app.py

from huggingface_hub import from_pretrained_keras
from keras_cv import models
import gradio as gr
import tensorflow as tf

tf.keras.mixed_precision.set_global_policy("mixed_float16")

# load keras model
resolution = 512
dreambooth_model = models.StableDiffusion(
        img_width=resolution, img_height=resolution, jit_compile=True, 
    )
loaded_diffusion_model = from_pretrained_keras("keras-dreambooth/dreambooth_diffusion_hokusai")
dreambooth_model._diffusion_model = loaded_diffusion_model


# generate images
def inference(prompt, negative_prompt, num_imgs_to_gen, num_steps, guidance_scale):
    generated_images = dreambooth_model.text_to_image(
        prompt,
        negative_prompt=negative_prompt,
        batch_size=num_imgs_to_gen,
        num_steps=num_steps,
        unconditional_guidance_scale=guidance_scale,
    )
    return generated_images 
    
    

# pass function, input type for prompt, the output for multiple images
gr.Interface(
    inference, [
        gr.Textbox(label="Positive Prompt", value="a painting image in hks## style"),
        gr.Textbox(label="Negative Prompt", value="bad anatomy, soft blurry"),
        gr.Slider(label='Number of gen image', minimum=1, maximum=4, value=2, step=1),
        gr.Slider(label="Inference Steps",value=100),
        gr.Number(label='Guidance scale', value=7.5),
    ], [
        gr.Gallery(show_label=False).style(grid=(1,2)),
    ],
    title="Keras Dreambooth - Hokusai artist 🏞",
    description = "This model has been fine-tuned to learn the concept of Hokusai artist. To use this demo, you should have {hks## style} in the input",
    examples = [["a painting image of a fishing village under a cherry blossom forest at sunset in hks## style, ultra realistic, 4k, 8k", "bad anatomy, soft blurry", 4, 100, 15],
               ["a beautiful and highly detailed oil painting of a lost valley in the mountains in hks## style, running river, intricate details, 8k, sharp focus, hyper realism", "(bad anatomy), (blurry), grain", 4, 100, 15]],
    cache_examples=True
    ).queue().launch(debug=True)