Constructor for a general sampler; supporting different sampling methods Args: - transport: an tranport object specify model predictio

The initialization

Initialize a diagonal Gaussian distribution with mean and log-variance parameters. Args: parameters (Union[torch.Tensor,

initialize ulip and uni3d model normalize: whether to normalize the mesh to fit in unit sphere device: torch device p

Return an iterator over the shards.

精确填充前景内部alpha=0的洞 参数: image: 输入图像(RGBA格式) 返回: 处理后的图像

r""" Returns the device on which the pipeline's models will be executed. After calling [`~DiffusionPipeline.enable_sequential_cpu_offl

Performs all_gather operation on the provided tensors.

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

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

Args: samples (list[dict]): combine_tensors: combine_scalars: Returns:

Compute coefficient of x1

Compute coefficient of x1

Special purposed function for computing numerical stabled d_alpha_t / alpha_t

Special purposed function for computing numerical stabled d_alpha_t / alpha_t

Compute the drift term of the SDE

Compute coefficient of x0

Compute coefficient of x0

function for creating Transport object **Note**: model prediction defaults to velocity Args: - path_type: type of path to use; default to

Overwrite model.train with this function to make sure train/eval mode does not change anymore.