oMj VSSKrSSKrSSKrSSKJr SSKJr SSK J r "SS\5r g)N)register_to_config)VaeImageProcessor)PIL_INTERPOLATIONc^\rSrSrSr\SS\S\S\S\\\4S\ S \S \S \S \S \S\ \ -\\ S4-S-4U4Sjjj5r S\ RRS\S\S\ RR4SjrSS\ RR\R -\R$-S\S-S\S-S\\\44SjjrSrU=r$)WanAnimateImageProcessora Image processor to preprocess the reference (character) image for the Wan Animate model. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to downscale the image's (height, width) dimensions to multiples of `vae_scale_factor`. Can accept `height` and `width` arguments from [`image_processor.VaeImageProcessor.preprocess`] method. vae_scale_factor (`int`, *optional*, defaults to `8`): VAE (spatial) scale factor. If `do_resize` is `True`, the image is automatically resized to multiples of this factor. vae_latent_channels (`int`, *optional*, defaults to `16`): VAE latent channels. spatial_patch_size (`tuple[int, int]`, *optional*, defaults to `(2, 2)`): The spatial patch size used by the diffusion transformer. For Wan models, this is typically (2, 2). resample (`str`, *optional*, defaults to `lanczos`): Resampling filter to use when resizing the image. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image to [-1,1]. do_binarize (`bool`, *optional*, defaults to `False`): Whether to binarize the image to 0/1. do_convert_rgb (`bool`, *optional*, defaults to be `False`): Whether to convert the images to RGB format. do_convert_grayscale (`bool`, *optional*, defaults to be `False`): Whether to convert the images to grayscale format. fill_color (`str` or `float` or `tuple[float, ...]`, *optional*, defaults to `None`): An optional fill color when `resize_mode` is set to `"fill"`. This will fill the empty space with that color instead of filling with data from the image. Any valid `color` argument to `PIL.Image.new` is valid; if `None`, will default to filling with data from `image`. N do_resizevae_scale_factorvae_latent_channelsspatial_patch_sizeresample reducing_gap do_normalize do_binarizedo_convert_rgbdo_convert_grayscale fill_color.c X>[T U]5 U (aU (a [SS5egg)Nz`do_convert_rgb` and `do_convert_grayscale` can not both be set to `True`, if you intended to convert the image into RGB format, please set `do_convert_grayscale = False`.z` if you intended to convert the image into grayscale format, please set `do_convert_rgb = False`)super__init__ ValueError) selfr r r r rrrrrrr __class__s ڵC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\diffusers/pipelines/wan/image_processor.pyr!WanAnimateImageProcessor.__init__8s3  2tr 3>imagewidthheightreturnc X#- nURUR- nURRSLnURR=(d SnXE:aUOURU-UR-nXE:aUOURU-UR-n UR X4[ URR S9n [RRSX#4US9n U RXS-US-- US-U S-- 4S9 U(aXE:apUS-U S-- n U S:a]U RU R X,4SSUS4S9SS9 U RU R X,4SU RX*R4S9SX-4S9 U $XE:aoUS-US-- n U S:a^U RU R X4SSSU4S9SS9 U RU R X4U RSU RU4S9X-S4S9 U $) a Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image. Args: image (`PIL.Image.Image`): The image to resize and fill. width (`int`): The width to resize the image to. height (`int`): The height to resize the image to. Returns: `PIL.Image.Image`: The resized and filled image. Nr)rRGB)color)box)rr) rr configrresizerrPILImagenewpaste)rrrr ratio src_ratiofill_with_image_datarsrc_wsrc_hresizedres fill_height fill_widths r_resize_and_fill)WanAnimateImageProcessor._resize_and_fillOs.KK%,,. #{{55=[[++0q * f0D 0T,%,,2F%++2U,,~8I$++J^J^8_,`iimmEE?*mE ' UaZ 71uPQz9QR S  $kEQJ6 ?IIgnne-A1eUVGWnX^dIeII';!W^^UZ\j\jAkl 34 ""aZ%1*4 >IIgnnj-A1aQWGXnY_eIfII ';'--QRT[TaTaciAjk'/3  rcPUct[U[RR5(a URnO>[U[R 5(aUR SnOUR SnUct[U[RR5(a URnO>[U[R 5(aUR SnOUR SnX2-nX#- nURRURRS-nURRURRS-n[[R"XE-55U-U-n[[R"XE- 55U-U-nX#4$)a Returns the height and width of the image, downscaled to the next integer multiple of `vae_scale_factor`. Args: image (`PIL.Image.Image | np.ndarray | torch.Tensor`): The image input, which can be a PIL image, NumPy array, or PyTorch tensor. If it is a NumPy array, it should have shape `[batch, height, width]` or `[batch, height, width, channels]`. If it is a PyTorch tensor, it should have shape `[batch, channels, height, width]`. height (`int | None`, *optional*, defaults to `None`): The height of the preprocessed image. If `None`, the height of the `image` input will be used. width (`int | None`, *optional*, defaults to `None`): The width of the preprocessed image. If `None`, the width of the `image` input will be used. Returns: `tuple[int, int]`: A tuple containing the height and width, both resized to the nearest integer multiple of `vae_scale_factor * spatial_patch_size`. r%rr) isinstancer)r*r torchTensorshaperr'r r roundnpsqrt)rrr rmax_area aspect_ratio mod_value_h mod_value_ws rget_default_height_width1WanAnimateImageProcessor.get_default_height_widths@2 >%11E5<<00QQ =%11 E5<<00 A A>~ kk22T[[5S5STU5VV kk22T[[5S5STU5VV rwwx678KG+Ubggh567;FT}r) T)r%r%lanczosNTFFFr)NN)__name__ __module__ __qualname____firstlineno____doc__rboolinttuplestrfloatrr)r*r6r?ndarrayr;r<rE__static_attributes__ __classcell__)rs@rrrsh< !#%.4! !!$%*=>!  "#s(O   #%K%s "33d:,5yy55 5  5t" 2yy+ell:2d 2Tz 2 sCx 22rr) numpyr? PIL.Imager)r;configuration_utilsrimage_processorrutilsrrrGrrr]s(  50&_0_r