Instructions to use buio/vq-vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use buio/vq-vae with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://buio/vq-vae") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f63e64c205b1acd3cc227776674fc9e27174afa8b6fce8a494900c8d147f3c6
- Size of remote file:
- 29.8 kB
- SHA256:
- 6c9ff128a6e79ebc9961ab1f83a31daf916ae274ab4337bf23c3b7abc163cbb8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.