Checking Stable Diffusion output
Check Stable Diffusion output — especially for what it memorized
The Stable Diffusion ecosystem is the most customizable in generative imaging, and the least predictable: checkpoints, samplers, LoRAs and prompts all shift the failure profile. It is also the family where researchers have directly measured training-data regurgitation — generations that near-copy existing images. If any output deserves a pre-publication scan, it is this one.
What typically goes wrong in Stable Diffusion images
- Rendered text remains the weakest area across most checkpoints
- Extra limbs and merged anatomy, especially at unusual aspect ratios
- Near-copies of training images — a measured, documented behaviour
- Artifact profiles that change with every checkpoint and sampler switch
- Duplicated faces and objects in wide or highly detailed scenes
How Chekr checks it
Chekr runs the same nine checks on every upload regardless of checkpoint or pipeline, pinning findings with severity and confidence. The reverse-image similarity check matters most here: research has shown diffusion models can reproduce training images closely, and the only reliable defence at publish time is comparing your output against what already exists online.
Our blog documents the underlying research — measured replication rates and why popular images are regurgitated most — in the Filippa K case study, where an AI campaign near-copied two real photographs and was withdrawn in two days.
Frequently asked
How real is the training-data regurgitation risk?
Measured and non-zero: research on Stable Diffusion found a small but real percentage of generations closely matching training images, with heavily duplicated images most at risk. At campaign volume, small percentages become expected events.
Does Chekr support SDXL, SD3 and community checkpoints?
All of them — the scan evaluates the finished image, so any checkpoint, sampler or LoRA combination is covered automatically.
Can the scan run inside our self-hosted pipeline?
Yes, via the API: send each render, get findings and a score back in seconds, and gate publishing on the result.