variational-autoencoder-pytorch-lib 1.0.1

A package to simplify the implementing a variational-autoencoder model with spacial latent heads.

🧠 Variational Autoencoder (VAE) in PyTorch

A modular and customizable implementation of a Convolutional Variational Autoencoder (VAE) in PyTorch, designed for image reconstruction and unsupervised representation learning. Built with residual blocks, RMS normalization, and flexible architecture scaling.

🚀 Features

• 🔁 Encoder–Decoder VAE with reparameterization trick
• 🧱 Residual blocks with RMS normalization
• 🧩 Fully modular, easy to customize
• 🔄 Downsampling/Upsampling using einops and nn.Conv2d
• 🧪 Dropout regularization for improved generalization
• ⚡ Fast inference with .reconstruct() method
• 🧼 Clean, production-ready code

📦 Installation

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

pip install variational-autoencoder-pytorch-lib

📁 Project Structure

variational-autoencoder-pytorch/
├── variational_autoencoder_pytorch/
│   ├── __init__.py
│   └── module.py        # All architecture classes and logic
├── pyproject.toml
├── LICENSE
└── README.md

🚀 Quick Start

1. Import the package and create the model

import torch
from variational_autoencoder_pytorch import VariationalAutoEncoder

model = AutoEncoder(
    dim=64,
    dim_mults=(1, 2, 4, 8),
    dim_latent=128,
    image_channels=3
)

2. Forward pass and reconstruction

x = torch.randn(8, 3, 256, 256)  # batch of images
x_recon, mu, logvar = model(x)

# Or just get the reconstruction
x_recon = model.reconstruct(x)

3. Training step (sample loop)

import torch.nn.functional as F
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)

def train_step(x):
    model.train()
    optimizer.zero_grad()
    x_recon, mu, logvar = model(x)
    loss = vae_loss(x, x_recon, mu, logvar)
    loss.backward()
    optimizer.step()
    return loss.item()
    

🧠 Model Output

• x_recon: Reconstructed image

• mu: Mean of the latent distribution

• logvar: Log-variance of the latent distribution

⚙️ Configuration Options

Argument	Type	Default	Description
dim	int	64	Base number of channels
dim_mults	tuple	(1, 2, 4, 8)	Multipliers for feature map dimensions
dim_latent	int	64	Latent space dimension
image_channels	int	3	Input/output image channels (e.g., 3)
dropout	float	0.0	Dropout probability

🧪 Example: Loss Function

Here's a basic VAE loss function combining reconstruction and KL divergence:

def vae_loss(x, x_recon, mu, logvar):
    recon_loss = F.mse_loss(x_recon, x, reduction='sum')
    kl_div = -0.5  *  torch.sum(torch.mean(1  +  logvar  -  mu.pow(2) -  logvar.exp(), dim=[2, 3]))
    loss = recon_loss + (kl_div * 0.0001) # beta = 0.0001
    return loss

🙋‍♂️ Author

Developed by Mehran Bazrafkan

Built from scratch with inspiration from modern deep generative modeling architectures. This package reflects personal experience with VAEs and convolutional design patterns.

⭐️ Support & Contribute

If you find this project useful, consider:

• ⭐️ Starring the repo

• 🐛 Submitting issues

• 📦 Suggesting improvements

🔗 Related Projects

• convolutional-autoencoder-pytorch · PyPI (Implemented by me)

• PyTorch VAE Tutorial (external)

📜 License

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