Just finished building a complex workflow using Google's new Nano Banana AI model through Fal.ai. Creates professional product photos by combining any model image with t-shirt designs - completely automated from Excel input to WooCommerce upload. How it Works (Step-by-Step):
1. Data Input & Batching:
- Manual trigger reads Excel file (model images + t-shirt designs + product IDs)
- Split into batches of 10 to prevent API overload
- Each row gets processed with model + design combination
2. Smart Caching System:
- Code node generates unique cache key for each image combo
- Checks if combination was processed before
- If cache exists → skip API call, use stored result
- If no cache → proceed to image generation
3. AI Image Generation:
- HTTP request to Fal.ai using Nano Banana model
- Prompt: "Photo of model wearing submitted clothing item, professional product photography"
- Smart Wait node polls every 5-10 seconds for completion (prevents rate limits)
4. Status Checking & Error Handling:
- Check Status node verifies successful generation
- If failed → loops back to Smart Wait
- If successful → proceeds to download
5. Storage & Integration:
- Downloads generated images
- Uploads to Google Drive for storage
- Updates Excel with new image URLs
- Optional: Auto-pushes to WooCommerce/Shopify
Key Technical Features:
- Batch processing prevents API failures
- Caching system eliminates duplicate charges
- Error handling with retry loops
- Rate limiting with smart wait intervals
Example Output: Takes a model photo + t-shirt design → generates professional product photo showing model wearing the shirt
The caching is the game-changer here - once an image combo is generated, it never gets charged again. Client processes 50+ products monthly and only pays for new combinations.
Built this over 2 weeks of testing different approaches. The Smart Wait node timing was crucial - too fast and Fal.ai rejects requests, too slow and workflow takes forever.