app.py

import random
import os
import uuid
from datetime import datetime
import gradio as gr
import numpy as np
import spaces
import torch
from diffusers import DiffusionPipeline
from PIL import Image

# Create permanent storage directory
SAVE_DIR = "saved_images"  # Gradio will handle the persistence
if not os.path.exists(SAVE_DIR):
    os.makedirs(SAVE_DIR, exist_ok=True)

device = "cuda" if torch.cuda.is_available() else "cpu"
repo_id = "black-forest-labs/FLUX.1-dev"
adapter_id = "seawolf2357/kim-korea"  # 특정 정치인을 학습한 LoRA 모델

pipeline = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16)
pipeline.load_lora_weights(adapter_id)
pipeline = pipeline.to(device)

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

def save_generated_image(image, prompt):
    # Generate unique filename with timestamp
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    unique_id = str(uuid.uuid4())[:8]
    filename = f"{timestamp}_{unique_id}.png"
    filepath = os.path.join(SAVE_DIR, filename)
    
    # Save the image
    image.save(filepath)
    
    # Save metadata
    metadata_file = os.path.join(SAVE_DIR, "metadata.txt")
    with open(metadata_file, "a", encoding="utf-8") as f:
        f.write(f"{filename}|{prompt}|{timestamp}\n")
    
    return filepath

@spaces.GPU(duration=60)
def inference(
    prompt,
    seed=42,
    randomize_seed=True,
    width=1024,
    height=768,
    guidance_scale=3.5,
    num_inference_steps=30,
    lora_scale=1.0,
    progress=None,
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator(device=device).manual_seed(int(seed))
    
    image = pipeline(
        prompt=prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
        joint_attention_kwargs={"scale": lora_scale},
    ).images[0]
    
    # Save the generated image
    filepath = save_generated_image(image, prompt)
    
    # Return just the image and seed
    return image, seed

# 예시 문구: 특정 정치인 Mr. KIM의 다양한 상황을 묘사
examples = [
    "Mr. KIM delivering a powerful speech in front of a large crowd with confident gestures and determined expression. [trigger]",
    "Mr. KIM attending an international summit, standing among other world leaders with a dignified and poised manner. [trigger]",
    "Mr. KIM holding a press conference, facing flashing cameras, wearing a tailored suit and a subtle lapel pin. [trigger]",
    "Mr. KIM visiting a rural area, warmly greeting local residents while discussing policies and improvements. [trigger]",
    "Mr. KIM in a dynamic interview setting, passionately outlining his visions for the future. [trigger]",
    "Mr. KIM preparing for an important debate, surrounded by paperwork, looking focused and resolute. [trigger]",
]

# UI를 붉은 계열 그라디에이션으로 디자인
custom_css = """
:root {
    --color-primary: #8F1A3A; /* 붉은 톤의 메인 컬러 */
    --color-secondary: #FF4B4B; /* 포인트 컬러(밝은 빨강) */
    --background-fill-primary: linear-gradient(to right, #FFF5F5, #FED7D7, #FEB2B2);
}

footer {
    visibility: hidden;
}

.gradio-container {
    background: var(--background-fill-primary);
}

.title {
    color: var(--color-primary) !important;
    font-size: 3rem !important;
    font-weight: 700 !important;
    text-align: center;
    margin: 1rem 0;
    text-shadow: 2px 2px 4px rgba(0,0,0,0.05);
    font-family: 'Playfair Display', serif;
}

.subtitle {
    color: #4A5568 !important;
    font-size: 1.2rem !important;
    text-align: center;
    margin-bottom: 1.5rem;
    font-style: italic;
}

.collection-link {
    text-align: center;
    margin-bottom: 2rem;
    font-size: 1.1rem;
}

.collection-link a {
    color: var(--color-primary);
    text-decoration: underline;
    transition: color 0.3s ease;
}

.collection-link a:hover {
    color: var(--color-secondary);
}

.model-description {
    background-color: rgba(255, 255, 255, 0.8);
    border-radius: 12px;
    padding: 24px;
    margin: 20px 0;
    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.05);
    border-left: 5px solid var(--color-primary);
}

button.primary {
    background-color: var(--color-primary) !important;
    transition: all 0.3s ease;
    color: #fff !important;
}

button:hover {
    transform: translateY(-2px);
    box-shadow: 0 5px 15px rgba(0,0,0,0.1);
}

.input-container {
    border-radius: 10px;
    box-shadow: 0 2px 8px rgba(0,0,0,0.05);
    background-color: rgba(255, 255, 255, 0.6);
    padding: 20px;
    margin-bottom: 1rem;
}

.advanced-settings {
    margin-top: 1rem;
    padding: 1rem;
    border-radius: 10px;
    background-color: rgba(255, 255, 255, 0.6);
}

.example-region {
    background-color: rgba(255, 255, 255, 0.5);
    border-radius: 10px;
    padding: 1rem;
    margin-top: 1rem;
}
"""

with gr.Blocks(css=custom_css, analytics_enabled=False) as demo:
    gr.HTML('<div class="title">Mr. KIM in KOREA</div>')
    
    # 컬렉션 링크 또는 안내문을 필요 시 수정/삭제
    gr.HTML('<div class="collection-link"><a href="https://huggingface.co/collections/openfree/painting-art-ai-681453484ec15ef5978bbeb1" target="_blank">Visit the LoRA Model Collection</a></div>')
    
    # 모델 설명: 특정 정치인에 대한 LoRA 모델임을 언급
    with gr.Group(elem_classes="model-description"):
        gr.HTML("""
        <p>
        본 모델은 특정 정치인의 얼굴과 외모를 학습한 LoRA 모델입니다.<br>
        개인 사진 등을 무단 사용하지 않도록 유의해 주세요.<br>
        (예시 prompt 사용 시 [trigger]를 추가하여 최적의 결과를 얻을 수 있습니다.)
        </p>
        """)

    # 메인 UI
    with gr.Column(elem_id="col-container"):
        with gr.Row(elem_classes="input-container"):
            prompt = gr.Text(
                label="Prompt",
                max_lines=1,
                placeholder="Enter your prompt (add [trigger] at the end)",
                value=examples[0]  # 기본 예시
            )
            run_button = gr.Button("Generate", variant="primary", scale=0)

        result = gr.Image(label="Generated Image")
        seed_output = gr.Number(label="Seed", visible=True)

        with gr.Accordion("Advanced Settings", open=False, elem_classes="advanced-settings"):
            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=42,
            )
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=768,
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=3.5,
                )
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=30,
                )
                lora_scale = gr.Slider(
                    label="LoRA scale",
                    minimum=0.0,
                    maximum=1.0,
                    step=0.1,
                    value=1.0,
                )

        with gr.Group(elem_classes="example-region"):
            gr.Markdown("### Examples")
            gr.Examples(
                examples=examples,
                inputs=prompt,
                outputs=None,  # Don't auto-run examples
                fn=None,       # No function to run for examples - just fill the prompt
                cache_examples=False,
            )

    # 이벤트 핸들러
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=inference,
        inputs=[
            prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            lora_scale,
        ],
        outputs=[result, seed_output],
    )

demo.queue()
demo.launch()