Fix issue with gligen.

comfy/ldm/modules/attention.py

@@ -16,6 +16,8 @@ if model_management.xformers_enabled():
     import xformers.ops
 
 from comfy.cli_args import args
+import comfy.ops
+
 # CrossAttn precision handling
 if args.dont_upcast_attention:
     print("disabling upcasting of attention")
@@ -51,7 +53,7 @@ def init_(tensor):
 
 # feedforward
 class GEGLU(nn.Module):
-    def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=None):
+    def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=comfy.ops):
         super().__init__()
         self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
 
@@ -61,7 +63,7 @@ class GEGLU(nn.Module):
 
 
 class FeedForward(nn.Module):
-    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=None):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=comfy.ops):
         super().__init__()
         inner_dim = int(dim * mult)
         dim_out = default(dim_out, dim)
@@ -147,7 +149,7 @@ class SpatialSelfAttention(nn.Module):
 
 
 class CrossAttentionBirchSan(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=None):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
         super().__init__()
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)
@@ -244,7 +246,7 @@ class CrossAttentionBirchSan(nn.Module):
 
 
 class CrossAttentionDoggettx(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=None):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
         super().__init__()
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)
@@ -342,7 +344,7 @@ class CrossAttentionDoggettx(nn.Module):
         return self.to_out(r2)
 
 class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=None):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
         super().__init__()
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)
@@ -398,7 +400,7 @@ class CrossAttention(nn.Module):
 
 class MemoryEfficientCrossAttention(nn.Module):
     # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None, operations=None):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None, operations=comfy.ops):
         super().__init__()
         print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
               f"{heads} heads.")
@@ -449,7 +451,7 @@ class MemoryEfficientCrossAttention(nn.Module):
         return self.to_out(out)
 
 class CrossAttentionPytorch(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=None):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
         super().__init__()
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)
@@ -507,7 +509,7 @@ else:
 
 class BasicTransformerBlock(nn.Module):
     def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
-                 disable_self_attn=False, dtype=None, device=None, operations=None):
+                 disable_self_attn=False, dtype=None, device=None, operations=comfy.ops):
         super().__init__()
         self.disable_self_attn = disable_self_attn
         self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
@@ -647,7 +649,7 @@ class SpatialTransformer(nn.Module):
     def __init__(self, in_channels, n_heads, d_head,
                  depth=1, dropout=0., context_dim=None,
                  disable_self_attn=False, use_linear=False,
-                 use_checkpoint=True, dtype=None, device=None, operations=None):
+                 use_checkpoint=True, dtype=None, device=None, operations=comfy.ops):
         super().__init__()
         if exists(context_dim) and not isinstance(context_dim, list):
             context_dim = [context_dim] * depth

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -70,7 +70,7 @@ class Upsample(nn.Module):
                  upsampling occurs in the inner-two dimensions.
     """
 
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=None):
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=comfy.ops):
         super().__init__()
         self.channels = channels
         self.out_channels = out_channels or channels
@@ -106,7 +106,7 @@ class Downsample(nn.Module):
                  downsampling occurs in the inner-two dimensions.
     """
 
-    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=None):
+    def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=comfy.ops):
         super().__init__()
         self.channels = channels
         self.out_channels = out_channels or channels
@@ -156,7 +156,7 @@ class ResBlock(TimestepBlock):
         down=False,
         dtype=None,
         device=None,
-        operations=None
+        operations=comfy.ops
     ):
         super().__init__()
         self.channels = channels