From e6e5d33b351fc5ed8334d74dac77b283ecea8708 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 25 Jul 2025 01:58:28 -0700 Subject: [PATCH] Remove useless code. (#9041) This is only needed on old pytorch 2.0 and older. --- comfy/ldm/wan/vae.py | 13 ++----------- 1 file changed, 2 insertions(+), 11 deletions(-) diff --git a/comfy/ldm/wan/vae.py b/comfy/ldm/wan/vae.py index a8ebc5ec6..a83c6edfd 100644 --- a/comfy/ldm/wan/vae.py +++ b/comfy/ldm/wan/vae.py @@ -52,15 +52,6 @@ class RMS_norm(nn.Module): x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma.to(x) + (self.bias.to(x) if self.bias is not None else 0) -class Upsample(nn.Upsample): - - def forward(self, x): - """ - Fix bfloat16 support for nearest neighbor interpolation. - """ - return super().forward(x.float()).type_as(x) - - class Resample(nn.Module): def __init__(self, dim, mode): @@ -73,11 +64,11 @@ class Resample(nn.Module): # layers if mode == 'upsample2d': self.resample = nn.Sequential( - Upsample(scale_factor=(2., 2.), mode='nearest-exact'), + nn.Upsample(scale_factor=(2., 2.), mode='nearest-exact'), ops.Conv2d(dim, dim // 2, 3, padding=1)) elif mode == 'upsample3d': self.resample = nn.Sequential( - Upsample(scale_factor=(2., 2.), mode='nearest-exact'), + nn.Upsample(scale_factor=(2., 2.), mode='nearest-exact'), ops.Conv2d(dim, dim // 2, 3, padding=1)) self.time_conv = CausalConv3d( dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))