[AnyFlow] tests: drop @slow integration test scaffolds for initial PR

.ai/skills/model-integration/SKILL.md is explicit: 'No integration / slow
tests in the initial PR — don't add anything gated on @slow / RUN_SLOW=1
yet.' Our two integration test classes were shape-only assertions with TODOs
for a future numeric reference, so dropping them loses no actual coverage —
the relevant rollouts are covered by H200 bit-exact replay outside the
pytest suite. Can land a follow-up PR after merge with proper numeric
reference slices once the maintainer is comfortable enabling slow tests.

Author: Enderfga

tests/pipelines/anyflow/test_anyflow.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import gc
 import unittest
 
 import torch
@@ -25,13 +24,7 @@
     FlowMapEulerDiscreteScheduler,
 )
 
-from ...testing_utils import (
-    backend_empty_cache,
-    enable_full_determinism,
-    require_torch_accelerator,
-    slow,
-    torch_device,
-)
+from ...testing_utils import enable_full_determinism
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
 from ..test_pipelines_common import PipelineTesterMixin
 
@@ -140,43 +133,3 @@ def test_save_load_float16(self):
     @unittest.skip("AnyFlow's custom attention processor does not support sliced attention.")
     def test_attention_slicing_forward_pass(self):
         pass
-
-
-@slow
-@require_torch_accelerator
-class AnyFlowPipelineIntegrationTests(unittest.TestCase):
-    """End-to-end integration tests against released NVIDIA AnyFlow checkpoints. Run with ``RUN_SLOW=1``."""
-
-    prompt = "A cat walks on the grass, realistic style."
-
-    def setUp(self):
-        super().setUp()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def test_anyflow_t2v_1_3b(self):
-        pipe = AnyFlowPipeline.from_pretrained(
-            "nvidia/AnyFlow-Wan2.1-T2V-1.3B-Diffusers",
-            torch_dtype=torch.bfloat16,
-        )
-        pipe.to(torch_device)
-
-        generator = torch.Generator(device=torch_device).manual_seed(0)
-        video = pipe(
-            prompt=self.prompt,
-            num_inference_steps=4,
-            num_frames=33,
-            height=480,
-            width=832,
-            generator=generator,
-            output_type="pt",
-        ).frames
-
-        self.assertEqual(video[0].shape, (33, 3, 480, 832))
-        # TODO: extend with a numeric reference slice once a GPU reference run is captured. The current
-        # shape-only assertion guards against regressions in the sampling loop's frame count.

tests/pipelines/anyflow/test_anyflow_far.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import gc
 import unittest
 
 import torch
@@ -25,13 +24,7 @@
     FlowMapEulerDiscreteScheduler,
 )
 
-from ...testing_utils import (
-    backend_empty_cache,
-    enable_full_determinism,
-    require_torch_accelerator,
-    slow,
-    torch_device,
-)
+from ...testing_utils import enable_full_determinism
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
 from ..test_pipelines_common import PipelineTesterMixin
 
@@ -162,42 +155,3 @@ def test_attention_slicing_forward_pass(self):
     )
     def test_callback_inputs(self):
         pass
-
-
-@slow
-@require_torch_accelerator
-class AnyFlowFARPipelineIntegrationTests(unittest.TestCase):
-    """End-to-end integration tests against released NVIDIA AnyFlow-FAR checkpoints. Run with ``RUN_SLOW=1``."""
-
-    prompt = "A cat walks on the grass, realistic style."
-
-    def setUp(self):
-        super().setUp()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def test_anyflow_far_t2v_1_3b(self):
-        pipe = AnyFlowFARPipeline.from_pretrained(
-            "nvidia/AnyFlow-FAR-Wan2.1-1.3B-Diffusers",
-            torch_dtype=torch.bfloat16,
-        )
-        pipe.to(torch_device)
-
-        generator = torch.Generator(device=torch_device).manual_seed(0)
-        video = pipe(
-            prompt=self.prompt,
-            num_inference_steps=4,
-            num_frames=33,
-            height=480,
-            width=832,
-            generator=generator,
-            output_type="pt",
-        ).frames
-
-        self.assertEqual(video[0].shape, (33, 3, 480, 832))
-        # TODO: extend with a numeric reference slice once a GPU reference run is captured.