[Tests] Improve unet / vae tests (#1018)

* improve tests

* up

* finish

* upload

* add init

* up

* finish vae

* finish

* reduce loading time with device_map

* remove device_map from CPU

* uP

setup.py

@@ -94,6 +94,7 @@ _deps = [
     "modelcards>=0.1.4",
     "numpy",
     "onnxruntime",
+    "parameterized",
     "pytest",
     "pytest-timeout",
     "pytest-xdist",
@@ -181,6 +182,7 @@ extras["test"] = deps_list(
     "accelerate",
     "datasets",
     "onnxruntime",
+    "parameterized",
     "pytest",
     "pytest-timeout",
     "pytest-xdist",

src/diffusers/dependency_versions_table.py

@@ -18,6 +18,7 @@ deps = {
     "modelcards": "modelcards>=0.1.4",
     "numpy": "numpy",
     "onnxruntime": "onnxruntime",
+    "parameterized": "parameterized",
     "pytest": "pytest",
     "pytest-timeout": "pytest-timeout",
     "pytest-xdist": "pytest-xdist",

src/diffusers/utils/__init__.py

@@ -40,7 +40,7 @@ from .outputs import BaseOutput
 
 
 if is_torch_available():
-    from .testing_utils import floats_tensor, load_image, parse_flag_from_env, slow, torch_device
+    from .testing_utils import floats_tensor, load_image, parse_flag_from_env, require_torch_gpu, slow, torch_device
 
 
 logger = get_logger(__name__)

tests/models/test_models_unet_1d.py

@@ -28,7 +28,7 @@ class UnetModel1DTests(unittest.TestCase):
     @slow
     def test_unet_1d_maestro(self):
         model_id = "harmonai/maestro-150k"
-        model = UNet1DModel.from_pretrained(model_id, subfolder="unet")
+        model = UNet1DModel.from_pretrained(model_id, subfolder="unet", device_map="auto")
         model.to(torch_device)
 
         sample_size = 65536

tests/models/test_models_unet_2d.py

@@ -21,9 +21,10 @@ import unittest
 import torch
 
 from diffusers import UNet2DConditionModel, UNet2DModel
-from diffusers.utils import floats_tensor, slow, torch_device
+from diffusers.utils import floats_tensor, require_torch_gpu, slow, torch_device
+from parameterized import parameterized
 
-from .test_modeling_common import ModelTesterMixin
+from ..test_modeling_common import ModelTesterMixin
 
 
 torch.backends.cuda.matmul.allow_tf32 = False
@@ -66,28 +67,6 @@ class Unet2DModelTests(ModelTesterMixin, unittest.TestCase):
         return init_dict, inputs_dict
 
 
-#    TODO(Patrick) - Re-add this test after having correctly added the final VE checkpoints
-#    def test_output_pretrained(self):
-#        model = UNet2DModel.from_pretrained("fusing/ddpm_dummy_update", subfolder="unet")
-#        model.eval()
-#
-#        torch.manual_seed(0)
-#        if torch.cuda.is_available():
-#            torch.cuda.manual_seed_all(0)
-#
-#        noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
-#        time_step = torch.tensor([10])
-#
-#        with torch.no_grad():
-#            output = model(noise, time_step).sample
-#
-#        output_slice = output[0, -1, -3:, -3:].flatten()
-# fmt: off
-#        expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053])
-# fmt: on
-#        self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
-
-
 class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
     model_class = UNet2DModel
 
@@ -170,7 +149,9 @@ class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
         torch.cuda.empty_cache()
         gc.collect()
 
-        model_normal_load, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
+        model_normal_load, _ = UNet2DModel.from_pretrained(
+            "fusing/unet-ldm-dummy-update", output_loading_info=True, device_map="auto"
+        )
         model_normal_load.to(torch_device)
         model_normal_load.eval()
         arr_normal_load = model_normal_load(noise, time_step)["sample"]
@@ -309,31 +290,6 @@ class UNet2DConditionModelTests(ModelTesterMixin, unittest.TestCase):
             self.assertTrue(torch.allclose(param.grad.data, named_params_2[name].grad.data, atol=5e-5))
 
 
-#    TODO(Patrick) - Re-add this test after having cleaned up LDM
-#    def test_output_pretrained_spatial_transformer(self):
-#        model = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy-spatial")
-#        model.eval()
-#
-#        torch.manual_seed(0)
-#        if torch.cuda.is_available():
-#            torch.cuda.manual_seed_all(0)
-#
-#        noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
-#        context = torch.ones((1, 16, 64), dtype=torch.float32)
-#        time_step = torch.tensor([10] * noise.shape[0])
-#
-#        with torch.no_grad():
-#            output = model(noise, time_step, context=context)
-#
-#        output_slice = output[0, -1, -3:, -3:].flatten()
-# fmt: off
-#        expected_output_slice = torch.tensor([61.3445, 56.9005, 29.4339, 59.5497, 60.7375, 34.1719, 48.1951, 42.6569, 25.0890])
-# fmt: on
-#
-#        self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
-#
-
-
 class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
     model_class = UNet2DModel
 
@@ -383,7 +339,9 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
 
     @slow
     def test_from_pretrained_hub(self):
-        model, loading_info = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256", output_loading_info=True)
+        model, loading_info = UNet2DModel.from_pretrained(
+            "google/ncsnpp-celebahq-256", output_loading_info=True, device_map="auto"
+        )
         self.assertIsNotNone(model)
         self.assertEqual(len(loading_info["missing_keys"]), 0)
 
@@ -397,7 +355,7 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
 
     @slow
     def test_output_pretrained_ve_mid(self):
-        model = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256")
+        model = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256", device_map="auto")
         model.to(torch_device)
 
         torch.manual_seed(0)
@@ -449,3 +407,189 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
     def test_forward_with_norm_groups(self):
         # not required for this model
         pass
+
+
+@slow
+class UNet2DConditionModelIntegrationTests(unittest.TestCase):
+    def tearDown(self):
+        # clean up the VRAM after each test
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def get_latents(self, seed=0, shape=(4, 4, 64, 64), fp16=False):
+        batch_size, channels, height, width = shape
+        generator = torch.Generator(device=torch_device).manual_seed(seed)
+        dtype = torch.float16 if fp16 else torch.float32
+        image = torch.randn(batch_size, channels, height, width, device=torch_device, generator=generator, dtype=dtype)
+
+        return image
+
+    def get_unet_model(self, fp16=False, model_id="CompVis/stable-diffusion-v1-4"):
+        revision = "fp16" if fp16 else None
+        torch_dtype = torch.float16 if fp16 else torch.float32
+
+        model = UNet2DConditionModel.from_pretrained(
+            model_id, subfolder="unet", torch_dtype=torch_dtype, revision=revision, device_map="auto"
+        )
+        model.to(torch_device).eval()
+
+        return model
+
+    def get_encoder_hidden_states(self, seed=0, shape=(4, 77, 768), fp16=False):
+        generator = torch.Generator(device=torch_device).manual_seed(seed)
+        dtype = torch.float16 if fp16 else torch.float32
+        return torch.randn(shape, device=torch_device, generator=generator, dtype=dtype)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [33, 4, [-0.4424, 0.1510, -0.1937, 0.2118, 0.3746, -0.3957, 0.0160, -0.0435]],
+            [47, 0.55, [-0.1508, 0.0379, -0.3075, 0.2540, 0.3633, -0.0821, 0.1719, -0.0207]],
+            [21, 0.89, [-0.6479, 0.6364, -0.3464, 0.8697, 0.4443, -0.6289, -0.0091, 0.1778]],
+            [9, 1000, [0.8888, -0.5659, 0.5834, -0.7469, 1.1912, -0.3923, 1.1241, -0.4424]],
+            # fmt: on
+        ]
+    )
+    def test_compvis_sd_v1_4(self, seed, timestep, expected_slice):
+        model = self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4")
+        latents = self.get_latents(seed)
+        encoder_hidden_states = self.get_encoder_hidden_states(seed)
+
+        with torch.no_grad():
+            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        assert sample.shape == latents.shape
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
+            [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
+            [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
+            [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
+            # fmt: on
+        ]
+    )
+    @require_torch_gpu
+    def test_compvis_sd_v1_4_fp16(self, seed, timestep, expected_slice):
+        model = self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4", fp16=True)
+        latents = self.get_latents(seed, fp16=True)
+        encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
+
+        with torch.no_grad():
+            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        assert sample.shape == latents.shape
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [33, 4, [-0.4430, 0.1570, -0.1867, 0.2376, 0.3205, -0.3681, 0.0525, -0.0722]],
+            [47, 0.55, [-0.1415, 0.0129, -0.3136, 0.2257, 0.3430, -0.0536, 0.2114, -0.0436]],
+            [21, 0.89, [-0.7091, 0.6664, -0.3643, 0.9032, 0.4499, -0.6541, 0.0139, 0.1750]],
+            [9, 1000, [0.8878, -0.5659, 0.5844, -0.7442, 1.1883, -0.3927, 1.1192, -0.4423]],
+            # fmt: on
+        ]
+    )
+    def test_compvis_sd_v1_5(self, seed, timestep, expected_slice):
+        model = self.get_unet_model(model_id="runwayml/stable-diffusion-v1-5")
+        latents = self.get_latents(seed)
+        encoder_hidden_states = self.get_encoder_hidden_states(seed)
+
+        with torch.no_grad():
+            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        assert sample.shape == latents.shape
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [83, 4, [-0.2695, -0.1669, 0.0073, -0.3181, -0.1187, -0.1676, -0.1395, -0.5972]],
+            [17, 0.55, [-0.1290, -0.2588, 0.0551, -0.0916, 0.3286, 0.0238, -0.3669, 0.0322]],
+            [8, 0.89, [-0.5283, 0.1198, 0.0870, -0.1141, 0.9189, -0.0150, 0.5474, 0.4319]],
+            [3, 1000, [-0.5601, 0.2411, -0.5435, 0.1268, 1.1338, -0.2427, -0.0280, -1.0020]],
+            # fmt: on
+        ]
+    )
+    @require_torch_gpu
+    def test_compvis_sd_v1_5_fp16(self, seed, timestep, expected_slice):
+        model = self.get_unet_model(model_id="runwayml/stable-diffusion-v1-5", fp16=True)
+        latents = self.get_latents(seed, fp16=True)
+        encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
+
+        with torch.no_grad():
+            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        assert sample.shape == latents.shape
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [33, 4, [-0.7639, 0.0106, -0.1615, -0.3487, -0.0423, -0.7972, 0.0085, -0.4858]],
+            [47, 0.55, [-0.6564, 0.0795, -1.9026, -0.6258, 1.8235, 1.2056, 1.2169, 0.9073]],
+            [21, 0.89, [0.0327, 0.4399, -0.6358, 0.3417, 0.4120, -0.5621, -0.0397, -1.0430]],
+            [9, 1000, [0.1600, 0.7303, -1.0556, -0.3515, -0.7440, -1.2037, -1.8149, -1.8931]],
+            # fmt: on
+        ]
+    )
+    def test_compvis_sd_inpaint(self, seed, timestep, expected_slice):
+        model = self.get_unet_model(model_id="runwayml/stable-diffusion-inpainting")
+        latents = self.get_latents(seed, shape=(4, 9, 64, 64))
+        encoder_hidden_states = self.get_encoder_hidden_states(seed)
+
+        with torch.no_grad():
+            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        assert sample.shape == (4, 4, 64, 64)
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [83, 4, [-0.1047, -1.7227, 0.1067, 0.0164, -0.5698, -0.4172, -0.1388, 1.1387]],
+            [17, 0.55, [0.0975, -0.2856, -0.3508, -0.4600, 0.3376, 0.2930, -0.2747, -0.7026]],
+            [8, 0.89, [-0.0952, 0.0183, -0.5825, -0.1981, 0.1131, 0.4668, -0.0395, -0.3486]],
+            [3, 1000, [0.4790, 0.4949, -1.0732, -0.7158, 0.7959, -0.9478, 0.1105, -0.9741]],
+            # fmt: on
+        ]
+    )
+    @require_torch_gpu
+    def test_compvis_sd_inpaint_fp16(self, seed, timestep, expected_slice):
+        model = self.get_unet_model(model_id="runwayml/stable-diffusion-inpainting", fp16=True)
+        latents = self.get_latents(seed, shape=(4, 9, 64, 64), fp16=True)
+        encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
+
+        with torch.no_grad():
+            sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
+
+        assert sample.shape == (4, 4, 64, 64)
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)

tests/models/test_models_vae.py

@@ -0,0 +1,303 @@
+# 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 gc
+import unittest
+
+import torch
+
+from diffusers import AutoencoderKL
+from diffusers.modeling_utils import ModelMixin
+from diffusers.utils import floats_tensor, require_torch_gpu, slow, torch_device
+from parameterized import parameterized
+
+from ..test_modeling_common import ModelTesterMixin
+
+
+torch.backends.cuda.matmul.allow_tf32 = False
+
+
+class AutoencoderKLTests(ModelTesterMixin, unittest.TestCase):
+    model_class = AutoencoderKL
+
+    @property
+    def dummy_input(self):
+        batch_size = 4
+        num_channels = 3
+        sizes = (32, 32)
+
+        image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
+
+        return {"sample": image}
+
+    @property
+    def input_shape(self):
+        return (3, 32, 32)
+
+    @property
+    def output_shape(self):
+        return (3, 32, 32)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
+            "block_out_channels": [32, 64],
+            "in_channels": 3,
+            "out_channels": 3,
+            "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
+            "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
+            "latent_channels": 4,
+        }
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+
+    def test_forward_signature(self):
+        pass
+
+    def test_training(self):
+        pass
+
+    def test_from_pretrained_hub(self):
+        model, loading_info = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy", output_loading_info=True)
+        self.assertIsNotNone(model)
+        self.assertEqual(len(loading_info["missing_keys"]), 0)
+
+        model.to(torch_device)
+        image = model(**self.dummy_input)
+
+        assert image is not None, "Make sure output is not None"
+
+    def test_output_pretrained(self):
+        model = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy")
+        model = model.to(torch_device)
+        model.eval()
+
+        # One-time warmup pass (see #372)
+        if torch_device == "mps" and isinstance(model, ModelMixin):
+            image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
+            image = image.to(torch_device)
+            with torch.no_grad():
+                _ = model(image, sample_posterior=True).sample
+            generator = torch.manual_seed(0)
+        else:
+            generator = torch.Generator(device=torch_device).manual_seed(0)
+
+        image = torch.randn(
+            1,
+            model.config.in_channels,
+            model.config.sample_size,
+            model.config.sample_size,
+            generator=torch.manual_seed(0),
+        )
+        image = image.to(torch_device)
+        with torch.no_grad():
+            output = model(image, sample_posterior=True, generator=generator).sample
+
+        output_slice = output[0, -1, -3:, -3:].flatten().cpu()
+
+        # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
+        # the expected output slices are not the same for CPU and GPU.
+        if torch_device == "mps":
+            expected_output_slice = torch.tensor(
+                [
+                    -4.0078e-01,
+                    -3.8323e-04,
+                    -1.2681e-01,
+                    -1.1462e-01,
+                    2.0095e-01,
+                    1.0893e-01,
+                    -8.8247e-02,
+                    -3.0361e-01,
+                    -9.8644e-03,
+                ]
+            )
+        elif torch_device == "cpu":
+            expected_output_slice = torch.tensor(
+                [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]
+            )
+        else:
+            expected_output_slice = torch.tensor(
+                [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]
+            )
+
+        self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
+
+
+@slow
+class AutoencoderKLIntegrationTests(unittest.TestCase):
+    def tearDown(self):
+        # clean up the VRAM after each test
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False):
+        batch_size, channels, height, width = shape
+        generator = torch.Generator(device=torch_device).manual_seed(seed)
+        dtype = torch.float16 if fp16 else torch.float32
+        image = torch.randn(batch_size, channels, height, width, device=torch_device, generator=generator, dtype=dtype)
+
+        return image
+
+    def get_sd_vae_model(self, model_id="CompVis/stable-diffusion-v1-4", fp16=False):
+        revision = "fp16" if fp16 else None
+        torch_dtype = torch.float16 if fp16 else torch.float32
+
+        model = AutoencoderKL.from_pretrained(
+            model_id, subfolder="vae", torch_dtype=torch_dtype, revision=revision, device_map="auto"
+        )
+        model.to(torch_device).eval()
+
+        return model
+
+    def get_generator(self, seed=0):
+        return torch.Generator(device=torch_device).manual_seed(seed)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824]],
+            [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089]],
+            # fmt: on
+        ]
+    )
+    def test_stable_diffusion(self, seed, expected_slice):
+        model = self.get_sd_vae_model()
+        image = self.get_sd_image(seed)
+        generator = self.get_generator(seed)
+
+        with torch.no_grad():
+            sample = model(image, generator=generator, sample_posterior=True).sample
+
+        assert sample.shape == image.shape
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
+            [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
+            # fmt: on
+        ]
+    )
+    @require_torch_gpu
+    def test_stable_diffusion_fp16(self, seed, expected_slice):
+        model = self.get_sd_vae_model(fp16=True)
+        image = self.get_sd_image(seed, fp16=True)
+        generator = self.get_generator(seed)
+
+        with torch.no_grad():
+            sample = model(image, generator=generator, sample_posterior=True).sample
+
+        assert sample.shape == image.shape
+
+        output_slice = sample[-1, -2:, :2, -2:].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814]],
+            [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085]],
+            # fmt: on
+        ]
+    )
+    def test_stable_diffusion_mode(self, seed, expected_slice):
+        model = self.get_sd_vae_model()
+        image = self.get_sd_image(seed)
+
+        with torch.no_grad():
+            sample = model(image).sample
+
+        assert sample.shape == image.shape
+
+        output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
+            [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
+            # fmt: on
+        ]
+    )
+    @require_torch_gpu
+    def test_stable_diffusion_decode(self, seed, expected_slice):
+        model = self.get_sd_vae_model()
+        encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))
+
+        with torch.no_grad():
+            sample = model.decode(encoding).sample
+
+        assert list(sample.shape) == [3, 3, 512, 512]
+
+        output_slice = sample[-1, -2:, :2, -2:].flatten().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
+            [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
+            # fmt: on
+        ]
+    )
+    def test_stable_diffusion_decode_fp16(self, seed, expected_slice):
+        model = self.get_sd_vae_model(fp16=True)
+        encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64), fp16=True)
+
+        with torch.no_grad():
+            sample = model.decode(encoding).sample
+
+        assert list(sample.shape) == [3, 3, 512, 512]
+
+        output_slice = sample[-1, -2:, :2, -2:].flatten().float().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
+
+    @parameterized.expand(
+        [
+            # fmt: off
+            [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
+            [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
+            # fmt: on
+        ]
+    )
+    def test_stable_diffusion_encode_sample(self, seed, expected_slice):
+        model = self.get_sd_vae_model()
+        image = self.get_sd_image(seed)
+        generator = self.get_generator(seed)
+
+        with torch.no_grad():
+            dist = model.encode(image).latent_dist
+            sample = dist.sample(generator=generator)
+
+        assert list(sample.shape) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
+
+        output_slice = sample[0, -1, -3:, -3:].flatten().cpu()
+        expected_output_slice = torch.tensor(expected_slice)
+
+        assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)

tests/models/test_models_vae_flax.py

@@ -4,7 +4,7 @@ from diffusers import FlaxAutoencoderKL
 from diffusers.utils import is_flax_available
 from diffusers.utils.testing_utils import require_flax
 
-from .test_modeling_common_flax import FlaxModelTesterMixin
+from ..test_modeling_common_flax import FlaxModelTesterMixin
 
 
 if is_flax_available():

tests/models/test_models_vq.py

@@ -20,7 +20,7 @@ import torch
 from diffusers import VQModel
 from diffusers.utils import floats_tensor, torch_device
 
-from .test_modeling_common import ModelTesterMixin
+from ..test_modeling_common import ModelTesterMixin
 
 
 torch.backends.cuda.matmul.allow_tf32 = False

tests/test_models_vae.py

@@ -1,132 +0,0 @@
-# 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 torch
-
-from diffusers import AutoencoderKL
-from diffusers.modeling_utils import ModelMixin
-from diffusers.utils import floats_tensor, torch_device
-
-from .test_modeling_common import ModelTesterMixin
-
-
-torch.backends.cuda.matmul.allow_tf32 = False
-
-
-class AutoencoderKLTests(ModelTesterMixin, unittest.TestCase):
-    model_class = AutoencoderKL
-
-    @property
-    def dummy_input(self):
-        batch_size = 4
-        num_channels = 3
-        sizes = (32, 32)
-
-        image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
-
-        return {"sample": image}
-
-    @property
-    def input_shape(self):
-        return (3, 32, 32)
-
-    @property
-    def output_shape(self):
-        return (3, 32, 32)
-
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
-            "block_out_channels": [32, 64],
-            "in_channels": 3,
-            "out_channels": 3,
-            "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            "latent_channels": 4,
-        }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
-
-    def test_forward_signature(self):
-        pass
-
-    def test_training(self):
-        pass
-
-    def test_from_pretrained_hub(self):
-        model, loading_info = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy", output_loading_info=True)
-        self.assertIsNotNone(model)
-        self.assertEqual(len(loading_info["missing_keys"]), 0)
-
-        model.to(torch_device)
-        image = model(**self.dummy_input)
-
-        assert image is not None, "Make sure output is not None"
-
-    def test_output_pretrained(self):
-        model = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy")
-        model = model.to(torch_device)
-        model.eval()
-
-        # One-time warmup pass (see #372)
-        if torch_device == "mps" and isinstance(model, ModelMixin):
-            image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
-            image = image.to(torch_device)
-            with torch.no_grad():
-                _ = model(image, sample_posterior=True).sample
-            generator = torch.manual_seed(0)
-        else:
-            generator = torch.Generator(device=torch_device).manual_seed(0)
-
-        image = torch.randn(
-            1,
-            model.config.in_channels,
-            model.config.sample_size,
-            model.config.sample_size,
-            generator=torch.manual_seed(0),
-        )
-        image = image.to(torch_device)
-        with torch.no_grad():
-            output = model(image, sample_posterior=True, generator=generator).sample
-
-        output_slice = output[0, -1, -3:, -3:].flatten().cpu()
-
-        # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
-        # the expected output slices are not the same for CPU and GPU.
-        if torch_device == "mps":
-            expected_output_slice = torch.tensor(
-                [
-                    -4.0078e-01,
-                    -3.8323e-04,
-                    -1.2681e-01,
-                    -1.1462e-01,
-                    2.0095e-01,
-                    1.0893e-01,
-                    -8.8247e-02,
-                    -3.0361e-01,
-                    -9.8644e-03,
-                ]
-            )
-        elif torch_device == "cpu":
-            expected_output_slice = torch.tensor(
-                [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]
-            )
-        else:
-            expected_output_slice = torch.tensor(
-                [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]
-            )
-
-        self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))