Looking for some modeling advice in the context of longitudinal Bayesian structural equation modeling with correlated latent outcomes when T = 2 (pre/post design). Most related posts seem to address crosssectional measurement models, singlefactor longitudinal measurement models, or largeT longitudinal SEMs, so this is a bit different.
Background
My data come from the responses of i \in \{1,...,1000\} respondents to the same 8 items \{x1,...x8\} recorded before/at baseline (t = 0) and after (t = 1) respondents randomly received either a low (z = 0) or high (z = 1) intensity treatment. The items measure two correlated latent factors, with four items apiece measuring each factor: F = \{F1, F2\}. For convenience, anything with a _1 subscript (e.g. x1_1, F2_1, F_1) refers to posttreatment values while the _0 subscript refers to pretreatment values.
I am trying to estimate the effect of receiving the high vs. low intensity treatment on the posttreatment latent variables: E[F_1z = 1]  E[F_1z = 0]. Since I have pretreatment responses to the same questions, I am also conditioning on the lagged scores of both latent factors (F_0) to likely improve precision. I prefer this to modeling change scores for efficiency reasons. McArdle (2009) calls this the (twooccasion) “multiple common factors cross lagged regression model.”
The associational structure I’m proposing is shown in the path diagram below. Note that (a) residual variances for each observed measure x are uncorrelated across time and there are no equality constraints on parameters because the pretreatment parameters are treated as nuisance parameters (I do not care if e.g. F1_0 and F1_1 are “actually” measuring the same thing provided that F_0 predict F_1).
Modeling Issues
As I’ve tried to think about this as a hierarchical model, I’ve struggled with two questions:

What should be hierarchical? What are the relevant “groupings” of the responses x? The typical approach to employing multilevel models here would be in an itemresponse framework where I model the responses themselves x as function of person and itemspecific variables. For example,
brms
syntax might be:response ~ (1  item ) + (0 + latent_factor_identifier  person)
, which would return a vector of length 4, where each element is a personspecific value for one of the 4 latent variables in the model. However, this seems to ignore that these 4 values are not independent: half of the responses used to measure them are pretreatment and half are post, half measure factor F1 and half measure F2, and roughly half of the respondents had z =1 and half z=0. This last point is particularly troublesome because everyone is treated to varying degrees, thus I expect F_0 \neq F_1 (even if they were measuring the same thing) and I would need to change z from a binary (“high”, “low” treatment intensity) variable to a nominal (“high”, “low”, “pretreatment”) variable to partition all x responses according to it. As it is now, it would be odd (or impossible) to model pretreatment factor scores (“ability” in IRTspeak) as functions of treatment status…Accordingly, I am not sure what type of hierarchical structure one would specify in this context: person:latent_factor_identifier? person:z? person:time? person:time:z? 
Are my groupings too small? Nearly every way of defining “groupings” has relatively few unique values of the grouping variable at some point. For example, z takes on two values, latent_factor_identifier would have 4 unique values, time has 2 values (pre/post), an identifier for which type of latent factor would have 2 value (i.e. F1 vs. F2). I know one can identify a model with two values for a grouping variable, but isn’t this more likely to introduce computational issues with very little regularization or gain in precision?
Many thanks.
Referenced
McCardle, John J. (2009). Latent Variable Modeling of Differences and Changes with Longitudinal Data. Annual Review of Psychology. Latent Variable Modeling of Differences and Changes with Longitudinal Data  Annual Review of Psychology