diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index bb539def..f1dca2c2 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -114,7 +114,11 @@ def attention_basic(q, k, v, heads, mask=None):
             mask = repeat(mask, 'b j -> (b h) () j', h=h)
             sim.masked_fill_(~mask, max_neg_value)
         else:
-            mask = mask.reshape(mask.shape[0], -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(sim.shape)
+            if len(mask.shape) == 2:
+                bs = 1
+            else:
+                bs = mask.shape[0]
+            mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
             sim.add_(mask)
 
     # attention, what we cannot get enough of
@@ -167,7 +171,11 @@ def attention_sub_quad(query, key, value, heads, mask=None):
         query_chunk_size = 512
 
     if mask is not None:
-        mask = mask.reshape(mask.shape[0], -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+        if len(mask.shape) == 2:
+            bs = 1
+        else:
+            bs = mask.shape[0]
+        mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
 
     hidden_states = efficient_dot_product_attention(
         query,
@@ -228,7 +236,11 @@ def attention_split(q, k, v, heads, mask=None):
                             f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
 
     if mask is not None:
-        mask = mask.reshape(mask.shape[0], -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+        if len(mask.shape) == 2:
+            bs = 1
+        else:
+            bs = mask.shape[0]
+        mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(-1, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
 
     # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size)
     first_op_done = False