r""" Attention processor for IP-Adapater. Args: hidden_size (`int`): The hidden size of the attention layer. cross

The output of [`TransformerTemporalModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size x num_frames, num_channels, heig

r""" Pipeline to generate video from an input image using Stable Video Diffusion. This model inherits from [`DiffusionPipeline`]. Check the s

r""" Default processor for performing attention-related computations.

A pose detect method for image-like data. Parameters: model_det: (str) serialized ONNX format model path, such

r""" Output class for mimicmotion pipeline. Args: frames (`[List[List[PIL.Image.Image]]`, `np.ndarray`, `torch.Tensor`]):

a tiny conv network for introducing pose sequence as the condition

a tiny conv network for introducing pose sequence as the conditionpose_net.py

The output of [`UNetSpatioTemporalConditionModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channel

detect human pose by dwpose

r""" Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).

r""" Attention processor for IP-Adapater for PyTorch 2.0. Args: hidden_size (`int`): The hidden size of the attention laye

A Transformer model for video-like data. Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to

A Transformer model for video-like data. Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to

r""" A conditional Spatio-Temporal UNet model that takes a noisy video frames, conditional state, and a timestep and returns a sample shaped o