import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
from optimum.intel.openvino.modeling_diffusion import OVModelVaeDecoder, OVBaseModel, OVStableDiffusionPipeline
import torch
from huggingface_hub import snapshot_download
import openvino.runtime as ov
from typing import Optional, Dict
from diffusers import EulerAncestralDiscreteScheduler, LCMScheduler, DDIMScheduler



#EulerDiscreteScheduler 尚可
#EulerAncestralDiscreteScheduler 


model_id = "hsuwill000/LCM-absolutereality-openvino-8bit"

#model_id = "spamsoms/LCM-anything-v5-openvino2"
#adapter_id = "latent-consistency/lcm-lora-sdv1-5"


HIGH=1024
WIDTH=512

batch_size = -1



pipe = OVStableDiffusionPipeline.from_pretrained(
        model_id, 
        compile = False, 
        ov_config = {"CACHE_DIR":""},
        torch_dtype=torch.uint8, 
        #variant="fp16", 
        #torch_dtype=torch.IntTensor, #慢,
        safety_checker=None,
        use_safetensors=False,
        )
print(pipe.scheduler.compatibles)


pipe.reshape( batch_size=-1, height=HIGH, width=WIDTH, num_images_per_prompt=1)

pipe.compile()

prompt=""
negative_prompt="EasyNegative, (animal, pet), close up,"

def infer(prompt,negative_prompt):

    image = pipe(
        prompt = prompt, 
        negative_prompt = negative_prompt,
        width = WIDTH, 
        height = HIGH,
        guidance_scale=1.0,
        num_inference_steps=6,
        num_images_per_prompt=1,
    ).images[0] 
    
    return image


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


power_device = "CPU"

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # {model_id.split('/')[1]} {WIDTH}x{HIGH}
        Currently running on {power_device}.
        """)
        
        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)

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

demo.queue().launch()