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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

summary_image

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

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Model Image

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,
)
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Inference Examples
Inference API (serverless) does not yet support tf-keras models for this pipeline type.

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