spandrel

import os import torch from spandrel import ModelLoader # This is a placeholder for a real .pth model file. # In a real scenario, you would have a path to a pre-trained PyTorch model. # For demonstration, we'll simulate loading a non-existent file. # Replace 'path/to/your/model.pth' with an actual model file path. # A common practice is to download models from official repositories. model_path = os.environ.get('SPANDREL_MODEL_PATH', 'path/to/your/model.pth') # Ensure the directory for the dummy model exists if needed for testing # For a real quickstart, the model_path would point to an existing file. if not os.path.exists(model_path): print(f"[NOTE]: Model file not found at '{model_path}'. This example requires a valid .pth model file.\n") print("You can download a sample model, e.g., from a Super-Resolution project, and update 'model_path'.") # Simulate a dummy model for demonstration purposes if no file exists # This part would typically not be in a quickstart as it expects a real file. # For the purpose of making this runnable *without* an actual file, # we'll create a minimal placeholder for the ModelLoader.load_from_file call to fail gracefully. try: # Attempt to load, expecting failure without a real file model = ModelLoader.load_from_file(model_path) # If it miraculously works (e.g., user provided a path to a dummy file), # then proceed to describe interaction. print(f"Successfully loaded model: {model.name}") # ModelDescriptor objects (like ImageModelDescriptor) are wrappers around the actual PyTorch model. # They provide a unified interface. # The actual forward pass depends on the model type. # For an ImageModelDescriptor, input is typically a torch.Tensor (batch, channels, height, width). # dummy_input = torch.randn(1, 3, 256, 256) # Example input for an image model # output = model(dummy_input) # print(f"Model output shape: {output.shape}") except FileNotFoundError: print("Failed to load model as expected, because the file does not exist.") print("Please provide a real .pth model path for a functional example.") except Exception as e: print(f"An error occurred during model loading: {e}") else: try: model = ModelLoader.load_from_file(model_path) print(f"Successfully loaded model: {model.name} (architecture: {model.architecture.name})") # Example of accessing metadata if hasattr(model, 'scale'): print(f"Model scale: {model.scale}x") if hasattr(model, 'upscale_latent'): # Specific to certain architectures print(f"Upscale latent: {model.upscale_latent}") # Note: ImageModelDescriptor will NOT convert an image to a tensor. # You need to provide a pre-processed tensor. # For demonstration, we'll create a dummy input tensor if the model expects one. if 'Image' in str(type(model)) or 'Upscaler' in str(type(model)): # Assuming a common image input format: NCHW (batch, channels, height, width) dummy_input = torch.randn(1, 3, 128, 128) # Example: batch size 1, 3 channels, 128x128 image print(f"Attempting forward pass with dummy input shape: {dummy_input.shape}") try: output = model(dummy_input) print(f"Model forward pass successful. Output shape: {output.shape}") except Exception as e: print(f"Error during model forward pass with dummy input: {e}") print("The actual input shape and type depend on the specific model architecture.") else: print("Model type not recognized for dummy image input. Skipping forward pass.") except Exception as e: print(f"Error loading or interacting with model from '{model_path}': {e}")