Struct stable_diffusion_trainer::trainer::Trainer

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pub struct Trainer {
Show 21 fields
    pub environment: Environment,
    pub training_images_repeat: usize,
    pub regularization_images_repeat: usize,
    pub pretrained_model_name_or_path: String,
    pub resolution: (usize, usize),
    pub save_model_as: ModelFileFormat,
    pub network_module: String,
    pub text_encoder_lr: f32,
    pub unet_lr: f32,
    pub lr_scheduler_num_cycles: usize,
    pub learning_rate: f32,
    pub lr_warmup_steps: usize,
    pub train_batch_size: usize,
    pub max_train_steps: usize,
    pub save_every_n_epochs: usize,
    pub mixed_precision: FloatPrecision,
    pub save_precision: FloatPrecision,
    pub max_grad_norm: f32,
    pub max_data_loader_n_workers: usize,
    pub bucket_reso_steps: usize,
    pub noise_offset: f32,

}
Expand description

The Trainer structure.

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§environment: Environment

The environment to use for the training process.

§training_images_repeat: usize

The number of times to repeat the training images.

§regularization_images_repeat: usize

The number of times to repeat the regularization images.

§pretrained_model_name_or_path: String

The name or path of the pretrained model to use for the training process.

§resolution: (usize, usize)

The maximum resolution of the images to use for the training process.

§save_model_as: ModelFileFormat

The format to save the model as.

§network_module: String

The module to use for the network.

§text_encoder_lr: f32

The learning rate for the text encoder.

§unet_lr: f32

The learning rate for the unet.

§lr_scheduler_num_cycles: usize

The number of cycles for the learning rate scheduler.

§learning_rate: f32

The learning rate for the training process.

§lr_warmup_steps: usize

The number of warmup steps for the learning rate.

§train_batch_size: usize

The batch size for the training process.

§max_train_steps: usize

The maximum number of training steps.

§save_every_n_epochs: usize

The frequency to

§mixed_precision: FloatPrecision

The precision to use for mixed precision training.

§save_precision: FloatPrecision

The precision to use for saving the model.

§max_grad_norm: f32

The maximum gradient norm.

§max_data_loader_n_workers: usize

The maximum number of data loader workers.

§bucket_reso_steps: usize

The number of steps for the bucket resolution.

§noise_offset: f32

The noise offset.

Implementations§

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impl Trainer

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pub fn new() -> Self

Create a new Trainer.

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pub fn start(&self, parameters: &Parameters)

Start the training process.

Trait Implementations§

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impl Default for Trainer

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fn default() -> Self

Returns the “default value” for a type. Read more

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