diffusion-pytorch-lib 1.0.0

A flexible PyTorch implementation of a UNet-based Denoising Diffusion Probabilistic Model (DDPM) with customizable architecture and training options.

UNet-based Denoising Diffusion Probabilistic Model (DDPM) in PyTorch

A modular and customizable PyTorch implementation of a UNet-based Denoising Diffusion Probabilistic Model for high-quality image generation.
Supports multiple beta schedules, flexible loss functions (MSE or L1), attention layers, and residual blocks for advanced denoising performance.

🚀 Features

• 🌀 UNet backbone for efficient denoising and image synthesis

• 📈 Multiple beta schedules (linear, cosine, etc.) for diffusion process customization

• 🔁 Residual and attention blocks for better feature preservation

• ⚙️ Configurable architecture via channel multipliers and attention resolution

• 🧮 Loss function options — Mean Squared Error (MSE) or L1 loss

• 🧪 Clean and modular code for research and experimentation

• 📦 Production-ready with training and sampling APIs

📦 Installation

You can access the PyPI page or install the package directly.

pip install diffusion-pytorch-lib

📁 Project Structure

diffusion-pytorch-lib/
├── diffusion_pytorch_lib/
│   ├── __init__.py
│   ├── module.py        # All architecture classes and logic
├── pyproject.toml
├── LICENSE
└── README.md

🚀 Quick Start

1. Import and create the model

import torch
from diffusion-pytorch-lib import UNet, Diffusion

# Define UNet
model = UNet(
    dim=64,
    dim_mults=(1, 2, 4, 8),
    channels=3,
)

# Define diffusion process
diffusion = GaussianDiffusion(
    model,
    image_size=256,
    timesteps=1000,
    beta_schedule="linear",  # or 'cosine'
    loss_type="l2"           # 'l2' (MSE) or 'l1'
)

2. Training step (sample loop)

optimizer = torch.optim.Adam(diffusion.parameters(), lr=1e-4)

def train_step(x):
    diffusion.train()
    optimizer.zero_grad()
    loss = diffusion(x)  # computes diffusion loss internally
    loss.backward()
    optimizer.step()
    return loss.item()

3. Sampling new images

diffusion.eval()
with torch.no_grad():
    samples = diffusion.sample(batch_size=8)  # (8, 3, 256, 256)

⚙️ Configuration Options

🧩 U-Net Architecture

Argument	Type	Default	Description
dim	int	64	Base number of feature channels in the first layer.
dim_mults	tuple	(1, 2, 4, 8)	Multipliers for feature map dimensions at each U-Net stage.
channels	int	3	Number of input/output image channels (e.g., 3 for RGB).
dropout	float	0.0	Dropout rate for regularization.

🌀 Diffusion Process

Argument	Type	Default	Description
image_size	int	256	Target image resolution
timesteps	int	1000	Number of diffusion steps (Higher values improve quality but increase training time).
beta_schedule	str	"linear"	Noise schedule type ("linear", "cosine", etc.).
loss_type	str	"mse"	Loss type ("mse" or "l1")

🙋‍♂️ Author

Developed by Mehran Bazrafkan

Built from scratch for research into diffusion models, with inspiration from modern generative modeling literature.

⭐️ Support & Contribute

If you find this project useful, consider:

• ⭐️ Starring the repo

• 🐛 Submitting issues

• 📦 Suggesting improvements

🔗 Related Projects

• variational-autoencoder-pytorch-lib · PyPI (by me)

• Original DDPM Paper (external)

📜 License

This project is licensed under the terms of the MIT LICENSE.