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


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

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


#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, 1024, 1024
pipeline.reshape(batch_size=batch_size, height=height, width=width, num_images_per_prompt=num_images)
pipeline.load_textual_inversion("./badhandv4.pt", "badhandv4")
pipeline.compile()

#TypeError: LatentConsistencyPipelineMixin.__call__() got an unexpected keyword argument 'negative_prompt'
negative_prompt="easynegative,bad anatomy, bad hands, missing fingers, extra fingers, three hands, three legs, bad arms, missing legs, missing arms, poorly drawn face, bad face, fused face, cloned face, three crus, fused feet, fused thigh, extra crus, ugly fingers, horn, cartoon, cg, 3d, unreal, animate, amputation, disconnected limbs, nsfw, nude, censored,  "

def infer(prompt, num_inference_steps):

    image = pipeline(
        prompt = prompt, 
        negative_prompt = negative_prompt,
        guidance_scale = 7.0,
        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):
            #with gr.Row():
            # negative_prompt = gr.Text(
            #     label="Negative prompt",
            #     max_lines=1,
            #     placeholder="Enter a negative prompt",
            # )
            
            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(share=True)