move pipeline based test skips out of pipeline mixin (#2486)

tests/pipelines/dance_diffusion/test_dance_diffusion.py

@@ -21,7 +21,7 @@ import torch
 
 from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel
 from diffusers.utils import slow, torch_device
-from diffusers.utils.testing_utils import require_torch_gpu
+from diffusers.utils.testing_utils import require_torch_gpu, skip_mps
 
 from ...test_pipelines_common import PipelineTesterMixin
 
@@ -87,6 +87,22 @@ class DanceDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         expected_slice = np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000])
         assert np.abs(audio_slice.flatten() - expected_slice).max() < 1e-2
 
+    @skip_mps
+    def test_save_load_local(self):
+        return super().test_save_load_local()
+
+    @skip_mps
+    def test_dict_tuple_outputs_equivalent(self):
+        return super().test_dict_tuple_outputs_equivalent()
+
+    @skip_mps
+    def test_save_load_optional_components(self):
+        return super().test_save_load_optional_components()
+
+    @skip_mps
+    def test_attention_slicing_forward_pass(self):
+        return super().test_attention_slicing_forward_pass()
+
 
 @slow
 @require_torch_gpu

tests/pipelines/repaint/test_repaint.py

@@ -20,7 +20,7 @@ import numpy as np
 import torch
 
 from diffusers import RePaintPipeline, RePaintScheduler, UNet2DModel
-from diffusers.utils.testing_utils import load_image, load_numpy, nightly, require_torch_gpu, torch_device
+from diffusers.utils.testing_utils import load_image, load_numpy, nightly, require_torch_gpu, skip_mps, torch_device
 
 from ...test_pipelines_common import PipelineTesterMixin
 
@@ -84,6 +84,28 @@ class RepaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
+    @skip_mps
+    def test_save_load_local(self):
+        return super().test_save_load_local()
+
+    # RePaint can hardly be made deterministic since the scheduler is currently always
+    # nondeterministic
+    @unittest.skip("non-deterministic pipeline")
+    def test_inference_batch_single_identical(self):
+        return super().test_inference_batch_single_identical()
+
+    @skip_mps
+    def test_dict_tuple_outputs_equivalent(self):
+        return super().test_dict_tuple_outputs_equivalent()
+
+    @skip_mps
+    def test_save_load_optional_components(self):
+        return super().test_save_load_optional_components()
+
+    @skip_mps
+    def test_attention_slicing_forward_pass(self):
+        return super().test_attention_slicing_forward_pass()
+
 
 @nightly
 @require_torch_gpu

tests/pipelines/stable_diffusion/test_cycle_diffusion.py

@@ -23,7 +23,7 @@ from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 
 from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNet2DConditionModel
 from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
-from diffusers.utils.testing_utils import require_torch_gpu
+from diffusers.utils.testing_utils import require_torch_gpu, skip_mps
 
 from ...test_pipelines_common import PipelineTesterMixin
 
@@ -149,6 +149,26 @@ class CycleDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
+    @skip_mps
+    def test_save_load_local(self):
+        return super().test_save_load_local()
+
+    @unittest.skip("non-deterministic pipeline")
+    def test_inference_batch_single_identical(self):
+        return super().test_inference_batch_single_identical()
+
+    @skip_mps
+    def test_dict_tuple_outputs_equivalent(self):
+        return super().test_dict_tuple_outputs_equivalent()
+
+    @skip_mps
+    def test_save_load_optional_components(self):
+        return super().test_save_load_optional_components()
+
+    @skip_mps
+    def test_attention_slicing_forward_pass(self):
+        return super().test_attention_slicing_forward_pass()
+
 
 @slow
 @require_torch_gpu

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -31,7 +31,7 @@ from diffusers import (
     UNet2DConditionModel,
 )
 from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
-from diffusers.utils.testing_utils import require_torch_gpu
+from diffusers.utils.testing_utils import require_torch_gpu, skip_mps
 
 from ...test_pipelines_common import PipelineTesterMixin
 
@@ -213,6 +213,22 @@ class StableDiffusionImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.Test
 
         assert images.shape == (batch_size * num_images_per_prompt, 32, 32, 3)
 
+    @skip_mps
+    def test_save_load_local(self):
+        return super().test_save_load_local()
+
+    @skip_mps
+    def test_dict_tuple_outputs_equivalent(self):
+        return super().test_dict_tuple_outputs_equivalent()
+
+    @skip_mps
+    def test_save_load_optional_components(self):
+        return super().test_save_load_optional_components()
+
+    @skip_mps
+    def test_attention_slicing_forward_pass(self):
+        return super().test_attention_slicing_forward_pass()
+
 
 @slow
 @require_torch_gpu

tests/pipelines/stable_diffusion/test_stable_diffusion_pix2pix_zero.py

@@ -223,6 +223,11 @@ class StableDiffusionPix2PixZeroPipelineFastTests(PipelineTesterMixin, unittest.
 
         assert images.shape == (batch_size * num_images_per_prompt, 64, 64, 3)
 
+    # Non-determinism caused by the scheduler optimizing the latent inputs during inference
+    @unittest.skip("non-deterministic pipeline")
+    def test_inference_batch_single_identical(self):
+        return super().test_inference_batch_single_identical()
+
 
 @slow
 @require_torch_gpu

tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py

@@ -382,6 +382,10 @@ class StableDiffusionDepth2ImgPipelineFastTests(PipelineTesterMixin, unittest.Te
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
+    @skip_mps
+    def test_attention_slicing_forward_pass(self):
+        return super().test_attention_slicing_forward_pass()
+
 
 @slow
 @require_torch_gpu

tests/test_pipelines_common.py

@@ -11,14 +11,7 @@ import numpy as np
 import torch
 
 import diffusers
-from diffusers import (
-    CycleDiffusionPipeline,
-    DanceDiffusionPipeline,
-    DiffusionPipeline,
-    RePaintPipeline,
-    StableDiffusionDepth2ImgPipeline,
-    StableDiffusionImg2ImgPipeline,
-)
+from diffusers import DiffusionPipeline
 from diffusers.utils import logging
 from diffusers.utils.import_utils import is_accelerate_available, is_xformers_available
 from diffusers.utils.testing_utils import require_torch, torch_device
@@ -83,15 +76,6 @@ class PipelineTesterMixin:
         torch.cuda.empty_cache()
 
     def test_save_load_local(self):
-        if torch_device == "mps" and self.pipeline_class in (
-            DanceDiffusionPipeline,
-            CycleDiffusionPipeline,
-            RePaintPipeline,
-            StableDiffusionImg2ImgPipeline,
-        ):
-            # FIXME: inconsistent outputs on MPS
-            return
-
         components = self.get_dummy_components()
         pipe = self.pipeline_class(**components)
         pipe.to(torch_device)
@@ -199,18 +183,6 @@ class PipelineTesterMixin:
     def _test_inference_batch_single_identical(
         self, test_max_difference=None, test_mean_pixel_difference=None, relax_max_difference=False
     ):
-        if self.pipeline_class.__name__ in [
-            "CycleDiffusionPipeline",
-            "RePaintPipeline",
-            "StableDiffusionPix2PixZeroPipeline",
-        ]:
-            # RePaint can hardly be made deterministic since the scheduler is currently always
-            # nondeterministic
-            # CycleDiffusion is also slightly nondeterministic
-            # There's a training loop inside Pix2PixZero and is guided by edit directions. This is
-            # why the slight non-determinism.
-            return
-
         if test_max_difference is None:
             # TODO(Pedro) - not sure why, but not at all reproducible at the moment it seems
             # make sure that batched and non-batched is identical
@@ -283,15 +255,6 @@ class PipelineTesterMixin:
             assert_mean_pixel_difference(output_batch[0][0], output[0][0])
 
     def test_dict_tuple_outputs_equivalent(self):
-        if torch_device == "mps" and self.pipeline_class in (
-            DanceDiffusionPipeline,
-            CycleDiffusionPipeline,
-            RePaintPipeline,
-            StableDiffusionImg2ImgPipeline,
-        ):
-            # FIXME: inconsistent outputs on MPS
-            return
-
         components = self.get_dummy_components()
         pipe = self.pipeline_class(**components)
         pipe.to(torch_device)
@@ -370,15 +333,6 @@ class PipelineTesterMixin:
         if not hasattr(self.pipeline_class, "_optional_components"):
             return
 
-        if torch_device == "mps" and self.pipeline_class in (
-            DanceDiffusionPipeline,
-            CycleDiffusionPipeline,
-            RePaintPipeline,
-            StableDiffusionImg2ImgPipeline,
-        ):
-            # FIXME: inconsistent outputs on MPS
-            return
-
         components = self.get_dummy_components()
         pipe = self.pipeline_class(**components)
         pipe.to(torch_device)
@@ -440,16 +394,6 @@ class PipelineTesterMixin:
         if not self.test_attention_slicing:
             return
 
-        if torch_device == "mps" and self.pipeline_class in (
-            DanceDiffusionPipeline,
-            CycleDiffusionPipeline,
-            RePaintPipeline,
-            StableDiffusionImg2ImgPipeline,
-            StableDiffusionDepth2ImgPipeline,
-        ):
-            # FIXME: inconsistent outputs on MPS
-            return
-
         components = self.get_dummy_components()
         pipe = self.pipeline_class(**components)
         pipe.to(torch_device)