diff --git a/sgm/modules/attention.py b/sgm/modules/attention.py
index 9340ddfb..1de2918f 100644
--- a/sgm/modules/attention.py
+++ b/sgm/modules/attention.py
@@ -1,5 +1,6 @@
 import logging
 import math
+from inspect import isfunction
 from typing import Any, Optional
 
 import torch
@@ -7,8 +8,6 @@
 from einops import rearrange, repeat
 from packaging import version
 from torch import nn
-from ..util import exists, default
-
 
 
 logger = logging.getLogger(__name__)
@@ -59,11 +58,25 @@
 from .diffusionmodules.util import checkpoint
 
 
-def uniq(arr):  # TODO: this seems unused
+def exists(val):
+    return val is not None
+
+
+def uniq(arr):
     return {el: True for el in arr}.keys()
 
 
-def init_(tensor):  # TODO: this seems unused
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def max_neg_value(t):
+    return -torch.finfo(t.dtype).max
+
+
+def init_(tensor):
     dim = tensor.shape[-1]
     std = 1 / math.sqrt(dim)
     tensor.uniform_(-std, std)
@@ -243,6 +256,23 @@ def forward(
 
         q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v))
 
+        ## old
+        """
+        sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+        del q, k
+
+        if exists(mask):
+            mask = rearrange(mask, 'b ... -> b (...)')
+            max_neg_value = -torch.finfo(sim.dtype).max
+            mask = repeat(mask, 'b j -> (b h) () j', h=h)
+            sim.masked_fill_(~mask, max_neg_value)
+
+        # attention, what we cannot get enough of
+        sim = sim.softmax(dim=-1)
+
+        out = einsum('b i j, b j d -> b i d', sim, v)
+        """
+        ## new
         with sdp_kernel(**BACKEND_MAP[self.backend]):
             # print("dispatching into backend", self.backend, "q/k/v shape: ", q.shape, k.shape, v.shape)
             out = F.scaled_dot_product_attention(