from huggingface_hub import from_pretrained_keras import keras_cv import gradio as gr def generate_images(prompt: str, num_imgs_to_gen: 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. Returns: all_images (List): List of images that were generated using the model """ dreambooth_model = keras_cv.models.StableDiffusion( img_width=resolution, img_height=resolution, jit_compile=True, ) loaded_diffusion_model = from_pretrained_keras("shivi/dreambooth_diffusion_model") dreambooth_model._diffusion_model = loaded_diffusion_model generated_img = dreambooth_model.text_to_image( prompt, batch_size=num_imgs_to_gen ) 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) run = gr.Button(value="Run") with gr.Column(): gallery = gr.Gallery(show_label=False) run.click(generate_images, inputs=[prompt,samples], outputs=gallery) gr.Examples([["A flower vase inspired by pink floyd division bell album cover", 1], ["A pendant in the style of pink floyd division bell album cover",1], ["A human skull inspired by pink floyd division bell album cover ",1], ["picture of pink floyd division bell album cover with a starry night on Mars theme", 1]], [prompt,samples,model_choice], gallery, generate_images, cache_examples=True) gr.Markdown("Some fun examples I created while playing with the model:") gr.Makdown("![Example 1](sample_outputs/1.png)") demo.launch(debug=True)