Move controlnet load local tests to nightly (#4543)

move controlnet load local tests to nihghtly

tests/pipelines/controlnet/test_controlnet.py

@@ -31,7 +31,7 @@ from diffusers import (
     UNet2DConditionModel,
 )
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
-from diffusers.utils import load_image, load_numpy, randn_tensor, slow, torch_device
+from diffusers.utils import load_image, load_numpy, nightly, randn_tensor, slow, torch_device
 from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.testing_utils import (
     enable_full_determinism,
@@ -925,42 +925,6 @@ class ControlNetPipelineSlowTests(unittest.TestCase):
         expected_slice = np.array([0.1338, 0.1597, 0.1202, 0.1687, 0.1377, 0.1017, 0.2070, 0.1574, 0.1348])
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
-    def test_load_local(self):
-        controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
-        pipe_1 = StableDiffusionControlNetPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
-        )
-
-        controlnet = ControlNetModel.from_single_file(
-            "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"
-        )
-        pipe_2 = StableDiffusionControlNetPipeline.from_single_file(
-            "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors",
-            safety_checker=None,
-            controlnet=controlnet,
-        )
-        pipes = [pipe_1, pipe_2]
-        images = []
-
-        for pipe in pipes:
-            pipe.enable_model_cpu_offload()
-            pipe.set_progress_bar_config(disable=None)
-
-            generator = torch.Generator(device="cpu").manual_seed(0)
-            prompt = "bird"
-            image = load_image(
-                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
-            )
-
-            output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
-            images.append(output.images[0])
-
-            del pipe
-            gc.collect()
-            torch.cuda.empty_cache()
-
-        assert np.abs(images[0] - images[1]).sum() < 1e-3
-
 
 @slow
 @require_torch_gpu
@@ -1000,3 +964,48 @@ class StableDiffusionMultiControlNetPipelineSlowTests(unittest.TestCase):
         )
 
         assert np.abs(expected_image - image).max() < 5e-2
+
+
+@nightly
+@require_torch_gpu
+class StableDiffusionMultiControlNetPipelineNightlyTests(unittest.TestCase):
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def test_load_local(self):
+        controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
+        pipe_1 = StableDiffusionControlNetPipeline.from_pretrained(
+            "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
+        )
+
+        controlnet = ControlNetModel.from_single_file(
+            "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"
+        )
+        pipe_2 = StableDiffusionControlNetPipeline.from_single_file(
+            "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors",
+            safety_checker=None,
+            controlnet=controlnet,
+        )
+        pipes = [pipe_1, pipe_2]
+        images = []
+
+        for pipe in pipes:
+            pipe.enable_model_cpu_offload()
+            pipe.set_progress_bar_config(disable=None)
+
+            generator = torch.Generator(device="cpu").manual_seed(0)
+            prompt = "bird"
+            image = load_image(
+                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
+            )
+
+            output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
+            images.append(output.images[0])
+
+            del pipe
+            gc.collect()
+            torch.cuda.empty_cache()
+
+        assert np.abs(images[0] - images[1]).sum() < 1e-3

tests/pipelines/controlnet/test_controlnet_img2img.py

@@ -33,7 +33,7 @@ from diffusers import (
     UNet2DConditionModel,
 )
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
-from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
+from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, randn_tensor, slow, torch_device
 from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
 
@@ -402,6 +402,15 @@ class ControlNetImg2ImgPipelineSlowTests(unittest.TestCase):
 
         assert np.abs(expected_image - image).max() < 9e-2
 
+
+@nightly
+@require_torch_gpu
+class ControlNetImg2ImgPipelineNightlyTests(unittest.TestCase):
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
     def test_load_local(self):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
         pipe_1 = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(

tests/pipelines/controlnet/test_controlnet_inpaint.py

@@ -33,7 +33,7 @@ from diffusers import (
     UNet2DConditionModel,
 )
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
-from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
+from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, randn_tensor, slow, torch_device
 from diffusers.utils.import_utils import is_xformers_available
 from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
 
@@ -544,6 +544,15 @@ class ControlNetInpaintPipelineSlowTests(unittest.TestCase):
 
         assert np.abs(expected_image - image).max() < 9e-2
 
+
+@nightly
+@require_torch_gpu
+class ControlNetInpaintPipelineNightlyTests(unittest.TestCase):
+    def tearDown(self):
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
     def test_load_local(self):
         controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
         pipe_1 = StableDiffusionControlNetInpaintPipeline.from_pretrained(