quality_validator.py

"""
CLIP-based Quality Validation System
Validates generated images for prompt alignment and quality
"""

import torch
from PIL import Image
from typing import Dict, Optional
import warnings

# Suppress warnings for cleaner output
warnings.filterwarnings("ignore")


class QualityValidator:
    """
    Uses CLIP to validate prompt-image alignment and quality
    """
    
    def __init__(self, device: Optional[str] = None):
        """
        Initialize the quality validator
        
        Args:
            device: Device to run validation on ('cuda' or 'cpu')
        """
        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
        self.model = None
        self.processor = None
        self._initialized = False
    
    def _lazy_load(self):
        """
        Lazy load CLIP model to save memory
        """
        if self._initialized:
            return
        
        try:
            from transformers import CLIPProcessor, CLIPModel
            
            print("Loading CLIP model for quality validation...")
            self.model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(self.device)
            self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
            self._initialized = True
            print("CLIP model loaded successfully!")
            
        except Exception as e:
            print(f"Warning: Could not load CLIP model: {e}")
            print("Quality validation will be disabled.")
            self._initialized = False
    
    def validate(
        self, 
        image: Image.Image, 
        prompt: str,
        return_details: bool = False
    ) -> float:
        """
        Validate image quality and prompt alignment
        
        Args:
            image: Generated PIL Image
            prompt: Original prompt used for generation
            return_details: Whether to return detailed validation info
            
        Returns:
            Quality score between 0 and 1 (higher is better)
        """
        self._lazy_load()
        
        if not self._initialized:
            # Return neutral score if CLIP is not available
            return 0.5
        
        try:
            # Prepare inputs
            inputs = self.processor(
                text=[prompt],
                images=image,
                return_tensors="pt",
                padding=True
            ).to(self.device)
            
            # Get CLIP similarity score
            with torch.no_grad():
                outputs = self.model(**inputs)
                logits_per_image = outputs.logits_per_image
                # Convert to probability (0-1 range)
                probs = logits_per_image.softmax(dim=1)
                score = probs[0][0].item()
            
            return score
            
        except Exception as e:
            print(f"Validation error: {e}")
            return 0.5  # Return neutral score on error
    
    def get_quality_feedback(self, score: float) -> str:
        """
        Get human-readable feedback based on quality score
        
        Args:
            score: Quality score from validation
            
        Returns:
            Feedback string
        """
        if score >= 0.35:
            return "✅ Excellent - Strong prompt alignment"
        elif score >= 0.28:
            return "✓ Good - Acceptable quality"
        elif score >= 0.20:
            return "⚠ Fair - Consider refining prompt"
        else:
            return "❌ Poor - Prompt may need significant changes"
    
    def should_retry(self, score: float, min_score: float = 0.25) -> bool:
        """
        Determine if image should be regenerated based on quality
        
        Args:
            score: Quality score
            min_score: Minimum acceptable score
            
        Returns:
            True if should retry generation
        """
        return score < min_score
    
    def batch_validate(
        self, 
        images: list,
        prompts: list
    ) -> list:
        """
        Validate multiple images at once
        
        Args:
            images: List of PIL Images
            prompts: List of corresponding prompts
            
        Returns:
            List of quality scores
        """
        scores = []
        for image, prompt in zip(images, prompts):
            score = self.validate(image, prompt)
            scores.append(score)
        return scores


# Test function
if __name__ == "__main__":
    print("=== Quality Validator Test ===\n")
    
    validator = QualityValidator()
    
    # Create a simple test image
    test_image = Image.new('RGB', (512, 512), color='red')
    test_prompt = "a red square"
    
    print(f"Testing with prompt: '{test_prompt}'")
    score = validator.validate(test_image, test_prompt)
    print(f"Quality Score: {score:.4f}")
    print(f"Feedback: {validator.get_quality_feedback(score)}")
    print(f"Should Retry: {validator.should_retry(score)}")