Delete app-backup.py

app-backup.py

@@ -1,450 +0,0 @@
-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
-)