def test_ip_adapter(self, expected_max_diff: float = 1e-4, expected_pipe_slice=None):
        r"""Tests for IP-Adapter.

        The following scenarios are tested:
          - Single IP-Adapter with scale=0 should produce same output as no IP-Adapter.
          - Multi IP-Adapter with scale=0 should produce same output as no IP-Adapter.
          - Single IP-Adapter with scale!=0 should produce different output compared to no IP-Adapter.
          - Multi IP-Adapter with scale!=0 should produce different output compared to no IP-Adapter.
        """
        # Raising the tolerance for this test when it's run on a CPU because we
        # compare against static slices and that can be shaky (with a VVVV low probability).
        expected_max_diff = 9e-4 if torch_device == "cpu" else expected_max_diff

        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components).to(torch_device)
        pipe.set_progress_bar_config(disable=None)
        cross_attention_dim = pipe.unet.config.get("cross_attention_dim", 32)

        # forward pass without ip adapter
        inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))
        if expected_pipe_slice is None:
            output_without_adapter = pipe(**inputs)[0]
        else:
            output_without_adapter = expected_pipe_slice

        # 1. Single IP-Adapter test cases
        adapter_state_dict = create_ip_adapter_state_dict(pipe.unet)
        pipe.unet._load_ip_adapter_weights(adapter_state_dict)

        # forward pass with single ip adapter, but scale=0 which should have no effect
        inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))
        inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)]
        pipe.set_ip_adapter_scale(0.0)
        output_without_adapter_scale = pipe(**inputs)[0]
        if expected_pipe_slice is not None:
            output_without_adapter_scale = output_without_adapter_scale[0, -3:, -3:, -1].flatten()

        # forward pass with single ip adapter, but with scale of adapter weights
        inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))
        inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)]
        pipe.set_ip_adapter_scale(42.0)
        output_with_adapter_scale = pipe(**inputs)[0]
        if expected_pipe_slice is not None:
            output_with_adapter_scale = output_with_adapter_scale[0, -3:, -3:, -1].flatten()

        max_diff_without_adapter_scale = np.abs(output_without_adapter_scale - output_without_adapter).max()
        max_diff_with_adapter_scale = np.abs(output_with_adapter_scale - output_without_adapter).max()

        self.assertLess(
            max_diff_without_adapter_scale,
            expected_max_diff,
            "Output without ip-adapter must be same as normal inference",
        )
        self.assertGreater(
            max_diff_with_adapter_scale, 1e-2, "Output with ip-adapter must be different from normal inference"
        )

        # 2. Multi IP-Adapter test cases
        adapter_state_dict_1 = create_ip_adapter_state_dict(pipe.unet)
        adapter_state_dict_2 = create_ip_adapter_state_dict(pipe.unet)
        pipe.unet._load_ip_adapter_weights([adapter_state_dict_1, adapter_state_dict_2])

        # forward pass with multi ip adapter, but scale=0 which should have no effect
        inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))
        inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] * 2
        pipe.set_ip_adapter_scale([0.0, 0.0])
        output_without_multi_adapter_scale = pipe(**inputs)[0]
        if expected_pipe_slice is not None:
            output_without_multi_adapter_scale = output_without_multi_adapter_scale[0, -3:, -3:, -1].flatten()

        # forward pass with multi ip adapter, but with scale of adapter weights
        inputs = self._modify_inputs_for_ip_adapter_test(self.get_dummy_inputs(torch_device))
        inputs["ip_adapter_image_embeds"] = [self._get_dummy_image_embeds(cross_attention_dim)] * 2
        pipe.set_ip_adapter_scale([42.0, 42.0])
        output_with_multi_adapter_scale = pipe(**inputs)[0]
        if expected_pipe_slice is not None:
            output_with_multi_adapter_scale = output_with_multi_adapter_scale[0, -3:, -3:, -1].flatten()

        max_diff_without_multi_adapter_scale = np.abs(
            output_without_multi_adapter_scale - output_without_adapter
        ).max()
        max_diff_with_multi_adapter_scale = np.abs(output_with_multi_adapter_scale - output_without_adapter).max()
        self.assertLess(
            max_diff_without_multi_adapter_scale,
            expected_max_diff,
            "Output without multi-ip-adapter must be same as normal inference",
        )
        self.assertGreater(
            max_diff_with_multi_adapter_scale,
            1e-2,
            "Output with multi-ip-adapter scale must be different from normal inference",
        )
Tests for IP-Adapter.

        The following scenarios are tested:
          - Single IP-Adapter with scale=0 should produce same output as no IP-Adapter.
          - Multi IP-Adapter with scale=0 should produce same output as no IP-Adapter.
          - Single IP-Adapter with scale!=0 should produce different output compared to no IP-Adapter.
          - Multi IP-Adapter with scale!=0 should produce different output compared to no IP-Adapter.
        
tests/pipelines/test_pipelines_common.py