---
title: Ai_Clothes_change
sdk: docker
emoji: ⚡
colorFrom: yellow
colorTo: blue
---
# ✦ AI Clothes Changer — Virtual Try-On

AI-powered virtual try-on app using **CatVTON** diffusion model. Upload a person photo + a clothing image → get a photorealistic try-on result in seconds.

---

## 🧠 Tech Stack

| Layer | Technology |
|-------|------------|
| AI Model | CatVTON (diffusion-based VTON) |
| Backend | Python · FastAPI · PyTorch |
| Frontend | React · Vite · Tailwind CSS |
| Inference | CUDA (GPU) / CPU fallback |
| Packaging | Docker · docker-compose |

---

## 📁 Project Structure

```
ai-clothes-changer/
├── backend/
│   ├── app/
│   │   ├── main.py              # FastAPI app + CORS
│   │   ├── routes/tryon.py      # POST /api/v1/try-on
│   │   ├── services/
│   │   │   ├── inference.py     # CatVTON model loading + inference
│   │   │   ├── preprocessing.py # Image resize, normalize
│   │   │   └── postprocessing.py# Tensor → PIL → base64
│   │   ├── schemas/request.py   # Pydantic models
│   │   └── utils/
│   │       ├── config.py        # Settings from .env
│   │       └── file_handler.py  # Upload + cleanup helpers
│   ├── weights/catvton/         # ← model weights go here
│   ├── requirements.txt
│   └── .env
│
├── frontend/
│   └── src/
│       ├── api/api.js           # Axios client
│       ├── components/
│       │   ├── Upload.jsx       # Drag-and-drop uploader
│       │   └── Result.jsx       # Output + download
│       └── pages/Home.jsx       # Main UI
│
├── scripts/
│   ├── download_model.py        # Downloads CatVTON weights
│   └── train.py                 # Fine-tuning stub
│
├── docker/Dockerfile
└── docker-compose.yml
```

---

## 🚀 Quick Start

### 1. Download model weights

```bash
python scripts/download_model.py
```
This downloads ~4GB of weights from Hugging Face into `backend/weights/catvton/`.

### 2. Backend (Python)

```bash
cd backend
python -m venv venv
source venv/bin/activate        # Windows: venv\Scripts\activate
pip install -r requirements.txt
uvicorn app.main:app --reload --port 8000
```

API docs: http://localhost:8000/docs

### 3. Frontend (React)

```bash
cd frontend
npm install
npm run dev
```

Open: http://localhost:5173

---

## 🐳 Docker (Production)

```bash
# Build and run backend
docker compose up --build

# Dev mode (backend + frontend)
docker compose --profile dev up --build
```

---

## 🔌 API Reference

### `POST /api/v1/try-on`

| Field | Type | Description |
|-------|------|-------------|
| `person_image` | File | Full-body person photo (JPEG/PNG/WEBP, max 10MB) |
| `cloth_image` | File | Clothing item photo |
| `cloth_type` | string | `upper` \| `lower` \| `overall` |
| `num_steps` | int | Inference steps 20–100 (default: 50) |

**Response:**
```json
{
  "status": "success",
  "output_image": "/outputs/results/abc123.png",
  "output_image_base64": "data:image/png;base64,...",
  "processing_time_ms": 4200.5,
  "message": "Try-on completed successfully"
}
```

### `GET /api/v1/health`

```json
{ "status": "healthy", "model_loaded": true, "device": "cuda" }
```

---

## ⚙️ Configuration (`.env`)

```env
DEVICE=cuda            # cuda | cpu
MODEL_PATH=weights/catvton
MIXED_PRECISION=fp16   # fp16 | no
OUTPUT_IMAGE_WIDTH=768
OUTPUT_IMAGE_HEIGHT=1024
CLOTH_TYPE=upper
MAX_IMAGE_SIZE_MB=10
```

---

## 💡 Tips for Best Results

- **Person image**: Full-body photo, neutral background, front-facing
- **Cloth image**: Flat-lay or product photo on white background
- **Steps**: 50 = balanced quality/speed · 80+ = best quality (slower)
- **GPU**: CUDA speeds up inference 10–20× vs CPU

---

## 🗺️ Roadmap

- [ ] Cloth library / preset gallery
- [ ] Video try-on (CatV2TON)
- [ ] Background removal (rembg)
- [ ] Body size / pose adjustment
- [ ] Cloudinary / S3 storage
- [ ] Redis queue for concurrent requests
- [ ] Mobile-optimized UI

---

## 📄 License

MIT — built on [CatVTON](https://github.com/Zheng-Chong/CatVTON) (Apache 2.0).