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"
model_repo_id = "stabilityai/sdxl-turbo"  # 사용하려는 모델 이름

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

def infer(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    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, seed


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: 640px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # 텍스트-이미지 생성 Gradio 템플릿")

        with gr.Row():
            prompt = gr.Text(
                label="프롬프트",
                show_label=False,
                max_lines=1,
                placeholder="생성하고 싶은 이미지를 입력하세요",
                container=False,
            )

            run_button = gr.Button("실행", scale=0, variant="primary")

        result = gr.Image(label="결과", show_label=False)

        with gr.Accordion("고급 설정", open=False):
            negative_prompt = gr.Text(
                label="네거티브 프롬프트",
                max_lines=1,
                placeholder="포함하지 않을 내용을 입력하세요",
                visible=False,
            )

            seed = gr.Slider(
                label="시드 값",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=0,
            )

            randomize_seed = gr.Checkbox(label="시드 랜덤화", value=True)

            with gr.Row():
                width = gr.Slider(
                    label="너비 (픽셀)",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # 모델에 적합한 기본값으로 설정
                )

                height = gr.Slider(
                    label="높이 (픽셀)",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # 모델에 적합한 기본값으로 설정
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="가이던스 스케일",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=0.0,  # 모델에 적합한 기본값으로 설정
                )

                num_inference_steps = gr.Slider(
                    label="추론 단계 수",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=2,  # 모델에 적합한 기본값으로 설정
                )

        gr.Examples(examples=examples, inputs=[prompt])
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.launch()