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README.md

@@ -1,64 +1,69 @@
 ---
-title: Spacely AI Interior Designer
+title: Virtual Staging AI
 emoji: 🏠
 colorFrom: blue
 colorTo: green
 sdk: gradio
-app_file: app_gradio.py
+app_file: virtual_staging_app.py
 pinned: false
 hardware: zero-gpu
 ---
 
-# 🏢 Spacely AI 가구 배치 디자이너
+# 🏠 Virtual Staging AI - 2-Stage Pipeline
 
-빈 방 사진을 업로드하면 AI가 전문적인 오피스 공간으로 디자인해주는 웹 애플리케이션입니다.
+**Advanced AI Interior Design with YOLO Detection & ControlNet Refinement**
+
+빈 방 사진을 업로드하면 2단계 AI 파이프라인이 전문적인 인테리어 디자인을 생성합니다.
 
 ## ✨ 주요 기능
 
-- **6가지 오피스 타입**: 개인사무실, 회의실, 휴게실, 오픈오피스, 리셉션, CEO실
-- **커스텀 프롬프트**: 원하는 스타일과 가구를 자유롭게 지정
-- **실시간 웹 UI**: 드래그 앤 드롭으로 간편한 이미지 업로드
-- **고품질 AI 생성**: Stable Diffusion + ControlNet 기반
+### 🎯 2-Stage Architecture
+1. **Stage 1**: ControlNet으로 초기 가구 배치 생성
+2. **Stage 2**: YOLO로 가구 영역 탐지 → ControlNet으로 품질 개선
+
+### 🏠 지원 공간 & 스타일
+- **4가지 공간 타입**: Living Room, Bedroom, Kitchen, Dining Room
+- **4가지 디자인 스타일**: Modern, Scandinavian, Industrial, Traditional
+- **16가지 조합**: 각 공간별 모든 스타일 지원
 
-## 🚀 배포 옵션
+## 🚀 배포 & 실행
 
 ### 1. Hugging Face Spaces (추천)
 ```bash
-# 1. huggingface.co에서 새 Space 생성
-# 2. 이 폴더의 모든 파일을 업로드
-# 3. 자동으로 웹 앱이 배포됩니다
-```
-
-### 2. Google Colab
-```python
-# Colab에서 실행
-!git clone [your-repo-url]
-%cd spacely-ai-furniture-designer
-!pip install -r requirements.txt
-!python app.py
+# 1. huggingface.co에서 새 Space 생성 (GPU 환경)
+# 2. 모든 파일 업로드 (virtual_staging_app.py가 메인)
+# 3. @spaces.GPU 자동 활성화로 GPU 가속
 ```
 
-### 3. 로컬 실행
+### 2. 로컬 개발 환경
 ```bash
-git clone [your-repo-url]
-cd spacely-ai-furniture-designer
-pip install -r requirements.txt
-python app.py
+# 가상환경 생성 및 의존성 설치
+python3 -m venv spacely_local
+source spacely_local/bin/activate
+pip install -r requirements_virtual_staging.txt
+
+# 로컬 실행
+python run_virtual_staging.py
 ```
 
 ## 🎯 사용법
 
-1. **이미지 업로드**: 빈방이나 사무실 사진을 업로드
-2. **공간 타입 선택**: 드롭다운에서 원하는 오피스 타입 선택
-3. **커스텀 설정** (선택): 특별한 요구사항이 있으면 프롬프트 입력
-4. **생성 버튼 클릭**: AI가 전문적인 오피스 디자인 생성
+1. **이미지 업로드**: 빈 방 사진을 업로드 (드래그 앤 드롭 지원)
+2. **공간 선택**: Living Room, Bedroom, Kitchen, Dining Room 중 선택
+3. **스타일 선택**: Modern, Scandinavian, Industrial, Traditional 중 선택
+4. **생성 시작**: AI가 2단계 파이프라인으로 전문 인테리어 생성
 
 ## 🔧 기술 스택
 
-- **AI 모델**: Realistic Vision V3.0 (Stable Diffusion)
-- **제어 기술**: ControlNet (세그멘테이션 + MLSD)
-- **웹 프레임워크**: Streamlit
-- **딥러닝**: PyTorch, Hugging Face Transformers
+### 🧠 AI 모델
+- **Base Model**: Realistic Vision V3.0 VAE (Stable Diffusion)
+- **ControlNet**: Segmentation + MLSD 듀얼 제어
+- **Object Detection**: YOLOv8n (가구 영역 자동 탐지)
+
+### 💻 프레임워크
+- **웹 UI**: Gradio 5.41.0
+- **딥러닝**: PyTorch, Diffusers, Transformers
+- **이미지 처리**: OpenCV, PIL, Ultralytics
 
 ## 📱 배포 플랫폼 비교
 
@@ -84,23 +89,39 @@ self.office_templates = {
 self.quality_suffix = "professional interior design, corporate style, clean, modern, functional, well-lit, 4K, high quality"
 ```
 
-## 🏗️ 아키텍처
+## 🏗️ 2-Stage Architecture
 
+```mermaid
+flowchart TD
+    A[Empty Room Image] --> B[Stage 1: ControlNet Layout Generation]
+    B --> C[Initial Furnished Image]
+    C --> D[Stage 2: YOLO Furniture Detection]
+    D --> E[Furniture Region Masks]
+    E --> F[Stage 2: ControlNet Refinement]
+    F --> G[Final High-Quality Result]
 ```
-빈방 이미지 → 세그멘테이션 → 마스크 생성 → ControlNet → AI 생성 → 결과 이미지
-           ↘ MLSD 라인 검출 ↗
-```
+
+### 🚀 Benefits Over Single-Stage
+
+| Aspect | Single-Stage | 2-Stage Virtual Staging |
+|--------|-------------|----------------------|
+| Quality | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
+| Furniture Detection | Manual masking | Automatic YOLO detection |
+| Refinement | None | Targeted region refinement |
+| Consistency | Variable | High |
+| Control | Limited | Precise furniture control |
 
 ---
 
-## 📚 원본 프로젝트 정보
+## 📚 프로젝트 기원
 
-이 프로젝트는 [neuralwork의 sd-interior-design](https://github.com/neuralwork/sd-interior-design)을 기반으로 회사용 오피스 디자인에 특화하여 개발되었습니다.
+이 프로���트는 [ArvidWartenberg/virtual-staging](https://github.com/ArvidWartenberg/virtual-staging) 접근법을 기반으로 하여 YOLO 통합과 Hugging Face Spaces 최적화를 추가한 완전한 재구현입니다.
 
-### 기술 세부사항
-- **Base Model**: [Realistic Vision V3.0](https://huggingface.co/SG161222/Realistic_Vision_V3.0_VAE) 
-- **ControlNets**: [Segmentation](https://huggingface.co/BertChristiaens/controlnet-seg-room) + [MLSD](https://huggingface.co/lllyasviel/sd-controlnet-mlsd)
-- **License**: MIT License
+### 주요 개선사항
+- 🔍 **YOLO v8 통합**: 자동 가구 영역 탐지
+- 🎨 **스타일 템플릿**: 4개 공간 × 4개 스타일 = 16가지 조합
+- 🚀 **HF Spaces 최적화**: @spaces.GPU와 메모리 최적화
+- 💾 **구조 보존**: 향상된 배경/구조물 보존 로직
 
-Original work from [neuralwork](https://neuralwork.ai/) ❤️  
-Enhanced for corporate office design by Spacely Team
+Original virtual-staging approach by [ArvidWartenberg](https://github.com/ArvidWartenberg) ❤️  
+Enhanced with YOLO integration by Spacely Team

requirements.txt

@@ -1,13 +1,12 @@
-streamlit
+gradio==5.41.0
 torch>=2.0.0
 torchvision
-diffusers>=0.25.0
-transformers>=4.36.0
-accelerate>=0.26.0
+diffusers>=0.30.0
+transformers>=4.50.0
 controlnet-aux
 opencv-python
-scipy
 Pillow
 numpy
-matplotlib
-huggingface_hub
+accelerate
+ultralytics>=8.3.0
+spaces

run_virtual_staging.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python3
+"""
+Run Virtual Staging AI locally for development
+"""
+
+import sys
+import os
+
+# Add current directory to path
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+# Import the new virtual staging app
+from virtual_staging_app import create_interface
+
+def main():
+    print("🏠 Starting Virtual Staging AI - 2-Stage Pipeline")
+    print("🔧 Stage 1: ControlNet generates initial layout")
+    print("🔍 Stage 2: YOLO detects furniture + ControlNet refines")
+    print("💻 Running locally with debug mode")
+    
+    # Create interface
+    demo = create_interface()
+    
+    # Launch with local settings
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_error=True,
+        debug=True
+    )
+
+if __name__ == "__main__":
+    main()

virtual_staging_app.py

@@ -0,0 +1,406 @@
+#!/usr/bin/env python3
+"""
+Virtual Staging AI - Complete Rebuild
+Based on ArvidWartenberg/virtual-staging approach with 2-stage pipeline
+
+Stage 1: Generate initial furniture layout using ControlNet
+Stage 2: Detect furniture regions with YOLO and refine with inpainting
+"""
+
+import gradio as gr
+import torch
+import numpy as np
+from PIL import Image
+import cv2
+import os
+from pathlib import Path
+
+# Handle spaces import for both local and Hugging Face deployment
+try:
+    import spaces  # Required for Hugging Face Spaces GPU
+    SPACES_AVAILABLE = True
+except ImportError:
+    # Local development - create dummy decorator
+    class DummySpaces:
+        @staticmethod
+        def GPU(func):
+            return func
+    
+    spaces = DummySpaces()
+    SPACES_AVAILABLE = False
+
+# Model imports with error handling
+try:
+    from diffusers import ControlNetModel, StableDiffusionControlNetInpaintPipeline, UniPCMultistepScheduler
+    from transformers import AutoImageProcessor, SegformerForSemanticSegmentation
+    from controlnet_aux import MLSDdetector
+    from ultralytics import YOLO
+    MODELS_AVAILABLE = True
+except ImportError as e:
+    print(f"Failed to load model libraries: {e}")
+    MODELS_AVAILABLE = False
+
+# Room style templates (keep the best from original)
+ROOM_STYLES = {
+    "Living Room": {
+        "Modern": "modern living room with sleek sectional sofa, glass coffee table, minimalist decor",
+        "Scandinavian": "scandinavian living room with cream linen sofa, light oak furniture, cozy textiles", 
+        "Industrial": "industrial living room with vintage leather sofa, exposed brick, metal fixtures",
+        "Traditional": "traditional living room with wingback chairs, mahogany table, classic patterns"
+    },
+    "Bedroom": {
+        "Modern": "modern bedroom with platform bed, integrated nightstands, clean geometric lines",
+        "Scandinavian": "scandinavian bedroom with light wood bed frame, white linens, hygge atmosphere",
+        "Industrial": "industrial bedroom with wrought iron bed, exposed elements, vintage touches",
+        "Traditional": "traditional bedroom with ornate four-poster bed, mahogany finish, luxury bedding"
+    },
+    "Kitchen": {
+        "Modern": "modern kitchen with handleless cabinets, quartz countertops, integrated appliances",
+        "Scandinavian": "scandinavian kitchen with light oak cabinets, white marble, clean design",
+        "Industrial": "industrial kitchen with concrete countertops, exposed brick, metal cabinets",
+        "Traditional": "traditional kitchen with raised panel cabinets, granite countertops, warm wood"
+    },
+    "Dining Room": {
+        "Modern": "modern dining room with glass-top table, sculptural chairs, linear lighting",
+        "Scandinavian": "scandinavian dining room with light oak table, wishbone chairs, natural textures",
+        "Industrial": "industrial dining room with reclaimed wood table, metal chairs, exposed beams",
+        "Traditional": "traditional dining room with mahogany pedestal table, upholstered chairs, crystal chandelier"
+    }
+}
+
+# Global models
+pipe = None
+yolo_model = None
+seg_processor = None
+seg_model = None
+mlsd_processor = None
+
+def load_models():
+    """Load all required models"""
+    global pipe, yolo_model, seg_processor, seg_model, mlsd_processor
+    
+    if not MODELS_AVAILABLE:
+        return "❌ Model libraries not available"
+    
+    try:
+        print("🔄 Loading Virtual Staging AI models...")
+        
+        # Load YOLO for furniture detection (Stage 2)
+        print("Loading YOLO model...")
+        yolo_model = YOLO('yolov8n.pt')  # Use nano model for speed
+        
+        # Load ControlNet for layout generation (Stage 1)  
+        print("Loading ControlNet models...")
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+        
+        controlnet = [
+            ControlNetModel.from_pretrained(
+                "BertChristiaens/controlnet-seg-room", 
+                torch_dtype=dtype,
+                low_cpu_mem_usage=True
+            ),
+            ControlNetModel.from_pretrained(
+                "lllyasviel/sd-controlnet-mlsd", 
+                torch_dtype=dtype,
+                low_cpu_mem_usage=True
+            ),
+        ]
+        
+        # Main pipeline
+        pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
+            "SG161222/Realistic_Vision_V3.0_VAE",
+            controlnet=controlnet,
+            safety_checker=None,
+            torch_dtype=dtype,
+            low_cpu_mem_usage=True,
+            variant="fp16" if torch.cuda.is_available() else None
+        )
+        
+        pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+        
+        if torch.cuda.is_available():
+            pipe = pipe.to("cuda")
+            # Enable memory efficient attention
+            try:
+                pipe.enable_xformers_memory_efficient_attention()
+                print("✅ XFormers enabled")
+            except:
+                print("⚠️ XFormers not available")
+                
+            # Enable model offloading to save GPU memory
+            pipe.enable_model_cpu_offload()
+            print("✅ CPU offloading enabled")
+        
+        # Load auxiliary models
+        seg_processor = AutoImageProcessor.from_pretrained("nvidia/segformer-b5-finetuned-ade-640-640")
+        seg_model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b5-finetuned-ade-640-640")
+        mlsd_processor = MLSDdetector.from_pretrained("lllyasviel/Annotators")
+        
+        print("✅ All models loaded successfully!")
+        return "✅ Models loaded successfully!"
+        
+    except Exception as e:
+        import traceback
+        error_details = traceback.format_exc()
+        print(f"❌ Model loading failed: {error_details}")
+        return f"❌ Failed to load models: {e}"
+
+def stage1_generate_initial_layout(input_image, room_type, design_style):
+    """Stage 1: Generate initial furniture layout using ControlNet"""
+    print("🏗️ Stage 1: Generating initial furniture layout...")
+    
+    if pipe is None:
+        raise Exception("Pipeline not loaded")
+    
+    # Get room prompt
+    prompt = ROOM_STYLES.get(room_type, ROOM_STYLES["Living Room"]).get(design_style, "modern furnished room")
+    full_prompt = f"photorealistic {prompt}, professional interior design photography, natural lighting, high quality, detailed furniture"
+    
+    # Resize for processing
+    orig_w, orig_h = input_image.size
+    max_size = 768
+    if max(orig_w, orig_h) > max_size:
+        if orig_w > orig_h:
+            new_w, new_h = max_size, int(max_size * orig_h / orig_w)
+        else:
+            new_w, new_h = int(max_size * orig_w / orig_h), max_size
+    else:
+        new_w, new_h = orig_w, orig_h
+        
+    resized_image = input_image.resize((new_w, new_h))
+    
+    # Create full mask for initial generation
+    mask_image = Image.new('RGB', (new_w, new_h), (255, 255, 255))
+    
+    # Prepare control images
+    seg_control = resized_image.copy()
+    if mlsd_processor:
+        mlsd_image = mlsd_processor(resized_image)
+        mlsd_image = mlsd_image.resize((new_w, new_h))
+    else:
+        mlsd_image = resized_image.copy()
+    
+    # Generate initial layout
+    print(f"Generating: {full_prompt}")
+    result = pipe(
+        prompt=full_prompt,
+        negative_prompt="empty room, no furniture, bad quality, distorted, blurry, unrealistic, floating furniture",
+        num_inference_steps=30,
+        strength=0.8,
+        guidance_scale=7.5,
+        image=resized_image,
+        mask_image=mask_image,
+        control_image=[seg_control, mlsd_image],
+        controlnet_conditioning_scale=[0.6, 0.4],
+        control_guidance_start=[0, 0],
+        control_guidance_end=[0.7, 0.5],
+    ).images[0]
+    
+    # Restore original size
+    stage1_result = result.resize((orig_w, orig_h), Image.Resampling.LANCZOS)
+    
+    print("✅ Stage 1 completed")
+    return stage1_result
+
+def stage2_detect_and_refine(original_image, stage1_result, room_type, design_style):
+    """Stage 2: Detect furniture regions and refine with inpainting"""
+    print("🔍 Stage 2: Detecting furniture and refining...")
+    
+    if yolo_model is None:
+        print("⚠️ YOLO model not loaded, skipping Stage 2")
+        return stage1_result
+    
+    # Convert PIL to numpy for YOLO
+    stage1_array = np.array(stage1_result)
+    
+    # Detect furniture objects
+    print("Detecting furniture with YOLO...")
+    results = yolo_model(stage1_array, verbose=False)
+    
+    # Create furniture mask based on detections
+    furniture_mask = np.zeros((stage1_result.height, stage1_result.width), dtype=np.uint8)
+    
+    furniture_classes = [56, 57, 58, 59, 60, 61, 62, 63, 64, 65]  # COCO furniture classes
+    
+    for result in results:
+        boxes = result.boxes
+        if boxes is not None:
+            for box in boxes:
+                class_id = int(box.cls.cpu().numpy()[0])
+                confidence = float(box.conf.cpu().numpy()[0])
+                
+                if class_id in furniture_classes and confidence > 0.3:
+                    # Get bounding box coordinates
+                    x1, y1, x2, y2 = box.xyxy.cpu().numpy()[0].astype(int)
+                    # Add to furniture mask
+                    furniture_mask[y1:y2, x1:x2] = 255
+    
+    # Expand mask slightly
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10, 10))
+    furniture_mask = cv2.dilate(furniture_mask, kernel, iterations=1)
+    
+    # Apply Gaussian blur for smooth edges
+    furniture_mask = cv2.GaussianBlur(furniture_mask, (21, 21), 0)
+    
+    # Convert mask to PIL
+    mask_pil = Image.fromarray(furniture_mask).convert('RGB')
+    
+    # If no furniture detected, return stage 1 result
+    if np.count_nonzero(furniture_mask) == 0:
+        print("⚠️ No furniture detected, returning Stage 1 result")
+        return stage1_result
+    
+    print(f"Detected furniture regions: {np.count_nonzero(furniture_mask)} pixels")
+    
+    # Stage 2 refinement with inpainting
+    prompt = ROOM_STYLES.get(room_type, ROOM_STYLES["Living Room"]).get(design_style, "modern furnished room")
+    refined_prompt = f"photorealistic {prompt}, high quality furniture, professional interior design, natural lighting, realistic textures"
+    
+    # Resize for processing
+    orig_w, orig_h = original_image.size
+    max_size = 768
+    if max(orig_w, orig_h) > max_size:
+        if orig_w > orig_h:
+            new_w, new_h = max_size, int(max_size * orig_h / orig_w)
+        else:
+            new_w, new_h = int(max_size * orig_w / orig_h), max_size
+    else:
+        new_w, new_h = orig_w, orig_h
+    
+    # Resize images
+    resized_original = original_image.resize((new_w, new_h))
+    resized_mask = mask_pil.resize((new_w, new_h))
+    
+    # Prepare control images
+    seg_control = resized_original.copy()
+    if mlsd_processor:
+        mlsd_image = mlsd_processor(resized_original)
+        mlsd_image = mlsd_image.resize((new_w, new_h))
+    else:
+        mlsd_image = resized_original.copy()
+    
+    # Refine furniture regions
+    print(f"Refining: {refined_prompt}")
+    refined_result = pipe(
+        prompt=refined_prompt,
+        negative_prompt="bad quality, distorted, blurry, unrealistic, empty room, no furniture, floating furniture, bad proportions",
+        num_inference_steps=25,
+        strength=0.6,
+        guidance_scale=8.0,
+        image=resized_original,
+        mask_image=resized_mask,
+        control_image=[seg_control, mlsd_image],
+        controlnet_conditioning_scale=[0.7, 0.5],
+        control_guidance_start=[0, 0],
+        control_guidance_end=[0.8, 0.6],
+    ).images[0]
+    
+    # Restore original size
+    final_result = refined_result.resize((orig_w, orig_h), Image.Resampling.LANCZOS)
+    
+    print("✅ Stage 2 completed")
+    return final_result
+
+@spaces.GPU
+def virtual_stage_room(input_image, room_type, design_style):
+    """Main virtual staging pipeline - 2-stage approach"""
+    
+    if input_image is None:
+        return None, "❌ Please upload an image!"
+    
+    try:
+        # Load models if needed
+        if pipe is None or yolo_model is None:
+            status = load_models()
+            if "❌" in status:
+                return None, status
+        
+        print(f"🏠 Starting Virtual Staging: {room_type} in {design_style} style")
+        
+        # Stage 1: Generate initial layout
+        stage1_result = stage1_generate_initial_layout(input_image, room_type, design_style)
+        
+        # Stage 2: Detect and refine furniture
+        final_result = stage2_detect_and_refine(input_image, stage1_result, room_type, design_style)
+        
+        success_msg = f"✅ Virtual staging completed! {room_type} furnished in {design_style} style using 2-stage AI pipeline."
+        
+        return final_result, success_msg
+        
+    except Exception as e:
+        import traceback
+        error_details = traceback.format_exc()
+        error_msg = f"❌ Virtual staging failed: {str(e)}\n\nDetails:\n{error_details}"
+        print(error_msg)
+        return None, error_msg
+
+def create_interface():
+    """Create Gradio interface for Virtual Staging"""
+    
+    with gr.Blocks(title="Virtual Staging AI - 2-Stage Pipeline", theme=gr.themes.Soft()) as demo:
+        gr.HTML("<h1>🏠 Virtual Staging AI - Complete Rebuild</h1>")
+        gr.Markdown("**2-Stage Pipeline**: Stage 1 generates initial layout, Stage 2 detects and refines furniture using YOLO + ControlNet")
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                # Input controls
+                input_image = gr.Image(
+                    label="Upload Empty Room Image",
+                    type="pil",
+                    height=300
+                )
+                
+                room_type = gr.Dropdown(
+                    choices=list(ROOM_STYLES.keys()),
+                    value="Living Room",
+                    label="Select Room Type"
+                )
+                
+                design_style = gr.Dropdown(
+                    choices=["Modern", "Scandinavian", "Industrial", "Traditional"],
+                    value="Modern", 
+                    label="Select Design Style"
+                )
+                
+                generate_btn = gr.Button("🚀 Start Virtual Staging", variant="primary", size="lg")
+                
+                gr.Markdown("""
+                ### 🔧 How it works:
+                1. **Stage 1**: ControlNet generates initial furniture layout
+                2. **Stage 2**: YOLO detects furniture → ControlNet refines regions
+                
+                ### ✨ Features:
+                - 🎯 Furniture detection with YOLO
+                - 🎨 Style-specific templates
+                - 🏗️ 2-stage quality improvement
+                - 💾 Background preservation
+                """)
+            
+            with gr.Column(scale=1):
+                # Output
+                output_image = gr.Image(
+                    label="Virtual Staging Result",
+                    height=400
+                )
+                result_message = gr.Textbox(
+                    label="Status",
+                    interactive=False,
+                    value="Ready to start virtual staging"
+                )
+        
+        # Event handler
+        generate_btn.click(
+            fn=virtual_stage_room,
+            inputs=[input_image, room_type, design_style],
+            outputs=[output_image, result_message]
+        )
+    
+    return demo
+
+if __name__ == "__main__":
+    print("🚀 Starting Virtual Staging AI - Complete Rebuild...")
+    
+    # Create and launch interface
+    demo = create_interface()
+    demo.launch(share=True)