forward loop of sde

Compute the noise coefficient along the path

Function to reshape time t to broadcastable dimension of x Args: t: [batch_dim,], time vector x: [batch_dim,...], data point

根据指定轴进行位移并保持维度一致

Iterate over tar file, yielding filename, content pairs for the given tar stream. Args: fileobj: the tar file stream. skip_meta:

We always output sde according to score parametrization;

Compute the data coefficient along the path

Take the mean over all non-batch dimensions.

Sampling x0 & t based on shape of x1 (if needed) Args: x1 - data point; [batch, *dim]

Sample points uniformly from the surface of the mesh along with their corresponding face normals. Args: mesh (trimesh.Trimesh): Inpu

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

Sample shards from the shard list with replacement. :param urls: a list of URLs as a Python list or brace notation string

Compute nearest-neighbor Euclidean distances from each point in a_pts to nearest in b_pts. Returns distances (not squared).

Args: image (np.ndarray): [3, h, w] Returns: image (np.ndarray): [h, w, 3], np.uint8, [0, 255].

Args: pl_module: batch (dict): the batch sample information, and it contains: - surface (torch.Floa

Normalize the mesh to fit inside a centered cube with a specified scale. The mesh is translated so that its bounding box center is at the or

recenter an image to leave some empty space at the image border. Args: image (ndarray): input image, float/uint8 [H, W, 3/4]

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

Uniformly sample points on mesh surface. vertices: (n,3) array or flattened (n*3,) faces: (f,3) array of indices or flattened (f*3,)

Returns the bounding box of the scene. Taken from Shap-E rendering script (https://github.com/openai/shap-e/blob/main/shap_e/rendering/blende

Sample points and normals preferentially from sharp edges of the mesh. Sharp edges are detected based on the angle between vertex normals an

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

Check whether a sample is valid. Args: sample: a Returns: boolean indicating whether the sample is valid.

TODO: generalize here by adding all private functions ending with steps to it

Get the last step function of the SDE solver