diff --git a/NEWS.md b/NEWS.md
index 1b91c7823c..9c4907dc33 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -11,6 +11,7 @@ been removed in favour of MLDatasets.jl.
 * Many utily functions and the `DataLoader` are [now provided by MLUtils.jl](https://github.com/FluxML/Flux.jl/pull/1874).
 * The DataLoader is now compatible with generic dataset types implementing `MLUtils.numobs` and `MLUtils.getobs`.
 * Added [truncated normal initialisation](https://github.com/FluxML/Flux.jl/pull/1877) of weights.
+* The `Flux.Diagonal` layer is now called `Scale`, and accepts an activation function.
 
 ## v0.12.10
 * `Dropout`/`AlphaDropout` now supports [user-specified RNGs](https://github.com/FluxML/Flux.jl/pull/1838)
diff --git a/docs/src/models/layers.md b/docs/src/models/layers.md
index d44e291031..81fbb60a2d 100644
--- a/docs/src/models/layers.md
+++ b/docs/src/models/layers.md
@@ -56,7 +56,7 @@ Maxout
 SkipConnection
 Parallel
 Flux.Bilinear
-Flux.Diagonal
+Flux.Scale
 Flux.Embedding
 ```
 
diff --git a/src/deprecations.jl b/src/deprecations.jl
index bce71bc2ab..597fc5a913 100644
--- a/src/deprecations.jl
+++ b/src/deprecations.jl
@@ -39,6 +39,15 @@ function Optimise.update!(x::AbstractArray, x̄)
   x .-= x̄
 end
 
+function Diagonal(size::Integer...; kw...)
+  Base.depwarn("Flux.Diagonal is now Flux.Scale, and also allows an activation function.", :Diagonal)
+  Scale(size...; kw...)
+end
+function Diagonal(size::Tuple; kw...)
+  Base.depwarn("Flux.Diagonal is now Flux.Scale, and also allows an activation function.", :Diagonal)
+  Scale(size...; kw...)
+end
+
 # Channel notation: Changed to match Conv, but very softly deprecated!
 # Perhaps change to @deprecate for v0.14, but there is no plan to remove these.
 Dense(in::Integer, out::Integer, σ = identity; kw...) =
diff --git a/src/layers/basic.jl b/src/layers/basic.jl
index 3374e2fc1c..eb3e9b5180 100644
--- a/src/layers/basic.jl
+++ b/src/layers/basic.jl
@@ -171,40 +171,69 @@ function Base.show(io::IO, l::Dense)
 end
 
 """
-    Diagonal(size::Integer...; σ = identity, bias=true, init=ones32)
-    Diagonal(scale::AbstractArray, [bias, activation])
+    Scale(size::Integer..., σ=identity; bias=true, init=ones32)
+    Scale(scale::AbstractArray, [bias, σ])
 
-Create an element-wise linear layer, which performs
+Create an element-wise layer, whose forward pass is given by:
 
     y = σ.(scale .* x .+ bias)
 
+This uses `.*` instead of matrix multiplication `*` of [`Dense`](@ref).
+    
 The learnable scale & bias are initialised `init(size...)` and `zeros32(size...)`,
 with `init=ones32` by default. You may specify the function `init`, 
 turn off trainable bias with `bias=false`, or provide the array(s) explicitly.
 
-Used by [`LayerNorm`](@ref).
+Used by [`LayerNorm`](@ref) with `affine=true`.
+
+# Examples
+```jldoctest
+julia> a = Flux.Scale(2)
+Scale(2)            # 4 parameters
+
+julia> Flux.params(a)
+Params([Float32[1.0, 1.0], Float32[0.0, 0.0]])
+
+julia> a([1 2 3])
+2×3 Matrix{Float32}:
+ 1.0  2.0  3.0
+ 1.0  2.0  3.0
+
+julia> b = Flux.Scale([1 2 3 4], false, abs2)
+Scale(1, 4, abs2; bias=false)  # 4 parameters
+
+julia> b([1, 10])
+2×4 Matrix{Int64}:
+   1    4    9    16
+ 100  400  900  1600
+
+julia> Flux.params(b)
+Params([[1 2 3 4]])
+```
 """
-struct Diagonal{A<:AbstractArray, B, F}
+struct Scale{F, A<:AbstractArray, B}
   scale::A
   bias::B
   σ::F
-  function Diagonal(W::M, bias = true, σ::F = identity) where {M<:AbstractArray, F}
-    b = create_bias(W, bias, size(W)...)
-    new{M, typeof(b), F}(W, b, σ)
+  function Scale(scale::A, bias::B = true, σ::F = identity) where {A<:AbstractArray, B<:Union{Bool, AbstractArray}, F}
+    b = create_bias(scale, bias, size(scale)...)
+    new{F, A, typeof(b)}(scale, b, σ)
   end
 end
 
-Diagonal(sz::Integer...; σ = identity, bias = true, init = ones32) = Diagonal(init(sz...), bias, σ)
+Scale(s1::Integer, s23::Integer...; bias = true, init = ones32, _act = identity) = Scale(init(s1, s23...), bias, _act)
+Scale(size_act...; bias = true, init = ones32) = Scale(size_act[1:end-1]...; bias, init, _act = size_act[end])
 
-@functor Diagonal
+@functor Scale
 
-function (a::Diagonal)(x::AbstractArray)
+function (a::Scale)(x::AbstractArray)
   σ = NNlib.fast_act(a.σ, x)  # replaces tanh => tanh_fast, etc
-  return σ === typeof(identity) ? a.scale .* x .+ a.bias : σ.(a.scale .* x .+ a.bias)
+  σ.(a.scale .* x .+ a.bias)
 end
 
-function Base.show(io::IO, l::Diagonal)
-  print(io, "Diagonal(", join(size(l.scale), ", "))
+function Base.show(io::IO, l::Scale)
+  print(io, "Scale(", join(size(l.scale), ", "))
+  l.σ == identity || print(io, ", ", l.σ)
   l.bias == false && print(io, "; bias=false")
   print(io, ")")
 end
diff --git a/src/layers/normalise.jl b/src/layers/normalise.jl
index 0324b4b90b..1a51615f6f 100644
--- a/src/layers/normalise.jl
+++ b/src/layers/normalise.jl
@@ -139,7 +139,7 @@ testmode!(m::AlphaDropout, mode=true) =
   (m.active = (isnothing(mode) || mode == :auto) ? nothing : !mode; m)
 
 """
-    LayerNorm(sz, λ=identity; affine=true, ϵ=1fe-5)
+    LayerNorm(size..., λ=identity; affine=true, ϵ=1fe-5)
 
 A [normalisation layer](https://arxiv.org/abs/1607.06450) designed to be
 used with recurrent hidden states.
@@ -151,10 +151,10 @@ for tuple `sz`, along the first dimension for integer `sz`.
 The input  is expected to have first dimensions' size equal to `sz`.
 
 If `affine=true` also applies a learnable shift and rescaling
-as in the [`Diagonal`](@ref) layer.
+using the [`Scale`](@ref) layer.
 
 
-Se also [`BatchNorm`](@ref), [`InstanceNorm`](@ref), [`GroupNorm`](@ref), and [`normalise`](@ref).
+See also [`BatchNorm`](@ref), [`InstanceNorm`](@ref), [`GroupNorm`](@ref), and [`normalise`](@ref).
 """
 struct LayerNorm{F,D,T,N}
   λ::F
@@ -164,17 +164,19 @@ struct LayerNorm{F,D,T,N}
   affine::Bool
 end
 
-function LayerNorm(sz, λ=identity; affine::Bool=true, ϵ::Real=1f-5)
-  diag = affine ? Diagonal(sz...; σ = λ) : Base.Fix1(broadcast, λ)
-  return LayerNorm(λ, diag, ϵ, Tuple(sz), affine)
+function LayerNorm(size::Tuple{Vararg{Int}}, λ=identity; affine::Bool=true, ϵ::Real=1f-5)
+  diag = affine ? Scale(size..., λ) : λ!=identity ? Base.Fix1(broadcast, λ) : identity
+  return LayerNorm(λ, diag, ϵ, size, affine)
 end
+LayerNorm(size::Integer...; kw...) = LayerNorm(Int.(size); kw...)
+LayerNorm(size_act...; kw...) = LayerNorm(Int.(size_act[1:end-1]), size_act[end]; kw...)
 
 @functor LayerNorm
 
 (a::LayerNorm)(x) = a.diag(normalise(x, dims=1:length(a.size), ϵ=a.ϵ))
 
 function Base.show(io::IO, l::LayerNorm)
-  print(io, "LayerNorm($(l.size)")
+  print(io, "LayerNorm(", join(l.size, ", "))
   l.λ === identity || print(io, ", ", l.λ)
   hasaffine(l) || print(io, ", affine=false")
   print(io, ")")
diff --git a/src/layers/show.jl b/src/layers/show.jl
index 47772f7e72..d393985042 100644
--- a/src/layers/show.jl
+++ b/src/layers/show.jl
@@ -55,7 +55,7 @@ _show_children(m::Maxout) = m.layers
 _show_children(p::Parallel) = (p.connection, p.layers...)
 
 for T in [
-    :Conv, :ConvTranspose, :CrossCor, :Dense, :Bilinear, :Embedding,
+    :Conv, :ConvTranspose, :CrossCor, :Dense, :Scale, :Bilinear, :Embedding,
     :BatchNorm, :LayerNorm, :InstanceNorm, :GroupNorm,
   ]
   @eval function Base.show(io::IO, m::MIME"text/plain", x::$T)
diff --git a/test/layers/basic.jl b/test/layers/basic.jl
index 8c8e15422d..6c9030874d 100644
--- a/test/layers/basic.jl
+++ b/test/layers/basic.jl
@@ -87,20 +87,29 @@ import Flux: activations
     end
   end
 
-  @testset "Diagonal" begin
-    @test length(Flux.Diagonal(10)(randn(10))) == 10
-    @test length(Flux.Diagonal(10)(randn(1))) == 10
-    @test length(Flux.Diagonal(10; bias = false)(randn(10))) == 10
-    @test_throws DimensionMismatch Flux.Diagonal(10)(randn(2))
-
-    @test Flux.Diagonal(2)([1 2]) == [1 2; 1 2]
-    @test Flux.Diagonal(2)([1, 2]) == [1, 2]
-    @test Flux.Diagonal(2; bias = false)([1 2; 3 4]) == [1 2; 3 4]
-
-    @test Flux.Diagonal(2)(rand(2, 3, 4)) |> size == (2, 3, 4)
-    @test Flux.Diagonal(2, 3;)(rand(2, 3, 4)) |> size == (2, 3, 4)
-    @test Flux.Diagonal(2, 3, 4; bias = false)(rand(2, 3, 4)) |> size == (2, 3, 4)
-    @test Flux.Diagonal(2, 3; bias = false)(rand(2, 1, 4)) |> size == (2, 3, 4)
+  @testset "Scale" begin
+    @test length(Flux.Scale(10)(randn(10))) == 10
+    @test length(Flux.Scale(10)(randn(1))) == 10
+    @test length(Flux.Scale(10; bias = false)(randn(10))) == 10
+    @test length(Flux.Scale(10, tanh)(randn(10))) == 10
+    @test_throws DimensionMismatch Flux.Scale(10)(randn(2))
+
+    @test Flux.Scale(2)([1 2]) == [1 2; 1 2]
+    @test Flux.Scale(2)([1, 2]) == [1, 2]
+    @test Flux.Scale(2; init = randn)([1, 2]) != [1, 2]
+    @test Flux.Scale(2; bias = false)([1 2; 3 4]) == [1 2; 3 4]
+    @test Flux.Scale(2, abs2; bias = false, init = ones)([1 2; 3 4]) == [1 4; 9 16]
+
+    @test Flux.Scale(2)(rand(2, 3, 4)) |> size == (2, 3, 4)
+    @test Flux.Scale(2, 3;)(rand(2, 3, 4)) |> size == (2, 3, 4)
+    @test Flux.Scale(2, 3, 4; bias = false)(rand(2, 3, 4)) |> size == (2, 3, 4)
+    @test Flux.Scale(2, 3; bias = false)(rand(2, 1, 4)) |> size == (2, 3, 4)
+    @test Flux.Scale(2, 3, tanh; bias = false, init = zeros)(rand(2, 1, 4)) == zeros(2, 3, 4)
+    
+    @test_throws MethodError Flux.Scale(1.)
+    @test_throws MethodError Flux.Scale(1., 2.)
+    @test_throws Exception Flux.Scale()
+    @test_throws MethodError Flux.Scale(sin)
   end
 
   @testset "Maxout" begin
diff --git a/test/utils.jl b/test/utils.jl
index cc97db4fc9..14b5ad9bbc 100644
--- a/test/utils.jl
+++ b/test/utils.jl
@@ -457,7 +457,7 @@ end
                                   +),
                                 LayerNorm(8)))
   @test length(mod_skip) == 6
-  @test mod_skip[end] isa Flux.Diagonal
+  @test mod_skip[end] isa Flux.Scale
 end
 
 @testset "Patience triggers" begin