This repository contains CVAE and CVQVAE implementations in PyTorch for MNIST dataset.

pip install -r requirements.txt
python main.py --latent_size 2 --batch_size 64

The model will be evaluated after every epoch. You can check the images generated by the decoder in the images folder.

When the training finishes, the model will be saved to model.pth.

There are two types of encoders and decoders: mlp and conv. You can use one of them by passing the --encoder and --decoder options.

There are two types of CVAE: cvae and cvqvae. You can use one of them by passing the --model_type option.

Run python main.py --help for more help on options.

You can export the decoder to the ONNX format and play with it in an interactive way using demo.html

# The onnx model will be saved as decoder.onnx
python main.py --export_onnx --latent_size 2 --encoder mlp --decoder mlp --model_path model.pth
python -m http.server
# Now open http://localhost:8000/demo.html

Note that because onnx.js does not support the ConvTranspose operator, you can only export the MLP decoder.

The web page only supports 2-dim latent representation. Feel free to hack around it!

Below are some generated images. All models use 2-dim latent representations.