Instructions to use leowajda/cosine_diffusion_ema with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use leowajda/cosine_diffusion_ema 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_ema") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 482849a66e45766c1477cf0f7e2426e2dc597645cfe10acd92efe9eb52ae3689
- Size of remote file:
- 5.18 MB
- SHA256:
- ef0f0c614ec959f4fff5b859ddf2d1715da47a4ee8236f2491ea1a611567da91
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