diff --git a/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py b/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py
index 9c65999e3a17..8a701e088806 100644
--- a/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py
+++ b/src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py
@@ -910,6 +910,9 @@ def __call__(
         controlnet_cond, controlnet_cond_mask = self.prepare_sparse_control_conditioning(
             conditioning_frames, num_frames, controlnet_frame_indices, device, controlnet.dtype
         )
+        if self.do_classifier_free_guidance and not guess_mode:
+            controlnet_cond = torch.cat([controlnet_cond] * 2)
+            controlnet_cond_mask = torch.cat([controlnet_cond_mask] * 2)
 
         # 6. Prepare timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=device)
@@ -976,6 +979,13 @@ def __call__(
                         return_dict=False,
                     )
 
+                    if guess_mode and self.do_classifier_free_guidance:
+                        # Copied from pipeline_controlnet: apply controlnet residuals only to the conditional batch.
+                        down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
+                        mid_block_res_sample = torch.cat(
+                            [torch.zeros_like(mid_block_res_sample), mid_block_res_sample]
+                        )
+
                     # predict the noise residual
                     noise_pred = self.unet(
                         latent_model_input,
diff --git a/tests/pipelines/animatediff/test_animatediff_sparsectrl.py b/tests/pipelines/animatediff/test_animatediff_sparsectrl.py
index 6b9f672cc4a1..f0a8efb62e93 100644
--- a/tests/pipelines/animatediff/test_animatediff_sparsectrl.py
+++ b/tests/pipelines/animatediff/test_animatediff_sparsectrl.py
@@ -492,3 +492,44 @@ def test_encode_prompt_works_in_isolation(self):
             "do_classifier_free_guidance": self.get_dummy_inputs(device=torch_device).get("guidance_scale", 1.0) > 1.0,
         }
         return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)
+
+    def test_conditioning_image_affects_output_with_unrelated_prompt(self):
+        """Regression for #9508: conditioning RGB should influence output even when prompt is unrelated.
+
+        Regression for #9508 after CFG conditioning batch alignment and guess_mode residual parity.
+        """
+        components = self.get_dummy_components()
+        pipe: AnimateDiffSparseControlNetPipeline = self.pipeline_class(**components)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.to(torch_device)
+
+        video_size = (32, 32)
+        red_frame = Image.new("RGB", video_size, color=(255, 0, 0))
+        black_frame = Image.new("RGB", video_size, color=(0, 0, 0))
+
+        base_inputs = self.get_dummy_inputs(torch_device, num_frames=2)
+        base_inputs.update(
+            {
+                "prompt": "a sky with clouds",
+                "num_inference_steps": 4,
+                "controlnet_frame_indices": [0],
+                "output_type": "pt",
+            }
+        )
+
+        red_inputs = {**base_inputs, "conditioning_frames": [red_frame, red_frame]}
+        black_inputs = {**base_inputs, "conditioning_frames": [black_frame, black_frame]}
+
+        red_output = pipe(**red_inputs).frames[0]
+        black_output = pipe(**black_inputs).frames[0]
+
+        red_mean = to_np(red_output).mean()
+        black_mean = to_np(black_output).mean()
+        diff = abs(red_mean - black_mean)
+
+        self.assertGreater(
+            diff,
+            1e-2,
+            f"Conditioning image should affect output with unrelated prompt (diff={diff:.4f}, "
+            f"red_mean={red_mean:.4f}, black_mean={black_mean:.4f})",
+        )