feat: add Real-ESRGAN AI upscaling support

- New module: modules/ai_upscaler.py
  - Real-ESRGAN integration for AI-powered upscaling
  - Designed specifically for low-res game textures/icons
  - 4x upscale factor with neural network enhancement
  - Falls back gracefully if model not available

- Updated TGA Converter UI:
  - New "AI Enhanced (Real-ESRGAN)" option in upscale dropdown
  - Only shown if Real-ESRGAN is installed
  - AI upscaling happens before canvas placement
  - Shows progress during AI model loading

Real-ESRGAN is much better than basic methods for rendered (non-pixel) icons!

To use AI upscaling:
1. pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
2. pip install realesrgan
3. Download RealESRGAN_x4plus.pth model
4. Select "AI Enhanced" in the UI

modules/ai_upscaler.py

@@ -0,0 +1,220 @@
+"""
+Lemontropia Suite - AI Image Upscaling with Real-ESRGAN
+Optional AI-powered upscaling for game icons and textures.
+
+Real-ESRGAN is specifically designed for low-resolution game graphics
+and produces excellent results for rendered icons (not pixel art).
+"""
+
+import logging
+from pathlib import Path
+from typing import Optional, Tuple
+import numpy as np
+
+try:
+    from PIL import Image
+    PIL_AVAILABLE = True
+except ImportError:
+    PIL_AVAILABLE = False
+
+logger = logging.getLogger(__name__)
+
+# Try to import Real-ESRGAN
+try:
+    import torch
+    from basicsr.archs.rrdbnet_arch import RRDBNet
+    from realesrgan import RealESRGANer
+    REALESRGAN_AVAILABLE = True
+except ImportError:
+    REALESRGAN_AVAILABLE = False
+    logger.info("Real-ESRGAN not available. Install with: pip install realesrgan")
+
+
+class AIIconUpscaler:
+    """
+    AI-powered upscaler for game icons using Real-ESRGAN.
+    
+    Real-ESRGAN is trained specifically on game textures and produces
+    excellent results for low-resolution rendered graphics (not pixel art).
+    
+    Usage:
+        upscaler = AIIconUpscaler()
+        if upscaler.is_available():
+            result = upscaler.upscale(image, scale=4)
+    """
+    
+    # Model download URLs
+    MODEL_URLS = {
+        'RealESRGAN_x4plus': 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth',
+        'RealESRGAN_x4plus_anime_6B': 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus_anime_6B.pth',
+        'RealESRGAN_x2plus': 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x2plus.pth',
+    }
+    
+    def __init__(self, model_name: str = 'RealESRGAN_x4plus', device: str = 'cpu'):
+        """
+        Initialize AI upscaler.
+        
+        Args:
+            model_name: Which model to use
+            device: 'cpu' or 'cuda' (GPU)
+        """
+        self.model_name = model_name
+        self.device = device
+        self.upsampler = None
+        
+        if REALESRGAN_AVAILABLE:
+            self._init_model()
+    
+    def _init_model(self):
+        """Initialize the Real-ESRGAN model."""
+        try:
+            # Model parameters
+            if 'anime' in self.model_name:
+                # Anime model (6B parameters)
+                model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, 
+                               num_block=6, num_grow_ch=32, scale=4)
+            elif 'x2' in self.model_name:
+                # 2x upscale
+                model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64,
+                               num_block=23, num_grow_ch=32, scale=2)
+            else:
+                # 4x upscale (default)
+                model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64,
+                               num_block=23, num_grow_ch=32, scale=4)
+            
+            # Get model path
+            model_path = self._get_model_path()
+            
+            if not model_path.exists():
+                logger.warning(f"Model not found: {model_path}")
+                logger.info(f"Download from: {self.MODEL_URLS.get(self.model_name)}")
+                return
+            
+            # Initialize upsampler
+            self.upsampler = RealESRGANer(
+                scale=4 if 'x4' in self.model_name else 2,
+                model_path=str(model_path),
+                model=model,
+                tile=0,  # No tiling (process whole image)
+                tile_pad=10,
+                pre_pad=0,
+                half=False if self.device == 'cpu' else True,
+                device=torch.device(self.device)
+            )
+            
+            logger.info(f"Real-ESRGAN initialized: {self.model_name} on {self.device}")
+            
+        except Exception as e:
+            logger.error(f"Failed to initialize Real-ESRGAN: {e}")
+            self.upsampler = None
+    
+    def _get_model_path(self) -> Path:
+        """Get path to model file."""
+        # Store models in user's home directory
+        model_dir = Path.home() / ".lemontropia" / "models"
+        model_dir.mkdir(parents=True, exist_ok=True)
+        return model_dir / f"{self.model_name}.pth"
+    
+    def is_available(self) -> bool:
+        """Check if AI upscaler is available and ready."""
+        return REALESRGAN_AVAILABLE and self.upsampler is not None
+    
+    def upscale(self, image: Image.Image, scale: int = 4) -> Optional[Image.Image]:
+        """
+        Upscale an image using Real-ESRGAN.
+        
+        Args:
+            image: PIL Image to upscale
+            scale: Upscale factor (2 or 4)
+            
+        Returns:
+            Upscaled PIL Image or None if failed
+        """
+        if not self.is_available():
+            logger.error("AI upscaler not available")
+            return None
+        
+        try:
+            # Convert PIL to numpy
+            img_np = np.array(image)
+            
+            # Remove alpha channel if present (Real-ESRGAN expects RGB)
+            has_alpha = img_np.shape[-1] == 4
+            if has_alpha:
+                alpha = img_np[:, :, 3]
+                img_rgb = img_np[:, :, :3]
+            else:
+                img_rgb = img_np
+            
+            # Upscale
+            output, _ = self.upsampler.enhance(img_rgb, outscale=scale)
+            
+            # Restore alpha channel if needed
+            if has_alpha:
+                # Upscale alpha with simple resize
+                from PIL import Image as PILImage
+                alpha_pil = PILImage.fromarray(alpha)
+                alpha_upscaled = alpha_pil.resize(
+                    (output.shape[1], output.shape[0]), 
+                    PILImage.Resampling.LANCZOS
+                )
+                alpha_np = np.array(alpha_upscaled)
+                output = np.dstack([output, alpha_np])
+            
+            # Convert back to PIL
+            result = Image.fromarray(output)
+            return result
+            
+        except Exception as e:
+            logger.error(f"Upscaling failed: {e}")
+            return None
+    
+    def get_info(self) -> dict:
+        """Get information about the upscaler status."""
+        return {
+            'available': self.is_available(),
+            'model': self.model_name,
+            'device': self.device,
+            'model_path': str(self._get_model_path()),
+            'dependencies': {
+                'torch': torch.__version__ if REALESRGAN_AVAILABLE else 'not installed',
+                'realesrgan': REALESRGAN_AVAILABLE
+            }
+        }
+
+
+# Convenience function
+def upscale_with_ai(image: Image.Image, scale: int = 4) -> Optional[Image.Image]:
+    """Quick AI upscale using Real-ESRGAN."""
+    upscaler = AIIconUpscaler()
+    return upscaler.upscale(image, scale)
+
+
+def check_ai_upscaler():
+    """Check if AI upscaler is available and print status."""
+    upscaler = AIIconUpscaler()
+    info = upscaler.get_info()
+    
+    print("=" * 50)
+    print("AI Upscaler Status (Real-ESRGAN)")
+    print("=" * 50)
+    print(f"Available: {info['available']}")
+    print(f"Model: {info['model']}")
+    print(f"Device: {info['device']}")
+    print(f"Model Path: {info['model_path']}")
+    print(f"PyTorch: {info['dependencies']['torch']}")
+    print("=" * 50)
+    
+    if not info['available']:
+        print("\nTo install AI upscaler:")
+        print("1. pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu")
+        print("2. pip install realesrgan")
+        print("3. Download model from:")
+        for name, url in AIIconUpscaler.MODEL_URLS.items():
+            print(f"   {name}: {url}")
+    
+    return info['available']
+
+
+if __name__ == "__main__":
+    check_ai_upscaler()

ui/tga_converter_dialog.py

@@ -17,6 +17,7 @@ from PyQt6.QtCore import Qt, QThread, pyqtSignal
 from PyQt6.QtGui import QPixmap
 
 from modules.tga_converter import TGAConverter
+from modules.ai_upscaler import AIIconUpscaler, REALESRGAN_AVAILABLE
 
 logger = logging.getLogger(__name__)
 
@@ -59,6 +60,15 @@ class TGAConvertWorker(QThread):
             total = len(tga_files)
             success = 0
             
+            # Initialize AI upscaler if needed
+            ai_upscaler = None
+            if self.upscale_method == 'ai' and REALESRGAN_AVAILABLE:
+                self.progress_update.emit("Loading AI upscaler (Real-ESRGAN)...")
+                ai_upscaler = AIIconUpscaler(device='cpu')
+                if not ai_upscaler.is_available():
+                    self.progress_update.emit("AI model not found, falling back to HQ4x")
+                    self.upscale_method = 'hq4x'
+            
             canvas_info = f" ({self.canvas_size[0]}x{self.canvas_size[1]} canvas)" if self.canvas_size else ""
             self.progress_update.emit(f"Found {total} TGA files to convert{canvas_info}")
             
@@ -68,12 +78,17 @@ class TGAConvertWorker(QThread):
                 
                 self.progress_update.emit(f"[{i+1}/{total}] Converting: {tga_path.name}")
                 
-                output_path = self.converter.convert_tga_to_png(
+                # Handle AI upscaling separately
-                    tga_path, 
+                if self.upscale_method == 'ai' and ai_upscaler and ai_upscaler.is_available():
-                    canvas_size=self.canvas_size, 
+                    output_path = self._convert_with_ai(tga_path, ai_upscaler)
-                    upscale=self.upscale,
+                else:
-                    upscale_method=self.upscale_method
+                    output_path = self.converter.convert_tga_to_png(
-                )
+                        tga_path, 
+                        canvas_size=self.canvas_size, 
+                        upscale=True,
+                        upscale_method=self.upscale_method
+                    )
+                
                 if output_path:
                     success += 1
                     self.file_converted.emit(tga_path.name, str(output_path))
@@ -83,6 +98,44 @@ class TGAConvertWorker(QThread):
         except Exception as e:
             self.conversion_error.emit(str(e))
     
+    def _convert_with_ai(self, tga_path: Path, ai_upscaler: AIIconUpscaler) -> Optional[Path]:
+        """Convert a single TGA file using AI upscaling."""
+        try:
+            from PIL import Image
+            
+            # Load TGA
+            image = Image.open(tga_path)
+            if image.mode != 'RGBA':
+                image = image.convert('RGBA')
+            
+            # AI upscale (4x)
+            self.progress_update.emit(f"  AI upscaling {tga_path.name}...")
+            upscaled = ai_upscaler.upscale(image, scale=4)
+            
+            if upscaled is None:
+                return None
+            
+            # Apply canvas if requested
+            if self.canvas_size:
+                upscaled = self.converter._apply_canvas(
+                    upscaled, self.canvas_size, upscale=False
+                )
+            
+            # Save
+            output_path = self.converter.output_dir / f"{tga_path.stem}.png"
+            upscaled.save(output_path, 'PNG')
+            
+            return output_path
+            
+        except Exception as e:
+            logger.error(f"AI conversion failed: {e}")
+            return None
+            
+            self.conversion_complete.emit(success, total)
+            
+        except Exception as e:
+            self.conversion_error.emit(str(e))
+    
     def stop(self):
         """Stop the conversion."""
         self._is_running = False
@@ -204,10 +257,16 @@ class TGAConverterDialog(QDialog):
         self.upscale_method_combo.addItem("Sharp Pixels (NEAREST)", "nearest")
         self.upscale_method_combo.addItem("Smooth (HQ4x-style)", "hq4x")
         self.upscale_method_combo.addItem("Photorealistic (LANCZOS)", "lanczos")
+        
+        # Add AI option if Real-ESRGAN is available
+        if REALESRGAN_AVAILABLE:
+            self.upscale_method_combo.addItem("🤖 AI Enhanced (Real-ESRGAN)", "ai")
+        
         self.upscale_method_combo.setToolTip(
             "NEAREST: Sharp pixel edges (best for pixel art)\n"
             "HQ4x: Smooth but keeps details (best for game icons)\n"
-            "LANCZOS: Very smooth (best for photos)"
+            "LANCZOS: Very smooth (best for photos)\n"
+            "AI: Neural network upscaling (best quality, requires model)"
         )
         canvas_layout.addWidget(self.upscale_method_combo)