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("

Keras Dreambooth - Pink Floyd Division Bell Demo

") 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: Shivalika Singh') demo.launch(debug=True)