Add some pytorch scaled_dot_product_attention code for testing.

--use-pytorch-cross-attention to use it.

comfy/ldm/modules/attention.py

@@ -442,11 +442,61 @@ 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.):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.heads = heads
+        self.dim_head = dim_head
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
+        self.attention_op: Optional[Any] = None
+
+    def forward(self, x, context=None, mask=None):
+        q = self.to_q(x)
+        context = default(context, x)
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        b, _, _ = q.shape
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, t.shape[1], self.heads, self.dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * self.heads, t.shape[1], self.dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
+
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
+
+        if exists(mask):
+            raise NotImplementedError
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, self.heads, out.shape[1], self.dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, out.shape[1], self.heads * self.dim_head)
+        )
+
+        return self.to_out(out)
+
 import sys
-if XFORMERS_IS_AVAILBLE == False:
+if XFORMERS_IS_AVAILBLE == False or "--disable-xformers" in sys.argv:
     if "--use-split-cross-attention" in sys.argv:
         print("Using split optimization for cross attention")
         CrossAttention = CrossAttentionDoggettx
+    else:
+        if "--use-pytorch-cross-attention" in sys.argv:
+            print("Using pytorch cross attention")
+            torch.backends.cuda.enable_math_sdp(False)
+            CrossAttention = CrossAttentionPytorch
         else:
             print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
             CrossAttention = CrossAttentionBirchSan