r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or o

r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or o

Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`)

Sample points from features of the input point cloud. Args: x (torch.Tensor): The input point cloud. shape: (B, N, C)

Args: geometric_func: device: bounds: dense_octree_depth: hierarchical_octree_depth: Returns:

Sample points from features of the input point cloud. Args: x (torch.Tensor): The input point cloud. shape: (B, N, C)

r""" Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing decoding in one st

r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in sev

Args: input_tensor: shape [D, D, D], torch.float16 alpha: isosurface offset Returns: mask: shape [D, D, D], torch.int

Returns the coordinates of all voxels in the occupancy_grid where |value| < band_threshold. Args: occupancy_grid (torch.Tensor): A 3

Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles custom timesteps. Any kwargs

Forward function for the noise scaling in the flow matching.

Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned m

The initialization

r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with

r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with

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

Computes loss weighting scheme for SD3 training. Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/

r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to computing decoding in on

r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to computing decoding in one

Sets the attention processor to use [feed forward chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).

r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in sev

r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and en

Encode a batch of point features into latents.

Forward process. Args: x: tensor of shape [..., dim] Returns: embedding: an embedding of `x` of shape [...,

The [`HunyuanDiT2DModel`] forward method. Args: hidden_states (`torch.Tensor` of shape `(batch size, dim, height, width)`):