[{"location":"references/#References","page":"References","title":"References","text":"","category":"section"},{"location":"references/","page":"References","title":"References","text":"","category":"page"},{"location":"references/","page":"References","title":"References","text":"*","category":"page"},{"location":"schedulers/","page":"Schedulers","title":"Schedulers","text":"(Image: Markovian Hierarchical Variational Autoencoder)","category":"page"},{"location":"schedulers/","page":"Schedulers","title":"Schedulers","text":"Modules = [Diffusers.Schedulers]","category":"page"},{"location":"schedulers/#Diffusers.Schedulers.DDIM","page":"Schedulers","title":"Diffusers.Schedulers.DDIM","text":"Denoising Diffusion Implicit Models (DDIM) scheduler.\n\nReferences\n\n[2010.02502] Denoising Diffusion Implicit Models\n\n\n\n\n\n","category":"type"},{"location":"schedulers/#Diffusers.Schedulers.DDPM","page":"Schedulers","title":"Diffusers.Schedulers.DDPM","text":"Denoising Diffusion Probabilistic Models (DDPM) scheduler.\n\nReferences\n\n[2006.11239] Denoising Diffusion Probabilistic Models\n\n\n\n\n\n","category":"type"},{"location":"schedulers/#Diffusers.Schedulers.Scheduler","page":"Schedulers","title":"Diffusers.Schedulers.Scheduler","text":"Abstract type for schedulers.\n\n\n\n\n\n","category":"type"},{"location":"schedulers/#Diffusers.Schedulers.forward-Tuple{Diffusers.Schedulers.Scheduler, AbstractArray, AbstractArray, AbstractArray}","page":"Schedulers","title":"Diffusers.Schedulers.forward","text":"Add noise to clean data using the forward diffusion process.\n\nInput\n\nscheduler::Scheduler: scheduler to use\nx₀::AbstractArray: clean data to add noise to\nϵ::AbstractArray: noise to add to clean data\nt::AbstractArray: timesteps used to weight the noise\n\nOutput\n\nxₜ::AbstractArray: noisy data at the given timesteps\n\n\n\n\n\n","category":"method"},{"location":"schedulers/#Diffusers.Schedulers.get_velocity-Tuple{Diffusers.Schedulers.Scheduler, AbstractArray, AbstractArray, AbstractArray}","page":"Schedulers","title":"Diffusers.Schedulers.get_velocity","text":"Compute the velocity of the diffusion process.\n\nInput\n\nscheduler::Scheduler: scheduler to use\nx₀::AbstractArray: clean data to add noise to\nϵ::AbstractArray: noise to add to clean data\nt::AbstractArray: timesteps used to weight the noise\n\nOutput\n\nvₜ::AbstractArray: velocity at the given timesteps\n\nReferences\n\n[2202.00512] Progressive Distillation for Fast Sampling of Diffusion Models (Ann. D)\n\n\n\n\n\n","category":"method"},{"location":"schedulers/#Diffusers.Schedulers.reverse-Tuple{Diffusers.Schedulers.Scheduler, AbstractArray, AbstractArray, AbstractArray}","page":"Schedulers","title":"Diffusers.Schedulers.reverse","text":"Remove noise from model output using the backward diffusion process.\n\nInput\n\nscheduler::Scheduler: scheduler to use\nxₜ::AbstractArray: sample to be denoised\nϵᵧ::AbstractArray: predicted noise to remove\nt::AbstractArray: timestep t of xₜ\n\nOutput\n\nxₜ₋₁::AbstractArray: denoised sample at t=t-1\nx̂₀::AbstractArray: denoised sample at t=0\n\n\n\n\n\n","category":"method"},{"location":"beta_schedules/","page":"Beta Schedules","title":"Beta Schedules","text":"using Diffusers.BetaSchedules\nusing LaTeXStrings\nusing PlotlyJS\n\nT = 1000\n\nβ_linear = linear_beta_schedule(T)\nβ_scaled_linear = scaled_linear_beta_schedule(T)\nβ_cosine = cosine_beta_schedule(T)\nβ_sigmoid = sigmoid_beta_schedule(T)\nβ_exponential = exponential_beta_schedule(T)\n\nα̅_linear = cumprod(1 .- β_linear)\nα̅_scaled_linear = cumprod(1 .- β_scaled_linear)\nα̅_cosine = cumprod(1 .- β_cosine)\nα̅_sigmoid = cumprod(1 .- β_sigmoid)\nα̅_exponential = cumprod(1 .- β_exponential)\n\np1 = plot(\n [\n scatter(y=β_linear, name=\"Linear\"),\n scatter(y=β_scaled_linear, name=\"Scaled linear\", visible=\"legendonly\"),\n scatter(y=β_cosine, name=\"Cosine\"),\n scatter(y=β_sigmoid, name=\"Sigmoid\", visible=\"legendonly\"),\n scatter(y=β_exponential, name=\"Exponential\", visible=\"legendonly\"),\n]
