Forward pass. Args: x (tensor): input Returns: tensor: interpolated data

Forward pass. Args: x (tensor): input Returns: tensor: output

Forward pass. Returns: tensor: output

Forward pass. Args: x (tensor): input Returns: tensor: output

Forward pass. Returns: tensor: output

Forward pass. Args: x (tensor): input data (image) Returns: tensor: depth

Args: x (torch.Tensor): Input image tensor of shape (B, C, H, W) return_final_centers (bool, optional): Whether to re

Forward pass Args: x (torch.Tensor - NCHW): Input feature tensor Returns: torch.Tensor - SNE: Transformer ou

Compute log binomial distribution for x Args: x (torch.Tensor - NCHW): probabilities t (float, torch.Tensor - NCHW, o

Forward pass Args: x (torch.Tensor - NCHW): Main feature cond (torch.Tensor - NCHW): condition feature Retur

Returns tensor of bin_width vectors (centers). One vector b for every pixel

Returns tensor of bin_width vectors (centers). One vector b for every pixel

x : feature block; shape - n, c, h, w b_prev : previous bin widths normed; shape - n, prev_nbins, h, w

Args: x (torch.Tensor) : feature block; shape - n, c, h, w b_prev (torch.Tensor) : previous bin centers normed; shape

Args: x (torch.Tensor) : feature block; shape - n, c, h, w b_prev (torch.Tensor) : previous bin centers normed; shape

Args: x (torch.Tensor): Input image tensor of shape (B, C, H, W). Assumes all images are from the same domain. return

Apply the module to `x` given `emb` timestep embeddings.

Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb:

Apply the module to `x` given `emb` timestep embeddings.

Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb: