calculate the number of frames for video used for model inputs. Args: ele (dict): a dict contains the configuration of video.

Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with the multistep UniPC.

Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with the multistep UniPC.

r""" Reconstruct video tensors from patch embeddings. Args: x (List[Tensor]): List of patchified features

r""" Reconstruct video tensors from patch embeddings. Args: x (List[Tensor]): List of patchified features

Extend the scale to match the given aspect ratio. Args: scale (np.ndarray): The image scale (w, h) in shape (2, ) aspect_ratio (f

Initialize the step_index counter for the scheduler.

Initialize the step_index counter for the scheduler.

Initialize the step_index counter for the scheduler.

Initialize the step_index counter for the scheduler.

Rotate a point by an angle. Args: pt (np.ndarray): 2D point coordinates (x, y) in shape (2, ) angle_rad (float): rotation angle i

Autoencoder3d adapted from Stable Diffusion 1.x, 2.x and XL.

Autoencoder3d adapted from Stable Diffusion 1.x, 2.x and XL.

Transform the bbox format from (x,y,w,h) into (center, scale) Args: bbox (ndarray): Bounding box(es) in shape (4,) or (n, 4), formatted

Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is designed to discretize an

r""" Convert the model output to the corresponding type the UniPC algorithm needs. Args: model_output (`torch.Tensor`):

Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is designed to discretize an

r""" Convert the model output to the corresponding type the UniPC algorithm needs. Args: model_output (`torch.Tensor`):

Modulate simcc distribution with Gaussian. Args: simcc_x (np.ndarray[K, Wx]): model predicted simcc in x. simcc_y (np.ndarray[K,

One step for the first-order DPMSolver (equivalent to DDIM). Args: model_output (`torch.Tensor`): The dir

One step for the first-order DPMSolver (equivalent to DDIM). Args: model_output (`torch.Tensor`): The dir

r""" Args: x(Tensor): Shape [B, L1, C] e(Tensor): Shape [B, C]

r""" Args: x(Tensor): Shape [B, L1, C] e(Tensor): Shape [B, C]

Get maximum response location and value from simcc representations. Note: instance number: N num_keypoints: K heatmap hei

Calculate the affine transformation matrix that can warp the bbox area in the input image to the output size. Args: center (np.ndarra

Inference RTMPose model. Args: sess (ort.InferenceSession): ONNXRuntime session. img (np.ndarray): Input image in shape. Ret

r""" Initialize model parameters using Xavier initialization.

r""" Initialize model parameters using Xavier initialization.

Multiclass NMS implemented in Numpy. Class-aware version.

One step for the second-order multistep DPMSolver. Args: model_output_list (`List[torch.Tensor]`): The di

One step for the second-order multistep DPMSolver. Args: model_output_list (`List[torch.Tensor]`): The di

One step for the third-order multistep DPMSolver. Args: model_output_list (`List[torch.Tensor]`): The dir

One step for the third-order multistep DPMSolver. Args: model_output_list (`List[torch.Tensor]`): The dir

One step for the UniC (B(h) version). Args: this_model_output (`torch.Tensor`): The model outputs at `x_