app-backup.py

import os
import gc
import uuid
import random
import tempfile
import time
from datetime import datetime
from typing import Any
from huggingface_hub import login, hf_hub_download
import spaces

import gradio as gr
import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont
from diffusers import FluxPipeline
from transformers import pipeline

# 메모리 정리 함수
def clear_memory():
    gc.collect()
    try:
        if torch.cuda.is_available():
            with torch.cuda.device(0):
                torch.cuda.empty_cache()
    except:
        pass

# GPU 설정
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

if torch.cuda.is_available():
    try:
        with torch.cuda.device(0):
            torch.cuda.empty_cache()
            torch.backends.cudnn.benchmark = True
            torch.backends.cuda.matmul.allow_tf32 = True
    except:
        print("Warning: Could not configure CUDA settings")

# HF 토큰 설정
HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN is None:
    raise ValueError("Please set the HF_TOKEN environment variable")

try:
    login(token=HF_TOKEN)
except Exception as e:
    raise ValueError(f"Failed to login to Hugging Face: {str(e)}")



translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device=-1)  # CPU에서 실행

def translate_to_english(text: str) -> str:
    """한글 텍스트를 영어로 번역"""
    try:
        if any(ord('가') <= ord(char) <= ord('힣') for char in text):
            translated = translator(text, max_length=128)[0]['translation_text']
            print(f"Translated '{text}' to '{translated}'")
            return translated
        return text
    except Exception as e:
        print(f"Translation error: {str(e)}")
        return text


# FLUX 파이프라인 초기화 부분 수정
print("Initializing FLUX pipeline...")
try:
    pipe = FluxPipeline.from_pretrained(
        "black-forest-labs/FLUX.1-dev",
        torch_dtype=torch.float16,
        use_auth_token=HF_TOKEN
    )
    print("FLUX pipeline initialized successfully")
    
    # 메모리 최적화 설정
    pipe.enable_attention_slicing(slice_size=1)
    
    # GPU 설정
    if torch.cuda.is_available():
        pipe = pipe.to("cuda:0")
        torch.cuda.empty_cache()
        torch.backends.cudnn.benchmark = True
        torch.backends.cuda.matmul.allow_tf32 = True
    
    print("Pipeline optimization settings applied")

except Exception as e:
    print(f"Error initializing FLUX pipeline: {str(e)}")
    raise

# LoRA 가중치 로드 부분 수정
print("Loading LoRA weights...")
try:
    # 로컬 LoRA 파일의 절대 경로 확인
    current_dir = os.path.dirname(os.path.abspath(__file__))
    lora_path = os.path.join(current_dir, "myt-flux-fantasy.safetensors")
    
    if not os.path.exists(lora_path):
        raise FileNotFoundError(f"LoRA file not found at: {lora_path}")
    
    print(f"Loading LoRA weights from: {lora_path}")
    
    # LoRA 가중치 로드
    pipe.load_lora_weights(lora_path)
    pipe.fuse_lora(lora_scale=0.75)  # lora_scale 값 조정
    
    # 메모리 정리
    torch.cuda.empty_cache()
    gc.collect()
    
    print("LoRA weights loaded and fused successfully")
    print(f"Current device: {pipe.device}")

except Exception as e:
    print(f"Error loading LoRA weights: {str(e)}")
    print(f"Full error details: {repr(e)}")
    raise ValueError(f"Failed to load LoRA weights: {str(e)}")
    

@spaces.GPU(duration=60)
def generate_image(
    prompt: str,
    seed: int,
    randomize_seed: bool,
    width: int,
    height: int,
    guidance_scale: float,
    num_inference_steps: int,
    progress: gr.Progress = gr.Progress()
):
    try:
        clear_memory()
        
        translated_prompt = translate_to_english(prompt)
        print(f"Processing prompt: {translated_prompt}")
        
        if randomize_seed:
            seed = random.randint(0, MAX_SEED)
        
        generator = torch.Generator(device=device).manual_seed(seed)
        
        print(f"Current device: {pipe.device}")
        print(f"Starting image generation...")
        
        with torch.inference_mode(), torch.cuda.amp.autocast(enabled=True):
            image = pipe(
                prompt=translated_prompt,
                width=width,
                height=height,
                num_inference_steps=num_inference_steps,
                guidance_scale=guidance_scale,
                generator=generator,
                num_images_per_prompt=1,
            ).images[0]
        
        filepath = save_generated_image(image, translated_prompt)
        print(f"Image generated and saved to: {filepath}")
        return image, seed
        
    except Exception as e:
        print(f"Generation error: {str(e)}")
        print(f"Full error details: {repr(e)}")
        raise gr.Error(f"Image generation failed: {str(e)}")
    finally:
        clear_memory()

# 저장 디렉토리 설정
SAVE_DIR = "saved_images"
if not os.path.exists(SAVE_DIR):
    os.makedirs(SAVE_DIR, exist_ok=True)

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

def save_generated_image(image, prompt):
    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)
    image.save(filepath)
    return filepath



def add_text_with_stroke(draw, text, x, y, font, text_color, stroke_width):
    """텍스트에 외곽선을 추가하는 함수"""
    for adj_x in range(-stroke_width, stroke_width + 1):
        for adj_y in range(-stroke_width, stroke_width + 1):
            draw.text((x + adj_x, y + adj_y), text, font=font, fill=text_color)

def add_text_to_image(
    input_image,
    text,
    font_size,
    color,
    opacity,
    x_position,
    y_position,
    thickness,
    text_position_type,
    font_choice
):
    try:
        if input_image is None or text.strip() == "":
            return input_image

        if not isinstance(input_image, Image.Image):
            if isinstance(input_image, np.ndarray):
                image = Image.fromarray(input_image)
            else:
                raise ValueError("Unsupported image type")
        else:
            image = input_image.copy()

        if image.mode != 'RGBA':
            image = image.convert('RGBA')

        font_files = {
            "Default": "DejaVuSans.ttf",
            "Korean Regular": "ko-Regular.ttf"
        }
        
        try:
            font_file = font_files.get(font_choice, "DejaVuSans.ttf")
            font = ImageFont.truetype(font_file, int(font_size))
        except Exception as e:
            print(f"Font loading error ({font_choice}): {str(e)}")
            font = ImageFont.load_default()
        
        color_map = {
            'White': (255, 255, 255),
            'Black': (0, 0, 0),
            'Red': (255, 0, 0),
            'Green': (0, 255, 0),
            'Blue': (0, 0, 255),
            'Yellow': (255, 255, 0),
            'Purple': (128, 0, 128)
        }
        rgb_color = color_map.get(color, (255, 255, 255))
        
        temp_draw = ImageDraw.Draw(image)
        text_bbox = temp_draw.textbbox((0, 0), text, font=font)
        text_width = text_bbox[2] - text_bbox[0]
        text_height = text_bbox[3] - text_bbox[1]

        actual_x = int((image.width - text_width) * (x_position / 100))
        actual_y = int((image.height - text_height) * (y_position / 100))

        text_color = (*rgb_color, int(opacity))
        
        txt_overlay = Image.new('RGBA', image.size, (255, 255, 255, 0))
        draw = ImageDraw.Draw(txt_overlay)
        
        add_text_with_stroke(
            draw,
            text,
            actual_x,
            actual_y,
            font,
            text_color,
            int(thickness)
        )
        output_image = Image.alpha_composite(image, txt_overlay)
        
        output_image = output_image.convert('RGB')
        
        return output_image

    except Exception as e:
        print(f"Error in add_text_to_image: {str(e)}")
        return input_image    


css = """
footer {display: none}
.main-title {
    text-align: center;
    margin: 1em 0;
    padding: 1.5em;
    background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
    border-radius: 15px;
    box-shadow: 0 4px 6px rgba(0,0,0,0.1);
}
.main-title h1 {
    color: #2196F3;
    font-size: 2.8em;
    margin-bottom: 0.3em;
    font-weight: 700;
}
.main-title p {
    color: #555;
    font-size: 1.3em;
    line-height: 1.4;
}
.container {
    max-width: 1200px;
    margin: auto;
    padding: 20px;
}
.input-panel, .output-panel {
    background: white;
    padding: 1.5em;
    border-radius: 12px;
    box-shadow: 0 2px 8px rgba(0,0,0,0.08);
    margin-bottom: 1em;
}
"""

with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
    gr.HTML("""
        <div class="main-title">
            <h1>🎨 Webtoon Studio</h1>
            <p>Generate webtoon-style images and add text with various styles and positions.</p>
        </div>
    """)

    with gr.Row():
        with gr.Column(scale=1):
            # 이미지 생성 섹션
            gen_prompt = gr.Textbox(
                label="Generation Prompt",
                placeholder="Enter your image generation prompt..."
            )
            with gr.Row():
                gen_width = gr.Slider(512, 1024, 768, step=64, label="Width")
                gen_height = gr.Slider(512, 1024, 768, step=64, label="Height")
            
            with gr.Row():
                guidance_scale = gr.Slider(1, 20, 7.5, step=0.5, label="Guidance Scale")
                num_steps = gr.Slider(1, 50, 30, step=1, label="Number of Steps")
            
            with gr.Row():
                seed = gr.Number(label="Seed", value=-1)
                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
            
            generate_btn = gr.Button("Generate Image", variant="primary")
            
            output_image = gr.Image(
                label="Generated Image",
                type="pil",
                show_download_button=True
            )
            output_seed = gr.Number(label="Used Seed", interactive=False)

            # 텍스트 추가 섹션
            with gr.Accordion("Text Options", open=False):
                text_input = gr.Textbox(
                    label="Text Content",
                    placeholder="Enter text to add..."
                )
                text_position_type = gr.Radio(
                    choices=["Text Over Image"],
                    value="Text Over Image",
                    label="Text Position",
                    visible=True
                )
                with gr.Row():
                    font_choice = gr.Dropdown(
                        choices=["Default", "Korean Regular"],
                        value="Default",
                        label="Font Selection",
                        interactive=True
                    )
                    font_size = gr.Slider(
                        minimum=10,
                        maximum=200,
                        value=40,
                        step=5,
                        label="Font Size"
                    )
                with gr.Row():
                    color_dropdown = gr.Dropdown(
                        choices=["White", "Black", "Red", "Green", "Blue", "Yellow", "Purple"],
                        value="White",
                        label="Text Color"
                    )
                    thickness = gr.Slider(
                        minimum=0,
                        maximum=10,
                        value=1,
                        step=1,
                        label="Text Thickness"
                    )
                with gr.Row():
                    opacity_slider = gr.Slider(
                        minimum=0,
                        maximum=255,
                        value=255,
                        step=1,
                        label="Opacity"
                    )
                with gr.Row():
                    x_position = gr.Slider(
                        minimum=0,
                        maximum=100,
                        value=50,
                        step=1,
                        label="Left(0%)~Right(100%)"
                    )
                    y_position = gr.Slider(
                        minimum=0,
                        maximum=100,
                        value=50,
                        step=1,
                        label="High(0%)~Low(100%)"
                    )
                add_text_btn = gr.Button("Apply Text", variant="primary")

    # 이벤트 바인딩
    generate_btn.click(
        fn=generate_image,
        inputs=[
            gen_prompt,
            seed,
            randomize_seed,
            gen_width,
            gen_height,
            guidance_scale,
            num_steps,
        ],
        outputs=[output_image, output_seed]
    )

    add_text_btn.click(
        fn=add_text_to_image,
        inputs=[
            output_image,
            text_input,
            font_size,
            color_dropdown,
            opacity_slider,
            x_position,
            y_position,
            thickness,
            text_position_type,
            font_choice
        ],
        outputs=output_image
    )

demo.queue(max_size=5)
demo.launch(
    server_name="0.0.0.0",
    server_port=7860,
    share=False,
    max_threads=2
)