I 4x'd a 367x479 stamp-sized photo through 8 upscaling models
enlarger.app
enlarger.app
Fuller disclaimer: this kind of a "postmark"-sized pic blowup test case is borderline unfair to every model in the comparison, mine included.
Most any upsize looks better if you toss hue protected noise back in; you've made good use of that point.
At scale, this tool should speed that process up a "bit". Let's say 100s of photos.
The end goal on working on this program has been that the user shouldn't care much about what model/architecture/whatever tech is used, and should be abstracted away as much as possible. Though in a blog post like this it should be mentioned, it was hard to break away from that self-imposed mental jail :)
Especially something as standardized as a passport photo.
See this article shared on HN a week ago:
https://blog.jimgrey.net/2026/06/30/what-happens-when-the-in...
The blog post you linked uses ChatGPT, which uses generative cloud AI with billions of parameters, which does indeed make up stuff.
The models that are diff'd on this blog post uses a whole different local architecture which has nothing to do with ChatGPT or Nano Banana 2, which the likes the blog post critiques. It is more rooted in older tech pre-generative era.
I do agree with the blog post, and "fool most people" -effect is especially strong when it's an old photo from a time that is not freshly in the viewers memory, and the subject no longer looks like it.
With the full images, the comparison is much more to the advantage of Enlarger.