♻️ rework docstrings and variable names

examples/ConditionalChain.jl

@@ -46,7 +46,7 @@ function Base.show(io::IO, c::ConditionalChain)
   print(io, ")")
 end
 
-function _big_show(io::IO, m::ConditionalChain{T}, indent::Int=0, name=nothing) where {T<:NamedTuple}
+function _big_show(io::IO, m::ConditionalChain{T}, indent::Integer=0, name=nothing) where {T<:NamedTuple}
   println(io, " "^indent, isnothing(name) ? "" : "$name = ", "ConditionalChain(")
   for k in Base.keys(m.layers)
     _big_show(io, m.layers[k], indent + 2, k)

examples/Embeddings.jl

@@ -7,12 +7,12 @@ end
 Flux.@functor SinusoidalPositionEmbedding
 Flux.trainable(emb::SinusoidalPositionEmbedding) = () # mark it as an non-trainable array
 
-function SinusoidalPositionEmbedding(in::Int, out::Int)
+function SinusoidalPositionEmbedding(in::Integer, out::Integer)
   W = make_positional_embedding(out, in)
   SinusoidalPositionEmbedding(W)
 end
 
-function make_positional_embedding(dim_embedding::Int, seq_length::Int=1000; n::Int=10000)
+function make_positional_embedding(dim_embedding::Integer, seq_length::Integer=1000; n::Integer=10000)
   embedding = Matrix{Float32}(undef, dim_embedding, seq_length)
   for pos in 1:seq_length
     for row in 0:2:(dim_embedding-1)

examples/swissroll.jl

@@ -8,7 +8,7 @@ using ProgressMeter
 include("Embeddings.jl")
 include("ConditionalChain.jl")
 
-function make_spiral(rng::AbstractRNG, n_samples::Int=1000)
+function make_spiral(rng::AbstractRNG, n_samples::Integer=1000)
   t_min = 1.5π
   t_max = 4.5π
 
@@ -20,7 +20,7 @@ function make_spiral(rng::AbstractRNG, n_samples::Int=1000)
   permutedims([x y], (2, 1))
 end
 
-make_spiral(n_samples::Int=1000) = make_spiral(Random.GLOBAL_RNG, n_samples)
+make_spiral(n_samples::Integer=1000) = make_spiral(Random.GLOBAL_RNG, n_samples)
 
 function normalize_zero_to_one(x)
   x_min, x_max = extrema(x)
@@ -42,7 +42,9 @@ scatter(data[1, :], data[2, :],
 num_timesteps = 100
 scheduler = Diffusers.DDPM(
   Vector{Float64},
-  Diffusers.cosine_beta_schedule(num_timesteps, 0.999f0, 0.001f0),
+  Diffusers.rescale_zero_terminal_snr(
+    Diffusers.cosine_beta_schedule(num_timesteps, 0.999f0, 0.001f0)
+  )
 )
 
 noise = randn(size(data))
@@ -104,7 +106,7 @@ model = ConditionalChain(
 
 model(data, [100])
 
-num_epochs = 1000
+num_epochs = 100
 loss = Flux.Losses.mse
 opt = Flux.setup(Adam(0.0001), model)
 dataloader = Flux.DataLoader(data |> cpu; batchsize=32, shuffle=true);

src/BetaSchedulers.jl

@@ -3,117 +3,122 @@ import NNlib: sigmoid
 """
 Linear beta schedule.
 
-cf. [[2006.11239] Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
-
 ## Input
-  * T (`Int`): number of timesteps
+  * `T::Integer`: number of timesteps
-  * β_1 (`Real := 0.0001f0`): initial value of β
+  * `β₁::Real=0.0001f0`: initial (t=1) value of β
-  * β_T (`Real := 0.02f0`): final value of β
+  * `β₋₁::Real=0.02f0`: final (t=T) value of β
 
 ## Output
-  * β (`Vector{Real}`): β_t values at each timestep t
+  * `β::Vector{Real}`: βₜ values at each timestep t
+
+## References
+  * [[2006.11239] Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
 """
-function linear_beta_schedule(T::Int, β_1::Real=0.0001f0, β_T::Real=0.02f0)
+function linear_beta_schedule(T::Integer, β₁::Real=0.0001f0, β₋₁::Real=0.02f0)
-  return range(start=β_1, stop=β_T, length=T)
+  return range(start=β₁, stop=β₋₁, length=T)
 end
 
 """
 Scaled linear beta schedule.
 
-cf. [[2006.11239] Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
-
 ## Input
-  * T (`Int`): number of timesteps
+  * `T::Int`: number of timesteps
-  * β_1 (`Real := 0.0001f0`): initial value of β
+  * `β₁::Real=0.0001f0`: initial value of β
-  * β_T (`Real := 0.02f0`): final value of β
+  * `β₋₁::Real=0.02f0`: final value of β
 
 ## Output
-  * β (`Vector{Real}`): β_t values at each timestep t
+  * `β::Vector{Real}`: βₜ values at each timestep t
+
+## References
+  * [[2006.11239] Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
 """
-function scaled_linear_beta_schedule(T::Int, β_1::Real=0.0001f0, β_T::Real=0.02f0)
+function scaled_linear_beta_schedule(T::Integer, β₁::Real=0.0001f0, β₋₁::Real=0.02f0)
-  return range(start=β_1^0.5, stop=β_T^0.5, length=T) .^ 2
+  return range(start=β₁^0.5, stop=β₋₁^0.5, length=T) .^ 2
 end
 
 """
 Sigmoid beta schedule.
 
-cf. [[2203.02923] GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation](https://arxiv.org/abs/2203.02923)
-and [github.com:MinkaiXu/GeoDiff](https://github.com/MinkaiXu/GeoDiff/blob/ea0ca48045a2f7abfccd7f0df449e45eb6eae638/models/epsnet/diffusion.py#L57)
-
 ## Input
-  * T (`Int`): number of timesteps
+  * `T::Int`: number of timesteps
-  * β_1 (`Real := 0.0001f0`): initial value of β
+  * `β₁::Real=0.0001f0`: initial value of β
-  * β_T (`Real := 0.02f0`): final value of β
+  * `β₋₁::Real=0.02f0`: final value of β
 
 ## Output
-  * β (`Vector{Real}`): β_t values at each timestep t
+  * `β::Vector{Real}`: βₜ values at each timestep t
+
+## References
+  * [[2203.02923] GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation](https://arxiv.org/abs/2203.02923)
+  * [github.com:MinkaiXu/GeoDiff](https://github.com/MinkaiXu/GeoDiff/blob/ea0ca48045a2f7abfccd7f0df449e45eb6eae638/models/epsnet/diffusion.py#L57)
 """
-function sigmoid_beta_schedule(T::Int, β_1::Real=0.0001f0, β_T::Real=0.02f0)
+function sigmoid_beta_schedule(T::Integer, β₁::Real=0.0001f0, β₋₁::Real=0.02f0)
   x = range(start=-6, stop=6, length=T)
-  return sigmoid(x) .* (β_T - β_1) .+ β_1
+  return sigmoid(x) .* (β₋₁ - β₁) .+ β₁
 end
 
 """
 Cosine beta schedule.
 
-cf. [[2102.09672] Improved Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2102.09672)
-
 ## Input
-  * T (`Int`): number of timesteps
+  * `T::Int`: number of timesteps
-  * β_max (`Real := 0.999f0`): maximum value of β
+  * `βₘₐₓ::Real=0.999f0`: maximum value of β
-  * ϵ (`Real := 1e-3f0`): small value used to avoid division by zero
+  * `ϵ::Real=1e-3f0`: small value used to avoid division by zero
 
 ## Output
-  * β (`Vector{Real}`): β_t values at each timestep t
+  * `β::Vector{Real}`: βₜ values at each timestep t
+
+## References
+  * [[2102.09672] Improved Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2102.09672)
+  * [github:openai/improved-diffusion](https://github.com/openai/improved-diffusion/blob/783b6740edb79fdb7d063250db2c51cc9545dcd1/improved_diffusion/gaussian_diffusion.py#L36)
 """
-function cosine_beta_schedule(T::Int, β_max::Real=0.999f0, ϵ::Real=0.001f0)
+function cosine_beta_schedule(T::Integer, βₘₐₓ::Real=0.999f0, ϵ::Real=0.001f0)
-  α_bar(t) = cos((t + ϵ) / (1 + ϵ) * π / 2)^2
+  α̅(t) = cos((t / T + ϵ) / (1 + ϵ) * π / 2)^2
 
-  β = Float32[]
+  β = Vector{Real}(undef, T)
   for t in 1:T
-    t1 = (t - 1) / T
+    αₜ = α̅(t) / α̅(t - 1)
-    t2 = t / T
 
-    β_t = 1 - α_bar(t2) / α_bar(t1)
+    βₜ = 1 - αₜ
-    β_t = min(β_max, β_t)
+    βₜ = min(βₘₐₓ, βₜ)
 
-    push!(β, β_t)
+    β[t] = βₜ
   end
 
   return β
 end
 
 """
-Rescale betas to have zero terminal SNR.
+Rescale betas to have zero terminal Signal to Noise Ratio (SNR).
-
-cf. [[2305.08891] Rescaling Diffusion Models](https://arxiv.org/abs/2305.08891) (Algorithm 1)
 
 ## Input
-  * β (`AbstractArray`): β_t values at each timestep t
+  * `β::AbstractArray`: βₜ values at each timestep t
 
 ## Output
-  * β (`Vector{Real}`): rescaled β_t values at each timestep t
+  * `β::Vector{Real}`: rescaled βₜ values at each timestep t
+
+## References
+  * [[2305.08891] Rescaling Diffusion Models](https://arxiv.org/abs/2305.08891) (Alg. 1)
 """
 function rescale_zero_terminal_snr(β::AbstractArray)
-  # convert β to sqrt_α_cumprods
+  # convert β to ⎷α̅
   α = 1 .- β
-  α_cumprod = cumprod(α)
+  α̅ = cumprod(α)
-  sqrt_α_cumprods = sqrt.(α_cumprod)
+  ⎷α̅ = sqrt.(α̅)
 
   # store old extrema values
-  sqrt_α_cumprod_1 = sqrt_α_cumprods[1]
+  ⎷α̅₁ = ⎷α̅[1]
-  sqrt_α_cumprod_T = sqrt_α_cumprods[end]
+  ⎷α̅₋₁ = ⎷α̅[end]
 
   # shift last timestep to zero
-  sqrt_α_cumprods .-= sqrt_α_cumprod_T
+  ⎷α̅ .-= ⎷α̅₋₁
 
   # scale so that first timestep reaches old values
-  sqrt_α_cumprods *= sqrt_α_cumprod_1 / (sqrt_α_cumprod_1 - sqrt_α_cumprod_T)
+  ⎷α̅ *= ⎷α̅₁ / (⎷α̅₁ - ⎷α̅₋₁)
 
-  # convert back sqrt_α_cumprods to β
+  # convert back ⎷α̅ to β
-  α_cumprod = sqrt_α_cumprods .^ 2
+  α̅ = ⎷α̅ .^ 2
-  α = α_cumprod[2:end] ./ α_cumprod[1:end-1]
+  α = α̅[2:end] ./ α̅[1:end-1]
-  α = vcat(α_cumprod[1], α)
+  α = vcat(α̅[1], α)
   β = 1 .- α
 
   return β

src/DDPM.jl

@@ -3,39 +3,65 @@ include("Schedulers.jl")
 """
 Denoising Diffusion Probabilistic Models (DDPM) scheduler.
 
-cf. [[2006.11239] Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
+## References
+  * [[2006.11239] Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
 """
 struct DDPM{V<:AbstractVector} <: Scheduler
-  # number of diffusion steps used to train the model.
+  T::Integer # length of markov chain
-  T_train::Int
 
-  # the betas used for the diffusion steps
+  β::V # beta variance schedule
-  β::V
+  α::V # 1 - beta
 
-  # internal variables used for computation (derived from β)
+  ⎷α::V # square root of α
-  α::V
+  ⎷β::V # square root of β
-  α_cumprods::V
+
-  α_cumprod_prevs::V
+  α̅::V # cumulative product of α
-  sqrt_α_cumprods::V
+  β̅::V # 1 - α̅ (≠ cumprod(β))
-  sqrt_one_minus_α_cumprods::V
+
+  α̅₋₁::V # right-shifted α̅
+  β̅₋₁::V # 1 - α̅₋₁
+
+  ⎷α̅::V # square root of α̅
+  ⎷β̅::V # square root of β̅
+
+  ⎷α̅₋₁::V # square root of α̅₋₁
+  ⎷β̅₋₁::V # square root of β̅₋₁
 end
 
 function DDPM(V::DataType, β::AbstractVector)
-  α = 1 .- β
+  T = length(β)
-  α_cumprods = cumprod(α)
-  α_cumprod_prevs = [1, (α_cumprods[1:end-1])...]
 
-  sqrt_α_cumprods = sqrt.(α_cumprods)
+  α = 1 .- β
-  sqrt_one_minus_α_cumprods = sqrt.(1 .- α_cumprods)
+
+  ⎷α = sqrt.(α)
+  ⎷β = sqrt.(β)
+
+  α̅ = cumprod(α)
+  β̅ = 1 .- α̅
+
+  α̅₋₁ = [1, (α̅[1:end-1])...]
+  β̅₋₁ = 1 .- α̅₋₁
+
+  ⎷α̅ = sqrt.(α̅)
+  ⎷β̅ = sqrt.(β̅)
+
+  ⎷α̅₋₁ = sqrt.(α̅₋₁)
+  ⎷β̅₋₁ = sqrt.(β̅₋₁)
 
   DDPM{V}(
-    length(β),
+    T,
     β,
     α,
-    α_cumprods,
+    ⎷α,
-    α_cumprod_prevs,
+    ⎷β,
-    sqrt_α_cumprods,
+    α̅,
-    sqrt_one_minus_α_cumprods,
+    β̅,
+    α̅₋₁,
+    β̅₋₁,
+    ⎷α̅,
+    ⎷β̅,
+    ⎷α̅₋₁,
+    ⎷β̅₋₁,
   )
 end
 
@@ -43,47 +69,47 @@ end
 Remove noise from model output using the backward diffusion process.
 
 ## Input
-  * scheduler (`DDPM`): scheduler to use
+  * `scheduler::DDPM`: scheduler to use
-  * sample (`AbstractArray`): sample to remove noise from, i.e. model_input
+  * `xₜ::AbstractArray`: sample to be denoised
-  * model_output (`AbstractArray`): predicted noise from the model
+  * `ϵᵧ::AbstractArray`: predicted noise to remove
-  * timesteps (`AbstractArray`): timesteps to remove noise from
+  * `t::AbstractArray`: timestep t of `xₜ`
 
 ## Output
-  * pred_prev_sample (`AbstractArray`): denoised sample at t=t-1
+  * `xₜ₋₁::AbstractArray`: denoised sample at t=t-1
-  * x_0_pred (`AbstractArray`): denoised sample at t=0
+  * `x̂₀::AbstractArray`: denoised sample at t=0
 """
 function step(
   scheduler::DDPM,
-  sample::AbstractArray,
+  xₜ::AbstractArray,
-  model_output::AbstractArray,
+  ϵᵧ::AbstractArray,
-  timesteps::AbstractArray,
+  t::AbstractArray,
 )
-  # 1. compute alphas, betas
+  # retreive scheduler variables at timesteps t
-  α_cumprod_t = scheduler.α_cumprods[timesteps]
+  βₜ = scheduler.β[t]
-  α_cumprod_t_prev = scheduler.α_cumprods[timesteps.-1]
+  β̅ₜ = scheduler.β̅[t]
-  β_cumprod_t = 1 .- α_cumprod_t
+  β̅ₜ₋₁ = scheduler.β̅₋₁[t]
-  β_cumprod_t_prev = 1 .- α_cumprod_t_prev
+  ⎷αₜ = scheduler.⎷α[t]
-  current_α_t = α_cumprod_t ./ α_cumprod_t_prev
+  ⎷α̅ₜ = scheduler.⎷α̅[t]
-  current_β_t = 1 .- current_α_t
+  ⎷α̅ₜ₋₁ = scheduler.⎷α̅₋₁[t]
+  ⎷β̅ₜ = scheduler.⎷β̅[t]
 
-  # 2. compute predicted original sample from predicted noise also called
+  # compute predicted previous sample x̂₀
-  # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
+  # arxiv:2006.11239 Eq. 15
-  # epsilon prediction type
+  # arxiv:2208.11970 Eq. 115
-  # print shapes of thingies
+  x̂₀ = (xₜ - ⎷β̅ₜ' .* ϵᵧ) ./ ⎷α̅ₜ'
-  x_0_pred = (sample - sqrt.(β_cumprod_t)' .* model_output) ./ sqrt.(α_cumprod_t)'
 
-  # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
+  # compute predicted previous sample μ̃ₜ
-  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
+  # arxiv:2006.11239 Eq. 7
-  pred_original_sample_coeff = (sqrt.(α_cumprod_t_prev) .* current_β_t) ./ β_cumprod_t
+  # arxiv:2208.11970 Eq. 84
-  current_sample_coeff = sqrt.(current_α_t) .* β_cumprod_t_prev ./ β_cumprod_t
+  λ₀ = ⎷α̅ₜ₋₁ .* βₜ ./ β̅ₜ
+  λₜ = ⎷αₜ .* β̅ₜ₋₁ ./ β̅ₜ  # TODO: this could be stored in the scheduler
+  μ̃ₜ = λ₀' .* x̂₀ + λₜ' .* xₜ
 
-  # 5. Compute predicted previous sample µ_t
+  # sample predicted previous sample xₜ₋₁
-  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
+  # arxiv:2006.11239 Eq. 6
-  pred_prev_sample = pred_original_sample_coeff' .* x_0_pred + current_sample_coeff' .* sample
+  # arxiv:2208.11970 Eq. 70
+  σₜ = β̅ₜ₋₁ ./ β̅ₜ .* βₜ # TODO: this could be stored in the scheduler
+  xₜ₋₁ = μ̃ₜ + σₜ' .* randn(size(ϵᵧ))
 
-  # 6. Add noise
+  return xₜ₋₁, x̂₀
-  variance = sqrt.(scheduler.β[timesteps])' .* randn(size(model_output))
-  pred_prev_sample = pred_prev_sample + variance
-
-  return pred_prev_sample, x_0_pred
 end

src/Diffusers.jl

@@ -6,5 +6,6 @@ include("BetaSchedulers.jl")
 
 # concrete types
 include("DDPM.jl")
+# include("DDIM.jl")
 
 end # module Diffusers

src/Schedulers.jl

@@ -6,24 +6,24 @@ Add noise to clean data using the forward diffusion process.
 cf. [[2006.11239] Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239) (Eq. 4)
 
 ## Input
-  * scheduler (`Scheduler`): scheduler to use
+  * `scheduler::Scheduler`: scheduler to use
-  * clean_data (`AbstractArray`): clean data to add noise to
+  * `clean_data::AbstractArray`: clean data to add noise to
-  * noise (`AbstractArray`): noise to add to clean data
+  * `noise::AbstractArray`: noise to add to clean data
-  * timesteps (`AbstractArray`): timesteps used to weight the noise
+  * `timesteps::AbstractArray`: timesteps used to weight the noise
 
 ## Output
-  * noisy_data (`AbstractArray`): noisy data at the given timesteps
+  * `noisy_data::AbstractArray`: noisy data at the given timesteps
 """
 function add_noise(
   scheduler::Scheduler,
-  clean_data::AbstractArray,
+  x₀::AbstractArray,
-  noise::AbstractArray,
+  ϵ::AbstractArray,
-  timesteps::AbstractArray,
+  t::AbstractArray,
 )
-  sqrt_α_cumprod_t = scheduler.sqrt_α_cumprods[timesteps]
+  ⎷α̅ₜ = scheduler.⎷α̅[t]
-  sqrt_one_minus_α_cumprod_t = scheduler.sqrt_one_minus_α_cumprods[timesteps]
+  ⎷β̅ₜ = scheduler.⎷β̅[t]
 
-  noisy_data = sqrt_α_cumprod_t' .* clean_data + sqrt_one_minus_α_cumprod_t' .* noise
+  xₜ = ⎷α̅ₜ' .* x₀ + ⎷β̅ₜ' .* ϵ
 
-  return noisy_data
+  return xₜ
 end