import gradio as gr
import numpy as np
from optimum.intel import OVStableDiffusionPipeline, OVStableDiffusionXLPipeline, OVLatentConsistencyModelPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker


# model_id = "echarlaix/sdxl-turbo-openvino-int8"
# model_id = "echarlaix/LCM_Dreamshaper_v7-openvino"

#safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")

model_id = "OpenVINO/LCM_Dreamshaper_v7-int8-ov"
#pipeline = OVLatentConsistencyModelPipeline.from_pretrained(model_id, compile=False, safety_checker=safety_checker)
pipeline = OVLatentConsistencyModelPipeline.from_pretrained(model_id, compile=False)


batch_size, num_images, height, width = 1, 1, 512, 512
pipeline.reshape(batch_size=batch_size, height=height, width=width, num_images_per_prompt=num_images)
pipeline.compile()

def infer(prompt, num_inference_steps):

    image = pipeline(
        prompt = prompt, 
        negative_prompt = negative_prompt,
        # guidance_scale = guidance_scale,
        num_inference_steps = num_inference_steps, 
        width = width,
        height = height,
        num_images_per_prompt=num_images,
    ).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(f"""
        # Demo : [Fast LCM](https://huggingface.co/OpenVINO/LCM_Dreamshaper_v7-int8-ov) quantized with NNCF ⚡
        """)

        with gr.Row():
            
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                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):
            
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
                 visible=True,
             )
            
            with gr.Row():
                
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=10,
                    step=1,
                    value=5,
                )
        
        gr.Examples(
            examples = examples,
            inputs = [prompt]
        )

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

demo.queue().launch()