diff --git a/src/conditional_layers/conditional_layer_glow.jl b/src/conditional_layers/conditional_layer_glow.jl
index ab36e064..2c3b895e 100644
--- a/src/conditional_layers/conditional_layer_glow.jl
+++ b/src/conditional_layers/conditional_layer_glow.jl
@@ -78,7 +78,12 @@ function ConditionalLayerGlow(n_in::Int64, n_cond::Int64, n_hidden::Int64;freeze
 
     # 1x1 Convolution and residual block for invertible layers
     C  = Conv1x1(n_in; freeze=freeze_conv)
-    RB = ResidualBlock(Int(n_in/2)+n_cond, n_hidden; n_out=n_in, activation=rb_activation, k1=k1, k2=k2, p1=p1, p2=p2, s1=s1, s2=s2, fan=true, ndims=ndims)
+
+    split_num = Int(round(n_in/2))
+    in_split   = n_in-split_num
+    out_chan  = 2*split_num
+
+    RB = ResidualBlock(in_split+n_cond, n_hidden; n_out=out_chan, activation=rb_activation, k1=k1, k2=k2, p1=p1, p2=p2, s1=s1, s2=s2, fan=true, ndims=ndims)
 
     return ConditionalLayerGlow(C, RB, logdet, activation)
 end
@@ -143,7 +148,7 @@ function backward(ΔY::AbstractArray{T, N}, Y::AbstractArray{T, N}, C::AbstractA
 
     # Backpropagate RB
     ΔX2_ΔC = L.RB.backward(tensor_cat(L.activation.backward(ΔS, S), ΔT), (tensor_cat(X2, C)))
-    ΔX2, ΔC = tensor_split(ΔX2_ΔC; split_index=Int(size(ΔY)[N-1]/2))
+    ΔX2, ΔC = tensor_split(ΔX2_ΔC; split_index=size(ΔY2)[N-1])
     ΔX2 += ΔY2
 
     # Backpropagate 1x1 conv
diff --git a/test/test_networks/test_conditional_glow_network.jl b/test/test_networks/test_conditional_glow_network.jl
index a2a0380d..946b39b0 100644
--- a/test/test_networks/test_conditional_glow_network.jl
+++ b/test/test_networks/test_conditional_glow_network.jl
@@ -9,6 +9,49 @@ device = InvertibleNetworks.CUDA.functional() ? gpu : cpu
 # Random seed
 Random.seed!(3);
 
+# Define network
+nx = 32; ny = 32; nz = 32
+n_in = 3
+n_cond = 3
+n_hidden = 4
+batchsize = 2
+L = 2
+K = 2
+split_scales = false
+N = (nx,ny)
+
+########################################### Test with split_scales = false N = (nx,ny) #########################
+# Invertibility
+
+# Network and input
+G = NetworkConditionalGlow(n_in, n_cond, n_hidden, L, K; split_scales=split_scales,ndims=length(N)) |> device
+X = rand(Float32, N..., n_in, batchsize)  |> device
+Cond = rand(Float32, N..., n_cond, batchsize)  |> device
+
+Y, Cond = G.forward(X,Cond)
+X_ = G.inverse(Y,Cond) # saving the cond is important in split scales because of reshapes
+
+@test isapprox(norm(X - X_)/norm(X), 0f0; atol=1f-5)
+
+# Test gradients are set and cleared
+G.backward(Y, Y, Cond)
+
+P = get_params(G)
+gsum = 0
+for p in P
+    ~isnothing(p.grad) && (global gsum += 1)
+end
+@test isequal(gsum, L*K*10+2)
+
+clear_grad!(G)
+gsum = 0
+for p in P
+    ~isnothing(p.grad) && (global gsum += 1)
+end
+@test isequal(gsum, 0)
+
+
+Random.seed!(3);
 # Define network
 nx = 32; ny = 32; nz = 32
 n_in = 2