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| # Copyright 2026 The AnyFlow Team, NVIDIA Corp., and The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from dataclasses import dataclass | |
| import torch | |
| from ...utils import BaseOutput | |
| class AnyFlowPipelineOutput(BaseOutput): | |
| r""" | |
| Output class for AnyFlow 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 | |