diff --git a/.github/workflows/Test.yml b/.github/workflows/Test.yml
index 54fdfeeb7..89223f597 100644
--- a/.github/workflows/Test.yml
+++ b/.github/workflows/Test.yml
@@ -51,8 +51,6 @@ jobs:
           - Back/Symbolics
           - Back/Tracker
           - Back/Zygote
-          - Down/Flux
-          - Down/Lux
         skip_lts:
           - ${{ github.event.pull_request.draft }}
         skip_pre:
@@ -79,14 +77,22 @@ jobs:
           arch: x64
       - uses: julia-actions/cache@v2
       - name: Install dependencies & run tests
-        # how to add the local DIT to the DI test env?
-        run: julia --project=./DifferentiationInterface --color=yes -e '
+        run: julia --color=yes -e '
           using Pkg;
           Pkg.Registry.update();
+          Pkg.activate("./DifferentiationInterface/test");
+          if VERSION < v"1.11";
+          Pkg.rm("DifferentiationInterfaceTest");
+          Pkg.resolve();
+          else;
+          Pkg.develop(; path="./DifferentiationInterfaceTest");
+          end;
+          Pkg.activate("./DifferentiationInterface");
+          test_kwargs = (; allow_reresolve=false, coverage=true);
           if ENV["JULIA_DI_PR_DRAFT"] == "true";
-          Pkg.test("DifferentiationInterface"; coverage=true, julia_args=["-O1"]);
+          Pkg.test("DifferentiationInterface"; julia_args=["-O1"], test_kwargs...);
           else;
-          Pkg.test("DifferentiationInterface"; coverage=true);
+          Pkg.test("DifferentiationInterface"; test_kwargs...);
           end;'
       - uses: julia-actions/julia-processcoverage@v1
         with:
diff --git a/DifferentiationInterface/test/Down/Flux/test.jl b/DifferentiationInterface/test/Down/Flux/test.jl
deleted file mode 100644
index da9a76b3a..000000000
--- a/DifferentiationInterface/test/Down/Flux/test.jl
+++ /dev/null
@@ -1,26 +0,0 @@
-using Pkg
-Pkg.add(["FiniteDifferences", "Enzyme", "Flux", "Zygote"])
-
-using DifferentiationInterface, DifferentiationInterfaceTest
-import DifferentiationInterfaceTest as DIT
-using Enzyme: Enzyme
-using FiniteDifferences: FiniteDifferences
-using Flux: Flux
-using Random
-using Zygote: Zygote
-using Test
-
-LOGGING = get(ENV, "CI", "false") == "false"
-
-test_differentiation(
-    [
-        AutoZygote(),
-        # AutoEnzyme(), # TODO a few scenarios fail
-    ],
-    DIT.flux_scenarios(Random.MersenneTwister(0));
-    isapprox = DIT.flux_isapprox,
-    rtol = 1.0e-2,
-    atol = 1.0e-4,
-    scenario_intact = false,  # TODO: why?
-    logging = LOGGING,
-)
diff --git a/DifferentiationInterface/test/Down/Lux/test.jl b/DifferentiationInterface/test/Down/Lux/test.jl
deleted file mode 100644
index b1d0652ff..000000000
--- a/DifferentiationInterface/test/Down/Lux/test.jl
+++ /dev/null
@@ -1,22 +0,0 @@
-using Pkg
-Pkg.add(["ForwardDiff", "Lux", "LuxTestUtils", "Zygote"])
-
-using ComponentArrays: ComponentArrays
-using DifferentiationInterface, DifferentiationInterfaceTest
-import DifferentiationInterfaceTest as DIT
-using ForwardDiff: ForwardDiff
-using Lux: Lux
-using LuxTestUtils: LuxTestUtils
-using Random
-
-LOGGING = get(ENV, "CI", "false") == "false"
-
-test_differentiation(
-    AutoZygote(),
-    DIT.lux_scenarios(Random.Xoshiro(63));
-    isapprox = DIT.lux_isapprox,
-    rtol = 1.0f-2,
-    atol = 1.0f-3,
-    scenario_intact = false,  # TODO: why?
-    logging = LOGGING,
-)
diff --git a/DifferentiationInterface/test/Project.toml b/DifferentiationInterface/test/Project.toml
index eee6a767e..350fcbb58 100644
--- a/DifferentiationInterface/test/Project.toml
+++ b/DifferentiationInterface/test/Project.toml
@@ -5,6 +5,7 @@ ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 DifferentiationInterface = "a0c0ee7d-e4b9-4e03-894e-1c5f64a51d63"
+DifferentiationInterfaceTest = "a82114a7-5aa3-49a8-9643-716bb13727a3"
 ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
@@ -26,6 +27,7 @@ ComponentArrays = "0.15.27"
 DataFrames = "1.7.0"
 Dates = "1"
 DifferentiationInterface = "0.7.10"
+DifferentiationInterfaceTest = "0.10.3"
 ExplicitImports = "1.10.1"
 InteractiveUtils = "1"
 JET = "0.9,0.10,0.11"
@@ -40,4 +42,4 @@ Test = "1"
 julia = "1.10.10"
 
 [sources]
-DifferentiationInterface = { path = ".." }
+DifferentiationInterface = { path = ".." }
\ No newline at end of file
diff --git a/DifferentiationInterface/test/runtests.jl b/DifferentiationInterface/test/runtests.jl
index df7884a27..66159c3d9 100644
--- a/DifferentiationInterface/test/runtests.jl
+++ b/DifferentiationInterface/test/runtests.jl
@@ -2,11 +2,13 @@ using DifferentiationInterface
 using Pkg
 using Test
 
-DIT_PATH = joinpath(@__DIR__, "..", "..", "DifferentiationInterfaceTest")
-if isdir(DIT_PATH)
-    Pkg.develop(; path = DIT_PATH)
-else
-    Pkg.add("DifferentiationInterfaceTest")
+@static if VERSION < v"1.11"
+    DIT_PATH = joinpath(@__DIR__, "..", "..", "DifferentiationInterfaceTest")
+    if isdir(DIT_PATH)
+        Pkg.develop(; path = DIT_PATH)
+    else
+        Pkg.add("DifferentiationInterfaceTest")
+    end
 end
 
 include("testutils.jl")
diff --git a/DifferentiationInterfaceTest/Project.toml b/DifferentiationInterfaceTest/Project.toml
index f73e7d1ba..bca102eda 100644
--- a/DifferentiationInterfaceTest/Project.toml
+++ b/DifferentiationInterfaceTest/Project.toml
@@ -21,26 +21,14 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [weakdeps]
 ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
-FiniteDifferences = "26cc04aa-876d-5657-8c51-4c34ba976000"
-Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
-Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 JLArrays = "27aeb0d3-9eb9-45fb-866b-73c2ecf80fcb"
-Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
-LuxTestUtils = "ac9de150-d08f-4546-94fb-7472b5760531"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [extensions]
 DifferentiationInterfaceTestComponentArraysExt = "ComponentArrays"
-DifferentiationInterfaceTestFluxExt = ["FiniteDifferences", "Flux", "Functors"]
 DifferentiationInterfaceTestJLArraysExt = "JLArrays"
-DifferentiationInterfaceTestLuxExt = [
-    "ComponentArrays",
-    "ForwardDiff",
-    "Lux",
-    "LuxTestUtils",
-]
 DifferentiationInterfaceTestStaticArraysExt = "StaticArrays"
 
 [compat]
@@ -51,15 +39,10 @@ ComponentArrays = "0.15"
 DataFrames = "1.6.1"
 DifferentiationInterface = "0.7.7"
 DocStringExtensions = "0.8,0.9"
-FiniteDifferences = "0.12"
-Flux = "0.16"
 ForwardDiff = "0.10.36,1"
-Functors = "0.4, 0.5"
 JET = "0.9,0.10,0.11"
 JLArrays = "0.1,0.2,0.3"
 LinearAlgebra = "1"
-Lux = "1.1.0"
-LuxTestUtils = "1.3.1, 2"
 PrecompileTools = "1.2.1"
 ProgressMeter = "1"
 Random = "1"
diff --git a/DifferentiationInterfaceTest/ext/DifferentiationInterfaceTestFluxExt/DifferentiationInterfaceTestFluxExt.jl b/DifferentiationInterfaceTest/ext/DifferentiationInterfaceTestFluxExt/DifferentiationInterfaceTestFluxExt.jl
deleted file mode 100644
index 3296995ec..000000000
--- a/DifferentiationInterfaceTest/ext/DifferentiationInterfaceTestFluxExt/DifferentiationInterfaceTestFluxExt.jl
+++ /dev/null
@@ -1,214 +0,0 @@
-module DifferentiationInterfaceTestFluxExt
-
-import DifferentiationInterface as DI
-import DifferentiationInterfaceTest as DIT
-using FiniteDifferences: FiniteDifferences
-using Flux:
-    Flux,
-    Bilinear,
-    Chain,
-    Conv,
-    ConvTranspose,
-    Dense,
-    GRU,
-    GRUCell,
-    LSTM,
-    LSTMCell,
-    Maxout,
-    MeanPool,
-    RNN,
-    RNNCell,
-    SamePad,
-    Scale,
-    SkipConnection,
-    destructure,
-    f64,
-    glorot_uniform,
-    relu
-using Functors: @functor, fmapstructure_with_path, fleaves
-using LinearAlgebra
-using Statistics: mean
-using Random: AbstractRNG, default_rng
-
-#=
-Relevant discussions:
-
-- https://github.com/JuliaDiff/DifferentiationInterface.jl/issues/105
-- https://github.com/JuliaDiff/DifferentiationInterface.jl/issues/343
-- https://github.com/FluxML/Flux.jl/issues/2469
-=#
-
-function gradient_finite_differences(loss, model, x)
-    v, re = destructure(model)
-    fdm = FiniteDifferences.central_fdm(5, 1)
-    gs = FiniteDifferences.grad(fdm, model -> loss(re(model), x), f64(v))
-    return re(only(gs))
-end
-
-function DIT.flux_isapprox(a, b; atol, rtol)
-    isapprox_results = fmapstructure_with_path(a, b) do kp, x, y
-        if x isa AbstractArray{<:Number}
-            return isapprox(x, y; atol, rtol)
-        else  # ignore non-arrays
-            return true
-        end
-    end
-    return all(fleaves(isapprox_results))
-end
-
-square_loss(model, x) = mean(abs2, model(x))
-
-function square_loss_iterated(cell, x)
-    y, st = cell(x) # uses default initial state
-    for _ in 1:2
-        y, st = cell(x, st)
-    end
-    return mean(abs2, y)
-end
-
-struct SimpleDense{W, B, F}
-    w::W
-    b::B
-    σ::F
-end
-
-(m::SimpleDense)(x) = m.σ.(m.w * x .+ m.b)
-
-@functor SimpleDense
-
-function DIT.flux_scenarios(rng::AbstractRNG = default_rng())
-    init = glorot_uniform(rng)
-
-    scens = DIT.Scenario[]
-
-    # Simple dense
-
-    d_in, d_out = 4, 2
-    w = randn(rng, d_out, d_in)
-    b = randn(rng, d_out)
-    model = SimpleDense(w, b, Flux.σ)
-
-    x = randn(rng, d_in)
-    g = gradient_finite_differences(square_loss, model, x)
-
-    scen = DIT.Scenario{:gradient, :out}(
-        square_loss,
-        model,
-        DI.Constant(x);
-        prep_args = (x = model, contexts = (DI.Constant(x),)),
-        res1 = g,
-    )
-    push!(scens, scen)
-
-    # Layers
-
-    models_and_xs = [
-        #! format: off
-        (
-            Dense(2, 4; init),
-            randn(rng, Float32, 2)
-        ),
-        (
-            Chain(Dense(2, 4, relu; init), Dense(4, 3; init)),
-            randn(rng, Float32, 2)),
-        (
-            f64(Chain(Dense(2, 4; init), Dense(4, 2; init))),
-            randn(rng, Float64, 2, 1)),
-        (
-            Scale([1.0f0 2.0f0 3.0f0 4.0f0], true, abs2),
-            randn(rng, Float32, 2)),
-        (
-            Conv((3, 3), 2 => 3; init),
-            randn(rng, Float32, 3, 3, 2, 1)),
-        (
-            Chain(Conv((3, 3), 2 => 3, relu; init), Conv((3, 3), 3 => 1, relu; init)),
-            rand(rng, Float32, 5, 5, 2, 1),
-        ),
-        (
-            Chain(Conv((4, 4), 2 => 2; pad=SamePad(), init), MeanPool((5, 5); pad=SamePad())),
-            rand(rng, Float32, 5, 5, 2, 2),
-        ),
-        (
-            Maxout(() -> Dense(5 => 4, tanh; init), 3),
-            randn(rng, Float32, 5, 1)
-        ),
-       (
-            SkipConnection(Dense(2 => 2; init), vcat),
-            randn(rng, Float32, 2, 3)
-        ),
-        (
-            Bilinear((2, 2) => 3; init),
-            randn(rng, Float32, 2, 1)
-        ),
-        (
-            ConvTranspose((3, 3), 3 => 2; stride=2, init),
-            rand(rng, Float32, 5, 5, 3, 1)
-        ),
-        (
-            RNN(3 => 4; init_kernel=init, init_recurrent_kernel=init),
-            randn(rng, Float32, 3, 2, 1)
-        ),
-        (
-            LSTM(3 => 4; init_kernel=init, init_recurrent_kernel=init),
-            randn(rng, Float32, 3, 2, 1)
-        ),
-        (
-            GRU(3 => 4; init_kernel=init, init_recurrent_kernel=init),
-            randn(rng, Float32, 3, 2, 1)
-        ),
-        (
-            Chain(LSTM(3 => 4), RNN(4 => 5), Dense(5 => 2)),
-            randn(rng, Float32, 3, 2, 1)
-        ),
-        #! format: on
-    ]
-
-    for (model, x) in models_and_xs
-        Flux.trainmode!(model)
-        g = gradient_finite_differences(square_loss, model, x)
-        scen = DIT.Scenario{:gradient, :out}(
-            square_loss,
-            model,
-            DI.Constant(x);
-            prep_args = (; x = model, contexts = (DI.Constant(x),)),
-            res1 = g,
-        )
-        push!(scens, scen)
-    end
-
-    # Recurrent Cells
-
-    recurrent_models_and_xs = [
-        #! format: off
-        (
-            RNNCell(3 => 3; init_kernel=init, init_recurrent_kernel=init),
-            randn(rng, Float32, 3, 2)
-        ),
-        (
-            LSTMCell(3 => 3; init_kernel=init, init_recurrent_kernel=init),
-            randn(rng, Float32, 3, 2)
-        ),
-        (
-            GRUCell(3 => 3; init_kernel=init, init_recurrent_kernel=init),
-            randn(rng, Float32, 3, 2)
-        ),
-        #! format: on
-    ]
-
-    for (model, x) in recurrent_models_and_xs
-        Flux.trainmode!(model)
-        g = gradient_finite_differences(square_loss_iterated, model, x)
-        scen = DIT.Scenario{:gradient, :out}(
-            square_loss_iterated,
-            model,
-            DI.Constant(x);
-            prep_args = (; x = model, contexts = (DI.Constant(x),)),
-            res1 = g,
-        )
-        push!(scens, scen)
-    end
-
-    return scens
-end
-
-end
diff --git a/DifferentiationInterfaceTest/ext/DifferentiationInterfaceTestLuxExt/DifferentiationInterfaceTestLuxExt.jl b/DifferentiationInterfaceTest/ext/DifferentiationInterfaceTestLuxExt/DifferentiationInterfaceTestLuxExt.jl
deleted file mode 100644
index 4dddb944d..000000000
--- a/DifferentiationInterfaceTest/ext/DifferentiationInterfaceTestLuxExt/DifferentiationInterfaceTestLuxExt.jl
+++ /dev/null
@@ -1,218 +0,0 @@
-module DifferentiationInterfaceTestLuxExt
-
-using ComponentArrays: ComponentArray
-import DifferentiationInterface as DI
-import DifferentiationInterfaceTest as DIT
-using ForwardDiff: ForwardDiff
-using Lux:
-    Lux,
-    BatchNorm,
-    Bilinear,
-    Chain,
-    Conv,
-    ConvTranspose,
-    Dense,
-    GroupNorm,
-    GRUCell,
-    InstanceNorm,
-    LayerNorm,
-    LSTMCell,
-    Maxout,
-    MaxPool,
-    MeanPool,
-    RNNCell,
-    SamePad,
-    Scale,
-    SkipConnection,
-    StatefulRecurrentCell,
-    gelu,
-    relu
-using LuxTestUtils: check_approx
-using Random: AbstractRNG, default_rng
-
-#=
-Relevant discussions:
-
-- https://github.com/LuxDL/Lux.jl/issues/769
-=#
-
-function DIT.lux_isapprox(a, b; atol, rtol)
-    return check_approx(a, b; atol, rtol)
-end
-
-function square_loss(ps, model, x, st)
-    return sum(abs2, first(model(x, ps, st)))
-end
-
-function DIT.lux_scenarios(rng::AbstractRNG = default_rng())
-    models_and_xs = [
-        #! format: off
-        (
-            Dense(2, 4),
-            randn(rng, Float32, 2, 3)
-        ),
-        (
-            Dense(2, 4, gelu),
-            randn(rng, Float32, 2, 3)
-        ),
-        (
-            Dense(2, 4, gelu; use_bias=false),
-            randn(rng, Float32, 2, 3)
-        ),
-        (
-            Chain(Dense(2, 4, relu), Dense(4, 3)),
-            randn(rng, Float32, 2, 3)
-        ),
-        (
-            Scale(2),
-            randn(rng, Float32, 2, 3)
-        ),
-        (
-            Conv((3, 3), 2 => 3),
-            randn(rng, Float32, 3, 3, 2, 2)
-        ),
-        (
-            Conv((3, 3), 2 => 3, gelu; pad=SamePad()),
-            randn(rng, Float32, 3, 3, 2, 2)
-        ),
-        (
-            Conv((3, 3), 2 => 3, relu; use_bias=false, pad=SamePad()),
-            randn(rng, Float32, 3, 3, 2, 2),
-        ),
-        (
-            Chain(Conv((3, 3), 2 => 3, gelu), Conv((3, 3), 3 => 1, gelu)),
-            rand(rng, Float32, 5, 5, 2, 2),
-        ),
-        (
-            Chain(Conv((4, 4), 2 => 2; pad=SamePad()), MeanPool((5, 5); pad=SamePad())),
-            rand(rng, Float32, 5, 5, 2, 2),
-        ),
-        (
-            Chain(Conv((3, 3), 2 => 3, relu; pad=SamePad()), MaxPool((2, 2))),
-            rand(rng, Float32, 5, 5, 2, 2),
-        ),
-        (
-            Maxout(() -> Dense(5 => 4, tanh), 3),
-            randn(rng, Float32, 5, 2)
-        ),
-        (
-            Bilinear((2, 2) => 3),
-            randn(rng, Float32, 2, 3)
-        ),
-        (
-            SkipConnection(Dense(2 => 2), vcat),
-            randn(rng, Float32, 2, 3)
-        ),
-        (
-            ConvTranspose((3, 3), 3 => 2; stride=2),
-            rand(rng, Float32, 5, 5, 3, 1)
-        ),
-        (
-            StatefulRecurrentCell(RNNCell(3 => 5)),
-            rand(rng, Float32, 3, 2)
-        ),
-        (
-            StatefulRecurrentCell(RNNCell(3 => 5, gelu)),
-            rand(rng, Float32, 3, 2)
-        ),
-        (
-            StatefulRecurrentCell(RNNCell(3 => 5, gelu; use_bias=false)),
-            rand(rng, Float32, 3, 2),
-        ),
-        (
-            Chain(StatefulRecurrentCell(RNNCell(3 => 5)), StatefulRecurrentCell(RNNCell(5 => 3)),),
-            rand(rng, Float32, 3, 2),
-        ),
-        (
-            StatefulRecurrentCell(LSTMCell(3 => 5)),
-            rand(rng, Float32, 3, 2)
-        ),
-        (
-            Chain(StatefulRecurrentCell(LSTMCell(3 => 5)), StatefulRecurrentCell(LSTMCell(5 => 3)),),
-            rand(rng, Float32, 3, 2),
-        ),
-        (
-            StatefulRecurrentCell(GRUCell(3 => 5)),
-            rand(rng, Float32, 3, 10)
-        ),
-        (
-            Chain(StatefulRecurrentCell(GRUCell(3 => 5)), StatefulRecurrentCell(GRUCell(5 => 3)),),
-            rand(rng, Float32, 3, 10),
-        ),
-        (
-            Chain(Dense(2, 4), BatchNorm(4)),
-            randn(rng, Float32, 2, 3)
-        ),
-        (
-            Chain(Dense(2, 4), BatchNorm(4, gelu)),
-            randn(rng, Float32, 2, 3)
-        ),
-        (
-            Chain(Dense(2, 4), BatchNorm(4, gelu; track_stats=false)),
-            randn(rng, Float32, 2, 3),
-        ),
-        (
-            Chain(Conv((3, 3), 2 => 6), BatchNorm(6)),
-            randn(rng, Float32, 6, 6, 2, 2)
-        ),
-        (
-            Chain(Conv((3, 3), 2 => 6, tanh), BatchNorm(6)),
-            randn(rng, Float32, 6, 6, 2, 2)
-        ),
-        (
-            Chain(Dense(2, 4), GroupNorm(4, 2, gelu)),
-            randn(rng, Float32, 2, 3)
-        ),
-        (
-            Chain(Dense(2, 4), GroupNorm(4, 2)),
-            randn(rng, Float32, 2, 3)
-        ),
-        (
-            Chain(Conv((3, 3), 2 => 6), GroupNorm(6, 3)),
-            randn(rng, Float32, 6, 6, 2, 2)
-        ),
-        (
-            Chain(Conv((3, 3), 2 => 6, tanh), GroupNorm(6, 3)),
-            randn(rng, Float32, 6, 6, 2, 2),
-        ),
-        (
-            Chain(Conv((3, 3), 2 => 3, gelu), LayerNorm((1, 1, 3))),
-            randn(rng, Float32, 4, 4, 2, 2),
-        ),
-        (
-            Chain(Conv((3, 3), 2 => 6), InstanceNorm(6)),
-            randn(rng, Float32, 6, 6, 2, 2)
-        ),
-        (
-            Chain(Conv((3, 3), 2 => 6, tanh), InstanceNorm(6)),
-            randn(rng, Float32, 6, 6, 2, 2),
-        ),
-        #! format: on
-    ]
-
-    scens = DIT.Scenario[]
-
-    for (model, x) in models_and_xs
-        ps, st = Lux.setup(rng, model)
-        g = DI.gradient(
-            ps -> square_loss(ps, model, x, st), DI.AutoForwardDiff(), ComponentArray(ps)
-        )
-        scen = DIT.Scenario{:gradient, :out}(
-            square_loss,
-            ComponentArray(ps),
-            DI.Constant(model),
-            DI.Constant(x),
-            DI.Constant(st);
-            prep_args = (
-                x = ComponentArray(ps),
-                contexts = (DI.Constant(model), DI.Constant(x), DI.Constant(st)),
-            ),
-            res1 = g,
-        )
-        push!(scens, scen)
-    end
-
-    return scens
-end
-
-end
diff --git a/DifferentiationInterfaceTest/test/weird.jl b/DifferentiationInterfaceTest/test/weird.jl
index 1a121ce36..f56831690 100644
--- a/DifferentiationInterfaceTest/test/weird.jl
+++ b/DifferentiationInterfaceTest/test/weird.jl
@@ -1,18 +1,12 @@
-using Pkg
-Pkg.add(["FiniteDiff", "Lux", "LuxTestUtils"])
-
 using ADTypes
 using ComponentArrays: ComponentArrays
 using DifferentiationInterface
 using DifferentiationInterfaceTest
 import DifferentiationInterfaceTest as DIT
-using FiniteDifferences: FiniteDifferences
+using FiniteDiff: FiniteDiff
 using ForwardDiff: ForwardDiff
-using Flux: Flux
 using ForwardDiff: ForwardDiff
 using JLArrays: JLArrays
-using Lux: Lux
-using LuxTestUtils: LuxTestUtils
 using Random
 using SparseConnectivityTracer
 using SparseMatrixColorings
@@ -64,25 +58,3 @@ test_differentiation(
     excluded = SECOND_ORDER,
     logging = LOGGING,
 );
-
-## Neural nets
-
-test_differentiation(
-    AutoZygote(),
-    DIT.flux_scenarios(Random.MersenneTwister(0));
-    isapprox = DIT.flux_isapprox,
-    rtol = 1.0e-2,
-    atol = 1.0e-4,
-    scenario_intact = false,
-    logging = LOGGING,
-)
-
-test_differentiation(
-    AutoZygote(),
-    DIT.lux_scenarios(Random.Xoshiro(63));
-    isapprox = DIT.lux_isapprox,
-    rtol = 1.0f-2,
-    atol = 1.0f-3,
-    scenario_intact = false,
-    logging = LOGGING,
-)