Reorganize pipeline tests (#963)

* Reorganize pipeline tests

* fix vq

src/diffusers/utils/testing_utils.py

@@ -14,7 +14,7 @@ import PIL.ImageOps
 import requests
 from packaging import version
 
-from .import_utils import is_flax_available, is_torch_available
+from .import_utils import is_flax_available, is_onnx_available, is_torch_available
 
 
 global_rng = random.Random()
@@ -100,6 +100,20 @@ def slow(test_case):
     return unittest.skipUnless(_run_slow_tests, "test is slow")(test_case)
 
 
+def require_torch(test_case):
+    """
+    Decorator marking a test that requires PyTorch. These tests are skipped when PyTorch isn't installed.
+    """
+    return unittest.skipUnless(is_torch_available(), "test requires PyTorch")(test_case)
+
+
+def require_torch_gpu(test_case):
+    """Decorator marking a test that requires CUDA and PyTorch."""
+    return unittest.skipUnless(is_torch_available() and torch_device == "cuda", "test requires PyTorch+CUDA")(
+        test_case
+    )
+
+
 def require_flax(test_case):
     """
     Decorator marking a test that requires JAX & Flax. These tests are skipped when one / both are not installed
@@ -107,6 +121,13 @@ def require_flax(test_case):
     return unittest.skipUnless(is_flax_available(), "test requires JAX & Flax")(test_case)
 
 
+def require_onnxruntime(test_case):
+    """
+    Decorator marking a test that requires onnxruntime. These tests are skipped when onnxruntime isn't installed.
+    """
+    return unittest.skipUnless(is_onnx_available(), "test requires onnxruntime")(test_case)
+
+
 def load_image(image: Union[str, PIL.Image.Image]) -> PIL.Image.Image:
     """
     Args:

tests/pipelines/ddim/test_ddim.py

+# coding=utf-8
+# Copyright 2022 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import torch
+
+from diffusers import DDIMPipeline, DDIMScheduler, UNet2DModel
+from diffusers.utils.testing_utils import require_torch, slow, torch_device
+
+from ...test_pipelines_common import PipelineTesterMixin
+
+
+torch.backends.cuda.matmul.allow_tf32 = False
+
+
+class DDIMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    @property
+    def dummy_uncond_unet(self):
+        torch.manual_seed(0)
+        model = UNet2DModel(
+            block_out_channels=(32, 64),
+            layers_per_block=2,
+            sample_size=32,
+            in_channels=3,
+            out_channels=3,
+            down_block_types=("DownBlock2D", "AttnDownBlock2D"),
+            up_block_types=("AttnUpBlock2D", "UpBlock2D"),
+        )
+        return model
+
+    def test_inference(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
+
+
+@slow
+@require_torch
+class DDIMPipelineIntegrationTests(unittest.TestCase):
+    def test_inference_ema_bedroom(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
+
+    def test_inference_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

tests/pipelines/ddpm/test_ddpm.py

+# coding=utf-8
+# Copyright 2022 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import torch
+
+from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
+from diffusers.utils.testing_utils import require_torch, slow, torch_device
+
+from ...test_pipelines_common import PipelineTesterMixin
+
+
+torch.backends.cuda.matmul.allow_tf32 = False
+
+
+class DDPMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    # FIXME: add fast tests
+    pass
+
+
+@slow
+@require_torch
+class DDPMPipelineIntegrationTests(unittest.TestCase):
+    def test_inference_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

tests/pipelines/karras_ve/test_karras_ve.py

+# coding=utf-8
+# Copyright 2022 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import torch
+
+from diffusers import KarrasVePipeline, KarrasVeScheduler, UNet2DModel
+from diffusers.utils.testing_utils import require_torch, slow, torch_device
+
+from ...test_pipelines_common import PipelineTesterMixin
+
+
+torch.backends.cuda.matmul.allow_tf32 = False
+
+
+class KarrasVePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    @property
+    def dummy_uncond_unet(self):
+        torch.manual_seed(0)
+        model = UNet2DModel(
+            block_out_channels=(32, 64),
+            layers_per_block=2,
+            sample_size=32,
+            in_channels=3,
+            out_channels=3,
+            down_block_types=("DownBlock2D", "AttnDownBlock2D"),
+            up_block_types=("AttnUpBlock2D", "UpBlock2D"),
+        )
+        return model
+
+    def test_inference(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
+
+
+@slow
+@require_torch
+class KarrasVePipelineIntegrationTests(unittest.TestCase):
+    def test_inference(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

tests/pipelines/latent_diffusion/test_latent_diffusion.py

+# coding=utf-8
+# Copyright 2022 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import torch
+
+from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNet2DConditionModel
+from diffusers.utils.testing_utils import require_torch, slow, torch_device
+from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
+
+from ...test_pipelines_common import PipelineTesterMixin
+
+
+torch.backends.cuda.matmul.allow_tf32 = False
+
+
+class LDMTextToImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    @property
+    def dummy_cond_unet(self):
+        torch.manual_seed(0)
+        model = UNet2DConditionModel(
+            block_out_channels=(32, 64),
+            layers_per_block=2,
+            sample_size=32,
+            in_channels=4,
+            out_channels=4,
+            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
+            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
+            cross_attention_dim=32,
+        )
+        return model
+
+    @property
+    def dummy_vae(self):
+        torch.manual_seed(0)
+        model = AutoencoderKL(
+            block_out_channels=[32, 64],
+            in_channels=3,
+            out_channels=3,
+            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
+            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
+            latent_channels=4,
+        )
+        return model
+
+    @property
+    def dummy_text_encoder(self):
+        torch.manual_seed(0)
+        config = CLIPTextConfig(
+            bos_token_id=0,
+            eos_token_id=2,
+            hidden_size=32,
+            intermediate_size=37,
+            layer_norm_eps=1e-05,
+            num_attention_heads=4,
+            num_hidden_layers=5,
+            pad_token_id=1,
+            vocab_size=1000,
+        )
+        return CLIPTextModel(config)
+
+    def test_inference_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
+
+
+@slow
+@require_torch
+class LDMTextToImagePipelineIntegrationTests(unittest.TestCase):
+    def test_inference_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
+
+    def test_inference_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

tests/pipelines/latent_diffusion/test_latent_diffusion_uncond.py

+# coding=utf-8
+# Copyright 2022 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import torch
+
+from diffusers import DDIMScheduler, LDMPipeline, UNet2DModel, VQModel
+from diffusers.utils.testing_utils import require_torch, slow, torch_device
+from transformers import CLIPTextConfig, CLIPTextModel
+
+from ...test_pipelines_common import PipelineTesterMixin
+
+
+torch.backends.cuda.matmul.allow_tf32 = False
+
+
+class LDMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    @property
+    def dummy_uncond_unet(self):
+        torch.manual_seed(0)
+        model = UNet2DModel(
+            block_out_channels=(32, 64),
+            layers_per_block=2,
+            sample_size=32,
+            in_channels=3,
+            out_channels=3,
+            down_block_types=("DownBlock2D", "AttnDownBlock2D"),
+            up_block_types=("AttnUpBlock2D", "UpBlock2D"),
+        )
+        return model
+
+    @property
+    def dummy_vq_model(self):
+        torch.manual_seed(0)
+        model = VQModel(
+            block_out_channels=[32, 64],
+            in_channels=3,
+            out_channels=3,
+            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
+            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
+            latent_channels=3,
+        )
+        return model
+
+    @property
+    def dummy_text_encoder(self):
+        torch.manual_seed(0)
+        config = CLIPTextConfig(
+            bos_token_id=0,
+            eos_token_id=2,
+            hidden_size=32,
+            intermediate_size=37,
+            layer_norm_eps=1e-05,
+            num_attention_heads=4,
+            num_hidden_layers=5,
+            pad_token_id=1,
+            vocab_size=1000,
+        )
+        return CLIPTextModel(config)
+
+    def test_inference_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
+
+
+@slow
+@require_torch
+class LDMPipelineIntegrationTests(unittest.TestCase):
+    def test_inference_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

tests/pipelines/pndm/test_pndm.py

+# coding=utf-8
+# Copyright 2022 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import torch
+
+from diffusers import PNDMPipeline, PNDMScheduler, UNet2DModel
+from diffusers.utils.testing_utils import require_torch, slow, torch_device
+
+from ...test_pipelines_common import PipelineTesterMixin
+
+
+torch.backends.cuda.matmul.allow_tf32 = False
+
+
+class PNDMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    @property
+    def dummy_uncond_unet(self):
+        torch.manual_seed(0)
+        model = UNet2DModel(
+            block_out_channels=(32, 64),
+            layers_per_block=2,
+            sample_size=32,
+            in_channels=3,
+            out_channels=3,
+            down_block_types=("DownBlock2D", "AttnDownBlock2D"),
+            up_block_types=("AttnUpBlock2D", "UpBlock2D"),
+        )
+        return model
+
+    def test_inference(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
+
+
+@slow
+@require_torch
+class PNDMPipelineIntegrationTests(unittest.TestCase):
+    def test_inference_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

tests/pipelines/score_sde_ve/test_score_sde_ve.py

+# coding=utf-8
+# Copyright 2022 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import torch
+
+from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNet2DModel
+from diffusers.utils.testing_utils import require_torch, slow, torch_device
+
+from ...test_pipelines_common import PipelineTesterMixin
+
+
+torch.backends.cuda.matmul.allow_tf32 = False
+
+
+class ScoreSdeVeipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    @property
+    def dummy_uncond_unet(self):
+        torch.manual_seed(0)
+        model = UNet2DModel(
+            block_out_channels=(32, 64),
+            layers_per_block=2,
+            sample_size=32,
+            in_channels=3,
+            out_channels=3,
+            down_block_types=("DownBlock2D", "AttnDownBlock2D"),
+            up_block_types=("AttnUpBlock2D", "UpBlock2D"),
+        )
+        return model
+
+    def test_inference(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
+
+
+@slow
+@require_torch
+class ScoreSdeVePipelineIntegrationTests(unittest.TestCase):
+    def test_inference(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

tests/pipelines/stable_diffusion/test_onnx_stable_diffusion.py

+# coding=utf-8
+# Copyright 2022 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+
+from diffusers import OnnxStableDiffusionPipeline
+from diffusers.utils.testing_utils import require_onnxruntime, slow
+
+from ...test_pipelines_onnx_common import OnnxPipelineTesterMixin
+
+
+class OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
+    # FIXME: add fast tests
+    pass
+
+
+@slow
+@require_onnxruntime
+class OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase):
+    def test_inference(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
+
+    def test_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

tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py

+# coding=utf-8
+# Copyright 2022 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+
+from diffusers import OnnxStableDiffusionImg2ImgPipeline
+from diffusers.utils.testing_utils import load_image, require_onnxruntime, slow
+
+from ...test_pipelines_onnx_common import OnnxPipelineTesterMixin
+
+
+class OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
+    # FIXME: add fast tests
+    pass
+
+
+@slow
+@require_onnxruntime
+class OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase):
+    def test_inference(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

tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_inpaint.py

+# coding=utf-8
+# Copyright 2022 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+
+from diffusers import OnnxStableDiffusionInpaintPipeline
+from diffusers.utils.testing_utils import load_image, require_onnxruntime, slow
+
+from ...test_pipelines_onnx_common import OnnxPipelineTesterMixin
+
+
+class OnnxStableDiffusionPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):
+    # FIXME: add fast tests
+    pass
+
+
+@slow
+@require_onnxruntime
+class OnnxStableDiffusionPipelineIntegrationTests(unittest.TestCase):
+    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

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -31,13 +31,16 @@ 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 StableDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     def tearDown(self):
         # clean up the VRAM after each test
         super().tearDown()
@@ -514,8 +517,8 @@ 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 StableDiffusionPipelineIntegrationTests(unittest.TestCase):
     def tearDown(self):
         # clean up the VRAM after each test
         super().tearDown()