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
| { | |
| "module": "keras_hub.src.models.clip.clip_tokenizer", | |
| "class_name": "CLIPTokenizer", | |
| "config": { | |
| "name": "clip_g_tokenizer", | |
| "trainable": true, | |
| "dtype": { | |
| "module": "keras", | |
| "class_name": "DTypePolicy", | |
| "config": { | |
| "name": "int32" | |
| }, | |
| "registered_name": null | |
| }, | |
| "config_file": "clip_g_tokenizer.json", | |
| "sequence_length": null, | |
| "add_prefix_space": false, | |
| "pad_with_end_token": false | |
| }, | |
| "registered_name": "keras_hub>CLIPTokenizer" | |
| } |