enable latent_diffusion, dance_diffusion, musicldm, shap_e integration

uts on xpu

Signed-off-by: Yao Matrix <[email protected]>

Author: yao-matrix

tests/pipelines/dance_diffusion/test_dance_diffusion.py

@@ -20,7 +20,7 @@
 import torch
 
 from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel
-from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, skip_mps, torch_device
+from diffusers.utils.testing_utils import backend_empty_cache, enable_full_determinism, nightly, require_torch_accelerator, require_torch_gpu, skip_mps, torch_device
 
 from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
 from ..test_pipelines_common import PipelineTesterMixin
@@ -116,19 +116,19 @@ def test_inference_batch_single_identical(self):
 
 
 @nightly
-@require_torch_gpu
+@require_torch_accelerator
 class PipelineIntegrationTests(unittest.TestCase):
     def setUp(self):
         # clean up the VRAM before each test
         super().setUp()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def tearDown(self):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def test_dance_diffusion(self):
         device = torch_device

tests/pipelines/latent_diffusion/test_latent_diffusion.py

@@ -22,9 +22,11 @@
 
 from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNet2DConditionModel
 from diffusers.utils.testing_utils import (
+    backend_empty_cache,
     enable_full_determinism,
     load_numpy,
     nightly,
+    require_torch_accelerator,
     require_torch_gpu,
     torch_device,
 )
@@ -136,17 +138,17 @@ def test_inference_text2img(self):
 
 
 @nightly
-@require_torch_gpu
+@require_torch_accelerator
 class LDMTextToImagePipelineSlowTests(unittest.TestCase):
     def setUp(self):
         super().setUp()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def tearDown(self):
         super().tearDown()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def get_inputs(self, device, dtype=torch.float32, seed=0):
         generator = torch.manual_seed(seed)
@@ -177,17 +179,17 @@ def test_ldm_default_ddim(self):
 
 
 @nightly
-@require_torch_gpu
+@require_torch_accelerator
 class LDMTextToImagePipelineNightlyTests(unittest.TestCase):
     def setUp(self):
         super().setUp()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def tearDown(self):
         super().tearDown()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def get_inputs(self, device, dtype=torch.float32, seed=0):
         generator = torch.manual_seed(seed)

tests/pipelines/musicldm/test_musicldm.py

@@ -39,7 +39,7 @@
     UNet2DConditionModel,
 )
 from diffusers.utils import is_xformers_available
-from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
+from diffusers.utils.testing_utils import backend_empty_cache, enable_full_determinism, nightly, require_torch_accelerator, require_torch_gpu, torch_device
 
 from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
 from ..test_pipelines_common import PipelineTesterMixin
@@ -408,17 +408,17 @@ def test_to_dtype(self):
 
 
 @nightly
-@require_torch_gpu
+@require_torch_accelerator
 class MusicLDMPipelineNightlyTests(unittest.TestCase):
     def setUp(self):
         super().setUp()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def tearDown(self):
         super().tearDown()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
         generator = torch.Generator(device=generator_device).manual_seed(seed)

tests/pipelines/shap_e/test_shap_e.py

@@ -21,7 +21,7 @@
 
 from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
 from diffusers.pipelines.shap_e import ShapERenderer
-from diffusers.utils.testing_utils import load_numpy, nightly, require_torch_gpu, torch_device
+from diffusers.utils.testing_utils import backend_empty_cache, load_numpy, nightly, require_torch_accelerator, require_torch_gpu, torch_device
 
 from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
 
@@ -222,19 +222,19 @@ def test_sequential_cpu_offload_forward_pass(self):
 
 
 @nightly
-@require_torch_gpu
+@require_torch_accelerator
 class ShapEPipelineIntegrationTests(unittest.TestCase):
     def setUp(self):
         # clean up the VRAM before each test
         super().setUp()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def tearDown(self):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def test_shap_e(self):
         expected_image = load_numpy(

tests/pipelines/shap_e/test_shap_e_img2img.py

@@ -23,10 +23,12 @@
 from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImg2ImgPipeline
 from diffusers.pipelines.shap_e import ShapERenderer
 from diffusers.utils.testing_utils import (
+    backend_empty_cache,
     floats_tensor,
     load_image,
     load_numpy,
     nightly,
+    require_torch_accelerator,
     require_torch_gpu,
     torch_device,
 )
@@ -250,19 +252,19 @@ def test_sequential_cpu_offload_forward_pass(self):
 
 
 @nightly
-@require_torch_gpu
+@require_torch_accelerator
 class ShapEImg2ImgPipelineIntegrationTests(unittest.TestCase):
     def setUp(self):
         # clean up the VRAM before each test
         super().setUp()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def tearDown(self):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
-        torch.cuda.empty_cache()
+        backend_empty_cache(torch_device)
 
     def test_shap_e_img2img(self):
         input_image = load_image(