applications in image generation, denoising, and more**
📌 **Use Stable Diffusion, DDPM, and advanced diffusion techniques**
---
## 🚀 Features
- 📖 **Theory & Fundamentals** of Diffusion Models
- 🖼️ **Image Generation with Denoising Diffusion Probabilistic Models (DDPM)**
- ⚡ **Implementation in PyTorch**
- 🌍 **Stable Diffusion & Latent Diffusion Models (LDMs)**
- 🔍 **Exploration of Variational Diffusion Models & Score-Based Methods**
- 📝 **Jupyter notebooks with step-by-step explanations**
---
## 📂 Repository Structure
```
Diffusion-ss/
│── theory/ # Theory & mathematical foundations
│── notebooks/ # Jupyter notebooks with implementations
│── models/ # PyTorch implementations of diffusion models
│── applications/ # Real-world applications (image generation, denoising, etc.)
│── experiments/ # Custom diffusion experiments & modifications
│── README.md # Documentation
└── requirements.txt # Python dependencies
```
---
## 🏆 Getting Started
### 1️⃣ Clone the Repository
```bash
git clone https://github.com/saadsalmanakram/Diffusion-ss.git
cd Diffusion-ss
```
### 2️⃣ Install Dependencies
```bash
pip install -r requirements.txt
```
### 3️⃣ Run a Simple Diffusion Model
```bash
python models/ddpm.py
```
---
## 🔍 Topics Covered
### 📖 **Theory & Fundamentals**
- What are **Diffusion Models**?
- Forward & Reverse Diffusion Process
- **Mathematical Formulation** (Stochastic Differential Equations)
- **DDPM vs. Score-Based Generative Models**
### 🖼️ **Image Generation with Diffusion Models**
- Implementing **Denoising Diffusion Probabilistic Models (DDPM)**
- Training diffusion models on **CIFAR-10, CelebA, and ImageNet**
- **Latent Diffusion Models (LDMs) & Stable Diffusion**
### ⚡ **Diffusion Models in PyTorch**
- Building a simple **DDPM from scratch**
- Training a model to **generate high-resolution images**
- Implementing **U-Net-based diffusion architectures**
### 🔍 **Advanced Diffusion Techniques**
- **Classifier-free guidance** for improved generation
- **Conditional diffusion models** (text-to-image)
- **Speeding up inference using fast sampling methods (DDIM, PNDM)**
### 🚀 **Real-World Applications**
- **Image Denoising & Super-Resolution**
- **Text-to-Image Generation (Stable Diffusion, Imagen, DALL·E 2)**
- **Video & 3D Diffusion Models**
---
## 🚀 Example Code
### 🖼️ **Simple Forward Diffusion Process**
```python
import torch
import torch.nn.functional as F
def forward_diffusion(x, noise, t, betas):
sqrt_alpha = (1 - betas).cumprod(dim=0).sqrt()
return sqrt_alpha[t] * x + torch.sqrt(1 - sqrt_alpha[t]) * noise
x = torch.randn(1, 3, 64, 64) # Random image
noise = torch.randn_like(x)
betas = torch.linspace(0.0001, 0.02, 1000) # Noise schedule
diffused_x = forward_diffusion(x, noise, 100, betas)
```
### 🔄 **Reverse Process with Learned Model**
```python
import torch.nn as nn
class SimpleUNet(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(64, 3, kernel_size=3, padding=1)
def forward(self, x, t):
x = F.relu(self.conv1(x))
return self.conv2(x)
model = SimpleUNet()
prediction = model(diffused_x, 100) # Reverse step prediction
```
---
## 🔥 Cutting-Edge Diffusion Models
📌 **Stable Diffusion** – Latent space diffusion for **high-resolution text-to-image generation**
📌 **DALL·E 2 & Imagen** – **Transformer-based conditional diffusion models**
📌 **Score-Based Generative Models** – SDE-based methods for **high-fidelity image synthesis**
📌 **Variational Diffusion Models (VDM)** – **Improving likelihood-based training**
---
## 🏆 Contributing
Contributions are welcome! 🚀
🔹 **Fork** the repository
🔹 Create a new branch (`git checkout -b feature-name`)
🔹 Commit changes (`git commit -m "Added DDIM sampling"`)
🔹 Push to your branch (`git push origin feature-name`)
🔹 Open a pull request
---
## 📜 License
This project is licensed under the **MIT License** – feel free to use, modify, and share the code.
---
## 📬 Contact
📧 **Email:** saadsalmanakram1@gmail.com
🌐 **GitHub:** [SaadSalmanAkram](https://github.com/saadsalmanakram)
💼 **LinkedIn:** [Saad Salman Akram](https://www.linkedin.com/in/saadsalmanakram/)
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⚡ **Master Diffusion Models & Unlock the Future of Generative AI!** ⚡
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