🎨 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

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

Instruction Format

# 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

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

For AI Artists

# 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

# 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

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

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

🛠️ 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

generation_config = {
    "max_length": 80,
    "num_beams": 2,
    "repetition_penalty": 2.0,
    "no_repeat_ngram_size": 3
}

For Creative Variation

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

# 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

# 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

@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

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

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