Instructions to use keras-io/super-resolution with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use keras-io/super-resolution with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy), and from_pretrained_keras was removed in huggingface_hub 1.0. # See https://github.com/keras-team/tf-keras for more details. # !pip install "huggingface_hub<1.0" tf_keras from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("keras-io/super-resolution") - Notebooks
- Google Colab
- Kaggle
Download saved_model.pb from keras-io/super-resolution: direct link, hf CLI and curl.
- Browser
- Download file 110 kB
-
https://huggingface.co/keras-io/super-resolution/resolve/refs%2Fpr%2F2/saved_model.pb
- Command line
-
hf download hf://keras-io/super-resolution@refs/pr/2/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/keras-io/super-resolution/resolve/refs%2Fpr%2F2/saved_model.pb
110 kB
- Xet hash:
- 993ca72b979357bf6b187e4b517190dcd3766e9b9e2246416e0a80b248ecd255
- Size of remote file:
- 110 kB
- SHA256:
- 41b81e6e72275c1935dbc703dea660d43613d8d510067a0fa3ba42e352b279dc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.