Instructions to use kfahn/dreambooth_diffusion_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use kfahn/dreambooth_diffusion_model with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://kfahn/dreambooth_diffusion_model") - Notebooks
- Google Colab
- Kaggle
| library_name: keras | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| | Hyperparameters | Value | | |
| | :-- | :-- | | |
| | inner_optimizer.class_name | Custom>RMSprop | | |
| | inner_optimizer.config.name | RMSprop | | |
| | inner_optimizer.config.weight_decay | None | | |
| | inner_optimizer.config.clipnorm | None | | |
| | inner_optimizer.config.global_clipnorm | None | | |
| | inner_optimizer.config.clipvalue | None | | |
| | inner_optimizer.config.use_ema | False | | |
| | inner_optimizer.config.ema_momentum | 0.99 | | |
| | inner_optimizer.config.ema_overwrite_frequency | 100 | | |
| | inner_optimizer.config.jit_compile | True | | |
| | inner_optimizer.config.is_legacy_optimizer | False | | |
| | inner_optimizer.config.learning_rate | 0.0010000000474974513 | | |
| | inner_optimizer.config.rho | 0.9 | | |
| | inner_optimizer.config.momentum | 0.0 | | |
| | inner_optimizer.config.epsilon | 1e-07 | | |
| | inner_optimizer.config.centered | False | | |
| | dynamic | True | | |
| | initial_scale | 32768.0 | | |
| | dynamic_growth_steps | 2000 | | |
| | training_precision | mixed_float16 | | |
| ## Model Plot | |
| <details> | |
| <summary>View Model Plot</summary> | |
|  | |
| </details> |