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("