import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

if torch.cuda.is_available():
    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
    # pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp32", use_safetensors=True)
    pipe.enable_xformers_memory_efficient_attention()
    pipe = pipe.to(device)
else: 
    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
    pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 512  # 이미지 크기를 512로 설정

def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator().manual_seed(seed)
    
    image = pipe(
        prompt=prompt, 
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale, 
        num_inference_steps=num_inference_steps, 
        width=width, 
        height=height,
        generator=generator
    ).images[0] 
    
    return image

examples = [
    "A playful Australian Shepherd dog running around in Central Park",
    "맛있는 바스크 치즈케이크 조각"
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 1024px;
}
"""

if torch.cuda.is_available():
    power_device = "GPU"
else:
    power_device = "CPU"

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # Text-to-Image Generation
        # 텍스트-이미지 생성기 
        Currently running on {power_device}.
        현재 {power_device}에서 실행 중입니다.
        """)
        
        with gr.Row():
            prompt = gr.Textbox(
                label="Prompt / 프롬프트",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt / 프롬프트를 입력하세요",
                container=False,
            )
            
            run_button = gr.Button("Run / 실행", scale=0)
        
        result = gr.Image(label="Result / 결과", show_label=False)

        with gr.Accordion("Advanced Settings / 고급 설정", open=False):
            
            negative_prompt = gr.Textbox(
                label="Negative prompt / 네거티브 프롬프트",
                max_lines=1,
                placeholder="Enter a negative prompt / 네거티브 프롬프트를 입력하세요",
                visible=False,
            )
            
            seed = gr.Slider(
                label="Seed / 시드",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )
            
            randomize_seed = gr.Checkbox(label="Randomize seed / 시드 랜덤화", value=True)
            
            with gr.Row():
                
                width = gr.Slider(
                    label="Width / 너비",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )
                
                height = gr.Slider(
                    label="Height / 높이",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,
                )
            
            with gr.Row():
                
                guidance_scale = gr.Slider(
                    label="Guidance scale / 가이던스 스케일",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=7.5,
                )
                
                num_inference_steps = gr.Slider(
                    label="Number of inference steps / 추론 단계 수",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=10,  # 추론 단계를 10으로 설정하여 시간 단축
                )
        
        gr.Examples(
            examples=examples,
            inputs=[prompt]
        )

    run_button.click(
        fn=infer,
        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs=[result]
    )

demo.queue().launch()