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✨ very basic working example of forward diffusion
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||||
version = "3.5.3"
|
||||
|
||||
[[deps.SHA]]
|
||||
uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
|
||||
version = "0.7.0"
|
||||
|
|
@ -1008,6 +1047,10 @@ git-tree-sha1 = "e2cc6d8c88613c05e1defb55170bf5ff211fbeac"
|
|||
uuid = "efcf1570-3423-57d1-acb7-fd33fddbac46"
|
||||
version = "1.1.1"
|
||||
|
||||
[[deps.SharedArrays]]
|
||||
deps = ["Distributed", "Mmap", "Random", "Serialization"]
|
||||
uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383"
|
||||
|
||||
[[deps.ShowCases]]
|
||||
git-tree-sha1 = "7f534ad62ab2bd48591bdeac81994ea8c445e4a5"
|
||||
uuid = "605ecd9f-84a6-4c9e-81e2-4798472b76a3"
|
||||
|
|
@ -1035,9 +1078,9 @@ uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
|
|||
|
||||
[[deps.SortingAlgorithms]]
|
||||
deps = ["DataStructures"]
|
||||
git-tree-sha1 = "c60ec5c62180f27efea3ba2908480f8055e17cee"
|
||||
git-tree-sha1 = "f6cb12bae7c2ecff6c4986f28defff8741747a9b"
|
||||
uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c"
|
||||
version = "1.1.1"
|
||||
version = "0.3.2"
|
||||
|
||||
[[deps.SparseArrays]]
|
||||
deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"]
|
||||
|
|
@ -1060,10 +1103,14 @@ uuid = "171d559e-b47b-412a-8079-5efa626c420e"
|
|||
version = "0.1.15"
|
||||
|
||||
[[deps.StaticArrays]]
|
||||
deps = ["LinearAlgebra", "Random", "StaticArraysCore", "Statistics"]
|
||||
git-tree-sha1 = "832afbae2a45b4ae7e831f86965469a24d1d8a83"
|
||||
deps = ["LinearAlgebra", "Random", "StaticArraysCore"]
|
||||
git-tree-sha1 = "0da7e6b70d1bb40b1ace3b576da9ea2992f76318"
|
||||
uuid = "90137ffa-7385-5640-81b9-e52037218182"
|
||||
version = "1.5.26"
|
||||
version = "1.6.0"
|
||||
weakdeps = ["Statistics"]
|
||||
|
||||
[deps.StaticArrays.extensions]
|
||||
StaticArraysStatisticsExt = "Statistics"
|
||||
|
||||
[[deps.StaticArraysCore]]
|
||||
git-tree-sha1 = "6b7ba252635a5eff6a0b0664a41ee140a1c9e72a"
|
||||
|
|
@ -1083,9 +1130,9 @@ version = "1.6.0"
|
|||
|
||||
[[deps.StatsBase]]
|
||||
deps = ["DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"]
|
||||
git-tree-sha1 = "75ebe04c5bed70b91614d684259b661c9e6274a4"
|
||||
git-tree-sha1 = "d1bf48bfcc554a3761a133fe3a9bb01488e06916"
|
||||
uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
|
||||
version = "0.34.0"
|
||||
version = "0.33.21"
|
||||
|
||||
[[deps.StructArrays]]
|
||||
deps = ["Adapt", "DataAPI", "GPUArraysCore", "StaticArraysCore", "Tables"]
|
||||
|
|
@ -1185,13 +1232,11 @@ deps = ["ConstructionBase", "Dates", "LinearAlgebra", "Random"]
|
|||
git-tree-sha1 = "ba4aa36b2d5c98d6ed1f149da916b3ba46527b2b"
|
||||
uuid = "1986cc42-f94f-5a68-af5c-568840ba703d"
|
||||
version = "1.14.0"
|
||||
weakdeps = ["InverseFunctions"]
|
||||
|
||||
[deps.Unitful.extensions]
|
||||
InverseFunctionsUnitfulExt = "InverseFunctions"
|
||||
|
||||
[deps.Unitful.weakdeps]
|
||||
InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112"
|
||||
|
||||
[[deps.UnitfulLatexify]]
|
||||
deps = ["LaTeXStrings", "Latexify", "Unitful"]
|
||||
git-tree-sha1 = "e2d817cc500e960fdbafcf988ac8436ba3208bfd"
|
||||
|
|
@ -1205,9 +1250,9 @@ version = "0.2.1"
|
|||
|
||||
[[deps.UnsafeAtomicsLLVM]]
|
||||
deps = ["LLVM", "UnsafeAtomics"]
|
||||
git-tree-sha1 = "ea37e6066bf194ab78f4e747f5245261f17a7175"
|
||||
git-tree-sha1 = "323e3d0acf5e78a56dfae7bd8928c989b4f3083e"
|
||||
uuid = "d80eeb9a-aca5-4d75-85e5-170c8b632249"
|
||||
version = "0.1.2"
|
||||
version = "0.1.3"
|
||||
|
||||
[[deps.Unzip]]
|
||||
git-tree-sha1 = "ca0969166a028236229f63514992fc073799bb78"
|
||||
|
|
@ -1399,9 +1444,9 @@ version = "0.2.3"
|
|||
|
||||
[[deps.cuDNN]]
|
||||
deps = ["CEnum", "CUDA", "CUDNN_jll"]
|
||||
git-tree-sha1 = "f65490d187861d6222cb38bcbbff3fd949a7ec3e"
|
||||
git-tree-sha1 = "ee79f97d07bf875231559f9b3f2649f34fac140b"
|
||||
uuid = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
|
||||
version = "1.0.4"
|
||||
version = "1.1.0"
|
||||
|
||||
[[deps.fzf_jll]]
|
||||
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
|
||||
|
|
|
|||
|
|
@ -1,3 +1,10 @@
|
|||
name = "Diffusers"
|
||||
uuid = "90edb7a8-79d7-49b2-b6b1-9322c3fdead8"
|
||||
authors = ["Laureηt <laurent@fainsin.bzh>"]
|
||||
version = "0.1.0"
|
||||
|
||||
[deps]
|
||||
Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
|
||||
NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
|
||||
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
|
||||
Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
|
||||
|
|
|
|||
68
examples/swissroll.jl
Normal file
68
examples/swissroll.jl
Normal file
|
|
@ -0,0 +1,68 @@
|
|||
import Diffusers
|
||||
using Random
|
||||
using Plots
|
||||
|
||||
function make_spiral(rng::AbstractRNG, n_samples::Int=1000)
|
||||
t_min = 1.5π
|
||||
t_max = 4.5π
|
||||
|
||||
t = rand(rng, n_samples) * (t_max - t_min) .+ t_min
|
||||
|
||||
x = t .* cos.(t)
|
||||
y = t .* sin.(t)
|
||||
|
||||
permutedims([x y], (2, 1))
|
||||
end
|
||||
|
||||
make_spiral(n_samples::Int=1000) = make_spiral(Random.GLOBAL_RNG, n_samples)
|
||||
|
||||
function normalize_zero_to_one(x)
|
||||
x_min, x_max = extrema(x)
|
||||
x_norm = (x .- x_min) ./ (x_max - x_min)
|
||||
x_norm
|
||||
end
|
||||
|
||||
function normalize_neg_one_to_one(x)
|
||||
2 * normalize_zero_to_one(x) .- 1
|
||||
end
|
||||
|
||||
n_samples = 1000
|
||||
data = normalize_neg_one_to_one(make_spiral(n_samples))
|
||||
scatter(data[1, :], data[2, :],
|
||||
alpha=0.5,
|
||||
aspectratio=:equal,
|
||||
)
|
||||
|
||||
num_timesteps = 1000
|
||||
scheduler = Diffusers.DDPM(
|
||||
Vector{Float64},
|
||||
Diffusers.cosine_beta_schedule(num_timesteps, 0.999f0, 0.001f0),
|
||||
)
|
||||
|
||||
noise = randn(size(X))
|
||||
|
||||
anim = @animate for i in 1:num_timesteps
|
||||
noisy_data = Diffusers.add_noise(scheduler, X, noise, [i])
|
||||
scatter(noise[1, :], noise[2, :],
|
||||
alpha=0.1,
|
||||
aspectratio=:equal,
|
||||
label="noise",
|
||||
legend=:topright,
|
||||
)
|
||||
scatter!(noisy_data[1, :], noisy_data[2, :],
|
||||
alpha=0.5,
|
||||
aspectratio=:equal,
|
||||
label="noisy data",
|
||||
)
|
||||
scatter!(data[1, :], data[2, :],
|
||||
alpha=0.5,
|
||||
aspectratio=:equal,
|
||||
label="data",
|
||||
)
|
||||
i_str = lpad(i, 3, "0")
|
||||
title!("t = $(i_str)")
|
||||
xlims!(-3, 3)
|
||||
ylims!(-3, 3)
|
||||
end
|
||||
|
||||
gif(anim, "swissroll.gif", fps=50)
|
||||
7
src/Diffusers.jl
Normal file
7
src/Diffusers.jl
Normal file
|
|
@ -0,0 +1,7 @@
|
|||
module Diffusers
|
||||
|
||||
include("scheduler.jl")
|
||||
include("beta_scheduler.jl")
|
||||
include("ddpm.jl")
|
||||
|
||||
end # module Diffusers
|
||||
27
src/beta_scheduler.jl
Normal file
27
src/beta_scheduler.jl
Normal file
|
|
@ -0,0 +1,27 @@
|
|||
import NNlib: sigmoid
|
||||
|
||||
function linear_beta_schedule(num_timesteps::Int, β_start=0.0001f0, β_end=0.02f0)
|
||||
range(β_start, β_end, length=num_timesteps)
|
||||
end
|
||||
|
||||
function scaled_linear_beta_schedule(num_timesteps::Int, β_start=0.0001f0, β_end=0.02f0)
|
||||
range(β_start^0.5, β_end^0.5, length=num_timesteps) .^ 2
|
||||
end
|
||||
|
||||
function cosine_beta_schedule(num_timesteps::Int, max_beta=0.999f0, ϵ=1e-3f0)
|
||||
α_bar(t) = cos((t + ϵ) / (1 + ϵ) * π / 2)^2
|
||||
|
||||
βs = Float32[]
|
||||
for i in 1:num_timesteps
|
||||
t1 = (i - 1) / num_timesteps
|
||||
t2 = i / num_timesteps
|
||||
push!(βs, min(1 - α_bar(t2) / α_bar(t1), max_beta))
|
||||
end
|
||||
|
||||
return βs
|
||||
end
|
||||
|
||||
function sigmoid_beta_schedule(num_timesteps::Int, β_start=0.0001f0, β_end=0.02f0)
|
||||
x = range(-6, 6, length=num_timesteps)
|
||||
sigmoid(x) * (β_end - β_start) + β_start
|
||||
end
|
||||
44
src/ddpm.jl
Normal file
44
src/ddpm.jl
Normal file
|
|
@ -0,0 +1,44 @@
|
|||
include("scheduler.jl")
|
||||
|
||||
"""
|
||||
Denoising Diffusion Probabilistic Models (DDPM) scheduler.
|
||||
|
||||
https://arxiv.org/abs/2006.11239
|
||||
"""
|
||||
struct DDPM{V<:AbstractVector} <: Scheduler
|
||||
# number of diffusion steps used to train the model.
|
||||
num_train_timesteps::Int
|
||||
|
||||
# the betas to use for the diffusion steps
|
||||
βs::V
|
||||
αs::V
|
||||
α_cumprods::V
|
||||
α_cumprod_prevs::V
|
||||
|
||||
sqrt_α_cumprods::V
|
||||
sqrt_one_minus_α_cumprods::V
|
||||
end
|
||||
|
||||
|
||||
function DDPM(V::DataType, βs::AbstractVector)
|
||||
αs = 1 .- βs
|
||||
α_cumprods = cumprod(αs)
|
||||
α_cumprod_prevs = [1, (α_cumprods[1:end-1])...]
|
||||
|
||||
sqrt_α_cumprods = sqrt.(α_cumprods)
|
||||
sqrt_one_minus_α_cumprods = sqrt.(1 .- α_cumprods)
|
||||
|
||||
DDPM{V}(
|
||||
length(βs),
|
||||
βs,
|
||||
αs,
|
||||
α_cumprods,
|
||||
α_cumprod_prevs,
|
||||
sqrt_α_cumprods,
|
||||
sqrt_one_minus_α_cumprods,
|
||||
)
|
||||
end
|
||||
|
||||
function DDPM(V::DataType, beta_scheduler)
|
||||
DDPM(V, beta_scheduler)
|
||||
end
|
||||
14
src/scheduler.jl
Normal file
14
src/scheduler.jl
Normal file
|
|
@ -0,0 +1,14 @@
|
|||
abstract type Scheduler end
|
||||
|
||||
function add_noise(
|
||||
scheduler::Scheduler,
|
||||
original_samples::AbstractArray,
|
||||
noise::AbstractArray,
|
||||
timesteps::AbstractArray,
|
||||
)
|
||||
alphas_cumprod = scheduler.α_cumprods[timesteps]
|
||||
sqrt_alpha_prod = sqrt.(alphas_cumprod)
|
||||
sqrt_one_minus_alpha_prod = sqrt.(1 .- alphas_cumprod)
|
||||
|
||||
sqrt_alpha_prod .* original_samples .+ sqrt_one_minus_alpha_prod .* noise
|
||||
end
|
||||
Loading…
Reference in a new issue