diff --git a/tests/pipelines/hunyuan_video/test_hunyuan_video_framepack.py b/tests/pipelines/hunyuan_video/test_hunyuan_video_framepack.py
index 3187aa88ddc4..ace06371be28 100644
--- a/tests/pipelines/hunyuan_video/test_hunyuan_video_framepack.py
+++ b/tests/pipelines/hunyuan_video/test_hunyuan_video_framepack.py
@@ -389,6 +389,14 @@ def test_sequential_offload_forward_pass_twice(self):
         # this test because of MHA (example: HunyuanDiT because of AttentionPooling layer).
         pass
 
+    @unittest.skip("The image_encoder uses SiglipVisionModel, which does not support group offloading.")
+    def test_pipeline_level_group_offloading_inference(self):
+        # Same root cause as the sequential CPU offloading skips above: the attention layer is a
+        # torch.nn.MultiheadAttention, which passes `self.out_proj.weight` to
+        # `torch.nn.functional.multi_head_attention_forward` instead of calling `self.out_proj`. The leaf-level
+        # onload hook on `out_proj` is therefore never triggered and its weights stay on the CPU.
+        pass
+
     # TODO(aryan): Create a dummy gemma model with smol vocab size
     @unittest.skip(
         "A very small vocab size is used for fast tests. So, Any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error."