This release is a pre-release and may not be stable for production use.
VIT-VQGAN
This is an unofficial implementation of both ViT-VQGAN and RQ-VAE in Pytorch. ViT-VQGAN is a simple ViT-based Vector Quantized AutoEncoder while RQ-VAE introduces a new residual quantization scheme. Further details can be viewed in the papers
Installation
pip install vitvqgan
Training
Train the model:
python -m vitvqgan.train_vim
You can add more options too:
python -m vitvqgan.train_vim -c imagenet_vitvq_small -lr 0.00001 -e 10
It uses Imagenette as the training dataset for demo purpose, to change it, modify dataloader init file.
Inference:
- download checkpoints from above in mbin folder
- Run the following command:
python -m vitvqgan.demo_recon
Checkpoints
Acknowledgements
The repo is modified from here with updates to latest dependencies and to be easily run in consumer-grade GPU for learning purpose.
1 maintainer
Metadata
1 maintainer
Release files for vitvqgan 0.0.1.dev1
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