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# Normalization layers
Customized normalization layers for supporting various models in 🤗 Diffusers.
## AdaLayerNorm[[diffusers.models.normalization.AdaLayerNorm]]
#### diffusers.models.normalization.AdaLayerNorm[[diffusers.models.normalization.AdaLayerNorm]]
[Source](https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/models/normalization.py#L28)
Norm layer modified to incorporate timestep embeddings.
**Parameters:**
embedding_dim (`int`) : The size of each embedding vector.
num_embeddings (`int`, *optional*) : The size of the embeddings dictionary.
output_dim (`int`, *optional*) --
norm_elementwise_affine (`bool`, defaults to `False) --
norm_eps (`bool`, defaults to `False`) --
chunk_dim (`int`, defaults to `0`) --
## AdaLayerNormZero[[diffusers.models.normalization.AdaLayerNormZero]]
#### diffusers.models.normalization.AdaLayerNormZero[[diffusers.models.normalization.AdaLayerNormZero]]
[Source](https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/models/normalization.py#L131)
Norm layer adaptive layer norm zero (adaLN-Zero).
**Parameters:**
embedding_dim (`int`) : The size of each embedding vector.
num_embeddings (`int`) : The size of the embeddings dictionary.
## AdaLayerNormSingle[[diffusers.models.normalization.AdaLayerNormSingle]]
#### diffusers.models.normalization.AdaLayerNormSingle[[diffusers.models.normalization.AdaLayerNormSingle]]
[Source](https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/models/normalization.py#L236)
Norm layer adaptive layer norm single (adaLN-single).
As proposed in PixArt-Alpha (see: https://huggingface.co/papers/2310.00426; Section 2.3).
**Parameters:**
embedding_dim (`int`) : The size of each embedding vector.
use_additional_conditions (`bool`) : To use additional conditions for normalization or not.
## AdaGroupNorm[[diffusers.models.normalization.AdaGroupNorm]]
#### diffusers.models.normalization.AdaGroupNorm[[diffusers.models.normalization.AdaGroupNorm]]
[Source](https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/models/normalization.py#L270)
GroupNorm layer modified to incorporate timestep embeddings.
**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.
## AdaLayerNormContinuous[[diffusers.models.normalization.AdaLayerNormContinuous]]
#### diffusers.models.normalization.AdaLayerNormContinuous[[diffusers.models.normalization.AdaLayerNormContinuous]]
[Source](https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/models/normalization.py#L308)
Adaptive normalization layer with a norm layer (layer_norm or rms_norm).
**Parameters:**
embedding_dim (`int`) : Embedding dimension to use during projection.
conditioning_embedding_dim (`int`) : Dimension of the input condition.
elementwise_affine (`bool`, defaults to `True`) : Boolean flag to denote if affine transformation should be applied.
eps (`float`, defaults to 1e-5) : Epsilon factor.
bias (`bias`, defaults to `True`) : Boolean flag to denote if bias should be use.
norm_type (`str`, defaults to `"layer_norm"`) : Normalization layer to use. Values supported: "layer_norm", "rms_norm".
## RMSNorm[[diffusers.models.normalization.RMSNorm]]
#### diffusers.models.normalization.RMSNorm[[diffusers.models.normalization.RMSNorm]]
[Source](https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/models/normalization.py#L511)
RMS Norm as introduced in https://huggingface.co/papers/1910.07467 by Zhang et al.
**Parameters:**
dim (`int`) : Number of dimensions to use for `weights`. Only effective when `elementwise_affine` is True.
eps (`float`) : Small value to use when calculating the reciprocal of the square-root.
elementwise_affine (`bool`, defaults to `True`) : Boolean flag to denote if affine transformation should be applied.
bias (`bool`, defaults to False) : If also training the `bias` param.
## GlobalResponseNorm[[diffusers.models.normalization.GlobalResponseNorm]]
#### diffusers.models.normalization.GlobalResponseNorm[[diffusers.models.normalization.GlobalResponseNorm]]
[Source](https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/models/normalization.py#L601)
Global response normalization as introduced in ConvNeXt-v2 (https://huggingface.co/papers/2301.00808).
**Parameters:**
dim (`int`) : Number of dimensions to use for the `gamma` and `beta`.
## LuminaLayerNormContinuous[[diffusers.models.normalization.LuminaLayerNormContinuous]]
#### diffusers.models.normalization.LuminaLayerNormContinuous[[diffusers.models.normalization.LuminaLayerNormContinuous]]
[Source](https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/models/normalization.py#L355)
## SD35AdaLayerNormZeroX[[diffusers.models.normalization.SD35AdaLayerNormZeroX]]
#### diffusers.models.normalization.SD35AdaLayerNormZeroX[[diffusers.models.normalization.SD35AdaLayerNormZeroX]]
[Source](https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/models/normalization.py#L97)
Norm layer adaptive layer norm zero (AdaLN-Zero).
**Parameters:**
embedding_dim (`int`) : The size of each embedding vector.
num_embeddings (`int`) : The size of the embeddings dictionary.
## AdaLayerNormZeroSingle[[diffusers.models.normalization.AdaLayerNormZeroSingle]]
#### diffusers.models.normalization.AdaLayerNormZeroSingle[[diffusers.models.normalization.AdaLayerNormZeroSingle]]
[Source](https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/models/normalization.py#L174)
Norm layer adaptive layer norm zero (adaLN-Zero).
**Parameters:**
embedding_dim (`int`) : The size of each embedding vector.
num_embeddings (`int`) : The size of the embeddings dictionary.
## LuminaRMSNormZero[[diffusers.models.normalization.LuminaRMSNormZero]]
#### diffusers.models.normalization.LuminaRMSNormZero[[diffusers.models.normalization.LuminaRMSNormZero]]
[Source](https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/models/normalization.py#L206)
Norm layer adaptive RMS normalization zero.
**Parameters:**
embedding_dim (`int`) : The size of each embedding vector.
## LpNorm[[diffusers.models.normalization.LpNorm]]
#### diffusers.models.normalization.LpNorm[[diffusers.models.normalization.LpNorm]]
[Source](https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/models/normalization.py#L621)
## CogView3PlusAdaLayerNormZeroTextImage[[diffusers.models.normalization.CogView3PlusAdaLayerNormZeroTextImage]]
#### diffusers.models.normalization.CogView3PlusAdaLayerNormZeroTextImage[[diffusers.models.normalization.CogView3PlusAdaLayerNormZeroTextImage]]
[Source](https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/models/normalization.py#L404)
Norm layer adaptive layer norm zero (adaLN-Zero).
**Parameters:**
embedding_dim (`int`) : The size of each embedding vector.
num_embeddings (`int`) : The size of the embeddings dictionary.
## CogVideoXLayerNormZero[[diffusers.models.normalization.CogVideoXLayerNormZero]]
#### diffusers.models.normalization.CogVideoXLayerNormZero[[diffusers.models.normalization.CogVideoXLayerNormZero]]
[Source](https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/models/normalization.py#L449)
## MochiRMSNormZero[[diffusers.models.transformers.transformer_mochi.MochiRMSNormZero]]
#### diffusers.models.transformers.transformer_mochi.MochiRMSNormZero[[diffusers.models.transformers.transformer_mochi.MochiRMSNormZero]]
[Source](https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/models/transformers/transformer_mochi.py#L88)
Adaptive RMS Norm used in Mochi.
**Parameters:**
embedding_dim (`int`) : The size of each embedding vector.
## MochiRMSNorm[[diffusers.models.normalization.MochiRMSNorm]]
#### diffusers.models.normalization.MochiRMSNorm[[diffusers.models.normalization.MochiRMSNorm]]
[Source](https://github.com/huggingface/diffusers/blob/vr_11636/src/diffusers/models/normalization.py#L573)

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