[Tests] Add single file tester mixin for Models and remove unittest dependency (#12352)

* update

* update

* update

* update

* update

tests/single_file/single_file_testing_utils.py

+import gc
 import tempfile
 from io import BytesIO
 
@@ -9,7 +10,10 @@ from diffusers.loaders.single_file_utils import _extract_repo_id_and_weights_nam
 from diffusers.models.attention_processor import AttnProcessor
 
 from ..testing_utils import (
+    backend_empty_cache,
+    nightly,
     numpy_cosine_similarity_distance,
+    require_torch_accelerator,
     torch_device,
 )
 
@@ -47,6 +51,93 @@ def download_diffusers_config(repo_id, tmpdir):
     return path
 
 
+@nightly
+@require_torch_accelerator
+class SingleFileModelTesterMixin:
+    def setup_method(self):
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def teardown_method(self):
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def test_single_file_model_config(self):
+        pretrained_kwargs = {}
+        single_file_kwargs = {}
+
+        if hasattr(self, "subfolder") and self.subfolder:
+            pretrained_kwargs["subfolder"] = self.subfolder
+
+        if hasattr(self, "torch_dtype") and self.torch_dtype:
+            pretrained_kwargs["torch_dtype"] = self.torch_dtype
+            single_file_kwargs["torch_dtype"] = self.torch_dtype
+
+        model = self.model_class.from_pretrained(self.repo_id, **pretrained_kwargs)
+        model_single_file = self.model_class.from_single_file(self.ckpt_path, **single_file_kwargs)
+
+        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
+        for param_name, param_value in model_single_file.config.items():
+            if param_name in PARAMS_TO_IGNORE:
+                continue
+            assert model.config[param_name] == param_value, (
+                f"{param_name} differs between pretrained loading and single file loading"
+            )
+
+    def test_single_file_model_parameters(self):
+        pretrained_kwargs = {}
+        single_file_kwargs = {}
+
+        if hasattr(self, "subfolder") and self.subfolder:
+            pretrained_kwargs["subfolder"] = self.subfolder
+
+        if hasattr(self, "torch_dtype") and self.torch_dtype:
+            pretrained_kwargs["torch_dtype"] = self.torch_dtype
+            single_file_kwargs["torch_dtype"] = self.torch_dtype
+
+        model = self.model_class.from_pretrained(self.repo_id, **pretrained_kwargs)
+        model_single_file = self.model_class.from_single_file(self.ckpt_path, **single_file_kwargs)
+
+        state_dict = model.state_dict()
+        state_dict_single_file = model_single_file.state_dict()
+
+        assert set(state_dict.keys()) == set(state_dict_single_file.keys()), (
+            "Model parameters keys differ between pretrained and single file loading"
+        )
+
+        for key in state_dict.keys():
+            param = state_dict[key]
+            param_single_file = state_dict_single_file[key]
+
+            assert param.shape == param_single_file.shape, (
+                f"Parameter shape mismatch for {key}: "
+                f"pretrained {param.shape} vs single file {param_single_file.shape}"
+            )
+
+            assert torch.allclose(param, param_single_file, rtol=1e-5, atol=1e-5), (
+                f"Parameter values differ for {key}: "
+                f"max difference {torch.max(torch.abs(param - param_single_file)).item()}"
+            )
+
+    def test_checkpoint_altered_keys_loading(self):
+        # Test loading with checkpoints that have altered keys
+        if not hasattr(self, "alternate_keys_ckpt_paths") or not self.alternate_keys_ckpt_paths:
+            return
+
+        for ckpt_path in self.alternate_keys_ckpt_paths:
+            backend_empty_cache(torch_device)
+
+            single_file_kwargs = {}
+            if hasattr(self, "torch_dtype") and self.torch_dtype:
+                single_file_kwargs["torch_dtype"] = self.torch_dtype
+
+            model = self.model_class.from_single_file(ckpt_path, **single_file_kwargs)
+
+            del model
+            gc.collect()
+            backend_empty_cache(torch_device)
+
+
 class SDSingleFileTesterMixin:
     single_file_kwargs = {}
 

tests/single_file/test_lumina2_transformer.py

@@ -13,26 +13,21 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import gc
-import unittest
 
 from diffusers import (
     Lumina2Transformer2DModel,
 )
 
 from ..testing_utils import (
-    backend_empty_cache,
     enable_full_determinism,
-    require_torch_accelerator,
-    torch_device,
 )
+from .single_file_testing_utils import SingleFileModelTesterMixin
 
 
 enable_full_determinism()
 
 
-@require_torch_accelerator
-class Lumina2Transformer2DModelSingleFileTests(unittest.TestCase):
+class TestLumina2Transformer2DModelSingleFile(SingleFileModelTesterMixin):
     model_class = Lumina2Transformer2DModel
     ckpt_path = "https://huggingface.co/Comfy-Org/Lumina_Image_2.0_Repackaged/blob/main/split_files/diffusion_models/lumina_2_model_bf16.safetensors"
     alternate_keys_ckpt_paths = [
@@ -40,34 +35,4 @@ class Lumina2Transformer2DModelSingleFileTests(unittest.TestCase):
     ]
 
     repo_id = "Alpha-VLLM/Lumina-Image-2.0"
-
-    def setUp(self):
-        super().setUp()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def test_single_file_components(self):
-        model = self.model_class.from_pretrained(self.repo_id, subfolder="transformer")
-        model_single_file = self.model_class.from_single_file(self.ckpt_path)
-
-        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
-        for param_name, param_value in model_single_file.config.items():
-            if param_name in PARAMS_TO_IGNORE:
-                continue
-            assert model.config[param_name] == param_value, (
-                f"{param_name} differs between single file loading and pretrained loading"
-            )
-
-    def test_checkpoint_loading(self):
-        for ckpt_path in self.alternate_keys_ckpt_paths:
-            backend_empty_cache(torch_device)
-            model = self.model_class.from_single_file(ckpt_path)
-
-            del model
-            gc.collect()
-            backend_empty_cache(torch_device)
+    subfolder = "transformer"

tests/single_file/test_model_autoencoder_dc_single_file.py

@@ -13,8 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import gc
-import unittest
 
 import torch
 
@@ -23,38 +21,24 @@ from diffusers import (
 )
 
 from ..testing_utils import (
-    backend_empty_cache,
     enable_full_determinism,
     load_hf_numpy,
     numpy_cosine_similarity_distance,
-    require_torch_accelerator,
-    slow,
     torch_device,
 )
+from .single_file_testing_utils import SingleFileModelTesterMixin
 
 
 enable_full_determinism()
 
 
-@slow
-@require_torch_accelerator
-class AutoencoderDCSingleFileTests(unittest.TestCase):
+class TestAutoencoderDCSingleFile(SingleFileModelTesterMixin):
     model_class = AutoencoderDC
     ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.0/blob/main/model.safetensors"
     repo_id = "mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers"
     main_input_name = "sample"
     base_precision = 1e-2
 
-    def setUp(self):
-        super().setUp()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
     def get_file_format(self, seed, shape):
         return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"
 
@@ -80,18 +64,6 @@ class AutoencoderDCSingleFileTests(unittest.TestCase):
 
         assert numpy_cosine_similarity_distance(output_slice_1, output_slice_2) < 1e-4
 
-    def test_single_file_components(self):
-        model = self.model_class.from_pretrained(self.repo_id)
-        model_single_file = self.model_class.from_single_file(self.ckpt_path)
-
-        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
-        for param_name, param_value in model_single_file.config.items():
-            if param_name in PARAMS_TO_IGNORE:
-                continue
-            assert model.config[param_name] == param_value, (
-                f"{param_name} differs between pretrained loading and single file loading"
-            )
-
     def test_single_file_in_type_variant_components(self):
         # `in` variant checkpoints require passing in a `config` parameter
         # in order to set the scaling factor correctly.

tests/single_file/test_model_controlnet_single_file.py

@@ -13,8 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import gc
-import unittest
 
 import torch
 
@@ -23,46 +21,19 @@ from diffusers import (
 )
 
 from ..testing_utils import (
-    backend_empty_cache,
     enable_full_determinism,
-    require_torch_accelerator,
-    slow,
-    torch_device,
 )
+from .single_file_testing_utils import SingleFileModelTesterMixin
 
 
 enable_full_determinism()
 
 
-@slow
-@require_torch_accelerator
-class ControlNetModelSingleFileTests(unittest.TestCase):
+class TestControlNetModelSingleFile(SingleFileModelTesterMixin):
     model_class = ControlNetModel
     ckpt_path = "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"
     repo_id = "lllyasviel/control_v11p_sd15_canny"
 
-    def setUp(self):
-        super().setUp()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def test_single_file_components(self):
-        model = self.model_class.from_pretrained(self.repo_id)
-        model_single_file = self.model_class.from_single_file(self.ckpt_path)
-
-        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
-        for param_name, param_value in model_single_file.config.items():
-            if param_name in PARAMS_TO_IGNORE:
-                continue
-            assert model.config[param_name] == param_value, (
-                f"{param_name} differs between single file loading and pretrained loading"
-            )
-
     def test_single_file_arguments(self):
         model_default = self.model_class.from_single_file(self.ckpt_path)
 

tests/single_file/test_model_flux_transformer_single_file.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 import gc
-import unittest
 
 from diffusers import (
     FluxTransformer2DModel,
@@ -23,52 +22,21 @@ from diffusers import (
 from ..testing_utils import (
     backend_empty_cache,
     enable_full_determinism,
-    require_torch_accelerator,
     torch_device,
 )
+from .single_file_testing_utils import SingleFileModelTesterMixin
 
 
 enable_full_determinism()
 
 
-@require_torch_accelerator
-class FluxTransformer2DModelSingleFileTests(unittest.TestCase):
+class TestFluxTransformer2DModelSingleFile(SingleFileModelTesterMixin):
     model_class = FluxTransformer2DModel
     ckpt_path = "https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/flux1-dev.safetensors"
     alternate_keys_ckpt_paths = ["https://huggingface.co/Comfy-Org/flux1-dev/blob/main/flux1-dev-fp8.safetensors"]
 
     repo_id = "black-forest-labs/FLUX.1-dev"
-
-    def setUp(self):
-        super().setUp()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def test_single_file_components(self):
-        model = self.model_class.from_pretrained(self.repo_id, subfolder="transformer")
-        model_single_file = self.model_class.from_single_file(self.ckpt_path)
-
-        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
-        for param_name, param_value in model_single_file.config.items():
-            if param_name in PARAMS_TO_IGNORE:
-                continue
-            assert model.config[param_name] == param_value, (
-                f"{param_name} differs between single file loading and pretrained loading"
-            )
-
-    def test_checkpoint_loading(self):
-        for ckpt_path in self.alternate_keys_ckpt_paths:
-            backend_empty_cache(torch_device)
-            model = self.model_class.from_single_file(ckpt_path)
-
-            del model
-            gc.collect()
-            backend_empty_cache(torch_device)
+    subfolder = "transformer"
 
     def test_device_map_cuda(self):
         backend_empty_cache(torch_device)

tests/single_file/test_model_motion_adapter_single_file.py

@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
 
 from diffusers import (
     MotionAdapter,
@@ -27,7 +26,7 @@ from ..testing_utils import (
 enable_full_determinism()
 
 
-class MotionAdapterSingleFileTests(unittest.TestCase):
+class MotionAdapterSingleFileTests:
     model_class = MotionAdapter
 
     def test_single_file_components_version_v1_5(self):

tests/single_file/test_model_sd_cascade_unet_single_file.py

@@ -14,7 +14,6 @@
 # limitations under the License.
 
 import gc
-import unittest
 
 import torch
 
@@ -37,14 +36,12 @@ enable_full_determinism()
 
 @slow
 @require_torch_accelerator
-class StableCascadeUNetSingleFileTest(unittest.TestCase):
-    def setUp(self):
-        super().setUp()
+class StableCascadeUNetSingleFileTest:
+    def setup_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
-    def tearDown(self):
-        super().tearDown()
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 

tests/single_file/test_model_vae_single_file.py

@@ -13,8 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import gc
-import unittest
 
 import torch
 
@@ -23,22 +21,18 @@ from diffusers import (
 )
 
 from ..testing_utils import (
-    backend_empty_cache,
     enable_full_determinism,
     load_hf_numpy,
     numpy_cosine_similarity_distance,
-    require_torch_accelerator,
-    slow,
     torch_device,
 )
+from .single_file_testing_utils import SingleFileModelTesterMixin
 
 
 enable_full_determinism()
 
 
-@slow
-@require_torch_accelerator
-class AutoencoderKLSingleFileTests(unittest.TestCase):
+class TestAutoencoderKLSingleFile(SingleFileModelTesterMixin):
     model_class = AutoencoderKL
     ckpt_path = (
         "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors"
@@ -47,16 +41,6 @@ class AutoencoderKLSingleFileTests(unittest.TestCase):
     main_input_name = "sample"
     base_precision = 1e-2
 
-    def setUp(self):
-        super().setUp()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
     def get_file_format(self, seed, shape):
         return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"
 
@@ -84,18 +68,6 @@ class AutoencoderKLSingleFileTests(unittest.TestCase):
 
         assert numpy_cosine_similarity_distance(output_slice_1, output_slice_2) < 1e-4
 
-    def test_single_file_components(self):
-        model = self.model_class.from_pretrained(self.repo_id)
-        model_single_file = self.model_class.from_single_file(self.ckpt_path, config=self.repo_id)
-
-        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
-        for param_name, param_value in model_single_file.config.items():
-            if param_name in PARAMS_TO_IGNORE:
-                continue
-            assert model.config[param_name] == param_value, (
-                f"{param_name} differs between pretrained loading and single file loading"
-            )
-
     def test_single_file_arguments(self):
         model_default = self.model_class.from_single_file(self.ckpt_path, config=self.repo_id)
 

tests/single_file/test_model_wan_autoencoder_single_file.py

@@ -13,50 +13,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import gc
-import unittest
 
 from diffusers import (
     AutoencoderKLWan,
 )
 
 from ..testing_utils import (
-    backend_empty_cache,
     enable_full_determinism,
-    require_torch_accelerator,
-    torch_device,
 )
+from .single_file_testing_utils import SingleFileModelTesterMixin
 
 
 enable_full_determinism()
 
 
-@require_torch_accelerator
-class AutoencoderKLWanSingleFileTests(unittest.TestCase):
+class TestAutoencoderKLWanSingleFile(SingleFileModelTesterMixin):
     model_class = AutoencoderKLWan
     ckpt_path = (
         "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/vae/wan_2.1_vae.safetensors"
     )
     repo_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-
-    def setUp(self):
-        super().setUp()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def test_single_file_components(self):
-        model = self.model_class.from_pretrained(self.repo_id, subfolder="vae")
-        model_single_file = self.model_class.from_single_file(self.ckpt_path)
-
-        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
-        for param_name, param_value in model_single_file.config.items():
-            if param_name in PARAMS_TO_IGNORE:
-                continue
-            assert model.config[param_name] == param_value, (
-                f"{param_name} differs between single file loading and pretrained loading"
-            )
+    subfolder = "vae"

tests/single_file/test_model_wan_transformer3d_single_file.py

@@ -13,8 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import gc
-import unittest
 
 import torch
 
@@ -23,72 +21,26 @@ from diffusers import (
 )
 
 from ..testing_utils import (
-    backend_empty_cache,
     enable_full_determinism,
     require_big_accelerator,
-    require_torch_accelerator,
-    torch_device,
 )
+from .single_file_testing_utils import SingleFileModelTesterMixin
 
 
 enable_full_determinism()
 
 
-@require_torch_accelerator
-class WanTransformer3DModelText2VideoSingleFileTest(unittest.TestCase):
+class TestWanTransformer3DModelText2VideoSingleFile(SingleFileModelTesterMixin):
     model_class = WanTransformer3DModel
     ckpt_path = "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/diffusion_models/wan2.1_t2v_1.3B_bf16.safetensors"
     repo_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-
-    def setUp(self):
-        super().setUp()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def test_single_file_components(self):
-        model = self.model_class.from_pretrained(self.repo_id, subfolder="transformer")
-        model_single_file = self.model_class.from_single_file(self.ckpt_path)
-
-        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
-        for param_name, param_value in model_single_file.config.items():
-            if param_name in PARAMS_TO_IGNORE:
-                continue
-            assert model.config[param_name] == param_value, (
-                f"{param_name} differs between single file loading and pretrained loading"
-            )
+    subfolder = "transformer"
 
 
 @require_big_accelerator
-@require_torch_accelerator
-class WanTransformer3DModelImage2VideoSingleFileTest(unittest.TestCase):
+class TestWanTransformer3DModelImage2VideoSingleFile(SingleFileModelTesterMixin):
     model_class = WanTransformer3DModel
     ckpt_path = "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/blob/main/split_files/diffusion_models/wan2.1_i2v_480p_14B_fp8_e4m3fn.safetensors"
     repo_id = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
     torch_dtype = torch.float8_e4m3fn
-
-    def setUp(self):
-        super().setUp()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def test_single_file_components(self):
-        model = self.model_class.from_pretrained(self.repo_id, subfolder="transformer", torch_dtype=self.torch_dtype)
-        model_single_file = self.model_class.from_single_file(self.ckpt_path, torch_dtype=self.torch_dtype)
-
-        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
-        for param_name, param_value in model_single_file.config.items():
-            if param_name in PARAMS_TO_IGNORE:
-                continue
-            assert model.config[param_name] == param_value, (
-                f"{param_name} differs between single file loading and pretrained loading"
-            )
+    subfolder = "transformer"

tests/single_file/test_sana_transformer.py

-import gc
-import unittest
-
 from diffusers import (
     SanaTransformer2DModel,
 )
 
 from ..testing_utils import (
-    backend_empty_cache,
     enable_full_determinism,
-    require_torch_accelerator,
-    torch_device,
 )
+from .single_file_testing_utils import SingleFileModelTesterMixin
 
 
 enable_full_determinism()
 
 
-@require_torch_accelerator
-class SanaTransformer2DModelSingleFileTests(unittest.TestCase):
+class TestSanaTransformer2DModelSingleFile(SingleFileModelTesterMixin):
     model_class = SanaTransformer2DModel
     ckpt_path = (
         "https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px/blob/main/checkpoints/Sana_1600M_1024px.pth"
@@ -27,34 +21,4 @@ class SanaTransformer2DModelSingleFileTests(unittest.TestCase):
     ]
 
     repo_id = "Efficient-Large-Model/Sana_1600M_1024px_diffusers"
-
-    def setUp(self):
-        super().setUp()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        backend_empty_cache(torch_device)
-
-    def test_single_file_components(self):
-        model = self.model_class.from_pretrained(self.repo_id, subfolder="transformer")
-        model_single_file = self.model_class.from_single_file(self.ckpt_path)
-
-        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
-        for param_name, param_value in model_single_file.config.items():
-            if param_name in PARAMS_TO_IGNORE:
-                continue
-            assert model.config[param_name] == param_value, (
-                f"{param_name} differs between single file loading and pretrained loading"
-            )
-
-    def test_checkpoint_loading(self):
-        for ckpt_path in self.alternate_keys_ckpt_paths:
-            backend_empty_cache(torch_device)
-            model = self.model_class.from_single_file(ckpt_path)
-
-            del model
-            gc.collect()
-            backend_empty_cache(torch_device)
+    subfolder = "transformer"

tests/single_file/test_stable_diffusion_controlnet_img2img_single_file.py

 import gc
 import tempfile
-import unittest
 
 import torch
 
@@ -29,7 +28,7 @@ enable_full_determinism()
 
 @slow
 @require_torch_accelerator
-class StableDiffusionControlNetPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
+class TestStableDiffusionControlNetPipelineSingleFileSlow(SDSingleFileTesterMixin):
     pipeline_class = StableDiffusionControlNetPipeline
     ckpt_path = (
         "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors"
@@ -39,13 +38,11 @@ class StableDiffusionControlNetPipelineSingleFileSlowTests(unittest.TestCase, SD
     )
     repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
 
-    def setUp(self):
-        super().setUp()
+    def setup_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
-    def tearDown(self):
-        super().tearDown()
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 

tests/single_file/test_stable_diffusion_controlnet_inpaint_single_file.py

 import gc
 import tempfile
-import unittest
 
+import pytest
 import torch
 
 from diffusers import ControlNetModel, StableDiffusionControlNetInpaintPipeline
@@ -29,19 +29,17 @@ enable_full_determinism()
 
 @slow
 @require_torch_accelerator
-class StableDiffusionControlNetInpaintPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
+class TestStableDiffusionControlNetInpaintPipelineSingleFileSlow(SDSingleFileTesterMixin):
     pipeline_class = StableDiffusionControlNetInpaintPipeline
     ckpt_path = "https://huggingface.co/botp/stable-diffusion-v1-5-inpainting/blob/main/sd-v1-5-inpainting.ckpt"
     original_config = "https://raw.githubusercontent.com/runwayml/stable-diffusion/main/configs/stable-diffusion/v1-inpainting-inference.yaml"
     repo_id = "stable-diffusion-v1-5/stable-diffusion-inpainting"
 
-    def setUp(self):
-        super().setUp()
+    def setup_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
-    def tearDown(self):
-        super().tearDown()
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
@@ -115,7 +113,7 @@ class StableDiffusionControlNetInpaintPipelineSingleFileSlowTests(unittest.TestC
 
         super()._compare_component_configs(pipe, pipe_single_file)
 
-    @unittest.skip("runwayml original config repo does not exist")
+    @pytest.mark.skip(reason="runwayml original config repo does not exist")
     def test_single_file_components_with_original_config(self):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny", variant="fp16")
         pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet)
@@ -125,7 +123,7 @@ class StableDiffusionControlNetInpaintPipelineSingleFileSlowTests(unittest.TestC
 
         super()._compare_component_configs(pipe, pipe_single_file)
 
-    @unittest.skip("runwayml original config repo does not exist")
+    @pytest.mark.skip(reason="runwayml original config repo does not exist")
     def test_single_file_components_with_original_config_local_files_only(self):
         controlnet = ControlNetModel.from_pretrained(
             "lllyasviel/control_v11p_sd15_canny", torch_dtype=torch.float16, variant="fp16"

tests/single_file/test_stable_diffusion_controlnet_single_file.py

 import gc
 import tempfile
-import unittest
 
 import torch
 
@@ -29,7 +28,7 @@ enable_full_determinism()
 
 @slow
 @require_torch_accelerator
-class StableDiffusionControlNetPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
+class TestStableDiffusionControlNetPipelineSingleFileSlow(SDSingleFileTesterMixin):
     pipeline_class = StableDiffusionControlNetPipeline
     ckpt_path = (
         "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors"
@@ -39,13 +38,11 @@ class StableDiffusionControlNetPipelineSingleFileSlowTests(unittest.TestCase, SD
     )
     repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
 
-    def setUp(self):
-        super().setUp()
+    def setup_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
-    def tearDown(self):
-        super().tearDown()
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 

tests/single_file/test_stable_diffusion_img2img_single_file.py

 import gc
-import unittest
 
 import torch
 
@@ -23,7 +22,7 @@ enable_full_determinism()
 
 @slow
 @require_torch_accelerator
-class StableDiffusionImg2ImgPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
+class TestStableDiffusionImg2ImgPipelineSingleFileSlow(SDSingleFileTesterMixin):
     pipeline_class = StableDiffusionImg2ImgPipeline
     ckpt_path = (
         "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors"
@@ -33,13 +32,11 @@ class StableDiffusionImg2ImgPipelineSingleFileSlowTests(unittest.TestCase, SDSin
     )
     repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
 
-    def setUp(self):
-        super().setUp()
+    def setup_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
-    def tearDown(self):
-        super().tearDown()
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
@@ -66,19 +63,17 @@ class StableDiffusionImg2ImgPipelineSingleFileSlowTests(unittest.TestCase, SDSin
 
 @slow
 @require_torch_accelerator
-class StableDiffusion21Img2ImgPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
+class TestStableDiffusion21Img2ImgPipelineSingleFileSlow(SDSingleFileTesterMixin):
     pipeline_class = StableDiffusionImg2ImgPipeline
     ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.safetensors"
     original_config = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml"
     repo_id = "stabilityai/stable-diffusion-2-1"
 
-    def setUp(self):
-        super().setUp()
+    def setup_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
-    def tearDown(self):
-        super().tearDown()
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 

tests/single_file/test_stable_diffusion_inpaint_single_file.py

 import gc
-import unittest
 
+import pytest
 import torch
 
 from diffusers import (
@@ -23,19 +23,17 @@ enable_full_determinism()
 
 @slow
 @require_torch_accelerator
-class StableDiffusionInpaintPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
+class TestStableDiffusionInpaintPipelineSingleFileSlow(SDSingleFileTesterMixin):
     pipeline_class = StableDiffusionInpaintPipeline
     ckpt_path = "https://huggingface.co/botp/stable-diffusion-v1-5-inpainting/blob/main/sd-v1-5-inpainting.ckpt"
     original_config = "https://raw.githubusercontent.com/runwayml/stable-diffusion/main/configs/stable-diffusion/v1-inpainting-inference.yaml"
     repo_id = "botp/stable-diffusion-v1-5-inpainting"
 
-    def setUp(self):
-        super().setUp()
+    def setup_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
-    def tearDown(self):
-        super().tearDown()
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
@@ -70,18 +68,18 @@ class StableDiffusionInpaintPipelineSingleFileSlowTests(unittest.TestCase, SDSin
 
         assert pipe.unet.config.in_channels == 4
 
-    @unittest.skip("runwayml original config has been removed")
+    @pytest.mark.skip(reason="runwayml original config has been removed")
     def test_single_file_components_with_original_config(self):
         return
 
-    @unittest.skip("runwayml original config has been removed")
+    @pytest.mark.skip(reason="runwayml original config has been removed")
     def test_single_file_components_with_original_config_local_files_only(self):
         return
 
 
 @slow
 @require_torch_accelerator
-class StableDiffusion21InpaintPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
+class TestStableDiffusion21InpaintPipelineSingleFileSlow(SDSingleFileTesterMixin):
     pipeline_class = StableDiffusionInpaintPipeline
     ckpt_path = (
         "https://huggingface.co/stabilityai/stable-diffusion-2-inpainting/blob/main/512-inpainting-ema.safetensors"
@@ -89,13 +87,11 @@ class StableDiffusion21InpaintPipelineSingleFileSlowTests(unittest.TestCase, SDS
     original_config = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inpainting-inference.yaml"
     repo_id = "stabilityai/stable-diffusion-2-inpainting"
 
-    def setUp(self):
-        super().setUp()
+    def setup_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
-    def tearDown(self):
-        super().tearDown()
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 

tests/single_file/test_stable_diffusion_single_file.py

 import gc
 import tempfile
-import unittest
 
 import torch
 
@@ -28,7 +27,7 @@ enable_full_determinism()
 
 @slow
 @require_torch_accelerator
-class StableDiffusionPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
+class TestStableDiffusionPipelineSingleFileSlow(SDSingleFileTesterMixin):
     pipeline_class = StableDiffusionPipeline
     ckpt_path = (
         "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors"
@@ -38,13 +37,11 @@ class StableDiffusionPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFile
     )
     repo_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
 
-    def setUp(self):
-        super().setUp()
+    def setup_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
-    def tearDown(self):
-        super().tearDown()
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
@@ -90,19 +87,17 @@ class StableDiffusionPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFile
 
 
 @slow
-class StableDiffusion21PipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
+class TestStableDiffusion21PipelineSingleFileSlow(SDSingleFileTesterMixin):
     pipeline_class = StableDiffusionPipeline
     ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.safetensors"
     original_config = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml"
     repo_id = "stabilityai/stable-diffusion-2-1"
 
-    def setUp(self):
-        super().setUp()
+    def setup_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
-    def tearDown(self):
-        super().tearDown()
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
@@ -125,7 +120,7 @@ class StableDiffusion21PipelineSingleFileSlowTests(unittest.TestCase, SDSingleFi
 @nightly
 @slow
 @require_torch_accelerator
-class StableDiffusionInstructPix2PixPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
+class TestStableDiffusionInstructPix2PixPipelineSingleFileSlow(SDSingleFileTesterMixin):
     pipeline_class = StableDiffusionInstructPix2PixPipeline
     ckpt_path = "https://huggingface.co/timbrooks/instruct-pix2pix/blob/main/instruct-pix2pix-00-22000.safetensors"
     original_config = (
@@ -134,13 +129,11 @@ class StableDiffusionInstructPix2PixPipelineSingleFileSlowTests(unittest.TestCas
     repo_id = "timbrooks/instruct-pix2pix"
     single_file_kwargs = {"extract_ema": True}
 
-    def setUp(self):
-        super().setUp()
+    def setup_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
-    def tearDown(self):
-        super().tearDown()
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 

tests/single_file/test_stable_diffusion_upscale_single_file.py

 import gc
-import unittest
 
 import pytest
 import torch
@@ -25,19 +24,17 @@ enable_full_determinism()
 
 @slow
 @require_torch_accelerator
-class StableDiffusionUpscalePipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
+class TestStableDiffusionUpscalePipelineSingleFileSlow(SDSingleFileTesterMixin):
     pipeline_class = StableDiffusionUpscalePipeline
     ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/blob/main/x4-upscaler-ema.safetensors"
     original_config = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/x4-upscaling.yaml"
     repo_id = "stabilityai/stable-diffusion-x4-upscaler"
 
-    def setUp(self):
-        super().setUp()
+    def setup_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
-    def tearDown(self):
-        super().tearDown()
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 

tests/single_file/test_stable_diffusion_xl_adapter_single_file.py

 import gc
 import tempfile
-import unittest
 
 import torch
 
@@ -32,7 +31,7 @@ enable_full_determinism()
 
 @slow
 @require_torch_accelerator
-class StableDiffusionXLAdapterPipelineSingleFileSlowTests(unittest.TestCase, SDXLSingleFileTesterMixin):
+class TestStableDiffusionXLAdapterPipelineSingleFileSlow(SDXLSingleFileTesterMixin):
     pipeline_class = StableDiffusionXLAdapterPipeline
     ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors"
     repo_id = "stabilityai/stable-diffusion-xl-base-1.0"
@@ -40,13 +39,11 @@ class StableDiffusionXLAdapterPipelineSingleFileSlowTests(unittest.TestCase, SDX
         "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml"
     )
 
-    def setUp(self):
-        super().setUp()
+    def setup_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
-    def tearDown(self):
-        super().tearDown()
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 

tests/single_file/test_stable_diffusion_xl_controlnet_single_file.py

 import gc
 import tempfile
-import unittest
 
 import torch
 
@@ -28,7 +27,7 @@ enable_full_determinism()
 
 @slow
 @require_torch_accelerator
-class StableDiffusionXLControlNetPipelineSingleFileSlowTests(unittest.TestCase, SDXLSingleFileTesterMixin):
+class TestStableDiffusionXLControlNetPipelineSingleFileSlow(SDXLSingleFileTesterMixin):
     pipeline_class = StableDiffusionXLControlNetPipeline
     ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors"
     repo_id = "stabilityai/stable-diffusion-xl-base-1.0"
@@ -36,13 +35,11 @@ class StableDiffusionXLControlNetPipelineSingleFileSlowTests(unittest.TestCase,
         "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml"
     )
 
-    def setUp(self):
-        super().setUp()
+    def setup_method(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
-    def tearDown(self):
-        super().tearDown()
+    def teardown_method(self):
         gc.collect()
         backend_empty_cache(torch_device)