[Tests] Speed up panorama tests (#3067)

* fix: norm group test for UNet3D.

* chore: speed up the panorama tests (fast).

* set default value of _test_inference_batch_single_identical.

* fix: batch_sizes default value.

Author: sayakpaul

tests/pipelines/stable_diffusion/test_stable_diffusion_panorama.py

@@ -49,7 +49,7 @@ def get_dummy_components(self):
         torch.manual_seed(0)
         unet = UNet2DConditionModel(
             block_out_channels=(32, 64),
-            layers_per_block=2,
+            layers_per_block=1,
             sample_size=32,
             in_channels=4,
             out_channels=4,
@@ -101,7 +101,7 @@ def get_dummy_inputs(self, device, seed=0):
             # Setting height and width to None to prevent OOMs on CPU.
             "height": None,
             "width": None,
-            "num_inference_steps": 2,
+            "num_inference_steps": 1,
             "guidance_scale": 6.0,
             "output_type": "numpy",
         }
@@ -119,10 +119,18 @@ def test_stable_diffusion_panorama_default_case(self):
         image_slice = image[0, -3:, -3:, -1]
         assert image.shape == (1, 64, 64, 3)
 
-        expected_slice = np.array([0.4794, 0.5084, 0.4992, 0.3941, 0.3555, 0.4754, 0.5248, 0.5224, 0.4839])
+        expected_slice = np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
+    # override to speed the overall test timing up.
+    def test_inference_batch_consistent(self):
+        super().test_inference_batch_consistent(batch_sizes=[1, 2])
+
+    # override to speed the overall test timing up.
+    def test_inference_batch_single_identical(self):
+        super().test_inference_batch_single_identical(batch_size=2)
+
     def test_stable_diffusion_panorama_negative_prompt(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         components = self.get_dummy_components()
@@ -138,7 +146,7 @@ def test_stable_diffusion_panorama_negative_prompt(self):
 
         assert image.shape == (1, 64, 64, 3)
 
-        expected_slice = np.array([0.5029, 0.5075, 0.5002, 0.3965, 0.3584, 0.4746, 0.5271, 0.5273, 0.4877])
+        expected_slice = np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -158,7 +166,7 @@ def test_stable_diffusion_panorama_euler(self):
 
         assert image.shape == (1, 64, 64, 3)
 
-        expected_slice = np.array([0.4934, 0.5455, 0.4847, 0.5022, 0.5572, 0.4833, 0.5207, 0.4952, 0.5051])
+        expected_slice = np.array([0.4886, 0.5586, 0.4476, 0.5053, 0.6013, 0.4737, 0.5538, 0.5100, 0.4927])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 

tests/test_pipelines_common.py

@@ -175,8 +175,8 @@ def test_pipeline_call_signature(self):
             f"Required optional parameters not present: {remaining_required_optional_parameters}",
         )
 
-    def test_inference_batch_consistent(self):
-        self._test_inference_batch_consistent()
+    def test_inference_batch_consistent(self, batch_sizes=[2, 4, 13]):
+        self._test_inference_batch_consistent(batch_sizes=batch_sizes)
 
     def _test_inference_batch_consistent(
         self, batch_sizes=[2, 4, 13], additional_params_copy_to_batched_inputs=["num_inference_steps"]
@@ -235,11 +235,12 @@ def _test_inference_batch_consistent(
 
         logger.setLevel(level=diffusers.logging.WARNING)
 
-    def test_inference_batch_single_identical(self):
-        self._test_inference_batch_single_identical()
+    def test_inference_batch_single_identical(self, batch_size=3):
+        self._test_inference_batch_single_identical(batch_size=batch_size)
 
     def _test_inference_batch_single_identical(
         self,
+        batch_size=3,
         test_max_difference=None,
         test_mean_pixel_difference=None,
         relax_max_difference=False,
@@ -267,7 +268,7 @@ def _test_inference_batch_single_identical(
 
         # batchify inputs
         batched_inputs = {}
-        batch_size = 3
+        batch_size = batch_size
         for name, value in inputs.items():
             if name in self.batch_params:
                 # prompt is string