diff --git a/comfy/ldm/models/autoencoder.py b/comfy/ldm/models/autoencoder.py
index d2f1d74a..b91ec324 100644
--- a/comfy/ldm/models/autoencoder.py
+++ b/comfy/ldm/models/autoencoder.py
@@ -8,6 +8,7 @@ from comfy.ldm.modules.distributions.distributions import DiagonalGaussianDistri
 
 from comfy.ldm.util import instantiate_from_config
 from comfy.ldm.modules.ema import LitEma
+import comfy.ops
 
 class DiagonalGaussianRegularizer(torch.nn.Module):
     def __init__(self, sample: bool = True):
@@ -161,12 +162,12 @@ class AutoencodingEngineLegacy(AutoencodingEngine):
             },
             **kwargs,
         )
-        self.quant_conv = torch.nn.Conv2d(
+        self.quant_conv = comfy.ops.disable_weight_init.Conv2d(
             (1 + ddconfig["double_z"]) * ddconfig["z_channels"],
             (1 + ddconfig["double_z"]) * embed_dim,
             1,
         )
-        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        self.post_quant_conv = comfy.ops.disable_weight_init.Conv2d(embed_dim, ddconfig["z_channels"], 1)
         self.embed_dim = embed_dim
 
     def get_autoencoder_params(self) -> list:
diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py
index fce29cb8..cc81c1f2 100644
--- a/comfy/ldm/modules/diffusionmodules/model.py
+++ b/comfy/ldm/modules/diffusionmodules/model.py
@@ -41,7 +41,7 @@ def nonlinearity(x):
 
 
 def Normalize(in_channels, num_groups=32):
-    return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+    return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
 
 
 class Upsample(nn.Module):