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
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LemonNexus
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"""
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Lemontropia Suite - AI Image Upscaling with Real-ESRGAN
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Optional AI-powered upscaling for game icons and textures.
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Real-ESRGAN is specifically designed for low-resolution game graphics
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and produces excellent results for rendered icons (not pixel art).
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"""
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import logging
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from pathlib import Path
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from typing import Optional, Tuple
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import numpy as np
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try:
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from PIL import Image
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PIL_AVAILABLE = True
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except ImportError:
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PIL_AVAILABLE = False
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logger = logging.getLogger(__name__)
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# Try to import Real-ESRGAN
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try:
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import torch
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from realesrgan import RealESRGANer
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REALESRGAN_AVAILABLE = True
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except ImportError:
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REALESRGAN_AVAILABLE = False
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logger.info("Real-ESRGAN not available. Install with: pip install realesrgan")
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class AIIconUpscaler:
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"""
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AI-powered upscaler for game icons using Real-ESRGAN.
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Real-ESRGAN is trained specifically on game textures and produces
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excellent results for low-resolution rendered graphics (not pixel art).
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Usage:
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upscaler = AIIconUpscaler()
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if upscaler.is_available():
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result = upscaler.upscale(image, scale=4)
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"""
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# Model download URLs
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MODEL_URLS = {
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'RealESRGAN_x4plus': 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth',
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'RealESRGAN_x4plus_anime_6B': 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus_anime_6B.pth',
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'RealESRGAN_x2plus': 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x2plus.pth',
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}
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def __init__(self, model_name: str = 'RealESRGAN_x4plus', device: str = 'cpu'):
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"""
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Initialize AI upscaler.
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Args:
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model_name: Which model to use
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device: 'cpu' or 'cuda' (GPU)
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"""
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self.model_name = model_name
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self.device = device
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self.upsampler = None
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if REALESRGAN_AVAILABLE:
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self._init_model()
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def _init_model(self):
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"""Initialize the Real-ESRGAN model."""
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try:
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# Model parameters
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if 'anime' in self.model_name:
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# Anime model (6B parameters)
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64,
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num_block=6, num_grow_ch=32, scale=4)
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elif 'x2' in self.model_name:
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# 2x upscale
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64,
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num_block=23, num_grow_ch=32, scale=2)
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else:
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# 4x upscale (default)
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model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64,
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num_block=23, num_grow_ch=32, scale=4)
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# Get model path
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model_path = self._get_model_path()
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if not model_path.exists():
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logger.warning(f"Model not found: {model_path}")
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logger.info(f"Download from: {self.MODEL_URLS.get(self.model_name)}")
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return
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# Initialize upsampler
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self.upsampler = RealESRGANer(
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scale=4 if 'x4' in self.model_name else 2,
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model_path=str(model_path),
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model=model,
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tile=0, # No tiling (process whole image)
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tile_pad=10,
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pre_pad=0,
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half=False if self.device == 'cpu' else True,
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device=torch.device(self.device)
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)
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logger.info(f"Real-ESRGAN initialized: {self.model_name} on {self.device}")
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except Exception as e:
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logger.error(f"Failed to initialize Real-ESRGAN: {e}")
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self.upsampler = None
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def _get_model_path(self) -> Path:
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"""Get path to model file."""
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# Store models in user's home directory
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model_dir = Path.home() / ".lemontropia" / "models"
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model_dir.mkdir(parents=True, exist_ok=True)
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return model_dir / f"{self.model_name}.pth"
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def is_available(self) -> bool:
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"""Check if AI upscaler is available and ready."""
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return REALESRGAN_AVAILABLE and self.upsampler is not None
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def upscale(self, image: Image.Image, scale: int = 4) -> Optional[Image.Image]:
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"""
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Upscale an image using Real-ESRGAN.
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Args:
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image: PIL Image to upscale
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scale: Upscale factor (2 or 4)
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Returns:
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Upscaled PIL Image or None if failed
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"""
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if not self.is_available():
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logger.error("AI upscaler not available")
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return None
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try:
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# Convert PIL to numpy
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img_np = np.array(image)
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# Remove alpha channel if present (Real-ESRGAN expects RGB)
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has_alpha = img_np.shape[-1] == 4
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if has_alpha:
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alpha = img_np[:, :, 3]
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img_rgb = img_np[:, :, :3]
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else:
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img_rgb = img_np
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# Upscale
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output, _ = self.upsampler.enhance(img_rgb, outscale=scale)
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# Restore alpha channel if needed
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if has_alpha:
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# Upscale alpha with simple resize
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from PIL import Image as PILImage
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alpha_pil = PILImage.fromarray(alpha)
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alpha_upscaled = alpha_pil.resize(
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(output.shape[1], output.shape[0]),
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PILImage.Resampling.LANCZOS
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)
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alpha_np = np.array(alpha_upscaled)
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output = np.dstack([output, alpha_np])
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# Convert back to PIL
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result = Image.fromarray(output)
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return result
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except Exception as e:
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logger.error(f"Upscaling failed: {e}")
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return None
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def get_info(self) -> dict:
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"""Get information about the upscaler status."""
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return {
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'available': self.is_available(),
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'model': self.model_name,
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'device': self.device,
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'model_path': str(self._get_model_path()),
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'dependencies': {
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'torch': torch.__version__ if REALESRGAN_AVAILABLE else 'not installed',
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'realesrgan': REALESRGAN_AVAILABLE
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}
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}
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# Convenience function
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def upscale_with_ai(image: Image.Image, scale: int = 4) -> Optional[Image.Image]:
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"""Quick AI upscale using Real-ESRGAN."""
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upscaler = AIIconUpscaler()
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return upscaler.upscale(image, scale)
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def check_ai_upscaler():
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"""Check if AI upscaler is available and print status."""
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upscaler = AIIconUpscaler()
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info = upscaler.get_info()
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print("=" * 50)
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print("AI Upscaler Status (Real-ESRGAN)")
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print("=" * 50)
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print(f"Available: {info['available']}")
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print(f"Model: {info['model']}")
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print(f"Device: {info['device']}")
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print(f"Model Path: {info['model_path']}")
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print(f"PyTorch: {info['dependencies']['torch']}")
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print("=" * 50)
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if not info['available']:
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print("\nTo install AI upscaler:")
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print("1. pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu")
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print("2. pip install realesrgan")
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print("3. Download model from:")
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for name, url in AIIconUpscaler.MODEL_URLS.items():
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print(f" {name}: {url}")
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return info['available']
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if __name__ == "__main__":
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check_ai_upscaler()
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@ -17,6 +17,7 @@ from PyQt6.QtCore import Qt, QThread, pyqtSignal
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from PyQt6.QtGui import QPixmap
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from modules.tga_converter import TGAConverter
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from modules.ai_upscaler import AIIconUpscaler, REALESRGAN_AVAILABLE
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logger = logging.getLogger(__name__)
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@ -59,6 +60,15 @@ class TGAConvertWorker(QThread):
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total = len(tga_files)
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success = 0
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# Initialize AI upscaler if needed
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ai_upscaler = None
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if self.upscale_method == 'ai' and REALESRGAN_AVAILABLE:
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self.progress_update.emit("Loading AI upscaler (Real-ESRGAN)...")
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ai_upscaler = AIIconUpscaler(device='cpu')
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if not ai_upscaler.is_available():
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self.progress_update.emit("AI model not found, falling back to HQ4x")
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self.upscale_method = 'hq4x'
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canvas_info = f" ({self.canvas_size[0]}x{self.canvas_size[1]} canvas)" if self.canvas_size else ""
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self.progress_update.emit(f"Found {total} TGA files to convert{canvas_info}")
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@ -68,12 +78,17 @@ class TGAConvertWorker(QThread):
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self.progress_update.emit(f"[{i+1}/{total}] Converting: {tga_path.name}")
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output_path = self.converter.convert_tga_to_png(
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tga_path,
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canvas_size=self.canvas_size,
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upscale=self.upscale,
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upscale_method=self.upscale_method
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)
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# Handle AI upscaling separately
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if self.upscale_method == 'ai' and ai_upscaler and ai_upscaler.is_available():
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output_path = self._convert_with_ai(tga_path, ai_upscaler)
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else:
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output_path = self.converter.convert_tga_to_png(
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tga_path,
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canvas_size=self.canvas_size,
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upscale=True,
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upscale_method=self.upscale_method
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)
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if output_path:
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success += 1
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self.file_converted.emit(tga_path.name, str(output_path))
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@ -83,6 +98,44 @@ class TGAConvertWorker(QThread):
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except Exception as e:
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self.conversion_error.emit(str(e))
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def _convert_with_ai(self, tga_path: Path, ai_upscaler: AIIconUpscaler) -> Optional[Path]:
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"""Convert a single TGA file using AI upscaling."""
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try:
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from PIL import Image
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# Load TGA
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image = Image.open(tga_path)
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if image.mode != 'RGBA':
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image = image.convert('RGBA')
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# AI upscale (4x)
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self.progress_update.emit(f" AI upscaling {tga_path.name}...")
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upscaled = ai_upscaler.upscale(image, scale=4)
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if upscaled is None:
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return None
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# Apply canvas if requested
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if self.canvas_size:
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upscaled = self.converter._apply_canvas(
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upscaled, self.canvas_size, upscale=False
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)
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# Save
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output_path = self.converter.output_dir / f"{tga_path.stem}.png"
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upscaled.save(output_path, 'PNG')
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return output_path
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except Exception as e:
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logger.error(f"AI conversion failed: {e}")
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return None
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self.conversion_complete.emit(success, total)
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except Exception as e:
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self.conversion_error.emit(str(e))
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def stop(self):
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"""Stop the conversion."""
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self._is_running = False
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@ -204,10 +257,16 @@ class TGAConverterDialog(QDialog):
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self.upscale_method_combo.addItem("Sharp Pixels (NEAREST)", "nearest")
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self.upscale_method_combo.addItem("Smooth (HQ4x-style)", "hq4x")
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self.upscale_method_combo.addItem("Photorealistic (LANCZOS)", "lanczos")
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# Add AI option if Real-ESRGAN is available
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if REALESRGAN_AVAILABLE:
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self.upscale_method_combo.addItem("🤖 AI Enhanced (Real-ESRGAN)", "ai")
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self.upscale_method_combo.setToolTip(
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"NEAREST: Sharp pixel edges (best for pixel art)\n"
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"HQ4x: Smooth but keeps details (best for game icons)\n"
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"LANCZOS: Very smooth (best for photos)"
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"LANCZOS: Very smooth (best for photos)\n"
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"AI: Neural network upscaling (best quality, requires model)"
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)
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canvas_layout.addWidget(self.upscale_method_combo)
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