QwenTest / pythonProject /diffusers-main /build /lib /diffusers /pipelines /hunyuan_video /pipeline_output.py
| from dataclasses import dataclass | |
| from typing import List, Union | |
| import numpy as np | |
| import PIL.Image | |
| import torch | |
| from diffusers.utils import BaseOutput | |
| class HunyuanVideoPipelineOutput(BaseOutput): | |
| r""" | |
| 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)`. | |
| """ | |
| frames: torch.Tensor | |
| class HunyuanVideoFramepackPipelineOutput(BaseOutput): | |
| r""" | |
| 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. | |
| """ | |
| frames: Union[torch.Tensor, np.ndarray, List[List[PIL.Image.Image]], List[torch.Tensor]] | |