Source code for neural_de.transformations.diffusion.unet.timestep_block

from abc import abstractmethod

from torch import nn




class TimestepBlock(nn.Module):
    """
    Any module where forward() takes timestep embeddings as a second argument.
    """



    @abstractmethod
    def forward(self, x, emb):
        """
        Apply the module to `x` given `emb` timestep embeddings.
        """