[tests] remove/speedup some low signal tests (#9285)

* remove 2 shapes from SDFunctionTesterMixin::test_vae_tiling

* combine freeu enable/disable test to reduce many inference runs

* remove low signal unet test for signature

* remove low signal embeddings test

* remove low signal progress bar test from PipelineTesterMixin

* combine ip-adapter single and multi tests to save many inferences

* fix broken tests

* Update tests/pipelines/test_pipelines_common.py

* Update tests/pipelines/test_pipelines_common.py

* add progress bar tests

tests/models/test_layers_utils.py

@@ -55,17 +55,6 @@ class EmbeddingsTests(unittest.TestCase):
             assert grad > prev_grad
             prev_grad = grad
 
-    def test_timestep_defaults(self):
-        embedding_dim = 16
-        timesteps = torch.arange(10)
-
-        t1 = get_timestep_embedding(timesteps, embedding_dim)
-        t2 = get_timestep_embedding(
-            timesteps, embedding_dim, flip_sin_to_cos=False, downscale_freq_shift=1, max_period=10_000
-        )
-
-        assert torch.allclose(t1.cpu(), t2.cpu(), 1e-3)
-
     def test_timestep_flip_sin_cos(self):
         embedding_dim = 16
         timesteps = torch.arange(10)

tests/models/test_modeling_common.py

@@ -183,17 +183,6 @@ class ModelUtilsTest(unittest.TestCase):
 
 
 class UNetTesterMixin:
-    def test_forward_signature(self):
-        init_dict, _ = self.prepare_init_args_and_inputs_for_common()
-
-        model = self.model_class(**init_dict)
-        signature = inspect.signature(model.forward)
-        # signature.parameters is an OrderedDict => so arg_names order is deterministic
-        arg_names = [*signature.parameters.keys()]
-
-        expected_arg_names = ["sample", "timestep"]
-        self.assertListEqual(arg_names[:2], expected_arg_names)
-
     def test_forward_with_norm_groups(self):
         init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
 

tests/pipelines/animatediff/test_animatediff.py

@@ -175,7 +175,7 @@ class AnimateDiffPipelineFastTests(
     def test_attention_slicing_forward_pass(self):
         pass
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
         if torch_device == "cpu":
             expected_pipe_slice = np.array(
@@ -209,7 +209,7 @@ class AnimateDiffPipelineFastTests(
                     0.5620,
                 ]
             )
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     def test_dict_tuple_outputs_equivalent(self):
         expected_slice = None

tests/pipelines/animatediff/test_animatediff_controlnet.py

@@ -193,7 +193,7 @@ class AnimateDiffControlNetPipelineFastTests(
     def test_attention_slicing_forward_pass(self):
         pass
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
         if torch_device == "cpu":
             expected_pipe_slice = np.array(
@@ -218,7 +218,7 @@ class AnimateDiffControlNetPipelineFastTests(
                     0.5155,
                 ]
             )
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     def test_dict_tuple_outputs_equivalent(self):
         expected_slice = None

tests/pipelines/animatediff/test_animatediff_sparsectrl.py

@@ -195,7 +195,7 @@ class AnimateDiffSparseControlNetPipelineFastTests(
     def test_attention_slicing_forward_pass(self):
         pass
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
         if torch_device == "cpu":
             expected_pipe_slice = np.array(
@@ -220,7 +220,7 @@ class AnimateDiffSparseControlNetPipelineFastTests(
                     0.5155,
                 ]
             )
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     def test_dict_tuple_outputs_equivalent(self):
         expected_slice = None

tests/pipelines/animatediff/test_animatediff_video2video.py

@@ -175,7 +175,7 @@ class AnimateDiffVideoToVideoPipelineFastTests(
     def test_attention_slicing_forward_pass(self):
         pass
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
 
         if torch_device == "cpu":
@@ -201,7 +201,7 @@ class AnimateDiffVideoToVideoPipelineFastTests(
                     0.5378,
                 ]
             )
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     def test_inference_batch_single_identical(
         self,

tests/pipelines/controlnet/test_controlnet.py

@@ -220,11 +220,11 @@ class ControlNetPipelineFastTests(
     def test_attention_slicing_forward_pass(self):
         return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
         if torch_device == "cpu":
             expected_pipe_slice = np.array([0.5234, 0.3333, 0.1745, 0.7605, 0.6224, 0.4637, 0.6989, 0.7526, 0.4665])
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available(),
@@ -460,11 +460,11 @@ class StableDiffusionMultiControlNetPipelineFastTests(
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3)
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
         if torch_device == "cpu":
             expected_pipe_slice = np.array([0.2422, 0.3425, 0.4048, 0.5351, 0.3503, 0.2419, 0.4645, 0.4570, 0.3804])
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     def test_save_pretrained_raise_not_implemented_exception(self):
         components = self.get_dummy_components()
@@ -679,11 +679,11 @@ class StableDiffusionMultiControlNetOneModelPipelineFastTests(
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3)
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
         if torch_device == "cpu":
             expected_pipe_slice = np.array([0.5264, 0.3203, 0.1602, 0.8235, 0.6332, 0.4593, 0.7226, 0.7777, 0.4780])
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     def test_save_pretrained_raise_not_implemented_exception(self):
         components = self.get_dummy_components()

tests/pipelines/controlnet/test_controlnet_img2img.py

@@ -173,11 +173,11 @@ class ControlNetImg2ImgPipelineFastTests(
     def test_attention_slicing_forward_pass(self):
         return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
         if torch_device == "cpu":
             expected_pipe_slice = np.array([0.7096, 0.5149, 0.3571, 0.5897, 0.4715, 0.4052, 0.6098, 0.6886, 0.4213])
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available(),
@@ -371,11 +371,11 @@ class StableDiffusionMultiControlNetPipelineFastTests(
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3)
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
         if torch_device == "cpu":
             expected_pipe_slice = np.array([0.5293, 0.7339, 0.6642, 0.3950, 0.5212, 0.5175, 0.7002, 0.5907, 0.5182])
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     def test_save_pretrained_raise_not_implemented_exception(self):
         components = self.get_dummy_components()

tests/pipelines/controlnet/test_controlnet_sdxl.py

@@ -190,14 +190,14 @@ class StableDiffusionXLControlNetPipelineFastTests(
     def test_attention_slicing_forward_pass(self):
         return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
 
-    def test_ip_adapter_single(self, from_ssd1b=False, expected_pipe_slice=None):
+    def test_ip_adapter(self, from_ssd1b=False, expected_pipe_slice=None):
         if not from_ssd1b:
             expected_pipe_slice = None
             if torch_device == "cpu":
                 expected_pipe_slice = np.array(
                     [0.7335, 0.5866, 0.5623, 0.6242, 0.5751, 0.5999, 0.4091, 0.4590, 0.5054]
                 )
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available(),
@@ -970,12 +970,12 @@ class StableDiffusionSSD1BControlNetPipelineFastTests(StableDiffusionXLControlNe
         # make sure that it's equal
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
         if torch_device == "cpu":
             expected_pipe_slice = np.array([0.7212, 0.5890, 0.5491, 0.6425, 0.5970, 0.6091, 0.4418, 0.4556, 0.5032])
 
-        return super().test_ip_adapter_single(from_ssd1b=True, expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(from_ssd1b=True, expected_pipe_slice=expected_pipe_slice)
 
     def test_controlnet_sdxl_lcm(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator

tests/pipelines/controlnet/test_controlnet_sdxl_img2img.py

@@ -175,12 +175,12 @@ class ControlNetPipelineSDXLImg2ImgFastTests(
 
         return inputs
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
         if torch_device == "cpu":
             expected_pipe_slice = np.array([0.6276, 0.5271, 0.5205, 0.5393, 0.5774, 0.5872, 0.5456, 0.5415, 0.5354])
         # TODO: update after slices.p
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     def test_stable_diffusion_xl_controlnet_img2img(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator

tests/pipelines/ip_adapters/test_ip_adapter_stable_diffusion.py

@@ -550,7 +550,7 @@ class IPAdapterSDXLIntegrationTests(IPAdapterNightlyTestsMixin):
         max_diff = numpy_cosine_similarity_distance(image_slice, expected_slice)
         assert max_diff < 5e-4
 
-    def test_ip_adapter_single_mask(self):
+    def test_ip_adapter_mask(self):
         image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="models/image_encoder")
         pipeline = StableDiffusionXLPipeline.from_pretrained(
             "stabilityai/stable-diffusion-xl-base-1.0",

tests/pipelines/latent_consistency_models/test_latent_consistency_models.py

@@ -108,11 +108,11 @@ class LatentConsistencyModelPipelineFastTests(
         }
         return inputs
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
         if torch_device == "cpu":
             expected_pipe_slice = np.array([0.1403, 0.5072, 0.5316, 0.1202, 0.3865, 0.4211, 0.5363, 0.3557, 0.3645])
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     def test_lcm_onestep(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator

tests/pipelines/latent_consistency_models/test_latent_consistency_models_img2img.py

@@ -119,11 +119,11 @@ class LatentConsistencyModelImg2ImgPipelineFastTests(
         }
         return inputs
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
         if torch_device == "cpu":
             expected_pipe_slice = np.array([0.4003, 0.3718, 0.2863, 0.5500, 0.5587, 0.3772, 0.4617, 0.4961, 0.4417])
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     def test_lcm_onestep(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator

tests/pipelines/pag/test_pag_animatediff.py

@@ -175,7 +175,7 @@ class AnimateDiffPAGPipelineFastTests(
     def test_attention_slicing_forward_pass(self):
         pass
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
 
         if torch_device == "cpu":
@@ -210,7 +210,7 @@ class AnimateDiffPAGPipelineFastTests(
                     0.5538,
                 ]
             )
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     def test_dict_tuple_outputs_equivalent(self):
         expected_slice = None

tests/pipelines/pia/test_pia.py

@@ -176,7 +176,7 @@ class PIAPipelineFastTests(IPAdapterTesterMixin, PipelineTesterMixin, PipelineFr
 
         assert isinstance(pipe.unet, UNetMotionModel)
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
 
         if torch_device == "cpu":
@@ -211,7 +211,7 @@ class PIAPipelineFastTests(IPAdapterTesterMixin, PipelineTesterMixin, PipelineFr
                     0.5538,
                 ]
             )
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     def test_dict_tuple_outputs_equivalent(self):
         expected_slice = None

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -253,11 +253,11 @@ class StableDiffusionImg2ImgPipelineFastTests(
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
         if torch_device == "cpu":
             expected_pipe_slice = np.array([0.4932, 0.5092, 0.5135, 0.5517, 0.5626, 0.6621, 0.6490, 0.5021, 0.5441])
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     def test_stable_diffusion_img2img_multiple_init_images(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -385,14 +385,14 @@ class StableDiffusionInpaintPipelineFastTests(
         # they should be the same
         assert torch.allclose(intermediate_latent, output_interrupted, atol=1e-4)
 
-    def test_ip_adapter_single(self, from_simple=False, expected_pipe_slice=None):
+    def test_ip_adapter(self, from_simple=False, expected_pipe_slice=None):
         if not from_simple:
             expected_pipe_slice = None
             if torch_device == "cpu":
                 expected_pipe_slice = np.array(
                     [0.4390, 0.5452, 0.3772, 0.5448, 0.6031, 0.4480, 0.5194, 0.4687, 0.4640]
                 )
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
 
 class StableDiffusionSimpleInpaintPipelineFastTests(StableDiffusionInpaintPipelineFastTests):
@@ -481,11 +481,11 @@ class StableDiffusionSimpleInpaintPipelineFastTests(StableDiffusionInpaintPipeli
         }
         return inputs
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
         if torch_device == "cpu":
             expected_pipe_slice = np.array([0.6345, 0.5395, 0.5611, 0.5403, 0.5830, 0.5855, 0.5193, 0.5443, 0.5211])
-        return super().test_ip_adapter_single(from_simple=True, expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(from_simple=True, expected_pipe_slice=expected_pipe_slice)
 
     def test_stable_diffusion_inpaint(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator

tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py

@@ -281,9 +281,6 @@ class StableDiffusionDepth2ImgPipelineFastTests(
         max_diff = np.abs(output - output_tuple).max()
         self.assertLess(max_diff, 1e-4)
 
-    def test_progress_bar(self):
-        super().test_progress_bar()
-
     def test_stable_diffusion_depth2img_default_case(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         components = self.get_dummy_components()

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py

@@ -330,12 +330,12 @@ class StableDiffusionXLPipelineFastTests(
         # make sure that it's equal
         assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
         if torch_device == "cpu":
             expected_pipe_slice = np.array([0.5388, 0.5452, 0.4694, 0.4583, 0.5253, 0.4832, 0.5288, 0.5035, 0.4766])
 
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     def test_attention_slicing_forward_pass(self):
         super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py

@@ -290,7 +290,7 @@ class StableDiffusionXLAdapterPipelineFastTests(
         }
         return inputs
 
-    def test_ip_adapter_single(self, from_multi=False, expected_pipe_slice=None):
+    def test_ip_adapter(self, from_multi=False, expected_pipe_slice=None):
         if not from_multi:
             expected_pipe_slice = None
             if torch_device == "cpu":
@@ -298,7 +298,7 @@ class StableDiffusionXLAdapterPipelineFastTests(
                     [0.5752, 0.6155, 0.4826, 0.5111, 0.5741, 0.4678, 0.5199, 0.5231, 0.4794]
                 )
 
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
 
     def test_stable_diffusion_adapter_default_case(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
@@ -448,12 +448,12 @@ class StableDiffusionXLMultiAdapterPipelineFastTests(
         expected_slice = np.array([0.5617, 0.6081, 0.4807, 0.5071, 0.5665, 0.4614, 0.5165, 0.5164, 0.4786])
         assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
 
-    def test_ip_adapter_single(self):
+    def test_ip_adapter(self):
         expected_pipe_slice = None
         if torch_device == "cpu":
             expected_pipe_slice = np.array([0.5617, 0.6081, 0.4807, 0.5071, 0.5665, 0.4614, 0.5165, 0.5164, 0.4786])
 
-        return super().test_ip_adapter_single(from_multi=True, expected_pipe_slice=expected_pipe_slice)
+        return super().test_ip_adapter(from_multi=True, expected_pipe_slice=expected_pipe_slice)
 
     def test_inference_batch_consistent(
         self, batch_sizes=[2, 4, 13], additional_params_copy_to_batched_inputs=["num_inference_steps"]