diff --git a/src/destructure.jl b/src/destructure.jl
index baea015..bc83ff1 100644
--- a/src/destructure.jl
+++ b/src/destructure.jl
@@ -35,9 +35,22 @@ end
 
 """
     destructure!(model) -> vector, reconstructor
+    destructure!(vector, model) -> vector, reconstructor
 
-This is a variant of [`destructure`](@ref), whose reconstruction function mutates the model.
-Requires that all trainable parameters in the model be mutable arrays!
+These are variants of [`destructure`](@ref), returning a reconstruction function
+which mutates the original model, instead of making a new one.
+The second method also mutates an existing flat vector.
+
+They require that all trainable parameters in the model be mutable arrays,
+else `re!` will give an error.
+
+!!! warning "Gradients"
+    Despite using mutation, they should be safe to use within Zygote,
+    with the important caveat that you must use the model returned, `m2 = re!(v)`, not the original.
+    Even though `m2 === m`, for Zygote to trace what results are used where, it has to see
+    the returned object being used.
+    If you discard `m2` and call for example `Flux.mse(m(x), y)` with the original model `m`,
+    Zygote will give silently wrong results.
 
 # Example
 ```jldoctest
@@ -51,6 +64,9 @@ true
 
 julia> m
 (x = [3.0, 5.0], y = (sin, Float32[7.0 9.0], cos))
+
+julia> v2, re2! = destructure!(rand(4), m)  # works the same way
+([3.0, 5.0, 7.0, 9.0], Restructure!(NamedTuple, ..., 4))
 ```
 """
 function destructure!(x)
@@ -58,10 +74,10 @@ function destructure!(x)
   flat, Restructure!(x, off, len)
 end
 
-# function destructure!(flat::AbstractVector, x)
-#   flat, off, len = _flatten!(flat, x)
-#   flat, Restructure!(x, off, len)
-# end
+function destructure!(flat::AbstractVector, x)
+  flat, off, len = _flatten!(flat, x)
+  flat, Restructure!(x, off, len)
+end
 
 """
     Restructure(Model, ..., length)
@@ -115,17 +131,17 @@ function _flatten(x)
   isempty(arrays) && return Bool[], off, 0
   return reduce(vcat, arrays), off, len[]
 end
-# function _flatten!(flat, x)
-#   isnumeric(x) && return copyto!(flat, _vec(x))  # trivial case
-#   len = Ref(0)
-#   off = fmap(x; exclude = isnumeric, walk = _TrainableStructWalk()) do y
-#     o = len[]
-#     copyto!(flat, o, _vec(y))
-#     len[] = o + length(y)
-#     o
-#   end
-#   flat, off, len[]
-# end
+function _flatten!(flat, x)
+  isnumeric(x) && return copyto!(flat, _vec(x))  # trivial case
+  len = Ref(0)
+  off = fmap(x; exclude = isnumeric, walk = TrainableStructWalk()) do y
+    o = len[]
+    copyto!(flat, o+1, _vec(y))
+    len[] = o + length(y)
+    o
+  end
+  flat, off, len[]
+end
 
 struct TrainableStructWalk <: AbstractWalk end