Activation functions

Customized activation functions for supporting various models in 🤗 Diffusers.

GELU

class diffusers.models.activations.GELU

< >

( dim_in: int dim_out: int approximate: str = 'none' bias: bool = True )

Parameters

  • dim_in (int) — The number of channels in the input.
  • dim_out (int) — The number of channels in the output.
  • approximate (str, optional, defaults to "none") — If "tanh", use tanh approximation.
  • bias (bool, defaults to True) — Whether to use a bias in the linear layer.

GELU activation function with tanh approximation support with approximate="tanh".

GEGLU

class diffusers.models.activations.GEGLU

< >

( dim_in: int dim_out: int bias: bool = True )

Parameters

  • dim_in (int) — The number of channels in the input.
  • dim_out (int) — The number of channels in the output.
  • bias (bool, defaults to True) — Whether to use a bias in the linear layer.

A variant of the gated linear unit activation function.

ApproximateGELU

class diffusers.models.activations.ApproximateGELU

< >

( dim_in: int dim_out: int bias: bool = True )

Parameters

  • dim_in (int) — The number of channels in the input.
  • dim_out (int) — The number of channels in the output.
  • bias (bool, defaults to True) — Whether to use a bias in the linear layer.

The approximate form of the Gaussian Error Linear Unit (GELU). For more details, see section 2 of this paper.

SwiGLU

class diffusers.models.activations.SwiGLU

< >

( dim_in: int dim_out: int bias: bool = True )

Parameters

  • dim_in (int) — The number of channels in the input.
  • dim_out (int) — The number of channels in the output.
  • bias (bool, defaults to True) — Whether to use a bias in the linear layer.

A variant of the gated linear unit activation function. It’s similar to GEGLU but uses SiLU / Swish instead of GeLU.

FP32SiLU

class diffusers.models.activations.FP32SiLU

< >

( )

SiLU activation function with input upcasted to torch.float32.

LinearActivation

class diffusers.models.activations.LinearActivation

< >

( dim_in: int dim_out: int bias: bool = True activation: str = 'silu' )

Update on GitHub