All the unet ops with weights are now handled by comfy.ops

comfy/controlnet.py

@@ -5,6 +5,7 @@ import comfy.utils
 import comfy.model_management
 import comfy.model_detection
 import comfy.model_patcher
+import comfy.ops
 
 import comfy.cldm.cldm
 import comfy.t2i_adapter.adapter
@@ -248,6 +249,15 @@ class ControlLoraOps:
         else:
             raise ValueError(f"unsupported dimensions: {dims}")
 
+    class Conv3d(comfy.ops.Conv3d):
+        pass
+
+    class GroupNorm(comfy.ops.GroupNorm):
+        pass
+
+    class LayerNorm(comfy.ops.LayerNorm):
+        pass
+
 
 class ControlLora(ControlNet):
     def __init__(self, control_weights, global_average_pooling=False, device=None):

comfy/ldm/modules/attention.py

@@ -83,16 +83,6 @@ class FeedForward(nn.Module):
     def forward(self, x):
         return self.net(x)
 
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
 def Normalize(in_channels, dtype=None, device=None):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
 
@@ -414,10 +404,10 @@ class BasicTransformerBlock(nn.Module):
 
             self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2,
                                 heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device, operations=operations)  # is self-attn if context is none
-            self.norm2 = nn.LayerNorm(inner_dim, dtype=dtype, device=device)
+            self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
 
-        self.norm1 = nn.LayerNorm(inner_dim, dtype=dtype, device=device)
-        self.norm3 = nn.LayerNorm(inner_dim, dtype=dtype, device=device)
+        self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
+        self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
         self.checkpoint = checkpoint
         self.n_heads = n_heads
         self.d_head = d_head
@@ -559,7 +549,7 @@ class SpatialTransformer(nn.Module):
             context_dim = [context_dim] * depth
         self.in_channels = in_channels
         inner_dim = n_heads * d_head
-        self.norm = Normalize(in_channels, dtype=dtype, device=device)
+        self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
         if not use_linear:
             self.proj_in = operations.Conv2d(in_channels,
                                      inner_dim,

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -177,7 +177,7 @@ class ResBlock(TimestepBlock):
             padding = kernel_size // 2
 
         self.in_layers = nn.Sequential(
-            nn.GroupNorm(32, channels, dtype=dtype, device=device),
+            operations.GroupNorm(32, channels, dtype=dtype, device=device),
             nn.SiLU(),
             operations.conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device),
         )
@@ -206,12 +206,11 @@ class ResBlock(TimestepBlock):
                 ),
             )
         self.out_layers = nn.Sequential(
-            nn.GroupNorm(32, self.out_channels, dtype=dtype, device=device),
+            operations.GroupNorm(32, self.out_channels, dtype=dtype, device=device),
             nn.SiLU(),
             nn.Dropout(p=dropout),
-            zero_module(
             operations.conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device)
-            ),
+            ,
         )
 
         if self.out_channels == channels:
@@ -810,13 +809,13 @@ class UNetModel(nn.Module):
                 self._feature_size += ch
 
         self.out = nn.Sequential(
-            nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
+            operations.GroupNorm(32, ch, dtype=self.dtype, device=device),
             nn.SiLU(),
             zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)),
         )
         if self.predict_codebook_ids:
             self.id_predictor = nn.Sequential(
-            nn.GroupNorm(32, ch, dtype=self.dtype, device=device),
+            operations.GroupNorm(32, ch, dtype=self.dtype, device=device),
             operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device),
             #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
         )

comfy/ops.py

@@ -13,6 +13,14 @@ class Conv3d(torch.nn.Conv3d):
     def reset_parameters(self):
         return None
 
+class GroupNorm(torch.nn.GroupNorm):
+    def reset_parameters(self):
+        return None
+
+class LayerNorm(torch.nn.LayerNorm):
+    def reset_parameters(self):
+        return None
+
 def conv_nd(dims, *args, **kwargs):
     if dims == 2:
         return Conv2d(*args, **kwargs)