Browser downloads of large AI models from Google Drive frequently fail, leaving users frustrated with incomplete files and wasted bandwidth. A new Python-based solution automates the entire process, using the gdown library to reliably fetch multi-gigabyte files that browsers can't handle.

The script reads a simple JSON configuration file containing model details and Google Drive IDs. It checks for existing files before downloading, saving time and data. If a download fails due to network issues, it automatically retries up to three times using exponential backoff to ensure completion.

File integrity is verified by comparing downloaded sizes against expected values within a 5% tolerance. Users can run commands to list models, download missing files, or force a complete redownload. This approach offers a robust workflow for managing AI model libraries without manual intervention.

This tool addresses a common pain point in AI development, where sharing large Stable Diffusion or LLM weights via Google Drive is standard but unreliable. By automating verification and retries, developers can maintain organized local repositories. The script essentially acts as a package manager for cloud-hosted model weights.