r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to

Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portio

Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portio

r"""Decode a batch of images using a tiled decoder. Args: When this option is enabled, the VAE will split the input tensor into tiles to compute

r"""Encode a batch of images using a tiled encoder. Args: When this option is enabled, the VAE will split the input tensor into tiles to compute

Convert tensor data type from uint8 to float, divide value by 255.0 and permute the dimensions of clip tensor Args: clip (torch.t

Drops text to enable classifier-free guidance.

Drops text to enable classifier-free guidance.

Drops text to enable classifier-free guidance.

Update the reweighting using losses from a model. Sub-classes should override this method to update the reweighting using los

Update the reweighting using losses from a model. Sub-classes should override this method to update the reweighting using los

Get a numpy array of weights, one per diffusion step. The weights needn't be normalized, but must be positive.

Get a numpy array of weights, one per diffusion step. The weights needn't be normalized, but must be positive.

r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*):

r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*):

Args: clip (torch.tensor, dtype=torch.uint8): Size is (T, C, H, W) Return: clip (torch.tensor, dtype=torch.fl

Args: clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W) Returns: torch.tensor: scale resize

r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the

r""" Returns the device on which the pipeline's models will be executed. After calling `pipeline.enable_sequential_cpu_offload()` the