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[tests] Fix controlnet tests #13736

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Merged
sayakpaul merged 8 commits into from
May 15, 2026
+14 −25
Merged

[tests] Fix controlnet tests #13736

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06353cb
add serge reviewer to enable claude for inline reviews.
sayakpaul May 13, 2026
31b949d
remove local settings
sayakpaul May 13, 2026
8d21c97
fix controlnet test failures.
sayakpaul May 13, 2026
784c78e
remove serge reviewer workflow from the mix
sayakpaul May 13, 2026
e44e1e7
Merge branch 'main' into fix-controlnet-tests
sayakpaul May 13, 2026
e549c23
Merge branch 'main' into fix-controlnet-tests
sayakpaul May 13, 2026
300223f
Merge branch 'main' into fix-controlnet-tests
sayakpaul May 13, 2026
b51a749
Merge branch 'main' into fix-controlnet-tests
sayakpaul May 15, 2026
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8 changes: 3 additions & 5 deletions tests/pipelines/controlnet_flux/test_controlnet_flux.py
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Original file line number Diff line number Diff line change
Expand Up @@ -143,7 +143,7 @@ def get_dummy_inputs(self, device, seed=0):
(1, 3, 32, 32),
generator=generator,
device=torch.device(device),
dtype=torch.float16,
dtype=torch.float32,
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Nit: there's another dtype=torch.float16 at line 201 in test_flux_image_output_shape that wasn't updated. The pipe in that test defaults to float32, so the control_image will be float16 while the rest of the pipeline is float32. It probably doesn't break anything since that test only checks output shape, but for consistency it should likely be torch.float32 as well.

)

controlnet_conditioning_scale = 0.5
Expand All @@ -163,7 +163,7 @@ def get_dummy_inputs(self, device, seed=0):
def test_controlnet_flux(self):
components = self.get_dummy_components()
flux_pipe = FluxControlNetPipeline(**components)
flux_pipe = flux_pipe.to(torch_device, dtype=torch.float16)
flux_pipe = flux_pipe.to(torch_device, dtype=torch.float32)
flux_pipe.set_progress_bar_config(disable=None)

inputs = self.get_dummy_inputs(torch_device)
Expand All @@ -174,9 +174,7 @@ def test_controlnet_flux(self):

assert image.shape == (1, 32, 32, 3)

expected_slice = np.array(
[0.47387695, 0.63134766, 0.5605469, 0.61621094, 0.7207031, 0.7089844, 0.70410156, 0.6113281, 0.64160156]
)
expected_slice = np.array([0.6677, 0.6138, 0.5296, 0.6109, 0.5672, 0.6373, 0.5463, 0.6068, 0.5569])

assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2, (
f"Expected: {expected_slice}, got: {image_slice.flatten()}"
Expand Down
13 changes: 3 additions & 10 deletions tests/pipelines/controlnet_hunyuandit/test_controlnet_hunyuandit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -126,7 +126,7 @@ def get_dummy_inputs(self, device, seed=0):
(1, 3, 16, 16),
generator=generator,
device=torch.device(device),
dtype=torch.float16,
dtype=torch.float32,
)

controlnet_conditioning_scale = 0.5
Expand All @@ -146,7 +146,7 @@ def get_dummy_inputs(self, device, seed=0):
def test_controlnet_hunyuandit(self):
components = self.get_dummy_components()
pipe = HunyuanDiTControlNetPipeline(**components)
pipe = pipe.to(torch_device, dtype=torch.float16)
pipe = pipe.to(torch_device, dtype=torch.float32)
pipe.set_progress_bar_config(disable=None)

inputs = self.get_dummy_inputs(torch_device)
Expand All @@ -156,14 +156,7 @@ def test_controlnet_hunyuandit(self):
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)

if torch_device == "xpu":
expected_slice = np.array(
[0.6948242, 0.89160156, 0.59375, 0.5078125, 0.57910156, 0.6035156, 0.58447266, 0.53564453, 0.52246094]
)
else:
expected_slice = np.array(
[0.6953125, 0.89208984, 0.59375, 0.5078125, 0.5786133, 0.6035156, 0.5839844, 0.53564453, 0.52246094]
)
expected_slice = np.array([0.5925, 0.5392, 0.4450, 0.7140, 0.3954, 0.3553, 0.3842, 0.5994, 0.3765])

assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2, (
f"Expected: {expected_slice}, got: {image_slice.flatten()}"
Expand Down
10 changes: 4 additions & 6 deletions tests/pipelines/controlnet_sd3/test_controlnet_inpaint_sd3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -156,14 +156,14 @@ def get_dummy_inputs(self, device, seed=0):
(1, 3, 32, 32),
generator=generator,
device=torch.device(device),
dtype=torch.float16,
dtype=torch.float32,
)

control_mask = randn_tensor(
(1, 1, 32, 32),
generator=generator,
device=torch.device(device),
dtype=torch.float16,
dtype=torch.float32,
)

controlnet_conditioning_scale = 0.95
Expand All @@ -184,7 +184,7 @@ def get_dummy_inputs(self, device, seed=0):
def test_controlnet_inpaint_sd3(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusion3ControlNetInpaintingPipeline(**components)
sd_pipe = sd_pipe.to(torch_device, dtype=torch.float16)
sd_pipe = sd_pipe.to(torch_device, dtype=torch.float32)
sd_pipe.set_progress_bar_config(disable=None)

inputs = self.get_dummy_inputs(torch_device)
Expand All @@ -195,9 +195,7 @@ def test_controlnet_inpaint_sd3(self):

assert image.shape == (1, 32, 32, 3)

expected_slice = np.array(
[0.51708984, 0.7421875, 0.4580078, 0.6435547, 0.65625, 0.43603516, 0.5151367, 0.65722656, 0.60839844]
)
expected_slice = np.array([0.2875, 0.3173, 0.4028, 0.7248, 0.6338, 0.4238, 0.1730, 0.4609, 0.5424])

assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2, (
f"Expected: {expected_slice}, got: {image_slice.flatten()}"
Expand Down
8 changes: 4 additions & 4 deletions tests/pipelines/controlnet_sd3/test_controlnet_sd3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -173,7 +173,7 @@ def get_dummy_inputs(self, device, seed=0):
(1, 3, 32, 32),
generator=generator,
device=torch.device(device),
dtype=torch.float16,
dtype=torch.float32,
)

controlnet_conditioning_scale = 0.5
Expand All @@ -192,7 +192,7 @@ def get_dummy_inputs(self, device, seed=0):

def run_pipe(self, components, use_sd35=False):
sd_pipe = StableDiffusion3ControlNetPipeline(**components)
sd_pipe = sd_pipe.to(torch_device, dtype=torch.float16)
sd_pipe = sd_pipe.to(torch_device, dtype=torch.float32)
sd_pipe.set_progress_bar_config(disable=None)

inputs = self.get_dummy_inputs(torch_device)
Expand All @@ -204,9 +204,9 @@ def run_pipe(self, components, use_sd35=False):
assert image.shape == (1, 32, 32, 3)

if not use_sd35:
expected_slice = np.array([0.5767, 0.7100, 0.5981, 0.5674, 0.5952, 0.4102, 0.5093, 0.5044, 0.6030])
expected_slice = np.array([0.4578, 0.3582, 0.4046, 0.0953, 0.6878, 0.5821, 0.5541, 0.5888, 0.4651])
else:
expected_slice = np.array([1.0000, 0.9072, 0.4209, 0.2744, 0.5737, 0.3840, 0.6113, 0.6250, 0.6328])
expected_slice = np.array([0.3721, 0.5626, 0.4657, 0.2845, 0.5241, 0.5917, 0.6265, 0.6955, 0.3969])

assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2, (
f"Expected: {expected_slice}, got: {image_slice.flatten()}"
Expand Down
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