From 22641129129928202519e98bd524fb16b19bff45 Mon Sep 17 00:00:00 2001
From: Devam0311 <devamjuthani@gmail.com>
Date: Tue, 1 Sep 2026 15:59:11 +0530
Subject: [PATCH] Fix controlnet_pooled_projections tensor handling in SD3
 ControlNet pipelines

Both SD3 ControlNet pipelines resolved the public
`controlnet_pooled_projections` argument with
`controlnet_pooled_projections or pooled_prompt_embeds`. `or` evaluates the
truthiness of the left operand, so passing any real pooled projection raises
`RuntimeError: Boolean value of Tensor with more than one value is
ambiguous`, making the argument unusable.

In the text-to-image pipeline, replace the `or` with an explicit `None`
check. In the inpainting pipeline the `else` branch is only reachable when
the value is not `None`, so it could only ever re-assign the value to itself
or raise; drop it.

Add a regression test that passes a tensor with a ControlNet configured with
`force_zeros_for_pooled_projection=False`, which is the path that raised.

Ref #13611 (Issue 3), #9686

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01XBYeq5vB4DNZDEaqUsroKR
---
 .../pipeline_stable_diffusion_3_controlnet.py |  4 +--
 ...table_diffusion_3_controlnet_inpainting.py |  2 --
 .../controlnet_sd3/test_controlnet_sd3.py     | 30 +++++++++++++++++++
 3 files changed, 32 insertions(+), 4 deletions(-)

diff --git a/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py b/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py
index 4530a424adb4..52ac7627c3c2 100644
--- a/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py
+++ b/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet.py
@@ -1137,8 +1137,8 @@ def __call__(
         if controlnet_config.force_zeros_for_pooled_projection:
             # instantx sd3 controlnet used zero pooled projection
             controlnet_pooled_projections = torch.zeros_like(pooled_prompt_embeds)
-        else:
-            controlnet_pooled_projections = controlnet_pooled_projections or pooled_prompt_embeds
+        elif controlnet_pooled_projections is None:
+            controlnet_pooled_projections = pooled_prompt_embeds
 
         if controlnet_config.joint_attention_dim is not None:
             controlnet_encoder_hidden_states = prompt_embeds
diff --git a/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py b/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py
index d2890d55811c..2929d12c9691 100644
--- a/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py
+++ b/src/diffusers/pipelines/controlnet_sd3/pipeline_stable_diffusion_3_controlnet_inpainting.py
@@ -1269,8 +1269,6 @@ def __call__(
 
         if controlnet_pooled_projections is None:
             controlnet_pooled_projections = torch.zeros_like(pooled_prompt_embeds)
-        else:
-            controlnet_pooled_projections = controlnet_pooled_projections or pooled_prompt_embeds
 
         # 4. Prepare timesteps
         if XLA_AVAILABLE:
diff --git a/tests/pipelines/controlnet_sd3/test_controlnet_sd3.py b/tests/pipelines/controlnet_sd3/test_controlnet_sd3.py
index 5fa6770dffdf..02200f4da87f 100644
--- a/tests/pipelines/controlnet_sd3/test_controlnet_sd3.py
+++ b/tests/pipelines/controlnet_sd3/test_controlnet_sd3.py
@@ -212,6 +212,36 @@ def test_controlnet_sd35(self):
         # fmt: on
         self._run_and_check_slice(components, expected_slice)
 
+    def test_controlnet_pooled_projections_accepts_tensor(self):
+        # Regression: when the ControlNet does not force zeroed pooled projections, the pipeline
+        # resolved this argument with `controlnet_pooled_projections or pooled_prompt_embeds`.
+        # `or` takes the truthiness of the tensor, which raises "Boolean value of Tensor with more
+        # than one value is ambiguous" for any real pooled projection. See huggingface/diffusers#9686.
+        components = self.get_dummy_components()
+        torch.manual_seed(0)
+        components["controlnet"] = SD3ControlNetModel(
+            sample_size=32,
+            patch_size=1,
+            in_channels=8,
+            num_layers=1,
+            attention_head_dim=8,
+            num_attention_heads=4,
+            joint_attention_dim=32,
+            caption_projection_dim=32,
+            pooled_projection_dim=64,
+            out_channels=8,
+            qk_norm="rms_norm",
+            force_zeros_for_pooled_projection=False,
+        )
+        pipe = self.get_pipeline(**components).to(torch_device, dtype=torch.float32)
+
+        inputs = self.get_dummy_inputs()
+        inputs["controlnet_pooled_projections"] = torch.zeros((1, 64), device=torch_device)
+
+        image = pipe(**inputs).images
+
+        assert image.shape == (1, *self.output_shape)
+
 
 class TestStableDiffusion3ControlNetPipelineMemory(StableDiffusion3ControlNetPipelineTesterConfig, MemoryTesterMixin):
     """Memory optimization tests (CPU offload, group offload, layerwise casting) for the SD3 ControlNet pipeline."""