#
Normalization layers

Customized normalization layers for supporting various models in 🤗 Diffusers.

##
AdaLayerNorm

###
class diffusers.models.normalization.AdaLayerNorm

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( embedding_dim: int num_embeddings: int )

Parameters

- embedding_dim (`int`) — The size of each embedding vector.
- num_embeddings (`int`) — The size of the embeddings dictionary.

Norm layer modified to incorporate timestep embeddings.

##
AdaLayerNormZero

###
class diffusers.models.normalization.AdaLayerNormZero

< source >

( embedding_dim: int num_embeddings: Optional = None norm_type = 'layer_norm' bias = True )

Parameters

- embedding_dim (`int`) — The size of each embedding vector.
- num_embeddings (`int`) — The size of the embeddings dictionary.

Norm layer adaptive layer norm zero (adaLN-Zero).

##
AdaLayerNormSingle

###
class diffusers.models.normalization.AdaLayerNormSingle

< source >

( embedding_dim: int use_additional_conditions: bool = False )

Parameters

- embedding_dim (`int`) — The size of each embedding vector.
- use_additional_conditions (`bool`) — To use additional conditions for normalization or not.

Norm layer adaptive layer norm single (adaLN-single).

As proposed in PixArt-Alpha (see: https://arxiv.org/abs/2310.00426; Section 2.3).

##
AdaGroupNorm

###
class diffusers.models.normalization.AdaGroupNorm

< source >

( embedding_dim: int out_dim: int num_groups: int act_fn: Optional = None eps: float = 1e-05 )

Parameters

- embedding_dim (`int`) — The size of each embedding vector.
- num_embeddings (`int`) — The size of the embeddings dictionary.
- num_groups (`int`) — The number of groups to separate the channels into.
- act_fn (`str`, optional, defaults to `None`) — The activation function to use.
- eps (`float`, optional, defaults to `1e-5`) — The epsilon value to use for numerical stability.

GroupNorm layer modified to incorporate timestep embeddings.

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