From e31499a4377b4ad29100ccd64e004a6aa177e8f6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Laure=CE=B7t?= <laurent@fainsin.bzh>
Date: Tue, 16 May 2023 07:51:09 +0000
Subject: [PATCH] Fix incomplete docstrings for resnet.py

---
 src/diffusers/models/resnet.py | 86 ++++++++++++++++++++++++----------
 1 file changed, 62 insertions(+), 24 deletions(-)

diff --git a/src/diffusers/models/resnet.py b/src/diffusers/models/resnet.py
index d9d539959c09..debe120e8ead 100644
--- a/src/diffusers/models/resnet.py
+++ b/src/diffusers/models/resnet.py
@@ -24,14 +24,17 @@
 
 
 class Upsample1D(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
+    """A 1D upsampling layer with an optional convolution.
 
     Parameters:
-            channels: channels in the inputs and outputs.
-            use_conv: a bool determining if a convolution is applied.
-            use_conv_transpose:
-            out_channels:
+        channels (`int`):
+            number of channels in the inputs and outputs.
+        use_conv (`bool`, default `False`):
+            option to use a convolution.
+        use_conv_transpose (`bool`, default `False`):
+            option to use a convolution transpose.
+        out_channels (`int`, optional):
+            number of output channels. Defaults to `channels`.
     """
 
     def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
@@ -62,14 +65,17 @@ def forward(self, x):
 
 
 class Downsample1D(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
+    """A 1D downsampling layer with an optional convolution.
 
     Parameters:
-        channels: channels in the inputs and outputs.
-        use_conv: a bool determining if a convolution is applied.
-        out_channels:
-        padding:
+        channels (`int`):
+            number of channels in the inputs and outputs.
+        use_conv (`bool`, default `False`):
+            option to use a convolution.
+        out_channels (`int`, optional):
+            number of output channels. Defaults to `channels`.
+        padding (`int`, default `1`):
+            padding for the convolution.
     """
 
     def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
@@ -93,14 +99,17 @@ def forward(self, x):
 
 
 class Upsample2D(nn.Module):
-    """
-    An upsampling layer with an optional convolution.
+    """A 2D upsampling layer with an optional convolution.
 
     Parameters:
-        channels: channels in the inputs and outputs.
-        use_conv: a bool determining if a convolution is applied.
-        use_conv_transpose:
-        out_channels:
+        channels (`int`):
+            number of channels in the inputs and outputs.
+        use_conv (`bool`, default `False`):
+            option to use a convolution.
+        use_conv_transpose (`bool`, default `False`):
+            option to use a convolution transpose.
+        out_channels (`int`, optional):
+            number of output channels. Defaults to `channels`.
     """
 
     def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
@@ -162,14 +171,17 @@ def forward(self, hidden_states, output_size=None):
 
 
 class Downsample2D(nn.Module):
-    """
-    A downsampling layer with an optional convolution.
+    """A 2D downsampling layer with an optional convolution.
 
     Parameters:
-        channels: channels in the inputs and outputs.
-        use_conv: a bool determining if a convolution is applied.
-        out_channels:
-        padding:
+        channels (`int`):
+            number of channels in the inputs and outputs.
+        use_conv (`bool`, default `False`):
+            option to use a convolution.
+        out_channels (`int`, optional):
+            number of output channels. Defaults to `channels`.
+        padding (`int`, default `1`):
+            padding for the convolution.
     """
 
     def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
@@ -209,6 +221,19 @@ def forward(self, hidden_states):
 
 
 class FirUpsample2D(nn.Module):
+    """A 2D FIR upsampling layer with an optional convolution.
+
+    Parameters:
+        channels (`int`):
+            number of channels in the inputs and outputs.
+        use_conv (`bool`, default `False`):
+            option to use a convolution.
+        out_channels (`int`, optional):
+            number of output channels. Defaults to `channels`.
+        fir_kernel (`tuple`, default `(1, 3, 3, 1)`):
+            kernel for the FIR filter.
+    """
+
     def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)):
         super().__init__()
         out_channels = out_channels if out_channels else channels
@@ -309,6 +334,19 @@ def forward(self, hidden_states):
 
 
 class FirDownsample2D(nn.Module):
+    """A 2D FIR downsampling layer with an optional convolution.
+
+    Parameters:
+        channels (`int`):
+            number of channels in the inputs and outputs.
+        use_conv (`bool`, default `False`):
+            option to use a convolution.
+        out_channels (`int`, optional):
+            number of output channels. Defaults to `channels`.
+        fir_kernel (`tuple`, default `(1, 3, 3, 1)`):
+            kernel for the FIR filter.
+    """
+
     def __init__(self, channels=None, out_channels=None, use_conv=False, fir_kernel=(1, 3, 3, 1)):
         super().__init__()
         out_channels = out_channels if out_channels else channels