Struct candle_transformers::models::stable_diffusion::euler_ancestral_discrete::EulerAncestralDiscreteScheduler

pub struct EulerAncestralDiscreteScheduler {
    pub config: EulerAncestralDiscreteSchedulerConfig,
    /* private fields */
}

The EulerAncestral Discrete scheduler.

Fields

config: EulerAncestralDiscreteSchedulerConfig

Implementations

impl EulerAncestralDiscreteScheduler

pub fn new( inference_steps: usize, config: EulerAncestralDiscreteSchedulerConfig ) -> Result<Self>

Creates a new EulerAncestral Discrete scheduler given the number of steps to be used for inference as well as the number of steps that was used during training.

Trait Implementations

impl Clone for EulerAncestralDiscreteScheduler

fn clone(&self) -> EulerAncestralDiscreteScheduler

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for EulerAncestralDiscreteScheduler

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Scheduler for EulerAncestralDiscreteScheduler

fn scale_model_input(&self, sample: Tensor, timestep: usize) -> Result<Tensor>

Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.

Scales the denoising model input by (sigma**2 + 1) ** 0.5 to match the K-LMS algorithm

fn step( &self, model_output: &Tensor, timestep: usize, sample: &Tensor ) -> Result<Tensor>

Performs a backward step during inference.

fn timesteps(&self) -> &[usize]

fn add_noise( &self, original: &Tensor, noise: Tensor, timestep: usize ) -> Result<Tensor>

fn init_noise_sigma(&self) -> f64