From a4bc76ce755632e998534f74ca22df420eba7fa2 Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Wed, 1 Mar 2023 16:20:52 +0000 Subject: [PATCH] make style --- src/diffusers/models/autoencoder_kl.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/src/diffusers/models/autoencoder_kl.py b/src/diffusers/models/autoencoder_kl.py index ca2056b5cc..1b5ef1fe54 100644 --- a/src/diffusers/models/autoencoder_kl.py +++ b/src/diffusers/models/autoencoder_kl.py @@ -191,12 +191,11 @@ class AutoencoderKL(ModelMixin, ConfigMixin): r"""Encode a batch of images using a tiled encoder. Args: When this option is enabled, the VAE will split the input tensor into tiles to compute encoding in several - steps. This is useful to keep memory use constant regardless of image size. The end result of tiled encoding is + steps. This is useful to keep memory use constant regardless of image size. The end result of tiled encoding is: different from non-tiled encoding due to each tile using a different encoder. To avoid tiling artifacts, the tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the look of the output, but they should be much less noticeable. - x (`torch.FloatTensor`): Input batch of images. - return_dict (`bool`, *optional*, defaults to `True`): + x (`torch.FloatTensor`): Input batch of images. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`AutoencoderKLOutput`] instead of a plain tuple. """ # Split the image into 512x512 tiles and encode them separately. @@ -234,12 +233,12 @@ class AutoencoderKL(ModelMixin, ConfigMixin): r"""Decode a batch of images using a tiled decoder. Args: When this option is enabled, the VAE will split the input tensor into tiles to compute decoding in several - steps. This is useful to keep memory use constant regardless of image size. The end result of tiled decoding is + steps. This is useful to keep memory use constant regardless of image size. The end result of tiled decoding is: different from non-tiled decoding due to each tile using a different decoder. To avoid tiling artifacts, the tiles overlap and are blended together to form a smooth output. You may still see tile-sized changes in the look of the output, but they should be much less noticeable. - z (`torch.FloatTensor`): Input batch of latent vectors. - return_dict (`bool`, *optional*, defaults to `True`): + z (`torch.FloatTensor`): Input batch of latent vectors. return_dict (`bool`, *optional*, defaults to + `True`): Whether or not to return a [`DecoderOutput`] instead of a plain tuple. """ # Split z into overlapping 64x64 tiles and decode them separately.