diff --git a/src/diffusers/hooks/layer_skip.py b/src/diffusers/hooks/layer_skip.py
index 112edfa2f79b..82106ece38fe 100644
--- a/src/diffusers/hooks/layer_skip.py
+++ b/src/diffusers/hooks/layer_skip.py
@@ -108,8 +108,17 @@ def __torch_function__(self, func, types, args=(), kwargs=None):
 
 
 class AttentionProcessorSkipHook(ModelHook):
-    def __init__(self, skip_processor_output_fn: Callable, skip_attention_scores: bool = False, dropout: float = 1.0):
+    def __init__(
+        self,
+        skip_processor_output_fn: Callable,
+        skip_attention_scores: bool = False,
+        dropout: float = 1.0,
+        skip_attn_scores_fn: Callable | None = None,
+    ):
+        super().__init__()
         self.skip_processor_output_fn = skip_processor_output_fn
+        # STG default: return the values as attention output
+        self.skip_attn_scores_fn = skip_attn_scores_fn or (lambda attn, q, k, v: v)
         self.skip_attention_scores = skip_attention_scores
         self.dropout = dropout
 
@@ -119,8 +128,22 @@ def new_forward(self, module: torch.nn.Module, *args, **kwargs):
                 raise ValueError(
                     "Cannot set `skip_attention_scores` to True when `dropout` is not 1.0. Please set `dropout` to 1.0."
                 )
-            with AttentionScoreSkipFunctionMode():
-                output = self.fn_ref.original_forward(*args, **kwargs)
+            processor_supports_skip_fn = hasattr(module.processor, "_skip_attn_scores")
+            if processor_supports_skip_fn:
+                module.processor._skip_attn_scores = True
+                module.processor._skip_attn_scores_fn = self.skip_attn_scores_fn
+            # Use try block in case attn processor raises an exception
+            try:
+                if processor_supports_skip_fn:
+                    output = self.fn_ref.original_forward(*args, **kwargs)
+                else:
+                    # Fallback to torch native SDPA intercept approach
+                    with AttentionScoreSkipFunctionMode():
+                        output = self.fn_ref.original_forward(*args, **kwargs)
+            finally:
+                if processor_supports_skip_fn:
+                    module.processor._skip_attn_scores = False
+                    module.processor._skip_attn_scores_fn = None
         else:
             if math.isclose(self.dropout, 1.0):
                 output = self.skip_processor_output_fn(module, *args, **kwargs)