make style

Author: patrickvonplaten

src/diffusers/models/autoencoder_kl.py

@@ -111,9 +111,9 @@ def __init__(
 
     def enable_tiling(self, use_tiling: bool = True):
         r"""
-        Enable tiled VAE decoding.
-        When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in
-        several steps. This is useful to save a large amount of memory and to allow the processing of larger images.
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful to save a large amount of memory and to allow
+        the processing of larger images.
         """
         self.use_tiling = use_tiling
 
@@ -126,9 +126,8 @@ def disable_tiling(self):
 
     def enable_slicing(self):
         r"""
-        Enable sliced VAE decoding.
-        When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
-        steps. This is useful to save some memory and allow larger batch sizes.
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
         """
         self.use_slicing = True
 
@@ -190,12 +189,12 @@ def blend_h(self, a, b, blend_width):
 
     def tiled_encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput:
         r"""Encode a batch of images using a tiled encoder.
-        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 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.
         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
+        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`):
                 Whether or not to return a [`AutoencoderKLOutput`] instead of a plain tuple.
@@ -233,12 +232,12 @@ def tiled_encode(self, x: torch.FloatTensor, return_dict: bool = True) -> Autoen
 
     def tiled_decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
         r"""Decode a batch of images using a tiled decoder.
-        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 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.
         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
+        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`):
                 Whether or not to return a [`DecoderOutput`] instead of a plain tuple.