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[tests] HunyuanDiTControlNetPipeline inference precision issue on XPU (#11197)

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* add xpu part

* fix more cases

* remove some cases

* no canny

* format fix
This commit is contained in:
Fanli Lin
2025-04-02 19:45:02 +08:00
committed by GitHub
parent d8c617ccb0
commit 52b460feb9
1 changed files with 9 additions and 3 deletions
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12
tests/pipelines/controlnet_hunyuandit/test_controlnet_hunyuandit.py
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@@ -153,9 +153,14 @@ class HunyuanDiTControlNetPipelineFastTests(unittest.TestCase, PipelineTesterMix
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
expected_slice = np.array(
[0.6953125, 0.89208984, 0.59375, 0.5078125, 0.5786133, 0.6035156, 0.5839844, 0.53564453, 0.52246094]
)
if torch_device == "xpu":
expected_slice = np.array(
[0.6376953, 0.84375, 0.58691406, 0.48046875, 0.43652344, 0.5517578, 0.54248047, 0.5644531, 0.48217773]
)
else:
expected_slice = np.array(
[0.6953125, 0.89208984, 0.59375, 0.5078125, 0.5786133, 0.6035156, 0.5839844, 0.53564453, 0.52246094]
)
assert (
np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@@ -351,6 +356,7 @@ class HunyuanDiTControlNetPipelineSlowTests(unittest.TestCase):
assert image.shape == (1024, 1024, 3)
original_image = image[-3:, -3:, -1].flatten()
expected_image = np.array(
[0.43652344, 0.44018555, 0.4494629, 0.44995117, 0.45654297, 0.44848633, 0.43603516, 0.4404297, 0.42626953]
)