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make style

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This commit is contained in:
Patrick von Platen
2023-10-02 16:14:53 +00:00
parent 2a62aadcff
commit db91e710da
1 changed files with 20 additions and 20 deletions
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src/diffusers/models/adapter.py
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@@ -259,10 +259,10 @@ class T2IAdapter(ModelMixin, ConfigMixin):
def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
r"""
This function processes the input tensor `x` through the adapter model and returns a list of feature tensors,
each representing information extracted at a different scale from the input.
The length of the list is determined by the number of downsample blocks in the Adapter, as specified
by the `channels` and `num_res_blocks` parameters during initialization.
This function processes the input tensor `x` through the adapter model and returns a list of feature tensors,
each representing information extracted at a different scale from the input. The length of the list is
determined by the number of downsample blocks in the Adapter, as specified by the `channels` and
`num_res_blocks` parameters during initialization.
"""
return self.adapter(x)
@@ -303,10 +303,10 @@ class FullAdapter(nn.Module):
def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
r"""
This method processes the input tensor `x` through the FullAdapter model and performs operations including
pixel unshuffling, convolution, and a stack of AdapterBlocks. It returns a list of feature tensors, each capturing information
at a different stage of processing within the FullAdapter model. The number of feature tensors in the list is determined
by the number of downsample blocks specified during initialization.
This method processes the input tensor `x` through the FullAdapter model and performs operations including
pixel unshuffling, convolution, and a stack of AdapterBlocks. It returns a list of feature tensors, each
capturing information at a different stage of processing within the FullAdapter model. The number of feature
tensors in the list is determined by the number of downsample blocks specified during initialization.
"""
x = self.unshuffle(x)
x = self.conv_in(x)
@@ -351,7 +351,7 @@ class FullAdapterXL(nn.Module):
def forward(self, x: torch.Tensor) -> List[torch.Tensor]:
r"""
This method takes the tensor x as input and processes it through FullAdapterXL model. It consists of operations
This method takes the tensor x as input and processes it through FullAdapterXL model. It consists of operations
including unshuffling pixels, applying convolution layer and appending each block into list of feature tensors.
"""
x = self.unshuffle(x)
@@ -384,9 +384,9 @@ class AdapterBlock(nn.Module):
def forward(self, x):
r"""
This method takes tensor x as input and performs operations downsampling and convolutional layers if the
self.downsample and self.in_conv properties of AdapterBlock model are specified. Then it applies a series
of residual blocks to the input tensor.
This method takes tensor x as input and performs operations downsampling and convolutional layers if the
self.downsample and self.in_conv properties of AdapterBlock model are specified. Then it applies a series of
residual blocks to the input tensor.
"""
if self.downsample is not None:
x = self.downsample(x)
@@ -408,8 +408,8 @@ class AdapterResnetBlock(nn.Module):
def forward(self, x):
r"""
This method takes input tensor x and applies a convolutional layer, ReLU activation,
and another convolutional layer on the input tensor. It returns addition with the input tensor.
This method takes input tensor x and applies a convolutional layer, ReLU activation, and another convolutional
layer on the input tensor. It returns addition with the input tensor.
"""
h = x
h = self.block1(h)
@@ -451,8 +451,8 @@ class LightAdapter(nn.Module):
def forward(self, x):
r"""
This method takes the input tensor x and performs downscaling and appends it in list of feature tensors.
Each feature tensor corresponds to a different level of processing within the LightAdapter.
This method takes the input tensor x and performs downscaling and appends it in list of feature tensors. Each
feature tensor corresponds to a different level of processing within the LightAdapter.
"""
x = self.unshuffle(x)
@@ -480,8 +480,8 @@ class LightAdapterBlock(nn.Module):
def forward(self, x):
r"""
This method takes tensor x as input and performs downsampling if required.
Then it applies in convolution layer, a sequence of residual blocks, and out convolutional layer.
This method takes tensor x as input and performs downsampling if required. Then it applies in convolution
layer, a sequence of residual blocks, and out convolutional layer.
"""
if self.downsample is not None:
x = self.downsample(x)
@@ -502,8 +502,8 @@ class LightAdapterResnetBlock(nn.Module):
def forward(self, x):
r"""
This function takes input tensor x and processes it through one convolutional layer, ReLU activation,
and another convolutional layer and adds it to input tensor.
This function takes input tensor x and processes it through one convolutional layer, ReLU activation, and
another convolutional layer and adds it to input tensor.
"""
h = x
h = self.block1(h)