integrated edits as recommended in the PR #15804

modules/sd_hijack_optimizations.py

@@ -486,18 +486,7 @@ def xformers_attention_forward(self, x, context=None, mask=None, **kwargs):
     k_in = self.to_k(context_k)
     v_in = self.to_v(context_v)
 
-    def _reshape(t):
+    q, k, v = (t.reshape(t.shape[0], t.shape[1], h, -1) for t in (q_in, k_in, v_in))
-        """rearrange(t, 'b n (h d) -> b n h d', h=h).
-        Using torch native operations to avoid overhead as this function is
-        called frequently. (70 times/it for SDXL)
-        """
-        b, n, _ = t.shape  # Get the batch size (b) and sequence length (n)
-        d = t.shape[2] // h  # Determine the depth per head
-        return t.reshape(b, n, h, d)
-
-    q = _reshape(q_in)
-    k = _reshape(k_in)
-    v = _reshape(v_in)
 
     del q_in, k_in, v_in
 
@@ -509,7 +498,6 @@ def xformers_attention_forward(self, x, context=None, mask=None, **kwargs):
 
     out = out.to(dtype)
 
-    # out = rearrange(out, 'b n h d -> b n (h d)', h=h)
     b, n, h, d = out.shape
     out = out.reshape(b, n, h * d)
     return self.to_out(out)