Reorganize pipeline tests (#963)

* Reorganize pipeline tests

* fix vq

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -30,13 +30,16 @@ from diffusers import (
     VQModel,
 )
 from diffusers.utils import floats_tensor, load_image, slow, torch_device
+from diffusers.utils.testing_utils import require_torch_gpu
 from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 
+from ...test_pipelines_common import PipelineTesterMixin
+
 
 torch.backends.cuda.matmul.allow_tf32 = False
 
 
-class PipelineFastTests(unittest.TestCase):
+class StableDiffusionImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     def tearDown(self):
         # clean up the VRAM after each test
         super().tearDown()
@@ -461,8 +464,8 @@ class PipelineFastTests(unittest.TestCase):
 
 
 @slow
-@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
-class PipelineIntegrationTests(unittest.TestCase):
+@require_torch_gpu
+class StableDiffusionImg2ImgPipelineIntegrationTests(unittest.TestCase):
     def tearDown(self):
         # clean up the VRAM after each test
         super().tearDown()

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -29,14 +29,17 @@ from diffusers import (
     VQModel,
 )
 from diffusers.utils import floats_tensor, load_image, slow, torch_device
+from diffusers.utils.testing_utils import require_torch_gpu
 from PIL import Image
 from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 
+from ...test_pipelines_common import PipelineTesterMixin
+
 
 torch.backends.cuda.matmul.allow_tf32 = False
 
 
-class PipelineFastTests(unittest.TestCase):
+class StableDiffusionInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     def tearDown(self):
         # clean up the VRAM after each test
         super().tearDown()
@@ -258,8 +261,8 @@ class PipelineFastTests(unittest.TestCase):
 
 
 @slow
-@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
-class PipelineIntegrationTests(unittest.TestCase):
+@require_torch_gpu
+class StableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
     def tearDown(self):
         # clean up the VRAM after each test
         super().tearDown()

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint_legacy.py

@@ -31,14 +31,17 @@ from diffusers import (
     VQModel,
 )
 from diffusers.utils import floats_tensor, load_image, slow, torch_device
+from diffusers.utils.testing_utils import require_torch_gpu
 from PIL import Image
 from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 
+from ...test_pipelines_common import PipelineTesterMixin
+
 
 torch.backends.cuda.matmul.allow_tf32 = False
 
 
-class PipelineFastTests(unittest.TestCase):
+class StableDiffusionInpaintLegacyPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     def tearDown(self):
         # clean up the VRAM after each test
         super().tearDown()
@@ -338,8 +341,8 @@ class PipelineFastTests(unittest.TestCase):
 
 
 @slow
-@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
-class PipelineIntegrationTests(unittest.TestCase):
+@require_torch_gpu
+class StableDiffusionInpaintLegacyPipelineIntegrationTests(unittest.TestCase):
     def tearDown(self):
         # clean up the VRAM after each test
         super().tearDown()

tests/test_pipelines.py

@@ -32,17 +32,7 @@ from diffusers import (
     DDIMScheduler,
     DDPMPipeline,
     DDPMScheduler,
-    KarrasVePipeline,
-    KarrasVeScheduler,
-    LDMPipeline,
-    LDMTextToImagePipeline,
-    OnnxStableDiffusionImg2ImgPipeline,
-    OnnxStableDiffusionInpaintPipeline,
-    OnnxStableDiffusionPipeline,
-    PNDMPipeline,
     PNDMScheduler,
-    ScoreSdeVePipeline,
-    ScoreSdeVeScheduler,
     StableDiffusionImg2ImgPipeline,
     StableDiffusionInpaintPipelineLegacy,
     StableDiffusionPipeline,
@@ -53,8 +43,8 @@ from diffusers import (
 )
 from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
-from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, load_image, slow, torch_device
-from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir
+from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, slow, torch_device
+from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir, require_torch_gpu
 from packaging import version
 from PIL import Image
 from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
@@ -145,12 +135,6 @@ class CustomPipelineTests(unittest.TestCase):
 
 
 class PipelineFastTests(unittest.TestCase):
-    def tearDown(self):
-        # clean up the VRAM after each test
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
     @property
     def dummy_image(self):
         batch_size = 1
@@ -261,174 +245,6 @@ class PipelineFastTests(unittest.TestCase):
 
         return extract
 
-    def test_ddim(self):
-        unet = self.dummy_uncond_unet
-        scheduler = DDIMScheduler()
-
-        ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
-        ddpm.to(torch_device)
-        ddpm.set_progress_bar_config(disable=None)
-
-        # Warmup pass when using mps (see #372)
-        if torch_device == "mps":
-            _ = ddpm(num_inference_steps=1)
-
-        generator = torch.manual_seed(0)
-        image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy").images
-
-        generator = torch.manual_seed(0)
-        image_from_tuple = ddpm(generator=generator, num_inference_steps=2, output_type="numpy", return_dict=False)[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array(
-            [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]
-        )
-        tolerance = 1e-2 if torch_device != "mps" else 3e-2
-        assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance
-
-    def test_pndm_cifar10(self):
-        unet = self.dummy_uncond_unet
-        scheduler = PNDMScheduler()
-
-        pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
-        pndm.to(torch_device)
-        pndm.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(0)
-        image = pndm(generator=generator, num_inference_steps=20, output_type="numpy").images
-
-        generator = torch.manual_seed(0)
-        image_from_tuple = pndm(generator=generator, num_inference_steps=20, output_type="numpy", return_dict=False)[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_ldm_text2img(self):
-        unet = self.dummy_cond_unet
-        scheduler = DDIMScheduler()
-        vae = self.dummy_vae
-        bert = self.dummy_text_encoder
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-
-        ldm = LDMTextToImagePipeline(vqvae=vae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
-        ldm.to(torch_device)
-        ldm.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-
-        # Warmup pass when using mps (see #372)
-        if torch_device == "mps":
-            generator = torch.manual_seed(0)
-            _ = ldm(
-                [prompt], generator=generator, guidance_scale=6.0, num_inference_steps=1, output_type="numpy"
-            ).images
-
-        generator = torch.manual_seed(0)
-        image = ldm(
-            [prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="numpy"
-        ).images
-
-        generator = torch.manual_seed(0)
-        image_from_tuple = ldm(
-            [prompt],
-            generator=generator,
-            guidance_scale=6.0,
-            num_inference_steps=2,
-            output_type="numpy",
-            return_dict=False,
-        )[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.5074, 0.5026, 0.4998, 0.4056, 0.3523, 0.4649, 0.5289, 0.5299, 0.4897])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_score_sde_ve_pipeline(self):
-        unet = self.dummy_uncond_unet
-        scheduler = ScoreSdeVeScheduler()
-
-        sde_ve = ScoreSdeVePipeline(unet=unet, scheduler=scheduler)
-        sde_ve.to(torch_device)
-        sde_ve.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(0)
-        image = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator).images
-
-        generator = torch.manual_seed(0)
-        image_from_tuple = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator, return_dict=False)[
-            0
-        ]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_ldm_uncond(self):
-        unet = self.dummy_uncond_unet
-        scheduler = DDIMScheduler()
-        vae = self.dummy_vq_model
-
-        ldm = LDMPipeline(unet=unet, vqvae=vae, scheduler=scheduler)
-        ldm.to(torch_device)
-        ldm.set_progress_bar_config(disable=None)
-
-        # Warmup pass when using mps (see #372)
-        if torch_device == "mps":
-            generator = torch.manual_seed(0)
-            _ = ldm(generator=generator, num_inference_steps=1, output_type="numpy").images
-
-        generator = torch.manual_seed(0)
-        image = ldm(generator=generator, num_inference_steps=2, output_type="numpy").images
-
-        generator = torch.manual_seed(0)
-        image_from_tuple = ldm(generator=generator, num_inference_steps=2, output_type="numpy", return_dict=False)[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 64, 64, 3)
-        expected_slice = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-
-    def test_karras_ve_pipeline(self):
-        unet = self.dummy_uncond_unet
-        scheduler = KarrasVeScheduler()
-
-        pipe = KarrasVePipeline(unet=unet, scheduler=scheduler)
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(0)
-        image = pipe(num_inference_steps=2, generator=generator, output_type="numpy").images
-
-        generator = torch.manual_seed(0)
-        image_from_tuple = pipe(num_inference_steps=2, generator=generator, output_type="numpy", return_dict=False)[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-
     def test_components(self):
         """Test that components property works correctly"""
         unet = self.dummy_cond_unet
@@ -489,7 +305,8 @@ class PipelineFastTests(unittest.TestCase):
         assert image_text2img.shape == (1, 128, 128, 3)
 
 
-class PipelineTesterMixin(unittest.TestCase):
+@slow
+class PipelineSlowTests(unittest.TestCase):
     def tearDown(self):
         # clean up the VRAM after each test
         super().tearDown()
@@ -556,7 +373,6 @@ class PipelineTesterMixin(unittest.TestCase):
 
         assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
 
-    @slow
     def test_from_pretrained_hub(self):
         model_path = "google/ddpm-cifar10-32"
 
@@ -577,7 +393,6 @@ class PipelineTesterMixin(unittest.TestCase):
 
         assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
 
-    @slow
     def test_from_pretrained_hub_pass_model(self):
         model_path = "google/ddpm-cifar10-32"
 
@@ -601,7 +416,6 @@ class PipelineTesterMixin(unittest.TestCase):
 
         assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
 
-    @slow
     def test_output_format(self):
         model_path = "google/ddpm-cifar10-32"
 
@@ -624,156 +438,6 @@ class PipelineTesterMixin(unittest.TestCase):
         assert isinstance(images, list)
         assert isinstance(images[0], PIL.Image.Image)
 
-    @slow
-    def test_ddpm_cifar10(self):
-        model_id = "google/ddpm-cifar10-32"
-
-        unet = UNet2DModel.from_pretrained(model_id)
-        scheduler = DDPMScheduler.from_config(model_id)
-
-        ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
-        ddpm.to(torch_device)
-        ddpm.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(0)
-        image = ddpm(generator=generator, output_type="numpy").images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.41995, 0.35885, 0.19385, 0.38475, 0.3382, 0.2647, 0.41545, 0.3582, 0.33845])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    @slow
-    def test_ddim_lsun(self):
-        model_id = "google/ddpm-ema-bedroom-256"
-
-        unet = UNet2DModel.from_pretrained(model_id)
-        scheduler = DDIMScheduler.from_config(model_id)
-
-        ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
-        ddpm.to(torch_device)
-        ddpm.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(0)
-        image = ddpm(generator=generator, output_type="numpy").images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 256, 256, 3)
-        expected_slice = np.array([0.00605, 0.0201, 0.0344, 0.00235, 0.00185, 0.00025, 0.00215, 0.0, 0.00685])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    @slow
-    def test_ddim_cifar10(self):
-        model_id = "google/ddpm-cifar10-32"
-
-        unet = UNet2DModel.from_pretrained(model_id)
-        scheduler = DDIMScheduler()
-
-        ddim = DDIMPipeline(unet=unet, scheduler=scheduler)
-        ddim.to(torch_device)
-        ddim.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(0)
-        image = ddim(generator=generator, eta=0.0, output_type="numpy").images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.17235, 0.16175, 0.16005, 0.16255, 0.1497, 0.1513, 0.15045, 0.1442, 0.1453])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    @slow
-    def test_pndm_cifar10(self):
-        model_id = "google/ddpm-cifar10-32"
-
-        unet = UNet2DModel.from_pretrained(model_id)
-        scheduler = PNDMScheduler()
-
-        pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
-        pndm.to(torch_device)
-        pndm.set_progress_bar_config(disable=None)
-        generator = torch.manual_seed(0)
-        image = pndm(generator=generator, output_type="numpy").images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    @slow
-    def test_ldm_text2img(self):
-        ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
-        ldm.to(torch_device)
-        ldm.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-        generator = torch.manual_seed(0)
-        image = ldm(
-            [prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="numpy"
-        ).images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 256, 256, 3)
-        expected_slice = np.array([0.9256, 0.9340, 0.8933, 0.9361, 0.9113, 0.8727, 0.9122, 0.8745, 0.8099])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    @slow
-    def test_ldm_text2img_fast(self):
-        ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
-        ldm.to(torch_device)
-        ldm.set_progress_bar_config(disable=None)
-
-        prompt = "A painting of a squirrel eating a burger"
-        generator = torch.manual_seed(0)
-        image = ldm(prompt, generator=generator, num_inference_steps=1, output_type="numpy").images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 256, 256, 3)
-        expected_slice = np.array([0.3163, 0.8670, 0.6465, 0.1865, 0.6291, 0.5139, 0.2824, 0.3723, 0.4344])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    @slow
-    def test_score_sde_ve_pipeline(self):
-        model_id = "google/ncsnpp-church-256"
-        model = UNet2DModel.from_pretrained(model_id)
-
-        scheduler = ScoreSdeVeScheduler.from_config(model_id)
-
-        sde_ve = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
-        sde_ve.to(torch_device)
-        sde_ve.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(0)
-        image = sde_ve(num_inference_steps=10, output_type="numpy", generator=generator).images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 256, 256, 3)
-
-        expected_slice = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    @slow
-    def test_ldm_uncond(self):
-        ldm = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
-        ldm.to(torch_device)
-        ldm.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(0)
-        image = ldm(generator=generator, num_inference_steps=5, output_type="numpy").images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 256, 256, 3)
-        expected_slice = np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    @slow
     def test_ddpm_ddim_equality(self):
         model_id = "google/ddpm-cifar10-32"
 
@@ -824,145 +488,7 @@ class PipelineTesterMixin(unittest.TestCase):
         # the values aren't exactly equal, but the images look the same visually
         assert np.abs(ddpm_images - ddim_images).max() < 1e-1
 
-    @slow
-    def test_karras_ve_pipeline(self):
-        model_id = "google/ncsnpp-celebahq-256"
-        model = UNet2DModel.from_pretrained(model_id)
-        scheduler = KarrasVeScheduler()
-
-        pipe = KarrasVePipeline(unet=model, scheduler=scheduler)
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(0)
-        image = pipe(num_inference_steps=20, generator=generator, output_type="numpy").images
-
-        image_slice = image[0, -3:, -3:, -1]
-        assert image.shape == (1, 256, 256, 3)
-        expected_slice = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-
-    @slow
-    def test_stable_diffusion_onnx(self):
-        sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider"
-        )
-
-        prompt = "A painting of a squirrel eating a burger"
-        np.random.seed(0)
-        output = sd_pipe([prompt], guidance_scale=6.0, num_inference_steps=5, output_type="np")
-        image = output.images
-
-        image_slice = image[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.3602, 0.3688, 0.3652, 0.3895, 0.3782, 0.3747, 0.3927, 0.4241, 0.4327])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
-
-    @slow
-    def test_stable_diffusion_img2img_onnx(self):
-        init_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/img2img/sketch-mountains-input.jpg"
-        )
-        init_image = init_image.resize((768, 512))
-        pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider"
-        )
-        pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A fantasy landscape, trending on artstation"
-
-        np.random.seed(0)
-        output = pipe(
-            prompt=prompt,
-            init_image=init_image,
-            strength=0.75,
-            guidance_scale=7.5,
-            num_inference_steps=8,
-            output_type="np",
-        )
-        images = output.images
-        image_slice = images[0, 255:258, 383:386, -1]
-
-        assert images.shape == (1, 512, 768, 3)
-        expected_slice = np.array([0.4830, 0.5242, 0.5603, 0.5016, 0.5131, 0.5111, 0.4928, 0.5025, 0.5055])
-        # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2
-
-    @slow
-    def test_stable_diffusion_inpaint_onnx(self):
-        init_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/in_paint/overture-creations-5sI6fQgYIuo.png"
-        )
-        mask_image = load_image(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-            "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
-        )
-
-        pipe = OnnxStableDiffusionInpaintPipeline.from_pretrained(
-            "runwayml/stable-diffusion-inpainting", revision="onnx", provider="CPUExecutionProvider"
-        )
-        pipe.set_progress_bar_config(disable=None)
-
-        prompt = "A red cat sitting on a park bench"
-
-        np.random.seed(0)
-        output = pipe(
-            prompt=prompt,
-            image=init_image,
-            mask_image=mask_image,
-            guidance_scale=7.5,
-            num_inference_steps=8,
-            output_type="np",
-        )
-        images = output.images
-        image_slice = images[0, 255:258, 255:258, -1]
-
-        assert images.shape == (1, 512, 512, 3)
-        expected_slice = np.array([0.2951, 0.2955, 0.2922, 0.2036, 0.1977, 0.2279, 0.1716, 0.1641, 0.1799])
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
-
-    @slow
-    def test_stable_diffusion_onnx_intermediate_state(self):
-        number_of_steps = 0
-
-        def test_callback_fn(step: int, timestep: int, latents: np.ndarray) -> None:
-            test_callback_fn.has_been_called = True
-            nonlocal number_of_steps
-            number_of_steps += 1
-            if step == 0:
-                assert latents.shape == (1, 4, 64, 64)
-                latents_slice = latents[0, -3:, -3:, -1]
-                expected_slice = np.array(
-                    [-0.5950, -0.3039, -1.1672, 0.1594, -1.1572, 0.6719, -1.9712, -0.0403, 0.9592]
-                )
-                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
-            elif step == 5:
-                assert latents.shape == (1, 4, 64, 64)
-                latents_slice = latents[0, -3:, -3:, -1]
-                expected_slice = np.array(
-                    [-0.4776, -0.0119, -0.8519, -0.0275, -0.9764, 0.9820, -0.3843, 0.3788, 1.2264]
-                )
-                assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
-
-        test_callback_fn.has_been_called = False
-
-        pipe = OnnxStableDiffusionPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider"
-        )
-        pipe.set_progress_bar_config(disable=None)
-
-        prompt = "Andromeda galaxy in a bottle"
-
-        np.random.seed(0)
-        pipe(prompt=prompt, num_inference_steps=5, guidance_scale=7.5, callback=test_callback_fn, callback_steps=1)
-        assert test_callback_fn.has_been_called
-        assert number_of_steps == 6
-
-    @slow
-    @unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
+    @require_torch_gpu
     def test_stable_diffusion_accelerate_load_works(self):
         if version.parse(version.parse(transformers.__version__).base_version) < version.parse("4.23"):
             return
@@ -975,8 +501,7 @@ class PipelineTesterMixin(unittest.TestCase):
             model_id, revision="fp16", torch_dtype=torch.float16, use_auth_token=True, device_map="auto"
         ).to(torch_device)
 
-    @slow
-    @unittest.skipIf(torch_device == "cpu", "This test is supposed to run on GPU")
+    @require_torch_gpu
     def test_stable_diffusion_accelerate_load_reduces_memory_footprint(self):
         if version.parse(version.parse(transformers.__version__).base_version) < version.parse("4.23"):
             return

tests/test_pipelines_common.py

+from diffusers.utils.testing_utils import require_torch
+
+
+@require_torch
+class PipelineTesterMixin:
+    """
+    This mixin is designed to be used with unittest.TestCase classes.
+    It provides a set of common tests for each PyTorch pipeline, e.g. saving and loading the pipeline,
+    equivalence of dict and tuple outputs, etc.
+    """
+
+    pass

tests/test_pipelines_flax.py

@@ -30,8 +30,8 @@ if is_flax_available():
     from jax import pmap
 
 
-@require_flax
 @slow
+@require_flax
 class FlaxPipelineTests(unittest.TestCase):
     def test_dummy_all_tpus(self):
         pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(

tests/test_pipelines_onnx_common.py

+from diffusers.utils.testing_utils import require_onnxruntime
+
+
+@require_onnxruntime
+class OnnxPipelineTesterMixin:
+    """
+    This mixin is designed to be used with unittest.TestCase classes.
+    It provides a set of common tests for each ONNXRuntime pipeline, e.g. saving and loading the pipeline,
+    equivalence of dict and tuple outputs, etc.
+    """
+
+    pass