From 7c9c7a4ab7ce0651183d8c63f0864c56939a24df Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Sun, 3 Sep 2023 18:51:05 +0000 Subject: [PATCH] build based on 0120c11 --- dev/beta_schedules/alpha_bar_schedules.html | 6 +++--- dev/beta_schedules/beta_schedules.html | 6 +++--- dev/beta_schedules/index.html | 2 +- dev/index.html | 2 +- dev/references/index.html | 2 +- dev/schedulers/index.html | 2 +- dev/search/index.html | 2 +- 7 files changed, 11 insertions(+), 11 deletions(-) diff --git a/dev/beta_schedules/alpha_bar_schedules.html b/dev/beta_schedules/alpha_bar_schedules.html index 8401dbf..8fbe94f 100644 --- a/dev/beta_schedules/alpha_bar_schedules.html +++ b/dev/beta_schedules/alpha_bar_schedules.html @@ -9,7 +9,7 @@ window.PlotlyConfig = {MathJaxConfig: 'local'};
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{"editable":false,"responsive":true,"staticPlot":false,"scrollZoom":true}, diff --git a/dev/beta_schedules/index.html b/dev/beta_schedules/index.html index 693c22d..4c5a57a 100644 --- a/dev/beta_schedules/index.html +++ b/dev/beta_schedules/index.html @@ -1,3 +1,3 @@ Beta Schedules · Diffusers.jl
Edit on GitHub
-
+
Diffusers.BetaSchedules.cosine_beta_schedule — Function

Cosine beta schedule.

Input

  • T::Int: number of timesteps
  • βₘₐₓ::Real=0.999f0: maximum value of β
  • ϵ::Real=1.0f-3: small value used to avoid division by zero

Output

  • β::Vector{Real}: βₜ values at each timestep t

References

source
Diffusers.BetaSchedules.exponential_beta_schedule — Function

Exponential beta schedule.

Input

  • T::Int: number of timesteps
  • βₘₐₓ::Real=0.999f0: maximum value of β

Output

  • β::Vector{Real}: βₜ values at each timestep t

References

source
Diffusers.BetaSchedules.linear_beta_schedule — Function

Linear beta schedule.

Input

  • T::Integer: number of timesteps
  • β₁::Real=1.0f-4: initial (t=1) value of β
  • β₋₁::Real=2.0f-2: final (t=T) value of β

Output

  • β::Vector{Real}: βₜ values at each timestep t

References

source
Diffusers.BetaSchedules.rescale_zero_terminal_snr — Method

Rescale betas to have zero terminal Signal to Noise Ratio (SNR).

Input

  • β::AbstractArray: βₜ values at each timestep t

Output

  • β::Vector{Real}: rescaled βₜ values at each timestep t

References

source
Diffusers.BetaSchedules.scaled_linear_beta_schedule — Function

Scaled linear beta schedule.

Input

  • T::Int: number of timesteps
  • β₁::Real=1.0f-4: initial value of β
  • β₋₁::Real=2.0f-2: final value of β

Output

  • β::Vector{Real}: βₜ values at each timestep t

References

source
Diffusers.BetaSchedules.sigmoid_beta_schedule — Function

Sigmoid beta schedule.

Input

  • T::Int: number of timesteps
  • β₁::Real=1.0f-4: initial value of β
  • β₋₁::Real=2.0f-2: final value of β

Output

  • β::Vector{Real}: βₜ values at each timestep t

References

source
diff --git a/dev/index.html b/dev/index.html index 0f2e624..d9451c6 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,2 +1,2 @@ -Home · Diffusers.jl
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+Home · Diffusers.jl
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diff --git a/dev/references/index.html b/dev/references/index.html index 762b129..b647ed3 100644 --- a/dev/references/index.html +++ b/dev/references/index.html @@ -13,4 +13,4 @@
A. Nichol and P. Dhariwal. Improved Denoising Diffusion Probabilistic Models. arXiv:2102.09672 [cs.LG] (2021).
- + diff --git a/dev/schedulers/index.html b/dev/schedulers/index.html index c67b2f4..0cc35ae 100644 --- a/dev/schedulers/index.html +++ b/dev/schedulers/index.html @@ -1,2 +1,2 @@ -Schedulers · Diffusers.jl
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Markovian Hierarchical Variational Autoencoder

Diffusers.Schedulers.forward — Method

Add noise to clean data using the forward diffusion process.

Input

  • scheduler::Scheduler: scheduler to use
  • x₀::AbstractArray: clean data to add noise to
  • ϵ::AbstractArray: noise to add to clean data
  • t::AbstractArray: timesteps used to weight the noise

Output

  • xₜ::AbstractArray: noisy data at the given timesteps
source
Diffusers.Schedulers.reverse — Method

Remove noise from model output using the backward diffusion process.

Input

  • scheduler::Scheduler: scheduler to use
  • xₜ::AbstractArray: sample to be denoised
  • ϵᵧ::AbstractArray: predicted noise to remove
  • t::AbstractArray: timestep t of xₜ

Output

  • xₜ₋₁::AbstractArray: denoised sample at t=t-1
  • x̂₀::AbstractArray: denoised sample at t=0
source
+Schedulers · Diffusers.jl
Edit on GitHub

Markovian Hierarchical Variational Autoencoder

Diffusers.Schedulers.forward — Method

Add noise to clean data using the forward diffusion process.

Input

  • scheduler::Scheduler: scheduler to use
  • x₀::AbstractArray: clean data to add noise to
  • ϵ::AbstractArray: noise to add to clean data
  • t::AbstractArray: timesteps used to weight the noise

Output

  • xₜ::AbstractArray: noisy data at the given timesteps
source
Diffusers.Schedulers.reverse — Method

Remove noise from model output using the backward diffusion process.

Input

  • scheduler::Scheduler: scheduler to use
  • xₜ::AbstractArray: sample to be denoised
  • ϵᵧ::AbstractArray: predicted noise to remove
  • t::AbstractArray: timestep t of xₜ

Output

  • xₜ₋₁::AbstractArray: denoised sample at t=t-1
  • x̂₀::AbstractArray: denoised sample at t=0
source
diff --git a/dev/search/index.html b/dev/search/index.html index f8cc6de..4c9b361 100644 --- a/dev/search/index.html +++ b/dev/search/index.html @@ -1,2 +1,2 @@ -Search · Diffusers.jl

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