import gradio as gr
from optimum.intel.openvino import OVStableDiffusionPipeline
from diffusers.training_utils import set_seed

quantized_pipe = OVStableDiffusionPipeline.from_pretrained("OpenVINO/Stable-Diffusion-Pokemon-en-quantized", compile=False)
quantized_pipe.reshape(batch_size=1, height=512, width=512, num_images_per_prompt=1)
quantized_pipe.compile()

prompt = "cartoon bird"

def generate(image):
    output = quantized_pipe(prompt, num_inference_steps=50, output_type="pil")
    return output.images[0]

examples = ["cartoon bird",
            "a drawing of a green pokemon with red eyes",
            "plant pokemon in jungle"]

gr.Interface(
    fn=generate,
    inputs=gr.inputs.Textbox(placeholder="cartoon bird", 
                             label="Prompt",
                             lines=1),
    outputs=gr.outputs.Image(type="pil", label="Generated Image"),
    title="OpenVINO-optimized Stable Diffusion",
    description="This is the Optimum-based demo for optimized Stable Diffusion pipeline trained on Pokemon dataset and running with OpenVINO",
    theme="huggingface",
).launch()