Instructions to use keras/stable_diffusion_3.5_medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/stable_diffusion_3.5_medium with KerasHub:
import keras_hub # Load TextToImage model (optional: use half precision for inference) text_to_image = keras_hub.models.TextToImage.from_preset("hf://keras/stable_diffusion_3.5_medium", dtype="bfloat16") # Generate images with a TextToImage model. text_to_image.generate("Astronaut in a jungle")import keras_hub # Create a ImageToImage model task = keras_hub.models.ImageToImage.from_preset("hf://keras/stable_diffusion_3.5_medium")import keras_hub # Create a Inpaint model task = keras_hub.models.Inpaint.from_preset("hf://keras/stable_diffusion_3.5_medium")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/stable_diffusion_3.5_medium") - Keras
How to use keras/stable_diffusion_3.5_medium with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://keras/stable_diffusion_3.5_medium") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3eb2f7e4a36541691fb5ad8f8f3c28704b53586d512cd0a93bc96194b9bc13b
- Size of remote file:
- 6.75 GB
- SHA256:
- df068258b964120bcf6035186d747ae46c088d3f8b59e333a771cc1ca3d50ee5
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