o GÆÏifã@s\ddlmZddlZddlZddlZddlmZeGdd„deƒƒZ eGdd„deƒƒZ dS)é)Ú dataclassN)Ú BaseOutputc@seZdZUdZejed<dS)ÚHunyuanVideoPipelineOutputa¶ 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)`. ÚframesN)Ú__name__Ú __module__Ú __qualname__Ú__doc__ÚtorchÚTensorÚ__annotations__©r r úe/home/ubuntu/.local/lib/python3.10/site-packages/diffusers/pipelines/hunyuan_video/pipeline_output.pyr s  rc@s<eZdZUdZejejBeee j j BeejBe d<dS)Ú#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. rN) rrrr r r ÚnpÚndarrayÚlistÚPILÚImager r r r rrs . r) Ú dataclassesrÚnumpyrÚ PIL.Imagerr Údiffusers.utilsrrrr r r rÚs