oMj `SSKrSSKrSSKrSSKrSSKJs Jr SSK J r J r J r "SS\ 5r g)N)VaeImageProcessoris_valid_imageis_valid_image_imagelistc ^\rSrSrSrSS\S-S\S-S\R4SjjrSS\RS \ S\ R\R-\ \ RR-4S jjr\S\S\S \S\\\44S j5r\S \RS\S\S\R4Sj5rSrg)VideoProcessorzSimple video processor.Nheightwidthreturnc [U[5(ae[US[R5(aCUSRS:Xa0[ R "S[5 [R"USS9n[U[5(ae[US[R5(aCUSRS:Xa0[ R "S[5 [R"USS9n[U[R[R45(aURS:Xa [U5nOr[U[5(a[US5(d[U5(aU/nO6[U[5(a[US5(aUnO [S5e[R"UVs/sHoPR "U4X#S.UD6PM snSS9nUR#SS S S S 5nU$s snf) a9 Preprocesses input video(s). Keyword arguments will be forwarded to `VaeImageProcessor.preprocess`. Args: video (`list[PIL.Image]`, `list[list[PIL.Image]]`, `torch.Tensor`, `np.array`, `list[torch.Tensor]`, `list[np.array]`): The input video. It can be one of the following: * list of the PIL images. * list of list of PIL images. * 4D Torch tensors (expected shape for each tensor `(num_frames, num_channels, height, width)`). * 4D NumPy arrays (expected shape for each array `(num_frames, height, width, num_channels)`). * list of 4D Torch tensors (expected shape for each tensor `(num_frames, num_channels, height, width)`). * list of 4D NumPy arrays (expected shape for each array `(num_frames, height, width, num_channels)`). * 5D NumPy arrays: expected shape for each array `(batch_size, num_frames, height, width, num_channels)`. * 5D Torch tensors: expected shape for each array `(batch_size, num_frames, num_channels, height, width)`. height (`int`, *optional*, defaults to `None`): The height in preprocessed frames of the video. If `None`, will use the `get_default_height_width()` to get default height. width (`int`, *optional*`, defaults to `None`): The width in preprocessed frames of the video. If `None`, will use get_default_height_width()` to get the default width. Returns: `torch.Tensor` of shape `(batch_size, num_channels, num_frames, height, width)`: A 5D tensor holding the batched channels-first video(s). rzPassing `video` as a list of 5d np.ndarray is deprecated.Please concatenate the list along the batch dimension and pass it as a single 5d np.ndarray)axiszPassing `video` as a list of 5d torch.Tensor is deprecated.Please concatenate the list along the batch dimension and pass it as a single 5d torch.TensorzeInput is in incorrect format. Currently, we only support numpy.ndarray, torch.Tensor, PIL.Image.Image)r r )dimr) isinstancelistnpndarrayndimwarningswarn FutureWarning concatenatetorchTensorcatrr ValueErrorstack preprocesspermute)selfvideor r kwargsimgs ڧC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\diffusers/video_processor.pypreprocess_videoVideoProcessor.preprocess_videos: eT " "z%(BJJ'G'GERSHMM]^L^ MMn  NN5q1E eT " "z%(ELL'I'IeTUhmm_`N` MMp  IIe!,E ebjj%,,7 8 8UZZ1_KE t $ $a)A)AE]^cEdEdGE t $ $)A%()K)KEw  chich\___SWWPVWchiopq aAq!, jsHr% output_typec `URSn/n[U5H=nXRSSSS5nUR"Xr40UD6nUR U5 M? US:Xa[ R "U5nU$US:Xa[R "U5nU$US:Xd[US35eU$) a Converts a video tensor to a list of frames for export. Keyword arguments will be forwarded to `VaeImageProcessor.postprocess`. Args: video (`torch.Tensor`): The video as a tensor. output_type (`str`, defaults to `"np"`): Output type of the postprocessed `video` tensor. rrrrrptpilz9 does not exist. Please choose one of ['np', 'pt', 'pil']) shaperanger# postprocessappendrr!rr ) r$r%r+r& batch_sizeoutputs batch_idx batch_vid batch_outputs r(postprocess_video VideoProcessor.postprocess_video]s[[^ z*I(00Aq![[U5T- 5$)N)absfloat)ratioars r(:VideoProcessor.classify_height_width_bin..sSuPRAR=Sr:)keyrr)r?minkeysint)r r r; closest_ratio default_hwrAs @r(classify_height_width_bin(VideoProcessor.classify_height_width_binxsI6> "FKKM/ST * :a=!3z!}#555r:samples new_width new_heightcURSURSpCX2:wdXA:wa[X#- X- 5n[XE-5n[X5-5nURuppn URSSSSS5R X-XU 5n[ R "XU4SSS9nXa- S-n X-nXr- S-nX-nUS S 2S S 2UU2X24nUR XXU5RSSSSS5nU$) a Resizes and crops a tensor of videos to the specified dimensions. Args: samples (`torch.Tensor`): A tensor of shape (N, C, T, H, W) where N is the batch size, C is the number of channels, T is the number of frames, H is the height, and W is the width. new_width (`int`): The desired width of the output videos. new_height (`int`): The desired height of the output videos. Returns: `torch.Tensor`: A tensor containing the resized and cropped videos. rrrrrbilinearF)sizemode align_cornersN)r/maxrGr#reshapeF interpolate)rLrMrN orig_height orig_widthr@ resized_widthresized_heightncthwstart_xend_xstart_yend_ys r(resize_and_crop_tensor%VideoProcessor.resize_and_crop_tensors #*--"2GMM!4DZ  $ (? 0)2HIE 23M !45N$MMMA!ooaAq!4<rys. XX[&[r: