HowToSD-ComfyUI-Pt-Wrapper/docs/reference/ptn_layer_norm.md

Ptn Layer Norm

A normalization model to normalize elements over specified axes.

Args:
    normalized_shape (list): Specifies the shape of elements to normalize.  
        - For an input of shape `[8, 1024, 768]` (where axes represent `[batch_size, sequence_length, hidden_dim]`):
        - `normalize_shape=[768]` normalizes within each token (across the hidden dimension).
        - `normalize_shape=[1024, 768]` normalizes within each sample (across both sequence and token dimensions).  
        - For an image input of shape `[8, 4, 256, 256]` (where axes represent `[batch_size, channel, height, width]`):
        - `normalize_shape=[256, 256]` normalizes within each channel (across spatial dimensions).
        - `normalize_shape=[4, 256, 256]` normalizes within each sample (across channels and spatial dimensions).

    elementwise_affine (bool): If `True`, applies a learnable scaling factor to the normalized elements.

    bias (bool): If `True` and elementwise_affine is also `True`, applies a learnable bias to the normalized elements.

Input

Name	Data type
normalized_shape	String
elementwise_affine	Boolean
bias	Boolean

Output

Data type
Ptmodel

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Category: PyTorch wrapper - Model

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