modelzoo.vision.pytorch.dit.input.DiffusionLatentImageNet1KProcessor.DiffusionLatentImageNet1KProcessor#

class modelzoo.vision.pytorch.dit.input.DiffusionLatentImageNet1KProcessor.DiffusionLatentImageNet1KProcessor[source]#

Bases: modelzoo.vision.pytorch.dit.input.DiffusionBaseProcessor.DiffusionBaseProcessor

Methods

check_split_valid

create_dataloader

Dataloader returns a dict with keys:

create_dataset

custom_collate_fn

process_transform

__init__(params)[source]#
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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