metadata
language:
- en
library_name: tf-keras
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
- keras-dreambooth
- wild-card
inference: true
Model description
The Ignatius Farray dreambooth model would be a sleek and modern diffusion model designed to transport users into a world of absurdity and hilarity. I cannot promise that all the images would be adorned with bright, eye-catching colors and images that reflect Ignatius' unique sense of style and humor.
Images generated by model
Intended uses & limitations
You can use to create images based on Ignatius and put him in different situations. Try not to use for bad purpose and use the "commedia" on it.
Training and evaluation data
To train this model, this was the training notebook and the trainig dataset was this one
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
View Model Plot
Usage
The instance token used is "ignatius". A prompt example is as follows "a photo of ignatius on a car"
from huggingface_hub import from_pretrained_keras
import keras_cv
sd_dreambooth_model = keras_cv.models.StableDiffusion(
img_width=resolution, img_height=resolution, jit_compile=True,
)
loaded_diffusion_model = from_pretrained_keras("keras-dreambooth/ignatius")
sd_dreambooth_model._diffusion_model = loaded_diffusion_model
prompt = f"ignatius on the moon"
#generated_img = sd_dreambooth_model.text_to_image(
generated_img = dreambooth_model.text_to_image(
prompt,
batch_size=4,
num_steps=150,
unconditional_guidance_scale=15,
)