diff --git a/dev/beta_schedules/alpha_bar_schedules.html b/dev/beta_schedules/alpha_bar_schedules.html index 9043591..cd689a7 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'};
@@ -17,9 +17,9 @@ window.PlotlyConfig = {MathJaxConfig: 'local'}; window.PLOTLYENV = window.PLOTLYENV || {} - if (document.getElementById('52f97157-52c6-4dbc-8430-e11ea079ff06')) { + if (document.getElementById('7cdf484a-61ee-4586-b8a9-778b36aa700c')) { Plotly.newPlot( - '52f97157-52c6-4dbc-8430-e11ea079ff06', + '7cdf484a-61ee-4586-b8a9-778b36aa700c', 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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 9aa3707..a3f6b31 100644 --- a/dev/beta_schedules/index.html +++ b/dev/beta_schedules/index.html @@ -1,3 +1,3 @@ Beta Schedules · Diffusers.jl
-
+
Diffusers.BetaSchedules.cosine_beta_scheduleFunction

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.linear_beta_scheduleFunction

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_snrMethod

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_scheduleFunction

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_scheduleFunction

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 f344ee8..2ee75b4 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,2 +1,2 @@ -Home · Diffusers.jl
+Home · Diffusers.jl
diff --git a/dev/schedulers/index.html b/dev/schedulers/index.html index 1e2a50d..d427816 100644 --- a/dev/schedulers/index.html +++ b/dev/schedulers/index.html @@ -1,2 +1,2 @@ -Schedulers · Diffusers.jl
Diffusers.Schedulers.add_noiseMethod

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.stepMethod

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
Diffusers.Schedulers.add_noiseMethod

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.stepMethod

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
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