realesrgan

import os from PIL import Image import numpy as np # Assuming realesrgan is installed via pip try: from realesrgan import RealESRGANer except ImportError: print("RealESRGAN is not installed. Please run 'pip install realesrgan'") exit(1) # Create a dummy image for demonstration input_image_path = "temp_input_64x64.png" output_image_path = "temp_output_upscaled.png" dummy_image = Image.new('RGB', (64, 64), color = 'red') dummy_image.save(input_image_path) try: # Initialize RealESRGANer with a common model (auto-downloads if not present) # Using 'cpu' for device ensures it runs without a CUDA setup. # For GPU, change 'cpu' to 'cuda' and ensure PyTorch with CUDA is installed. upscaler = RealESRGANer( model_name='RealESRGAN_x4plus', # A widely used general-purpose model netscale=4, # The upsampling factor the model was trained for outscale=4, # Desired output upsampling factor (should match netscale for best results) tile=0, # Set to 0 to process the entire image at once. For large images/limited VRAM, use e.g. 600. tile_pad=10, pre_pad=0, device='cpu' # Use 'cuda' if a compatible GPU and PyTorch+CUDA are available ) # Load and convert image img = Image.open(input_image_path).convert('RGB') img_np = np.array(img) # Convert PIL Image to NumPy array for processing # Perform enhancement upscaled_image_np, _ = upscaler.enhance(img_np, outscale=4) # Convert back to PIL Image and save upscaled_image_pil = Image.fromarray(upscaled_image_np) upscaled_image_pil.save(output_image_path) print(f"Image upscaled successfully and saved to {output_image_path}") except Exception as e: print(f"An error occurred during upscaling: {e}") print("Common issues: missing models (check network), CUDA not available/misconfigured, out of memory.") if "cuda" in str(e).lower() and "memory" in str(e).lower(): print("Consider reducing `tile` size in RealESRGANer or processing smaller images.") if "model_name" in str(e) or "model_path" in str(e): print("Ensure the specified `model_name` is valid or `model_path` points to an existing model file.") finally: # Clean up dummy files if os.path.exists(input_image_path): os.remove(input_image_path) if os.path.exists(output_image_path): os.remove(output_image_path)