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attend and excite tests disable determinism on the class level #3478

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merged 1 commit into from
May 18, 2023
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attend and excite tests disable determinism on the class level #3478

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27 changes: 26 additions & 1 deletion tests/pipelines/stable_diffusion_2/test_stable_diffusion_attend_and_excite.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,6 @@


torch.backends.cuda.matmul.allow_tf32 = False
torch.use_deterministic_algorithms(False)
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This will get ran once when the module is loaded and then determinism will get turned back on later



@skip_mps
Expand All @@ -47,6 +46,19 @@ class StableDiffusionAttendAndExcitePipelineFastTests(
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS.union({"token_indices"})
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS

# Attend and excite requires being able to run a backward pass at
# inference time. There's no deterministic backward operator for pad

@classmethod
def setUpClass(cls):
super().setUpClass()
torch.use_deterministic_algorithms(False)

@classmethod
def tearDownClass(cls):
super().tearDownClass()
torch.use_deterministic_algorithms(True)

def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
Expand Down Expand Up @@ -171,6 +183,19 @@ def test_save_load_optional_components(self):
@require_torch_gpu
@slow
class StableDiffusionAttendAndExcitePipelineIntegrationTests(unittest.TestCase):
# Attend and excite requires being able to run a backward pass at
# inference time. There's no deterministic backward operator for pad

@classmethod
def setUpClass(cls):
super().setUpClass()
torch.use_deterministic_algorithms(False)

@classmethod
def tearDownClass(cls):
super().tearDownClass()
torch.use_deterministic_algorithms(True)

def tearDown(self):
super().tearDown()
gc.collect()
Expand Down