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extend tests/others/test_cli_commands.py::TestRunCommand::test_group_… #14697

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extend tests/others/test_cli_commands.py::TestRunCommand::test_group_… #14697

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3 changes: 2 additions & 1 deletion tests/others/test_cli_commands.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,6 +49,7 @@
from diffusers.utils.testing_utils import (
require_accelerator,
require_kernels_version_greater_or_equal,
require_torch_accelerator,
require_torch_gpu,
)

Expand Down Expand Up @@ -201,7 +202,7 @@ def _parse_run_argv(self, extra_argv: list[str]) -> Namespace:
]
)

@require_torch_gpu
@require_torch_accelerator
def test_group_offload_arg(self):
from diffusers.hooks.group_offloading import _is_group_offload_enabled

Expand Down
13 changes: 7 additions & 6 deletions tests/pipelines/dreamlite/test_pipeline_dreamlite.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,9 +37,10 @@
from diffusers import DreamLitePipeline, DreamLiteUNetModel

from ...testing_utils import (
backend_empty_cache,
enable_full_determinism,
nightly,
require_torch_gpu,
require_torch_accelerator,
torch_device,
)
from ..testing_utils import MemoryTesterMixin, PipelineTesterMixin
Expand Down Expand Up @@ -221,7 +222,7 @@ class TestDreamLitePipelineMemory(DreamLitePipelineTesterConfig, MemoryTesterMix


@nightly
@require_torch_gpu
@require_torch_accelerator
class TestDreamLitePipelineIntegration:
"""End-to-end test against the real DreamLite-base checkpoint on the Hub.

Expand All @@ -236,10 +237,10 @@ class TestDreamLitePipelineIntegration:
@pytest.fixture(autouse=True)
def cleanup(self):
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
yield
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def _from_pretrained_kwargs(self):
local = os.getenv("DREAMLITE_BASE_PATH")
Expand All @@ -249,7 +250,7 @@ def _from_pretrained_kwargs(self):

def test_dreamlite_t2i_real_checkpoint(self):
pipe = DreamLitePipeline.from_pretrained(**self._from_pretrained_kwargs(), torch_dtype=torch.bfloat16).to(
"cuda"
torch_device
)
out = pipe(
prompt="a dog running on the grass",
Expand All @@ -266,7 +267,7 @@ def test_dreamlite_t2i_real_checkpoint(self):

def test_dreamlite_i2i_real_checkpoint(self):
pipe = DreamLitePipeline.from_pretrained(**self._from_pretrained_kwargs(), torch_dtype=torch.bfloat16).to(
"cuda"
torch_device
)

src = Image.fromarray((np.random.RandomState(0).rand(1024, 1024, 3) * 255).astype(np.uint8))
Expand Down
13 changes: 7 additions & 6 deletions tests/pipelines/dreamlite/test_pipeline_dreamlite_mobile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,9 +31,10 @@
from diffusers import DreamLiteMobilePipeline

from ...testing_utils import (
backend_empty_cache,
enable_full_determinism,
nightly,
require_torch_gpu,
require_torch_accelerator,
torch_device,
)
from ..testing_utils import MemoryTesterMixin, PipelineTesterMixin
Expand Down Expand Up @@ -153,7 +154,7 @@ class TestDreamLiteMobilePipelineMemory(DreamLiteMobilePipelineTesterConfig, Mem


@nightly
@require_torch_gpu
@require_torch_accelerator
class TestDreamLiteMobilePipelineIntegration:
"""End-to-end test against the real DreamLite-mobile checkpoint on the Hub.

Expand All @@ -168,10 +169,10 @@ class TestDreamLiteMobilePipelineIntegration:
@pytest.fixture(autouse=True)
def cleanup(self):
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
yield
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def _from_pretrained_kwargs(self):
local = os.getenv("DREAMLITE_MOBILE_PATH")
Expand All @@ -182,7 +183,7 @@ def _from_pretrained_kwargs(self):
def test_mobile_t2i_real_checkpoint(self):
pipe = DreamLiteMobilePipeline.from_pretrained(
**self._from_pretrained_kwargs(), torch_dtype=torch.bfloat16
).to("cuda")
).to(torch_device)
out = pipe(
prompt="a dog running on the grass",
num_inference_steps=4,
Expand All @@ -198,7 +199,7 @@ def test_mobile_t2i_real_checkpoint(self):
def test_mobile_i2i_real_checkpoint(self):
pipe = DreamLiteMobilePipeline.from_pretrained(
**self._from_pretrained_kwargs(), torch_dtype=torch.bfloat16
).to("cuda")
).to(torch_device)

src = Image.fromarray((np.random.RandomState(0).rand(1024, 1024, 3) * 255).astype(np.uint8))
out = pipe(
Expand Down
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