Create app-backup.py

app-backup.py

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+import tempfile
+import time
+from collections.abc import Sequence
+from typing import Any, cast
+import os
+from huggingface_hub import login, hf_hub_download
+
+import gradio as gr
+import numpy as np
+import pillow_heif
+import spaces
+import torch
+from gradio_image_annotation import image_annotator
+from gradio_imageslider import ImageSlider
+from PIL import Image
+from pymatting.foreground.estimate_foreground_ml import estimate_foreground_ml
+from refiners.fluxion.utils import no_grad
+from refiners.solutions import BoxSegmenter
+from transformers import GroundingDinoForObjectDetection, GroundingDinoProcessor
+from diffusers import FluxPipeline
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+import gc
+
+from PIL import Image, ImageDraw, ImageFont
+from PIL import Image
+from gradio_client import Client, handle_file
+import uuid
+
+
+def clear_memory():
+    """메모리 정리 함수"""
+    gc.collect()
+    try:
+        if torch.cuda.is_available():
+            with torch.cuda.device(0):  # 명시적으로 device 0 사용
+                torch.cuda.empty_cache()
+    except:
+        pass
+
+# GPU 설정
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")  # 명시적으로 cuda:0 지정
+
+# GPU 설정을 try-except로 감싸기
+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")
+
+# 번역 모델 초기화
+model_name = "Helsinki-NLP/opus-mt-ko-en"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to('cpu')
+translator = pipeline("translation", model=model, tokenizer=tokenizer, device=-1)
+
+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
+
+BoundingBox = tuple[int, int, int, int]
+
+pillow_heif.register_heif_opener()
+pillow_heif.register_avif_opener()
+
+# 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)}")
+
+# 모델 초기화
+segmenter = BoxSegmenter(device="cpu")
+segmenter.device = device
+segmenter.model = segmenter.model.to(device=segmenter.device)
+
+gd_model_path = "IDEA-Research/grounding-dino-base"
+gd_processor = GroundingDinoProcessor.from_pretrained(gd_model_path)
+gd_model = GroundingDinoForObjectDetection.from_pretrained(gd_model_path, torch_dtype=torch.float32)
+gd_model = gd_model.to(device=device)
+assert isinstance(gd_model, GroundingDinoForObjectDetection)
+
+# FLUX 파이프라인 초기화
+pipe = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    torch_dtype=torch.float16,
+    use_auth_token=HF_TOKEN
+)
+pipe.enable_attention_slicing(slice_size="auto")
+
+# LoRA 가중치 로드
+pipe.load_lora_weights(
+    hf_hub_download(
+        "ByteDance/Hyper-SD",
+        "Hyper-FLUX.1-dev-8steps-lora.safetensors",
+        use_auth_token=HF_TOKEN
+    )
+)
+pipe.fuse_lora(lora_scale=0.125)
+
+# GPU 설정을 try-except로 감싸기
+try:
+    if torch.cuda.is_available():
+        pipe = pipe.to("cuda:0")  # 명시적으로 cuda:0 지정
+except Exception as e:
+    print(f"Warning: Could not move pipeline to CUDA: {str(e)}")
+
+client = Client("NabeelShar/BiRefNet_for_text_writing")
+
+class timer:
+    def __init__(self, method_name="timed process"):
+        self.method = method_name
+    def __enter__(self):
+        self.start = time.time()
+        print(f"{self.method} starts")
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        end = time.time()
+        print(f"{self.method} took {str(round(end - self.start, 2))}s")
+
+def bbox_union(bboxes: Sequence[list[int]]) -> BoundingBox | None:
+    if not bboxes:
+        return None
+    for bbox in bboxes:
+        assert len(bbox) == 4
+        assert all(isinstance(x, int) for x in bbox)
+    return (
+        min(bbox[0] for bbox in bboxes),
+        min(bbox[1] for bbox in bboxes),
+        max(bbox[2] for bbox in bboxes),
+        max(bbox[3] for bbox in bboxes),
+    )
+
+def corners_to_pixels_format(bboxes: torch.Tensor, width: int, height: int) -> torch.Tensor:
+    x1, y1, x2, y2 = bboxes.round().to(torch.int32).unbind(-1)
+    return torch.stack((x1.clamp_(0, width), y1.clamp_(0, height), x2.clamp_(0, width), y2.clamp_(0, height)), dim=-1)
+
+def gd_detect(img: Image.Image, prompt: str) -> BoundingBox | None:
+    inputs = gd_processor(images=img, text=f"{prompt}.", return_tensors="pt").to(device=device)
+    with no_grad():
+        outputs = gd_model(**inputs)
+    width, height = img.size
+    results: dict[str, Any] = gd_processor.post_process_grounded_object_detection(
+        outputs,
+        inputs["input_ids"],
+        target_sizes=[(height, width)],
+    )[0]
+    assert "boxes" in results and isinstance(results["boxes"], torch.Tensor)
+    bboxes = corners_to_pixels_format(results["boxes"].cpu(), width, height)
+    return bbox_union(bboxes.numpy().tolist())
+
+def apply_mask(img: Image.Image, mask_img: Image.Image, defringe: bool = True) -> Image.Image:
+    assert img.size == mask_img.size
+    img = img.convert("RGB")
+    mask_img = mask_img.convert("L")
+    if defringe:
+        rgb, alpha = np.asarray(img) / 255.0, np.asarray(mask_img) / 255.0
+        foreground = cast(np.ndarray[Any, np.dtype[np.uint8]], estimate_foreground_ml(rgb, alpha))
+        img = Image.fromarray((foreground * 255).astype("uint8"))
+    result = Image.new("RGBA", img.size)
+    result.paste(img, (0, 0), mask_img)
+    return result
+
+
+def adjust_size_to_multiple_of_8(width: int, height: int) -> tuple[int, int]:
+    """이미지 크기를 8의 배수로 조정하는 함수"""
+    new_width = ((width + 7) // 8) * 8
+    new_height = ((height + 7) // 8) * 8
+    return new_width, new_height
+
+def calculate_dimensions(aspect_ratio: str, base_size: int = 512) -> tuple[int, int]:
+    """선택된 비율에 따라 이미지 크기 계산"""
+    if aspect_ratio == "1:1":
+        return base_size, base_size
+    elif aspect_ratio == "16:9":
+        return base_size * 16 // 9, base_size
+    elif aspect_ratio == "9:16":
+        return base_size, base_size * 16 // 9
+    elif aspect_ratio == "4:3":
+        return base_size * 4 // 3, base_size
+    return base_size, base_size
+
+@spaces.GPU(duration=20)  # 40초에서 20초로 감소
+def generate_background(prompt: str, aspect_ratio: str) -> Image.Image:
+    try:
+        width, height = calculate_dimensions(aspect_ratio)
+        width, height = adjust_size_to_multiple_of_8(width, height)
+        
+        max_size = 768
+        if width > max_size or height > max_size:
+            ratio = max_size / max(width, height)
+            width = int(width * ratio)
+            height = int(height * ratio)
+            width, height = adjust_size_to_multiple_of_8(width, height)
+
+        with timer("Background generation"):
+            try:
+                with torch.inference_mode():
+                    image = pipe(
+                        prompt=prompt,
+                        width=width,
+                        height=height,
+                        num_inference_steps=8,
+                        guidance_scale=4.0
+                    ).images[0]
+            except Exception as e:
+                print(f"Pipeline error: {str(e)}")
+                return Image.new('RGB', (width, height), 'white')
+                
+        return image
+    except Exception as e:
+        print(f"Background generation error: {str(e)}")
+        return Image.new('RGB', (512, 512), 'white')
+
+def create_position_grid():
+    return """
+    <div class="position-grid" style="display: grid; grid-template-columns: repeat(3, 1fr); gap: 10px; width: 150px; margin: auto;">
+        <button class="position-btn" data-pos="top-left">↖</button>
+        <button class="position-btn" data-pos="top-center">↑</button>
+        <button class="position-btn" data-pos="top-right">↗</button>
+        <button class="position-btn" data-pos="middle-left">←</button>
+        <button class="position-btn" data-pos="middle-center">•</button>
+        <button class="position-btn" data-pos="middle-right">→</button>
+        <button class="position-btn" data-pos="bottom-left">↙</button>
+        <button class="position-btn" data-pos="bottom-center" data-default="true">↓</button>
+        <button class="position-btn" data-pos="bottom-right">↘</button>
+    </div>
+    """
+
+def calculate_object_position(position: str, bg_size: tuple[int, int], obj_size: tuple[int, int]) -> tuple[int, int]:
+    """오브젝트의 위치 계산"""
+    bg_width, bg_height = bg_size
+    obj_width, obj_height = obj_size
+    
+    positions = {
+        "top-left": (0, 0),
+        "top-center": ((bg_width - obj_width) // 2, 0),
+        "top-right": (bg_width - obj_width, 0),
+        "middle-left": (0, (bg_height - obj_height) // 2),
+        "middle-center": ((bg_width - obj_width) // 2, (bg_height - obj_height) // 2),
+        "middle-right": (bg_width - obj_width, (bg_height - obj_height) // 2),
+        "bottom-left": (0, bg_height - obj_height),
+        "bottom-center": ((bg_width - obj_width) // 2, bg_height - obj_height),
+        "bottom-right": (bg_width - obj_width, bg_height - obj_height)
+    }
+    
+    return positions.get(position, positions["bottom-center"])
+
+def resize_object(image: Image.Image, scale_percent: float) -> Image.Image:
+    """오브젝트 크기 조정"""
+    width = int(image.width * scale_percent / 100)
+    height = int(image.height * scale_percent / 100)
+    return image.resize((width, height), Image.Resampling.LANCZOS)
+
+def combine_with_background(foreground: Image.Image, background: Image.Image, 
+                          position: str = "bottom-center", scale_percent: float = 100) -> Image.Image:
+    """전경과 배경 합성 함수"""
+    # 배경 이미지 준비
+    result = background.convert('RGBA')
+    
+    # 오브젝트 크기 조정
+    scaled_foreground = resize_object(foreground, scale_percent)
+    
+    # 오브젝트 위치 계산
+    x, y = calculate_object_position(position, result.size, scaled_foreground.size)
+    
+    # 합성
+    result.paste(scaled_foreground, (x, y), scaled_foreground)
+    return result
+
+@spaces.GPU(duration=30)  # 120초에서 30초로 감소
+def _gpu_process(img: Image.Image, prompt: str | BoundingBox | None) -> tuple[Image.Image, BoundingBox | None, list[str]]:
+    time_log: list[str] = []
+    try:
+        if isinstance(prompt, str):
+            t0 = time.time()
+            bbox = gd_detect(img, prompt)
+            time_log.append(f"detect: {time.time() - t0}")
+            if not bbox:
+                print(time_log[0])
+                raise gr.Error("No object detected")
+        else:
+            bbox = prompt
+        t0 = time.time()
+        mask = segmenter(img, bbox)
+        time_log.append(f"segment: {time.time() - t0}")
+        return mask, bbox, time_log
+    except Exception as e:
+        print(f"GPU process error: {str(e)}")
+        raise
+
+def _process(img: Image.Image, prompt: str | BoundingBox | None, bg_prompt: str | None = None, aspect_ratio: str = "1:1") -> tuple[tuple[Image.Image, Image.Image, Image.Image], gr.DownloadButton]:
+    try:
+        # 입력 이미지 크기 제한
+        max_size = 1024
+        if img.width > max_size or img.height > max_size:
+            ratio = max_size / max(img.width, img.height)
+            new_size = (int(img.width * ratio), int(img.height * ratio))
+            img = img.resize(new_size, Image.LANCZOS)
+
+        # CUDA 메모리 관리 수정
+        try:
+            if torch.cuda.is_available():
+                current_device = torch.cuda.current_device()
+                with torch.cuda.device(current_device):
+                    torch.cuda.empty_cache()
+        except Exception as e:
+            print(f"CUDA memory management failed: {e}")
+
+        with torch.cuda.amp.autocast(enabled=torch.cuda.is_available()):
+            mask, bbox, time_log = _gpu_process(img, prompt)
+            masked_alpha = apply_mask(img, mask, defringe=True)
+
+        if bg_prompt:
+            background = generate_background(bg_prompt, aspect_ratio)
+            combined = background
+        else:
+            combined = Image.alpha_composite(Image.new("RGBA", masked_alpha.size, "white"), masked_alpha)
+
+        clear_memory()
+
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp:
+            combined.save(temp.name)
+            return (img, combined, masked_alpha), gr.DownloadButton(value=temp.name, interactive=True)
+    except Exception as e:
+        clear_memory()
+        print(f"Processing error: {str(e)}")
+        raise gr.Error(f"Processing failed: {str(e)}")
+
+def on_change_bbox(prompts: dict[str, Any] | None):
+    return gr.update(interactive=prompts is not None)
+
+
+def on_change_prompt(img: Image.Image | None, prompt: str | None, bg_prompt: str | None = None):
+    return gr.update(interactive=bool(img and prompt))
+
+
+        
+def process_prompt(img: Image.Image, prompt: str, bg_prompt: str | None = None, 
+                  aspect_ratio: str = "1:1", position: str = "bottom-center", 
+                  scale_percent: float = 100) -> tuple[Image.Image, Image.Image]:
+    try:
+        if img is None or prompt.strip() == "":
+            raise gr.Error("Please provide both image and prompt")
+        
+        print(f"Processing with position: {position}, scale: {scale_percent}")
+        
+        try:
+            prompt = translate_to_english(prompt)
+            if bg_prompt:
+                bg_prompt = translate_to_english(bg_prompt)
+        except Exception as e:
+            print(f"Translation error (continuing with original text): {str(e)}")
+        
+        results, _ = _process(img, prompt, bg_prompt, aspect_ratio)
+        
+        if bg_prompt:
+            try:
+                combined = combine_with_background(
+                    foreground=results[2],
+                    background=results[1],
+                    position=position,
+                    scale_percent=scale_percent
+                )
+                print(f"Combined image created with position: {position}")
+                return combined, results[2]
+            except Exception as e:
+                print(f"Combination error: {str(e)}")
+                return results[1], results[2]
+        
+        return results[1], results[2]
+    except Exception as e:
+        print(f"Error in process_prompt: {str(e)}")
+        raise gr.Error(str(e))
+    finally:
+        clear_memory()
+        
+def process_bbox(img: Image.Image, box_input: str) -> tuple[Image.Image, Image.Image]:
+    try:
+        if img is None or box_input.strip() == "":
+            raise gr.Error("Please provide both image and bounding box coordinates")
+        
+        try:
+            coords = eval(box_input)
+            if not isinstance(coords, list) or len(coords) != 4:
+                raise ValueError("Invalid box format")
+            bbox = tuple(int(x) for x in coords)
+        except:
+            raise gr.Error("Invalid box format. Please provide [xmin, ymin, xmax, ymax]")
+        
+        # Process the image
+        results, _ = _process(img, bbox)
+        
+        # 합성된 이미지와 추출된 이미지만 반환
+        return results[1], results[2]
+    except Exception as e:
+        raise gr.Error(str(e))
+
+# Event handler functions 수정
+def update_process_button(img, prompt):
+    return gr.update(
+        interactive=bool(img and prompt),
+        variant="primary" if bool(img and prompt) else "secondary"
+    )
+
+def update_box_button(img, box_input):
+    try:
+        if img and box_input:
+            coords = eval(box_input)
+            if isinstance(coords, list) and len(coords) == 4:
+                return gr.update(interactive=True, variant="primary")
+        return gr.update(interactive=False, variant="secondary")
+    except:
+        return gr.update(interactive=False, variant="secondary")
+
+
+# CSS 정의
+css = """
+footer {display: none}
+.main-title {
+    text-align: center;
+    margin: 2em 0;
+    padding: 1em;
+    background: #f7f7f7;
+    border-radius: 10px;
+}
+.main-title h1 {
+    color: #2196F3;
+    font-size: 2.5em;
+    margin-bottom: 0.5em;
+}
+.main-title p {
+    color: #666;
+    font-size: 1.2em;
+}
+.container {
+    max-width: 1200px;
+    margin: auto;
+    padding: 20px;
+}
+.tabs {
+    margin-top: 1em;
+}
+.input-group {
+    background: white;
+    padding: 1em;
+    border-radius: 8px;
+    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+}
+.output-group {
+    background: white;
+    padding: 1em;
+    border-radius: 8px;
+    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+}
+button.primary {
+    background: #2196F3;
+    border: none;
+    color: white;
+    padding: 0.5em 1em;
+    border-radius: 4px;
+    cursor: pointer;
+    transition: background 0.3s ease;
+}
+button.primary:hover {
+    background: #1976D2;
+}
+.position-btn {
+    transition: all 0.3s ease;
+}
+.position-btn:hover {
+    background-color: #e3f2fd;
+}
+.position-btn.selected {
+    background-color: #2196F3;
+    color: white;
+}
+"""
+
+
+
+def add_text_with_stroke(draw, text, x, y, font, text_color, stroke_width):
+    """Helper function to draw text with stroke"""
+    # Draw the stroke/outline
+    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 remove_background(image):
+    # Save the image to a specific location
+    filename = f"image_{uuid.uuid4()}.png"  # Generates a universally unique identifier (UUID) for the filename
+    image.save(filename)
+    # Call gradio client for background removal
+    result = client.predict(images=handle_file(filename), api_name="/image")
+    return Image.open(result[0])
+
+def superimpose(image_with_text, overlay_image):
+    # Open image as RGBA to handle transparency
+    overlay_image = overlay_image.convert("RGBA")
+    # Paste overlay on the background
+    image_with_text.paste(overlay_image, (0, 0), overlay_image)
+    # Save the final image
+    # image_with_text.save("output_image.png")
+    return image_with_text
+
+def add_text_to_image(
+    input_image,
+    text,
+    font_size,
+    color,
+    opacity,
+    x_position,
+    y_position,
+    thickness
+):
+    """
+    Add text to an image with customizable properties
+    """
+    # Convert gradio image (numpy array) to PIL Image
+    if input_image is None:
+        return None
+    
+    image = Image.fromarray(input_image)
+    # remove background 
+    overlay_image = remove_background(image)
+
+    # Create a transparent overlay for the text
+    txt_overlay = Image.new('RGBA', image.size, (255, 255, 255, 0))
+    draw = ImageDraw.Draw(txt_overlay)
+    
+    # Create a font with specified size
+    try:
+        font = ImageFont.truetype("DejaVuSans.ttf", int(font_size))
+    except:
+        # If DejaVu font is not found, try to use Arial or default
+        try:
+            font = ImageFont.truetype("arial.ttf", int(font_size))
+        except:
+            print("Using default font as system fonts not found")
+            font = ImageFont.load_default()
+    
+    # Convert color name to RGB
+    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))
+    
+    # Get text size for positioning
+    text_bbox = draw.textbbox((0, 0), text, font=font)
+    text_width = text_bbox[2] - text_bbox[0]
+    text_height = text_bbox[3] - text_bbox[1]
+
+    # Calculate actual x and y positions based on percentages
+    actual_x = int((image.width - text_width) * (x_position / 100))
+    actual_y = int((image.height - text_height) * (y_position / 100))
+
+    # Create final color with opacity
+    text_color = (*rgb_color, int(opacity))
+    
+    # Draw the text with stroke for thickness
+    add_text_with_stroke(
+        draw, 
+        text, 
+        actual_x, 
+        actual_y, 
+        font, 
+        text_color, 
+        int(thickness)
+    )
+
+    # Combine the original image with the text overlay
+    if image.mode != 'RGBA':
+        image = image.convert('RGBA')
+    output_image = Image.alpha_composite(image, txt_overlay)
+    
+    # Convert back to RGB for display
+    output_image = output_image.convert('RGB')
+
+    # superimpose images
+    output_image = superimpose(output_image, overlay_image)
+    
+    # Convert PIL image back to numpy array for Gradio
+    return np.array(output_image)
+    
+# UI 구성
+with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
+    gr.HTML("""
+        <div class="main-title">
+            <h1>🎨GiniGen Canvas</h1>
+            <p>AI Integrated Image Creator: Extract objects, generate backgrounds, and adjust ratios and positions to create complete images with AI.</p>
+        </div>
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_image = gr.Image(
+                type="pil",
+                label="Upload Image",
+                interactive=True
+            )
+            text_prompt = gr.Textbox(
+                label="Object to Extract",
+                placeholder="Enter what you want to extract...",
+                interactive=True
+            )
+            with gr.Row():
+                bg_prompt = gr.Textbox(
+                    label="Background Prompt (optional)",
+                    placeholder="Describe the background...",
+                    interactive=True,
+                    scale=3
+                )
+                aspect_ratio = gr.Dropdown(
+                    choices=["1:1", "16:9", "9:16", "4:3"],
+                    value="1:1",
+                    label="Aspect Ratio",
+                    interactive=True,
+                    visible=True,
+                    scale=1
+                )
+
+            with gr.Row(visible=False) as object_controls:
+                with gr.Column(scale=1):
+                    with gr.Row():
+                        position = gr.State(value="bottom-center")
+                        btn_top_left = gr.Button("↖")
+                        btn_top_center = gr.Button("↑")
+                        btn_top_right = gr.Button("↗")
+                    with gr.Row():
+                        btn_middle_left = gr.Button("←")
+                        btn_middle_center = gr.Button("•")
+                        btn_middle_right = gr.Button("→")
+                    with gr.Row():
+                        btn_bottom_left = gr.Button("↙")
+                        btn_bottom_center = gr.Button("↓")
+                        btn_bottom_right = gr.Button("↘")
+                with gr.Column(scale=1):
+                    scale_slider = gr.Slider(
+                        minimum=10,
+                        maximum=200,
+                        value=50,
+                        step=5,
+                        label="Object Size (%)"
+                    )
+
+            process_btn = gr.Button(
+                "Process",
+                variant="primary",
+                interactive=False
+            )
+
+            # 각 버튼에 대한 클릭 이벤트 처리
+            def update_position(new_position):
+                return new_position
+
+            btn_top_left.click(fn=lambda: update_position("top-left"), outputs=position)
+            btn_top_center.click(fn=lambda: update_position("top-center"), outputs=position)
+            btn_top_right.click(fn=lambda: update_position("top-right"), outputs=position)
+            btn_middle_left.click(fn=lambda: update_position("middle-left"), outputs=position)
+            btn_middle_center.click(fn=lambda: update_position("middle-center"), outputs=position)
+            btn_middle_right.click(fn=lambda: update_position("middle-right"), outputs=position)
+            btn_bottom_left.click(fn=lambda: update_position("bottom-left"), outputs=position)
+            btn_bottom_center.click(fn=lambda: update_position("bottom-center"), outputs=position)
+            btn_bottom_right.click(fn=lambda: update_position("bottom-right"), outputs=position)
+
+        with gr.Column(scale=1):
+            with gr.Row():
+                combined_image = gr.Image(
+                    label="Combined Result",
+                    show_download_button=True,
+                    type="pil",
+                    height=512
+                )
+            with gr.Row():
+                extracted_image = gr.Image(
+                    label="Extracted Object",
+                    show_download_button=True,
+                    type="pil",
+                    height=256
+                )
+
+    # Event bindings
+    input_image.change(
+        fn=update_process_button,
+        inputs=[input_image, text_prompt],
+        outputs=process_btn,
+        queue=False
+    )
+    
+    text_prompt.change(
+        fn=update_process_button,
+        inputs=[input_image, text_prompt],
+        outputs=process_btn,
+        queue=False
+    )
+
+    def update_controls(bg_prompt):
+        """배경 프롬프트 입력 여부에 따라 컨트롤 표시 업데이트"""
+        is_visible = bool(bg_prompt)
+        return [
+            gr.update(visible=is_visible),  # aspect_ratio
+            gr.update(visible=is_visible),  # object_controls
+        ]
+
+    bg_prompt.change(
+        fn=update_controls,
+        inputs=bg_prompt,
+        outputs=[aspect_ratio, object_controls],
+        queue=False
+    )
+
+    process_btn.click(
+        fn=process_prompt,
+        inputs=[
+            input_image,
+            text_prompt,
+            bg_prompt,
+            aspect_ratio,
+            position,
+            scale_slider
+        ],
+        outputs=[combined_image, extracted_image],
+        queue=True
+    )
+
+
+demo.queue(max_size=5)  # 큐 크기 제한
+demo.launch(
+    server_name="0.0.0.0",
+    server_port=7860,
+    share=False,
+    max_threads=2  # 스레드 수 제한
+)