cerebras.modelzoo.data.vision.diffusion.DiffusionLatentImageNet1KProcessor.DiffusionLatentImageNet1KProcessor

class cerebras.modelzoo.data.vision.diffusion.DiffusionLatentImageNet1KProcessor.DiffusionLatentImageNet1KProcessor(params)

Bases: cerebras.modelzoo.data.vision.diffusion.DiffusionBaseProcessor.DiffusionBaseProcessor

Methods

check_split_valid
create_dataloader	Dataloader returns a dict with keys:
create_dataset
custom_collate_fn
process_transform

create_dataloader(dataset, is_training=False)

Dataloader returns a dict with keys:

“input”: Tensor of shape (batch_size, latent_channels, latent_height, latent_width) “label”: Tensor of shape (batch_size, ) with dropout applied with label_dropout_rate “diffusion_noise”: Tensor of shape (batch_size, latent_channels, latent_height, latent_width)

represents diffusion noise to be applied

“timestep”: Tensor of shape (batch_size, ) that

indicates the timesteps for each diffusion sample

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