danwaldie/dreambooth
Viewer • Updated • 7 • 11
How to use danwaldie/dreambooth_teddy with Keras:
# Available backend options are: "jax", "torch", "tensorflow".
import os
os.environ["KERAS_BACKEND"] = "jax"
import keras
model = keras.saving.load_model("hf://danwaldie/dreambooth_teddy")
Dreambooth fine-tuned for pictures of my dog, Teddy. Prompts with "teddy_holmes dog" as the unique identifier + class label.
The model seems overtrained on the poses supplied in the training images.
Teddy is a Mini Double Doodle. He's the offspring of a Labradoodle and a Mini Golden Doodle, so he is one half Poodle, one quarter Labrador Retriever, and one quarter Golden Retriever.
Anyone is welcome to use the model if they want to generate pictures of an adorable dog doing interesting things.
More information needed
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 |