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@@ -106,7 +106,21 @@ class AutoencoderKLTests(ModelTesterMixin, unittest.TestCase):
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# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
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# the expected output slices are not the same for CPU and GPU.
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if torch_device in ("mps", "cpu"):
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if torch_device == "mps":
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expected_output_slice = torch.tensor(
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[
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-4.0078e-01,
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-3.8323e-04,
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-1.2681e-01,
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-1.1462e-01,
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2.0095e-01,
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1.0893e-01,
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-8.8247e-02,
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-3.0361e-01,
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-9.8644e-03,
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]
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)
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elif torch_device == "cpu":
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expected_output_slice = torch.tensor(
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[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]
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)
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@@ -455,7 +455,13 @@ class PipelineFastTests(unittest.TestCase):
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text2img = StableDiffusionPipeline(**inpaint.components).to(torch_device)
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prompt = "A painting of a squirrel eating a burger"
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generator = torch.Generator(device=torch_device).manual_seed(0)
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# Device type MPS is not supported for torch.Generator() api.
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if torch_device == "mps":
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generator = torch.manual_seed(0)
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else:
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generator = torch.Generator(device=torch_device).manual_seed(0)
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image_inpaint = inpaint(
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[prompt],
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generator=generator,
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