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from huggingface_hub import from_pretrained_keras
import keras_cv
import gradio as gr
keras.mixed_precision.set_global_policy("mixed_float16")
# load keras model
resolution = 512
dreambooth_model = keras_cv.models.StableDiffusion(
img_width=resolution, img_height=resolution, jit_compile=True,
)
loaded_diffusion_model = from_pretrained_keras("keras-dreambooth/pink-floyd-division-bell")
dreambooth_model._diffusion_model = loaded_diffusion_model
def generate_images(prompt: str, num_imgs_to_gen: int, num_steps: int):
"""
This function is used to generate images using our fine-tuned keras dreambooth stable diffusion model.
Args:
prompt (str): The text input given by the user based on which images will be generated.
num_imgs_to_gen (int): The number of images to be generated using given prompt.
num_steps (int): The number of denoising steps
Returns:
generated_img (List): List of images that were generated using the model
"""
generated_img = dreambooth_model.text_to_image(
prompt, batch_size=num_imgs_to_gen,
num_steps=num_steps,
)
return generated_img
with gr.Blocks() as demo:
gr.HTML("<h2 style=\"font-size: 2em; font-weight: bold\" align=\"center\">Keras Dreambooth - Pink Floyd Division Bell Demo</h2>")
gr.Markdown("This model has been fine tuned to learn the concept of Division Bell from Pink Floyd's famous album `The Division Bell`")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="prompt")
samples = gr.Slider(label="No. of Images",value=1)
num_steps = gr.Slider(label="Inference Steps",value=50)
run = gr.Button(value="Run")
with gr.Column():
gallery = gr.Gallery(show_label=False)
run.click(generate_images, inputs=[prompt,samples, num_steps], outputs=gallery)
gr.Examples([["pink floyd division bell album cover with a starry night on Mars background", 1,50],
["Flower vase inspired by pink floyd division bell",1, 50],
["Pendant jewellery in the style of pink floyd division bell", 1,50]],
[prompt,samples,num_steps], gallery, generate_images, cache_examples=False)
gr.Markdown('\n Demo created by: <a href=\"https://huggingface.co/shivi/\">Shivalika Singh</a>')
demo.launch(debug=True)