import gradio as gr
import numpy as np
from optimum.intel import OVStableDiffusionPipeline, OVStableDiffusionXLPipeline, OVLatentConsistencyModelPipeline
from optimum.intel.openvino.modeling_diffusion import OVModelVaeDecoder, OVBaseModel
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from diffusers import DiffusionPipeline
from diffusers.schedulers import EulerDiscreteScheduler
import openvino.runtime as ov
from typing import Optional, Dict
from huggingface_hub import snapshot_download

#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, 512


class CustomOVModelVaeDecoder(OVModelVaeDecoder):
    def __init__(
        self, model: ov.Model, parent_model: OVBaseModel, ov_config: Optional[Dict[str, str]] = None, model_dir: str = None,
    ):
        super(OVModelVaeDecoder, self).__init__(model, parent_model, ov_config, "vae_decoder", model_dir)


pipeline = OVStableDiffusionPipeline.from_pretrained(model_id, compile = False, ov_config = {"CACHE_DIR":""})

taesd_dir = snapshot_download(repo_id="deinferno/taesd-openvino")
pipeline.vae_decoder = CustomOVModelVaeDecoder(model = OVBaseModel.load_model(f"{taesd_dir}/vae_decoder/openvino_model.xml"), parent_model = pipeline, model_dir = taesd_dir)


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

#不可用lora
#pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
#pipeline.set_adapters("pixel")


# 选择采样方法（调度器） 可以新增但是跑就死
#scheduler = EulerDiscreteScheduler()
#pipeline.scheduler = scheduler

#badhandv4
#pipeline.load_textual_inversion("./badhandv4.pt", "badhandv4")
#hiten1
#pipeline.load_textual_inversion("./hiten1.pt", "hiten1")
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)