SfBCUNet

CLASS cleandiffuser.nn_diffusion.SfBCUNet(act_dim: int, emb_dim: int = 64, hidden_dims: List[int] = [512, 256, 128], timestep_emb_type: str = “positional”, timestep_emb_params: Optional[dict] = None) [SOURCE]

A modified U-Net architecture to process low-dim vectors, proposed in SfBC. Compared to the original U-Net, it uses MLPs instead of convolutional layers.

Parameters:

• act_dim (int): The dimension of the action tensor \(\bm x_t\).
• emb_dim (int): The dimension of the time embedding. Default is 256.
• hidden_dims (List[int]): The dimensions of the hidden layers of the MLP. Default is [512, 256, 128].
• timestep_emb_type (str): The type of the time embedding. It can be either “positional” or “fourier”. Default is “positional”.
• timestep_emb_params (Optional[dict]): The parameters for the time embedding. Default is None.

forward(x: torch.Tensor, t: torch.Tensor, c: torch.Tensor) -> torch.Tensor

Parameters:

• x (torch.Tensor): The input tensor \(\bm x_t\) in shape (..., act_dim).
• t (torch.Tensor): The time tensor \(t\) in shape (..., 1).
• c (torch.Tensor): The context tensor \(\bm c\) in shape (..., emb_dim).

Returns:

• torch.Tensor: The output tensor in shape (..., act_dim).