From d1f2e3e47bb878bf623a42f9ac80325f9396fcb2 Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Wed, 29 Jun 2022 11:43:30 +0000 Subject: [PATCH] up --- src/diffusers/models/resnet.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/diffusers/models/resnet.py b/src/diffusers/models/resnet.py index 462f87d8d8..0b5262907a 100644 --- a/src/diffusers/models/resnet.py +++ b/src/diffusers/models/resnet.py @@ -167,8 +167,8 @@ class Downsample(nn.Module): # class GlideUpsample(nn.Module): # """ # An upsampling layer with an optional convolution. # # :param channels: channels in the inputs and outputs. :param -use_conv: a bool determining if a convolution is # applied. :param dims: determines if the signal is 1D, 2D, or 3D. If -3D, then # upsampling occurs in the inner-two dimensions. #""" +# use_conv: a bool determining if a convolution is # applied. :param dims: determines if the signal is 1D, 2D, or 3D. If +# 3D, then # upsampling occurs in the inner-two dimensions. #""" # # def __init__(self, channels, use_conv, dims=2, out_channels=None): # super().__init__() @@ -193,8 +193,8 @@ use_conv: a bool determining if a convolution is # applied. :param dims: determi # class LDMUpsample(nn.Module): # """ # An upsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param # -use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. # If -3D, then # upsampling occurs in the inner-two dimensions. #""" +# use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. # If +# 3D, then # upsampling occurs in the inner-two dimensions. #""" # # def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): # super().__init__()