How to scale outputs from AutoEncoder from multiple models?
I always recommend to use StandardScaler() rather than MinMaxScaler(). The reason is that the latter is too sensitive to outliers. Imagine shrinking the given vector in the [0, 1] interval:
[1, 2, 3, 4, 999]

Most of your data would be squeezed in a tiny subspace of the [0, 1] interval, causing absurd compression. The problem doesn't exist if you use the StandardScaler(). This looks particularly relevant given that you're trying to detect abnormal observations.
(I would use min-max scaling only if I'm sure that a given variable is bounded within a given range of values.)
But most importantly: What are the problems of your output? As far as we know, they might not be due to scaling.

UPDATE:
Lately, a scaling technique that I found very effective is robust min-max scaling. It works just like a min-max, but instead of being done between min and max value, it is done between min and a certain percentile (usually in 95-99). That's very effective when your dataset suffers from very few (but influent) outliers. This method makes sure that 95-99% of your dataset is properly scaled in [0,1], while few outliers take reach out-of-range values.