+hSSKJr SSKJrJr SSKrSSKrSSK r SSK J r \"SS\ 55r \"SS\ 55r g) ) dataclass)ListUnionN) BaseOutputc8\rSrSr%Sr\R \S'Srg)HunyuanVideoPipelineOutput a Output class for HunyuanVideo pipelines. Args: frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]): List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape `(batch_size, num_frames, channels, height, width)`. framesN) __name__ __module__ __qualname____firstlineno____doc__torchTensor__annotations____static_attributes__r k/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/pipelines/hunyuan_video/pipeline_output.pyrr s LLrrc\rSrSr%Sr\\R\R\ \ \ RR\ \R4\ S'Srg)#HunyuanVideoFramepackPipelineOutputa  Output class for HunyuanVideo pipelines. Args: frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]): List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape `(batch_size, num_frames, channels, height, width)`. Or, a list of torch tensors where each tensor corresponds to a latent that decodes to multiple frames. r r N)r r rrrrrrnpndarrayrPILImagerrr rrrrs@  %,, Dciioo1F,GellI[[ \\rr) dataclassesrtypingrrnumpyr PIL.Imagerrdiffusers.utilsrrrr rrr#sP! &       ]* ]  ]r