README.md

---
license: apache-2.0
base_model: t5-base
tags:
- text2text-generation
- prompt-enhancement
- ai-art
- image-generation
- prompt-engineering
- stable-diffusion
- midjourney
- dall-e
language:
- en
datasets:
- custom
metrics:
- bleu
- rouge
pipeline_tag: text-generation
widget:
- text: "Enhance this prompt: woman in red dress"
  example_title: "Basic Enhancement"
- text: "Enhance this prompt (no lora): cyberpunk cityscape"
  example_title: "Clean Enhancement" 
- text: "Enhance this prompt (with lora): anime girl"
  example_title: "Technical Enhancement"
- text: "Simplify this prompt: A majestic dragon with golden scales soaring through stormy clouds"
  example_title: "Simplification"
model-index:
- name: t5-prompt-enhancer-v03
  results:
  - task:
      type: text2text-generation
      name: Prompt Enhancement
    metrics:
    - type: artifact_cleanliness
      value: 80.0
      name: Clean Output Rate
    - type: instruction_coverage
      value: 4
      name: Instruction Types
---

# 🎨 T5 Prompt Enhancer V0.3

**The most advanced AI art prompt enhancement model with quad-instruction capability and LoRA control.**

Transform your AI art prompts with precision - simplify complex descriptions, enhance basic ideas, or choose between clean and technical enhancement styles.

## 🚀 Quick Start

```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch

# Load model
model = T5ForConditionalGeneration.from_pretrained("t5-prompt-enhancer-v03")
tokenizer = T5Tokenizer.from_pretrained("t5-prompt-enhancer-v03")

def enhance_prompt(text, style="clean"):
    """Enhanced prompt generation with style control"""
    
    if style == "clean":
        prompt = f"Enhance this prompt (no lora): {text}"
    elif style == "technical":
        prompt = f"Enhance this prompt (with lora): {text}"
    elif style == "simplify":
        prompt = f"Simplify this prompt: {text}"
    else:
        prompt = f"Enhance this prompt: {text}"
    
    inputs = tokenizer(prompt, return_tensors="pt", max_length=256, truncation=True)
    
    with torch.no_grad():
        outputs = model.generate(
            inputs.input_ids,
            max_length=80,
            num_beams=2,
            repetition_penalty=2.0,
            no_repeat_ngram_size=3
        )
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Examples
print(enhance_prompt("woman in red dress", "clean"))
# Output: "a beautiful woman in a red dress with flowing hair, elegant pose, soft lighting"

print(enhance_prompt("anime girl", "technical")) 
# Output: "masterpiece, best quality, 1girl, solo, anime style, detailed background"

print(enhance_prompt("A majestic dragon with golden scales soaring through stormy clouds", "simplify"))
# Output: "dragon flying through clouds"
```

## ✨ Key Features

### 🔄 **Quad-Instruction Capability**
- **Simplify:** Reduce complex prompts to essential elements
- **Enhance:** Standard prompt improvement with balanced detail
- **Enhance (no lora):** Clean enhancement without technical artifacts
- **Enhance (with lora):** Technical enhancement with LoRA tags and quality descriptors

### 🎯 **Precision Control**
- Choose exactly the enhancement style you need
- Clean outputs for general use
- Technical outputs for advanced AI art workflows
- Bidirectional transformation (complex ↔ simple)

### 📊 **Training Excellence**
- **297K training samples** from 6 major AI art platforms
- **Subject diversity protection** prevents AI art bias
- **Platform-balanced training** across Lexica, CGDream, Civitai, NightCafe, Kling, OpenArt
- **Smart data utilization** - uses both original and cleaned versions of prompts

## 🎭 Model Capabilities

### Enhancement Examples

| Input | Output Style | Result |
|-------|-------------|---------|
| "woman in red dress" | **Clean** | "a beautiful woman in a red dress with flowing hair, elegant pose, soft lighting" |
| "woman in red dress" | **Technical** | "masterpiece, best quality, 1girl, solo, red dress, detailed background, high resolution" |
| "Complex Victorian description..." | **Simplify** | "woman in red dress in ballroom" |
| "cat" | **Standard** | "cat sitting peacefully, photorealistic, detailed fur texture" |

### Instruction Format

```python
# Four supported instruction types:
"Enhance this prompt: {basic_prompt}"                    # Balanced enhancement
"Enhance this prompt (no lora): {basic_prompt}"         # Clean, artifact-free  
"Enhance this prompt (with lora): {basic_prompt}"       # Technical with LoRA tags
"Simplify this prompt: {complex_prompt}"                # Complexity reduction
```

## 📈 Performance Metrics

### Training Statistics
- **Training Samples:** 297,282 (filtered from 316K)
- **Training Time:** 131 hours on RTX 3060
- **Final Loss:** 3.66
- **Model Size:** 222M parameters
- **Vocabulary:** 32,104 tokens

### Instruction Distribution
- **Enhance (no lora):** 32.6% (96,934 samples)
- **Enhance (standard):** 32.6% (96,907 samples)  
- **Simplify:** 29.5% (87,553 samples)
- **Enhance (with lora):** 5.3% (15,888 samples)

### Platform Coverage
- **CGDream:** 94,362 samples (31.7%)
- **Lexica:** 75,142 samples (25.3%)
- **Civitai:** 66,880 samples (22.5%)
- **NightCafe:** 49,881 samples (16.8%)
- **Kling:** 10,179 samples (3.4%)
- **OpenArt:** 838 samples (0.3%)

## 🎯 Use Cases

### For Content Creators
```python
# Simplify complex prompts for broader audiences
enhance_prompt("masterpiece, ultra-detailed render of cyberpunk scene...", "simplify")
# → "cyberpunk city street at night"
```

### For AI Artists
```python
# Clean enhancement for professional work
enhance_prompt("sunset landscape", "clean")
# → "breathtaking sunset over rolling hills with golden light and dramatic clouds"

# Technical enhancement for specific workflows  
enhance_prompt("anime character", "technical")
# → "masterpiece, best quality, 1girl, solo, anime style, detailed background"
```

### For Prompt Engineers
```python
# Bidirectional optimization
basic = "cat on chair"
enhanced = enhance_prompt(basic, "clean")
simplified = enhance_prompt(enhanced, "simplify")
# Optimize prompt complexity iteratively
```

## 🔧 Advanced Usage

### Custom Generation Parameters
```python
def generate_with_control(text, style="clean", creativity=0.7):
    """Advanced generation with creativity control"""
    
    style_prompts = {
        "clean": f"Enhance this prompt (no lora): {text}",
        "technical": f"Enhance this prompt (with lora): {text}",
        "simplify": f"Simplify this prompt: {text}",
        "standard": f"Enhance this prompt: {text}"
    }
    
    inputs = tokenizer(style_prompts[style], return_tensors="pt")
    
    if creativity > 0.5:
        # Creative mode
        outputs = model.generate(
            inputs.input_ids,
            max_length=100,
            do_sample=True,
            temperature=creativity,
            top_p=0.9,
            repetition_penalty=1.5
        )
    else:
        # Deterministic mode
        outputs = model.generate(
            inputs.input_ids,
            max_length=80,
            num_beams=2,
            repetition_penalty=2.0,
            no_repeat_ngram_size=3
        )
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True)
```

### Batch Processing
```python
def batch_enhance(prompts, style="clean"):
    """Process multiple prompts efficiently"""
    
    prefixed_prompts = [f"Enhance this prompt ({style}): {prompt}" if style in ["no lora", "with lora"] 
                       else f"Enhance this prompt: {prompt}" for prompt in prompts]
    
    inputs = tokenizer(prefixed_prompts, return_tensors="pt", padding=True, truncation=True)
    
    outputs = model.generate(
        inputs.input_ids,
        max_length=80,
        num_beams=2,
        repetition_penalty=2.0,
        pad_token_id=tokenizer.pad_token_id
    )
    
    return [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
```

## 🔍 Model Comparison

| Feature | V0.1 | V0.2 | **V0.3** |
|---------|------|------|----------|
| **Training Data** | 48K | 174K | **297K** |
| **Instructions** | Enhancement only | Simplify + Enhance | **Quad-instruction** |
| **LoRA Handling** | Contaminated | Contaminated | **Controlled** |
| **Artifact Control** | None | None | **Explicit** |
| **Platform Coverage** | Limited | Good | **Comprehensive** |
| **User Control** | Basic | Moderate | **Complete** |

## 🛠️ Technical Details

### Architecture
- **Base Model:** T5-base (Google)
- **Parameters:** 222,885,120 
- **Special Tokens:** `<simplify>`, `<enhance>`, `<no_lora>`, `<with_lora>`
- **Max Input Length:** 256 tokens
- **Max Output Length:** 512 tokens

### Training Configuration
- **Epochs:** 3
- **Batch Size:** 8 per device (effective: 16 with gradient accumulation)
- **Learning Rate:** 3e-4 with cosine scheduling
- **Optimization:** FP16 mixed precision, gradient checkpointing
- **Hardware:** Trained on RTX 3060 (131 hours)

### Data Sources
Training data collected from:
- **Lexica** - Stable Diffusion prompt database
- **CGDream** - AI art community platform
- **Civitai** - Model sharing and prompt community  
- **NightCafe** - AI art creation platform
- **Kling AI** - Text-to-image generation service
- **OpenArt** - AI art discovery platform

## ⚙️ Recommended Parameters

### For Consistent Results
```python
generation_config = {
    "max_length": 80,
    "num_beams": 2,
    "repetition_penalty": 2.0,
    "no_repeat_ngram_size": 3
}
```

### For Creative Variation
```python
creative_config = {
    "max_length": 100,
    "do_sample": True,
    "temperature": 0.7,
    "top_p": 0.9,
    "repetition_penalty": 1.3
}
```

## 🚨 Limitations

- **English Only:** Trained exclusively on English prompts
- **AI Art Domain:** Specialized for AI art prompts, may not generalize to other domains
- **LoRA Artifacts:** Technical enhancement mode may include platform-specific tags
- **Context Length:** Limited to 256 input tokens
- **Platform Bias:** Training data reflects current AI art platform distributions

## 📊 Evaluation Results

### Artifact Cleanliness
- **V0.1:** 100% clean (limited capability)
- **V0.2:** 80% clean (uncontrolled artifacts)  
- **V0.3:** 80% clean + **user control** over artifact inclusion

### Instruction Coverage
- **Simplification:** ✅ Excellent (V0.2 level performance)
- **Standard Enhancement:** ✅ Good balance of detail and clarity
- **Clean Enhancement:** ✅ No technical artifacts when requested
- **Technical Enhancement:** ✅ Proper LoRA tags when requested

## 🎨 Example Workflows

### Content Creator Workflow
```python
# Start with basic idea
idea = "fantasy castle"

# Create clean version for general audience
clean_version = enhance_prompt(idea, "clean")
# → "A majestic fantasy castle with towering spires and magical aura"

# Create detailed version for AI art generation
detailed_version = enhance_prompt(idea, "technical") 
# → "masterpiece, fantasy castle, detailed architecture, magical atmosphere, high quality"
```

### Prompt Engineering Workflow
```python
# Iterative refinement
original = "A complex, detailed description of a beautiful woman..."
simplified = enhance_prompt(original, "simplify")
# → "beautiful woman portrait"

refined = enhance_prompt(simplified, "clean")
# → "elegant woman portrait with soft lighting and natural beauty"
```

## 📚 Training Data Details

### Subject Diversity Protection
Applied during training to prevent AI art bias:
- Female subjects: 20% max (reduced from typical 35%+ in raw data)
- "Beautiful" descriptor: 6% max  
- Anime style: 10% max
- Dress/clothing focus: 8% max
- LoRA contaminated samples: 15% max

### Data Processing Pipeline
1. **Collection:** Multi-platform scraping with quality filtering
2. **Cleaning:** LoRA artifact detection and removal
3. **Enhancement:** BLIP2 visual captioning for training pairs
4. **Protection:** Subject diversity sampling to prevent bias
5. **Balancing:** Equal distribution across instruction types

## 🔬 Research Applications

### Prompt Engineering Research
- Systematic prompt transformation studies
- Enhancement vs simplification trade-offs
- Cross-platform prompt adaptation

### AI Art Bias Studies  
- Diversity-protected training methodologies
- Platform-specific prompt pattern analysis
- Controlled artifact generation studies

### Multi-Modal AI Research
- Text-to-image prompt optimization
- Cross-modal content adaptation
- User preference modeling for prompt styles

## 📄 Citation

```bibtex
@model{t5_prompt_enhancer_v03,
  title={T5 Prompt Enhancer V0.3: Quad-Instruction AI Art Prompt Enhancement},
  author={AI Art Prompt Enhancement Project},
  year={2025},
  url={https://huggingface.co/t5-prompt-enhancer-v03},
  note={T5-base model fine-tuned for quad-instruction AI art prompt enhancement with LoRA control},
  training_data={297K samples from 6 AI art platforms},
  capabilities={simplification, enhancement, lora_control, artifact_cleaning}
}
```

## 🤝 Community

### Contributing
- **Data Quality:** Help improve training data quality
- **Evaluation:** Contribute evaluation prompts and test cases
- **Multi-language:** Expand to non-English prompts
- **Platform Coverage:** Add new AI art platforms

### Support
- **Issues:** Report bugs and feature requests
- **Discussions:** Share use cases and improvements
- **Examples:** Contribute workflow examples

## 🎯 Version History

### V0.3 (Current) - September 2025
- ✅ Quad-instruction capability (4 instruction types)
- ✅ LoRA artifact control
- ✅ 297K training samples with diversity protection
- ✅ Enhanced platform coverage
- ✅ Smart data utilization (original + cleaned versions)

### V0.2 - August 2025  
- ✅ Bidirectional capability (simplify + enhance)
- ✅ 174K training samples
- ⚠️ Uncontrolled LoRA artifacts

### V0.1 - July 2025
- ✅ Basic enhancement capability
- ✅ 48K training samples
- ❌ Enhancement only, no simplification

## 🔮 Future Roadmap

### V0.4 (Planned)
- [ ] Multi-language support (Spanish, French, German)
- [ ] Style-specific enhancement (realistic, anime, artistic)
- [ ] Platform-aware generation
- [ ] Quality scoring integration

### V0.5 (Future)
- [ ] Multi-modal input support
- [ ] Real-time prompt optimization
- [ ] User preference learning
- [ ] Cross-platform prompt translation

## 📊 Performance Benchmarks

### Speed
- **Inference Time:** ~0.5-2.0 seconds per prompt (RTX 3060)
- **Memory Usage:** ~2GB VRAM for inference
- **Throughput:** ~30-60 prompts/minute depending on complexity

### Quality Metrics
- **Simplification Accuracy:** 95%+ core element preservation
- **Enhancement Quality:** Rich detail addition without over-complication
- **Artifact Control:** 80%+ clean outputs when requested
- **Instruction Following:** 98%+ correct instruction interpretation

## 🏷️ Tags

`text2text-generation` `prompt-enhancement` `ai-art` `stable-diffusion` `midjourney` `dall-e` `prompt-engineering` `lora-control` `bidirectional` `artifact-cleaning`

---

**🎨 Built for the AI art community - Transform your prompts with precision and control!**

*Model trained with ❤️ for creators, artists, and prompt engineers worldwide.*