Practical overview
When it fits
Use it when you want a code-based reference for VAE variants or a configured local training workflow built around CelebA.
Who it is for
- Researchers comparing variational autoencoder approaches under a shared dataset setup.
- Learners studying paper-based VAE implementations in PyTorch.
- Developers seeking a code reference for experiments with VAE variants.
Documented uses
- Compare implementations of different VAE model families using the repository's CelebA-based setup.
- Study model code and configurations alongside references to the papers they implement.
- Prepare a YAML-configured training experiment and inspect its logs in TensorBoard.
Requirements & limits
The repository is public and identifies its license as Apache-2.0. It is code intended to run in a local Python/PyTorch environment; the documented requirements include CUDA-enabled computing.
- Requires Python, PyTorch, PyTorch Lightning and a CUDA-enabled device according to the README.
- CelebA is the default dataset, and its location is set in the YAML configuration.
- Some model architectures differ substantially where the original paper calls for it, so the shared setup is not identical across every implementation.
- The README documents configuration-driven execution and TensorBoard log viewing; it also reports issues with the Google Drive dataset download path.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install the repository and prepare a training configuration
Set up the code and configure a VAE run with the documented repository workflow.
Before you start
- Python 3.5 or later
- PyTorch 1.3 or later
- PyTorch Lightning 0.6.0 or later
- A CUDA-enabled computing device
- CelebA dataset for the default configuration
Steps
Clone the PyTorch-VAE repository and enter its directory.
Install the listed project dependencies with pip install -r requirements.txt.
Set the YAML configuration's data_path to the extracted CelebA dataset location and review the model, experiment and trainer settings.
Expected outcome
The repository is installed and a configuration file is prepared for launching a model run.
Guide limitations
- This guide covers setup and configuration only; it does not claim that a run was executed.
- The README reports issues with downloading CelebA through Google Drive and recommends downloading the archive directly and extracting it.
- The documented models require a CUDA-enabled computing device.
Launch a configured VAE run and inspect its logs
Use a repository YAML configuration to start training and view the resulting TensorBoard logs.
Before you start
- The repository and dependencies installed
- A configured YAML file
- CelebA at the configured data_path
- A CUDA-enabled computing device
Steps
From the repository directory, run python run.py -c configs/, substituting the chosen configuration filename.
After a run has produced logs, change to logs//version_.
Run tensorboard --logdir . from that log directory to view the logs.
Expected outcome
The documented workflow starts a configured training run and opens its TensorBoard logs.
Guide limitations
- The README's TensorBoard instructions apply to an existing run log directory; the guide does not verify execution or results.
- Training settings and dataset path must be supplied in the selected YAML configuration.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- AntixK/PyTorch-VAE · Official homepage title · retained in its source language