[Tests] Speed up some fast pipeline tests

src/diffusers/utils/testing_utils.py

@@ -105,10 +105,21 @@ def numpy_cosine_similarity_distance(a, b):
     return distance
 
 
-def print_tensor_test(tensor, filename="test_corrections.txt", expected_tensor_name="expected_slice"):
+def print_tensor_test(
+    tensor,
+    limit_to_slices=None,
+    max_torch_print=None,
+    filename="test_corrections.txt",
+    expected_tensor_name="expected_slice",
+):
+    if max_torch_print:
+        torch.set_printoptions(threshold=10_000)
+
     test_name = os.environ.get("PYTEST_CURRENT_TEST")
     if not torch.is_tensor(tensor):
         tensor = torch.from_numpy(tensor)
+    if limit_to_slices:
+        tensor = tensor[0, -3:, -3:, -1]
 
     tensor_str = str(tensor.detach().cpu().flatten().to(torch.float32)).replace("\n", "")
     # format is usually:
@@ -117,7 +128,7 @@ def print_tensor_test(tensor, filename="test_corrections.txt", expected_tensor_n
     test_file, test_class, test_fn = test_name.split("::")
     test_fn = test_fn.split()[0]
     with open(filename, "a") as f:
-        print(";".join([test_file, test_class, test_fn, output_str]), file=f)
+        print("::".join([test_file, test_class, test_fn, output_str]), file=f)
 
 
 def get_tests_dir(append_path=None):

tests/pipelines/animatediff/test_animatediff.py

@@ -131,6 +131,42 @@ def test_motion_unet_loading(self):
     def test_attention_slicing_forward_pass(self):
         pass
 
+    def test_ip_adapter_single(self):
+        expected_pipe_slice = None
+        if torch_device == "cpu":
+            expected_pipe_slice = np.array(
+                [
+                    0.5541,
+                    0.5802,
+                    0.5074,
+                    0.4583,
+                    0.4729,
+                    0.5374,
+                    0.4051,
+                    0.4495,
+                    0.4480,
+                    0.5292,
+                    0.6322,
+                    0.6265,
+                    0.5455,
+                    0.4771,
+                    0.5795,
+                    0.5845,
+                    0.4172,
+                    0.6066,
+                    0.6535,
+                    0.4113,
+                    0.6833,
+                    0.5736,
+                    0.3589,
+                    0.5730,
+                    0.4205,
+                    0.3786,
+                    0.5323,
+                ]
+            )
+        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+
     def test_inference_batch_single_identical(
         self,
         batch_size=2,
@@ -299,6 +335,9 @@ def test_xformers_attention_forwardGenerator_pass(self):
         max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()
         self.assertLess(max_diff, 1e-4, "XFormers attention should not affect the inference results")
 
+    def test_vae_slicing(self):
+        return super().test_vae_slicing(image_count=2)
+
 
 @slow
 @require_torch_gpu

tests/pipelines/animatediff/test_animatediff_video2video.py

@@ -135,6 +135,34 @@ def test_motion_unet_loading(self):
     def test_attention_slicing_forward_pass(self):
         pass
 
+    def test_ip_adapter_single(self):
+        expected_pipe_slice = None
+
+        if torch_device == "cpu":
+            expected_pipe_slice = np.array(
+                [
+                    0.4947,
+                    0.4780,
+                    0.4340,
+                    0.4666,
+                    0.4028,
+                    0.4645,
+                    0.4915,
+                    0.4101,
+                    0.4308,
+                    0.4581,
+                    0.3582,
+                    0.4953,
+                    0.4466,
+                    0.5348,
+                    0.5863,
+                    0.5299,
+                    0.5213,
+                    0.5017,
+                ]
+            )
+        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+
     def test_inference_batch_single_identical(
         self,
         batch_size=2,

tests/pipelines/controlnet/test_controlnet.py

@@ -221,6 +221,12 @@ def get_dummy_inputs(self, device, seed=0):
     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):
+        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)
+
     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available(),
         reason="XFormers attention is only available with CUDA and `xformers` installed",
@@ -455,6 +461,12 @@ def test_xformers_attention_forwardGenerator_pass(self):
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3)
 
+    def test_ip_adapter_single(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)
+
     def test_save_pretrained_raise_not_implemented_exception(self):
         components = self.get_dummy_components()
         pipe = self.pipeline_class(**components)
@@ -668,6 +680,12 @@ def test_xformers_attention_forwardGenerator_pass(self):
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3)
 
+    def test_ip_adapter_single(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)
+
     def test_save_pretrained_raise_not_implemented_exception(self):
         components = self.get_dummy_components()
         pipe = self.pipeline_class(**components)

tests/pipelines/controlnet/test_controlnet_img2img.py

@@ -174,6 +174,12 @@ def get_dummy_inputs(self, device, seed=0):
     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):
+        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)
+
     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available(),
         reason="XFormers attention is only available with CUDA and `xformers` installed",
@@ -366,6 +372,12 @@ def test_xformers_attention_forwardGenerator_pass(self):
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3)
 
+    def test_ip_adapter_single(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)
+
     def test_save_pretrained_raise_not_implemented_exception(self):
         components = self.get_dummy_components()
         pipe = self.pipeline_class(**components)

tests/pipelines/controlnet/test_controlnet_sdxl.py

@@ -191,6 +191,15 @@ def get_dummy_inputs(self, device, seed=0):
     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):
+        if not from_ssd1b:
+            expected_pipe_slice = None
+            if torch_device == "cpu":
+                expected_pipe_slice = np.array(
+                    [0.7331, 0.5907, 0.5667, 0.6029, 0.5679, 0.5968, 0.4033, 0.4761, 0.5090]
+                )
+        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+
     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available(),
         reason="XFormers attention is only available with CUDA and `xformers` installed",
@@ -1042,6 +1051,12 @@ def test_controlnet_sdxl_guess(self):
         # make sure that it's equal
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4
 
+    def test_ip_adapter_single(self):
+        expected_pipe_slice = None
+        if torch_device == "cpu":
+            expected_pipe_slice = np.array([0.6832, 0.5703, 0.5460, 0.6300, 0.5856, 0.6034, 0.4494, 0.4613, 0.5036])
+        return super().test_ip_adapter_single(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

@@ -170,6 +170,12 @@ def get_dummy_inputs(self, device, seed=0):
 
         return inputs
 
+    def test_ip_adapter_single(self):
+        expected_pipe_slice = None
+        if torch_device == "cpu":
+            expected_pipe_slice = np.array([0.6265, 0.5441, 0.5384, 0.5446, 0.5810, 0.5908, 0.5414, 0.5428, 0.5353])
+        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+
     def test_stable_diffusion_xl_controlnet_img2img(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         components = self.get_dummy_components()

tests/pipelines/latent_consistency_models/test_latent_consistency_models.py

@@ -108,6 +108,12 @@ def get_dummy_inputs(self, device, seed=0):
         }
         return inputs
 
+    def test_ip_adapter_single(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)
+
     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

@@ -119,6 +119,12 @@ def get_dummy_inputs(self, device, seed=0):
         }
         return inputs
 
+    def test_ip_adapter_single(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)
+
     def test_lcm_onestep(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
 

tests/pipelines/pia/test_pia.py

@@ -138,6 +138,43 @@ def test_motion_unet_loading(self):
 
         assert isinstance(pipe.unet, UNetMotionModel)
 
+    def test_ip_adapter_single(self):
+        expected_pipe_slice = None
+
+        if torch_device == "cpu":
+            expected_pipe_slice = np.array(
+                [
+                    0.5609,
+                    0.5756,
+                    0.4830,
+                    0.4420,
+                    0.4547,
+                    0.5129,
+                    0.3779,
+                    0.4042,
+                    0.3772,
+                    0.4450,
+                    0.5710,
+                    0.5536,
+                    0.4835,
+                    0.4308,
+                    0.5578,
+                    0.5578,
+                    0.4395,
+                    0.5440,
+                    0.6051,
+                    0.4651,
+                    0.6258,
+                    0.5662,
+                    0.3988,
+                    0.5108,
+                    0.4153,
+                    0.3993,
+                    0.4803,
+                ]
+            )
+        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+
     @unittest.skip("Attention slicing is not enabled in this pipeline")
     def test_attention_slicing_forward_pass(self):
         pass

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -370,6 +370,12 @@ def test_stable_diffusion_prompt_embeds_with_plain_negative_prompt_list(self):
 
         assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
 
+    def test_ip_adapter_single(self):
+        expected_pipe_slice = None
+        if torch_device == "cpu":
+            expected_pipe_slice = np.array([0.3203, 0.4555, 0.4711, 0.3505, 0.3973, 0.4650, 0.5137, 0.3392, 0.4045])
+        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+
     def test_stable_diffusion_ddim_factor_8(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
 

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -253,6 +253,12 @@ def test_stable_diffusion_img2img_negative_prompt(self):
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
 
+    def test_ip_adapter_single(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)
+
     def test_stable_diffusion_img2img_multiple_init_images(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         components = self.get_dummy_components()

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -388,6 +388,15 @@ def callback_on_step_end(pipe, i, t, callback_kwargs):
         # 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):
+        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)
+
 
 class StableDiffusionSimpleInpaintPipelineFastTests(StableDiffusionInpaintPipelineFastTests):
     pipeline_class = StableDiffusionInpaintPipeline
@@ -475,6 +484,12 @@ def get_dummy_inputs_2images(self, device, seed=0, img_res=64):
         }
         return inputs
 
+    def test_ip_adapter_single(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)
+
     def test_stable_diffusion_inpaint(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         components = self.get_dummy_components()

tests/pipelines/stable_diffusion_2/test_stable_diffusion_attend_and_excite.py

@@ -185,6 +185,9 @@ def test_save_load_local(self):
     def test_save_load_optional_components(self):
         super().test_save_load_optional_components(expected_max_difference=4e-4)
 
+    def test_karras_schedulers_shape(self):
+        super().test_karras_schedulers_shape(num_inference_steps_for_strength_for_iterations=3)
+
 
 @require_torch_gpu
 @nightly

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl.py

@@ -292,6 +292,12 @@ def test_stable_diffusion_xl_negative_prompt_embeds(self):
         # 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):
+        expected_pipe_slice = None
+        if torch_device == "cpu":
+            expected_pipe_slice = np.array([0.5552, 0.5569, 0.4725, 0.4348, 0.4994, 0.4632, 0.5142, 0.5012, 0.4700])
+        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
+
     def test_attention_slicing_forward_pass(self):
         super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)