From my experience working a bit with image-processing models, I’d say many of the technical problems are underestimated by users but very obvious to developers. Things like pose detection errors, inconsistent depth estimation, or edge blending are not trivial at all. Even small mistakes can snowball into clearly fake-looking results. Some platforms, like the one discussed on AI Undress , try to address this by restricting inputs and applying automated checks before processing, which is actually a smart safeguard even if users find it annoying.
What I personally find important is the use of internal filters and limitations: for example, blocking uploads that are too low-res, heavily edited, or obviously scraped from social media. That’s not about censorship, it’s about model stability and misuse prevention. Another challenge is preventing re-uploads of generated images to retrain the same system, which can cause quality collapse over time. These are very practical, unglamorous problems, but they matter more than flashy features in the long run.
