I found this old, damaged photo online — it had stains, bends, scratches, and even some small missing parts. Instead of just restoring it, I decided to use it as a test subject to fine-tune and perfect my AI-powered restoration workflow with ComfyUI.
Why This Image Was Perfect for Workflow Testing
Because the photo had a mix of common restoration challenges — discoloration, physical damage, missing details — it was a great real-world example to work through. I wanted to create a reliable, repeatable process that could handle photos like this consistently and efficiently.
How I Used This Photo to Refine My Process
- I experimented with different prompt formulations to get the best detail recovery.
- Tweaked inpainting nodes to fill missing or damaged areas convincingly.
- Balanced clean-up and upscaling steps to avoid artifacts while restoring texture.
- Tested batch processing potential, aiming to save hours on future restorations.
The Result
Within a surprisingly short time, I achieved a restored version I was happy with. More importantly, I now have a solid baseline workflow that I can confidently reuse and improve over time for other images.
What’s Next
This photo was my stepping stone. Moving forward, I’ll keep refining the pipeline and applying it to more challenging restorations — turning old, damaged memories into vivid, beautiful images with minimal manual effort.