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

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

The [`ControlNetModel`] forward method. Args: sample (`torch.Tensor`): The noisy input tensor.

r""" The [`UNet2DConditionModel`] forward method. Args: sample (`torch.Tensor`): The noisy input tensor w

Args: x (torch.Tensor): image features shape (b, n1, D) latent (torch.Tensor): latent features

r""" Instantiate a [`ControlNetModel`] from [`UNet2DConditionModel`]. Parameters: unet (`UNet2DConditionModel`):

Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) are fused. For cross-att

Returns vocab as a dict

Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be converted to ``torch.Tens

Read image and output RGB image (0-1). Args: path (str): path to file Returns: array: RGB image (0-1)

Read pfm file. Args: path (str): path to file Returns: tuple: (data, scale)

Resize depth map and bring to CPU (numpy). Args: depth (tensor): depth width (int): image width height (int): image heigh

Resize image and make it fit for network. Args: img (array): image Returns: tensor: data ready for network

Save the vocabulary and special tokens file to a directory. Args: save_directory (`str`): The directory

Disables custom attention processors and sets the default attention implementation.

Disables custom attention processors and sets the default attention implementation.

Disables the fused QKV projection if enabled. <Tip warning={true}> This API is 🧪 experimental. </Tip>

Write depth map to pfm and png file. Args: path (str): filepath without extension depth (array): depth