update

tests/pipelines/animatediff/test_animatediff_controlnet.py

@@ -20,6 +20,7 @@ from diffusers import (
 )
 from diffusers.models.attention import FreeNoiseTransformerBlock
 from diffusers.utils import logging
+from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.testing_utils import torch_device
 
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
@@ -329,6 +330,13 @@ class AnimateDiffControlNetPipelineFastTests(
         inputs["prompt_embeds"] = torch.randn((1, 4, pipe.text_encoder.config.hidden_size), device=torch_device)
         pipe(**inputs)
 
+    @unittest.skipIf(
+        torch_device != "cuda" or not is_xformers_available(),
+        reason="XFormers attention is only available with CUDA and `xformers` installed",
+    )
+    def test_xformers_attention_forwardGenerator_pass(self):
+        super()._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False)
+
     def test_free_init(self):
         components = self.get_dummy_components()
         pipe: AnimateDiffControlNetPipeline = self.pipeline_class(**components)

tests/pipelines/animatediff/test_animatediff_sparsectrl.py

@@ -19,6 +19,7 @@ from diffusers import (
     UNetMotionModel,
 )
 from diffusers.utils import logging
+from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.testing_utils import torch_device
 
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
@@ -393,6 +394,13 @@ class AnimateDiffSparseControlNetPipelineFastTests(
         inputs["prompt_embeds"] = torch.randn((1, 4, pipe.text_encoder.config.hidden_size), device=torch_device)
         pipe(**inputs)
 
+    @unittest.skipIf(
+        torch_device != "cuda" or not is_xformers_available(),
+        reason="XFormers attention is only available with CUDA and `xformers` installed",
+    )
+    def test_xformers_attention_forwardGenerator_pass(self):
+        super()._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False)
+
     def test_free_init(self):
         components = self.get_dummy_components()
         pipe: AnimateDiffSparseControlNetPipeline = self.pipeline_class(**components)

tests/pipelines/cogvideox/test_cogvideox.py

@@ -275,6 +275,10 @@ class CogVideoXPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             "VAE tiling should not affect the inference results",
         )
 
+    @unittest.skip("xformers attention processor does not exist for CogVideoX")
+    def test_xformers_attention_forwardGenerator_pass(self):
+        pass
+
 
 @slow
 @require_torch_gpu

tests/pipelines/latte/test_latte.py

@@ -28,6 +28,7 @@ from diffusers import (
     LattePipeline,
     LatteTransformer3DModel,
 )
+from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.testing_utils import (
     enable_full_determinism,
     numpy_cosine_similarity_distance,
@@ -256,6 +257,13 @@ class LattePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         max_diff = np.abs(to_np(output) - to_np(output_loaded)).max()
         self.assertLess(max_diff, 1.0)
 
+    @unittest.skipIf(
+        torch_device != "cuda" or not is_xformers_available(),
+        reason="XFormers attention is only available with CUDA and `xformers` installed",
+    )
+    def test_xformers_attention_forwardGenerator_pass(self):
+        super()._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False)
+
 
 @slow
 @require_torch_gpu