[Tests] Fix some slow tests (#3989)

fix some slow tests

Author: patrickvonplaten

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py

@@ -183,7 +183,7 @@ def enable_sequential_cpu_offload(self, gpu_id=0):
             self.to("cpu", silence_dtype_warnings=True)
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
 
-        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.text_encoder_2, self.vae]:
             cpu_offload(cpu_offloaded_model, device)
 
     def enable_model_cpu_offload(self, gpu_id=0):

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -191,7 +191,7 @@ def enable_sequential_cpu_offload(self, gpu_id=0):
             self.to("cpu", silence_dtype_warnings=True)
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
 
-        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.text_encoder_2, self.vae]:
             cpu_offload(cpu_offloaded_model, device)
 
     def enable_model_cpu_offload(self, gpu_id=0):

tests/pipelines/test_pipelines_common.py

@@ -699,12 +699,16 @@ def _test_xformers_attention_forwardGenerator_pass(
 
         inputs = self.get_dummy_inputs(torch_device)
         output_without_offload = pipe(**inputs)[0]
-        output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload
+        output_without_offload = (
+            output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload
+        )
 
         pipe.enable_xformers_memory_efficient_attention()
         inputs = self.get_dummy_inputs(torch_device)
         output_with_offload = pipe(**inputs)[0]
-        output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload
+        output_with_offload = (
+            output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload
+        )
 
         if test_max_difference:
             max_diff = np.abs(output_with_offload - output_without_offload).max()

tests/pipelines/text_to_video/test_text_to_video.py

@@ -26,7 +26,7 @@
     TextToVideoSDPipeline,
     UNet3DConditionModel,
 )
-from diffusers.utils import load_numpy, skip_mps, slow
+from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
 from diffusers.utils.testing_utils import enable_full_determinism
 
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
@@ -143,6 +143,13 @@ def test_text_to_video_default_case(self):
     def test_attention_slicing_forward_pass(self):
         self._test_attention_slicing_forward_pass(test_mean_pixel_difference=False, expected_max_diff=3e-3)
 
+    @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):
+        self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False, expected_max_diff=1e-2)
+
     # (todo): sayakpaul
     @unittest.skip(reason="Batching needs to be properly figured out first for this pipeline.")
     def test_inference_batch_consistent(self):