Ë ¢Ùbi+ãó€—ddlmZddlmZmZmZddlZddlZ ddl m Z eGd„de ««Z eGd„d e ««Z y) é)Ú dataclass)ÚListÚOptionalÚUnionNé)Ú BaseOutputcó~—eZdZUdZeeejjejfe d<e ee e d<y)ÚLEditsPPDiffusionPipelineOutputuð Output class for LEdits++ Diffusion pipelines. Args: images (`List[PIL.Image.Image]` or `np.ndarray`) List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width, num_channels)`. nsfw_content_detected (`List[bool]`) List indicating whether the corresponding generated image contains “not-safe-for-work†(nsfw) content or `None` if safety checking could not be performed. ÚimagesÚnsfw_content_detectedN) Ú__name__Ú __module__Ú __qualname__Ú__doc__rrÚPILÚImageÚnpÚndarrayÚ__annotations__rÚbool©óúh/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/pipelines/ledits_pp/pipeline_output.pyr r s9…ñ ð $s—y‘y—‘Ñ'¨¯©Ð3Ñ 4Ó4Ø# D¨¡JÑ/Ô/rr có¾—eZdZUdZeeejjejfe d<eeejjejfe d<y)ÚLEditsPPInversionPipelineOutputa0 Output class for LEdits++ Diffusion pipelines. Args: input_images (`List[PIL.Image.Image]` or `np.ndarray`) List of the cropped and resized input images as PIL images of length `batch_size` or NumPy array of shape ` (batch_size, height, width, num_channels)`. vae_reconstruction_images (`List[PIL.Image.Image]` or `np.ndarray`) List of VAE reconstruction of all input images as PIL images of length `batch_size` or NumPy array of shape ` (batch_size, height, width, num_channels)`. r Úvae_reconstruction_imagesN) r rrrrrrrrrrrrrrrsK…ñ ð $s—y‘y—‘Ñ'¨¯©Ð3Ñ 4Ó4Ø$ T¨#¯)©)¯/©/Ñ%:¸B¿J¹JÐ%FÑGÔGrr)Ú dataclassesrÚtypingrrrÚnumpyrÚ PIL.ImagerÚutilsrr rrrrúr"sPðÝ!ß(Ñ(ãÛåð ô0 jó0ó ð0ð" ôH jóHó ñHr