import gradio as gr
from optimum.intel import OVStableDiffusionPipeline

model_id = "yujiepan/dreamshaper-8-lcm-openvino-w8a8"
pipeline = OVStableDiffusionPipeline.from_pretrained(model_id, device='CPU')
pipeline.sampler = "dpm++2s_a" 
num_inference_steps = 28

def infer(prompt):
    image = pipeline(
        prompt=prompt,
        guidance_scale=1.0,
        num_inference_steps=num_inference_steps,
        width=512,
        height=512,
        num_images_per_prompt=1,
    ).images[0]
    return image

examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("# Demo : yujiepan/dreamshaper-8-lcm-openvino-w8a8 ⚡")
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                placeholder="Enter your prompt",
                container=False,
            )
            
            run_button = gr.Button("Run", scale=0)
        result = gr.Image(label="Result", show_label=False)

        #with gr.Accordion("Advanced Settings", open=False):
        #    num_inference_steps = gr.Slider(
        #        label="Number of inference steps",
        #        minimum=1,
        #        maximum=50,
        #        step=1,
        #        value=28,
        #    )

        gr.Examples(
            examples=examples,
            fn=infer,
            inputs=[prompt],
            outputs=[result]
        )

    run_button.click(
        fn=infer,
        #inputs=[prompt, num_inference_steps],
        inputs=[prompt],
        outputs=[result]
    )

demo.queue().launch(share=True)