---
title: ColorCraft SDXL - Advanced LineArt Controls
emoji: 🎨
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.44.1
app_file: app.py
pinned: false
license: mit
hardware: t4-medium
---

# 🎨 ColorCraft SDXL - Advanced LineArt Controls

A professional multi-model line art generator powered by **SDXL + ControlNet/T2I-Adapter** for creating high-quality coloring book images.

## 🚀 Core Features

- **Multi-Model Architecture**: Support for both ControlNet and T2I-Adapter models
- **3 LineArt Models**: Standard, Anime-specialized, and TencentARC research models
- **Advanced Controls**: Fine-tuned parameters for optimal coloring book output
- **Storage Efficient**: 20GB persistent storage with intelligent caching
- **Real-time Monitoring**: Live storage status and model loading feedback

## 🖌️ LineArt Models

| Model | Type | Architecture | Specialization |
|-------|------|-------------|----------------|
| **Standard ControlNet** | ControlNet | `ShermanG/ControlNet-Standard-Lineart-for-SDXL` | General purpose |
| **Anime ControlNet** | ControlNet | `r3gm/controlnet-lineart-anime-sdxl-fp16` | Anime-style images |
| **TencentARC T2I-Adapter** | T2I-Adapter | `TencentARC/t2i-adapter-lineart-sdxl-1.0` | Research-grade |

## 🎯 Style Presets

- **Ultra Clean**: Pure B&W, minimal details, vector art style
- **Clean**: Clear outlines, simple design, minimal shading
- **Detailed**: Intricate outlines, fine details, precise lines
- **Bold**: Strong contrast, thick lines, dramatic effect
- **Simple**: Basic outlines, easy coloring, child-friendly

## 🔧 Advanced Controls

### 📊 Generation Parameters
- **Inference Steps**: 10-50 (default: 25)
- **Guidance Scale**: 3-15 (default: 9)
- **ControlNet Strength**: 0.5-2.0 (default: 1.4)
- **Output Resolution**: 512-1536px (default: 1024px)

### 🎨 Processing Options
- **Depth Preprocessing**: Optional structural guidance with MidasDetector/DPT
- **Pure B&W Post-Processing**: OpenCV enhancement for stark black/white output
- **Negative Prompts**: Toggle for quality improvement

### 📈 Monitoring Panels
- **LineArt Preprocessor Output**: See the detected line art
- **Depth Preprocessor Output**: View depth map when enabled
- **Storage Status**: Monitor cache usage and performance

## 🏗️ Technical Architecture

### 🤖 AI/ML Stack
- **Base Model**: `stabilityai/stable-diffusion-xl-base-1.0`
- **VAE**: `madebyollin/sdxl-vae-fp16-fix` (FP16 optimized)
- **Depth Model**: `diffusers/controlnet-depth-sdxl-1.0`
- **Preprocessors**: `controlnet-aux` (LineartDetector, MidasDetector)

### ⚡ Framework Stack
- **ML Framework**: PyTorch 2.0+
- **Diffusion**: Diffusers 0.26.3
- **Transformers**: 4.40.2
- **UI**: Gradio 4.0+
- **Computer Vision**: OpenCV 4.8+, Pillow 10.0+

### 🔄 Pipeline Flow
1. **Input**: User uploads image via Gradio
2. **Model Selection**: Choose ControlNet or T2I-Adapter architecture
3. **Preprocessing**: LineArt detection + optional depth processing
4. **Generation**: SDXL with LineArt guidance
5. **Post-Processing**: Optional OpenCV B&W enhancement
6. **Output**: Multi-panel results with monitoring

## 💾 Storage Management

- **Persistent Cache**: 20GB storage for model caching
- **On-Demand Loading**: Models loaded only when selected
- **Intelligent Cleanup**: Automatic cache management when space is low
- **Multi-Model Caching**: Each model cached independently

## 🔗 API Usage

```python
import requests
import base64
from PIL import Image

def generate_lineart(image_path, model="standard", style="ultra_clean"):
    # Load and encode image
    with open(image_path, "rb") as f:
        image_data = base64.b64encode(f.read()).decode()
    
    # Call ColorCraft SDXL API
    response = requests.post(
        "https://YOUR_USERNAME-colorcraft-sdxl.hf.space/api/predict",
        json={
            "data": [
                f"data:image/jpeg;base64,{image_data}",
                "",  # custom_prompt
                style,  # style_preset
                25,  # num_steps
                9,   # guidance_scale
                1.4, # controlnet_scale
                True,  # use_negative_prompt
                1024,  # output_resolution
                False, # use_depth_preprocessor
                model, # lineart_model
                False  # apply_post_processing
            ]
        }
    )
    
    result = response.json()
    return result["data"]  # [main_image, lineart_preview, depth_preview, status]

# Usage
result = generate_lineart("input.jpg", model="anime", style="clean")
```

## 🎯 Use Cases

- **Mobile Apps**: Multi-model line art generation with quality options
- **Art Education**: Compare different model approaches for learning
- **Research**: Test ControlNet vs T2I-Adapter architectures
- **Print Services**: Generate coloring books with style consistency
- **Creative Tools**: Professional line art with real-time feedback

## 🚀 Deployment

### Quick Deploy
1. Create HuggingFace Space (Gradio SDK)
2. Upload: `app.py`, `requirements.txt`, `README.md`
3. Set Hardware: T4-medium (16GB VRAM + 20GB storage)
4. Deploy: Available at `https://USERNAME-SPACENAME.hf.space`

### Local Development
```bash
# Install dependencies
pip install -r requirements.txt

# Run locally (requires GPU for optimal performance)
python app.py
```

## 📊 Performance

- **Startup Time**: ~2-3 minutes (first load), ~30 seconds (cached)
- **Generation Time**: 15-30 seconds per image
- **Memory Usage**: ~12GB VRAM (T4-medium recommended)
- **Storage**: 20GB persistent recommended for all models

## 🔬 Model Comparison

| Feature | Standard | Anime | TencentARC |
|---------|----------|-------|------------|
| Architecture | ControlNet | ControlNet | T2I-Adapter |
| Training Data | General | Anime-focused | Research dataset |
| Line Quality | Balanced | Clean/smooth | Technical precision |
| Best For | All images | Cartoon/anime | Complex scenes |

---

**Built with ❤️ for the ColorCraft ecosystem**