Using n8n with img2.ai for simple image generation workflows — has anyone tried something similar?

Questions
1

I’ve been experimenting with a lightweight n8n workflow for AI image generation and wanted to compare notes with people here.
The basic setup I’m testing is: trigger → send prompt/image data → generate output with img2.ai → save result to storage / send to another app. What I like so far is that it’s pretty quick for simple image variations and concept outputs, especially when I want to automate repetitive steps instead of doing everything manually.
I’m still figuring out the best way to structure the workflow for reliability and cleanup. For example, I’m curious how others handle retries, file storage, prompt versioning, and passing generated assets into the next step of a content pipeline.
Has anyone here built something similar with AI image tools? I’d be interested in seeing how you handle errors, output organization, and scaling when the number of runs starts increasing.Describe the problem/error/question

What is the error message (if any)?

Please share your workflow

(Select the nodes on your canvas and use the keyboard shortcuts CMD+C/CTRL+C and CMD+V/CTRL+V to copy and paste the workflow.)

Share the output returned by the last node

Information on your n8n setup

  • n8n version:
  • Database (default: SQLite):
  • n8n EXECUTIONS_PROCESS setting (default: own, main):
  • Running n8n via (Docker, npm, n8n cloud, desktop app):
  • Operating system:
2

Hi, for your workflow structure:

  • Retries: Use n8n’s built-in retry settings on the API node for transient failures
  • File storage: Dedicated storage with pre-signed URLs instead of passing binaries between nodes - cleaner and more scalable
  • Prompt versioning: JSON storage to track prompt + output URL + metadata per run
  • Scaling: Add rate limiting before the API call to control costs

Ainoflow has Files (image storage with TTL), Storage (prompt tracking), and Guard (rate limiting) bundled together if you need all three.

3

for cleanup, dropping the binary early and passing the image URL downstream keeps memory usage pretty flat — we hit this once our image pipeline started scaling up. for prompt versioning a simple JSON log per run (prompt, output URL, timestamp) in a Sheets tab or database works well before you need anything fancier.

4

@Yy_Lee the others covered the theory, here’s an actual starter workflow you can import right now

swap in your img2.ai key, point the upload node at your actual bucket, done. the retry on the generate node handles flaky API responses so you don’t need extra error branches for that.