Instructions to use leowajda/cosine_diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use leowajda/cosine_diffusion with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://leowajda/cosine_diffusion") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bd6e5814443d93036f7859cb8d8b62ee76d0034c7b853ff9b592562b194bb81b
- Size of remote file:
- 54 Bytes
- SHA256:
- 70d20190d4132bd4c96da3814a26af221bd84c2104be476eba3e99b92caafc9e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.