Create app-backup.py

app-backup.py

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+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
+)