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.

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Hey guys created a video on youtube to x posting n8n automation. Would love your feedback. https://youtu.be/XIl3ASH84Cc

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