r""" Initialize the diffusion model backbone. Args: model_type (`str`, *optional*, defaults to 't2v'): Mo

Initialize the diffusion forcing pipeline class Args: model_path (str): Path to the model dit_path (str): Pa

we will divide the token sequence into the following format [1 latent frame] [1 latent frame] ... [1 latent frame] We use fle

The index for the first timestep. It should be set from pipeline with `set_begin_index` method.

x: [B, L, C].

x: [B, L, C].

imgs: [B, 3, H, W] of torch.float32. - mean: [0.48145466, 0.4578275, 0.40821073] - std: [0.26862954, 0.2613025

x: [B, L1, C]. context: [B, L2, C] or None. mask: [B, L2] or [B, L1, L2] or None.

Fix bfloat16 support for nearest neighbor interpolation.

r""" Args: x(Tensor): Shape [B, L, C]

r""" Args: x(Tensor): Shape [B, L, C]

r""" Args: x(Tensor): Shape [B, L, num_heads, C / num_heads] seq_lens(Tensor): Shape [B] grid_sizes(Tensor

r""" Args: x(Tensor): Shape [B, L1, C] context(Tensor): Shape [B, L2, C] context_lens(Tensor): Shape [B]

r""" Args: x(Tensor): Shape [B, L1, C] context(Tensor): Shape [B, L2, C] context_lens(Tensor): Shape [B]

r""" Args: x(Tensor): Shape [B, L, C] e(Tensor): Shape [B, 6, C] seq_lens(Tensor): Shape [B], length of ea

r""" Forward pass through the diffusion model Args: x (List[Tensor]): List of input video tensors, each w

x: [B, L, C].

ids: [B, L] of torch.LongTensor.

Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. A

Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_inde

The index counter for current timestep. It will increase 1 after each scheduler step.

r""" Args: x(Tensor): Shape [B, L, num_heads, C / num_heads] seq_lens(Tensor): Shape [B] grid_sizes(Tensor): Shape [B, 3],

x: A list of videos each with shape [C, T, H, W]. t: [B]. context: A list of text embeddings each with s

XLMRobertaLarge adapted from Huggingface.