Improve Error Handling for Model Loading and File Operations

[BackendTrip]

Description

The codebase lacks robust error handling, particularly when loading models and processing files. This can lead to cryptic errors or silent failures when running inference with missing or corrupt model files.

Specific Issues

1. No comprehensive error handling when loading model weights in initialize_model function
2. The image processing function image2vae_canny has minimal error checking for file existence but doesn't handle other potential issues like corrupt images
3. Missing validation for model parameters before inference

Example

In pipeline/test_relactrl_pixart_1024.py:

def image2vae_canny(image_path, device, low_threshold=100, high_threshold=200):
    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
    if image is None:
        raise ValueError(f"Unable to read image: {image_path}")
    refer_img = cv2.Canny(image, low_threshold, high_threshold)
    refer_img_vae = load_img_2_vae_feature(refer_img).unsqueeze(0).to(device)
    return refer_img_vae

Proposed Solution

1. Add comprehensive try/except blocks for file operations and model loading
2. Implement proper validation for input parameters
3. Add informative error messages that guide users to resolve issues
4. Consider adding a validation step before running inference to check if all required models are available and valid

[MaAo]

Thank you for your attention. We will address this issue in the next pull request.