[feature] Optimize does not work on `Colorant{T,1}`

When trying to fit an image of a bead, it is most commonly represented as an array of colorants. The most likely for this use in particular is for it to be some subtype of AbstractGray. But when trying to fit some kind of Gray array, there it throws:

ERROR: MethodError: no method matching DiffResults.DiffResult(::Gray{Float64}, ::Vector{Float64})
Closest candidates are:
DiffResults.DiffResult(::Union{Number, AbstractArray}, ::Union{Number, AbstractArray}...) at ~/.julia/packages/DiffResults/wASAy/src/DiffResults.jl:52
Stacktrace:
[1] (::NLSolversBase.var"#14#18"{Gray{Float64}, PSFModels.var"#_loss#42"{NamedTuple{(), Tuple{}}, typeof(abs2), Float64, typeof(gaussian), Matrix{Gray{Float64}}, Tuple{Int64, Int64}, Tuple{Int64, Int64}, CartesianIndices{2, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, NTuple{4, Symbol}}, ForwardDiff.GradientConfig{ForwardDiff.Tag{PSFModels.var"#_loss#42"{NamedTuple{(), Tuple{}}, typeof(abs2), Float64, typeof(gaussian), Matrix{Gray{Float64}}, Tuple{Int64, Int64}, Tuple{Int64, Int64}, CartesianIndices{2, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, NTuple{4, Symbol}}, Gray{Float64}}, Gray{Float64}, 5, Vector{ForwardDiff.Dual{ForwardDiff.Tag{PSFModels.var"#_loss#42"{NamedTuple{(), Tuple{}}, typeof(abs2), Float64, typeof(gaussian), Matrix{Gray{Float64}}, Tuple{Int64, Int64}, Tuple{Int64, Int64}, CartesianIndices{2, Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}}, NTuple{4, Symbol}}, Gray{Float64}}, Gray{Float64}, 5}}}})(out::Vector{Float64}, x::Vector{Gray{Float64}})
@ NLSolversBase ~/.julia/packages/NLSolversBase/cfJrN/src/objective_types/oncedifferentiable.jl:69
[2] value_gradient!!(obj::NLSolversBase.OnceDifferentiable{Float64, Vector{Float64}, Vector{Gray{Float64}}}, x::Vector{Gray{Float64}})
@ NLSolversBase ~/.julia/packages/NLSolversBase/cfJrN/src/interface.jl:82
[3] initial_state(method::Optim.LBFGS{Nothing, LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Optim.var"#19#21"}, options::Optim.Options{Float64, Nothing}, d::NLSolversBase.OnceDifferentiable{Float64, Vector{Float64}, Vector{Gray{Float64}}}, initial_x::Vector{Gray{Float64}})
@ Optim ~/.julia/packages/Optim/wFOeG/src/multivariate/solvers/first_order/l_bfgs.jl:164
[4] optimize(d::NLSolversBase.OnceDifferentiable{Float64, Vector{Float64}, Vector{Gray{Float64}}}, initial_x::Vector{Gray{Float64}}, method::Optim.LBFGS{Nothing, LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Optim.var"#19#21"}, options::Optim.Options{Float64, Nothing})
@ Optim ~/.julia/packages/Optim/wFOeG/src/multivariate/optimize/optimize.jl:36
[5] optimize(f::Function, initial_x::Vector{Gray{Float64}}, method::Optim.LBFGS{Nothing, LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Optim.var"#19#21"}, options::Optim.Options{Float64, Nothing}; inplace::Bool, autodiff::Symbol)
@ Optim ~/.julia/packages/Optim/wFOeG/src/multivariate/optimize/interface.jl:142
[6] fit(model::typeof(gaussian), params::NamedTuple{(:x, :y, :fwhm, :amp), Tuple{Float64, Float64, Tuple{Int64, Int64}, Int64}}, image::Matrix{Gray{Float64}}, inds::Tuple{Base.OneTo{Int64}, Base.OneTo{Int64}}; func_kwargs::NamedTuple{(), Tuple{}}, loss::typeof(abs2), alg::Optim.LBFGS{Nothing, LineSearches.InitialStatic{Float64}, LineSearches.HagerZhang{Float64, Base.RefValue{Bool}}, Optim.var"#19#21"}, maxfwhm::Float64, kwargs::Base.Pairs{Symbol, Union{}, Tuple{}, NamedTuple{(), Tuple{}}})
@ PSFModels ~/.julia/packages/PSFModels/G3WuK/src/fitting.jl:99
[7] fit (repeats 2 times)
@ ~/.julia/packages/PSFModels/G3WuK/src/fitting.jl:76 [inlined]
[8] top-level scope
@ REPL[167]:1

Could there be definition such as:

function fit(..., image::AbstractArray{T}, ...) where {T<:AbstractGray}
    fit(..., Real.(image), ...)
end

Do you think it is a good idea?

Start at fit in fitting.jl:76 and fitting.jl:99, then reproduce the reported optimize failure with a Matrix{Gray{Float64}}. Determine whether the fit entry point should support AbstractGray inputs as proposed, and verify that fitting such an image completes without the DiffResult MethodError.