fix

src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py

@@ -1068,7 +1068,6 @@ class StableDiffusionXLAdapterPipeline(
 
         return StableDiffusionXLPipelineOutput(images=image)
 
-
     # Overrride to properly handle the loading and unloading of the additional text encoder.
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.load_lora_weights
     def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py

@@ -13,8 +13,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import random
 import gc
+import random
 import unittest
 
 import numpy as np
@@ -30,14 +30,17 @@ from diffusers import (
     StableDiffusionXLAdapterPipeline,
     T2IAdapter,
     UNet2DConditionModel,
-    EulerAncestralDiscreteScheduler,
-
 )
-from diffusers.utils import logging
-from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, torch_device
-from diffusers.utils import load_image
-from diffusers.utils.torch_utils import randn_tensor
-from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
+from diffusers.utils import load_image, logging
+from diffusers.utils.testing_utils import (
+    enable_full_determinism,
+    floats_tensor,
+    numpy_cosine_similarity_distance,
+    require_torch_gpu,
+    slow,
+    torch_device,
+)
+
 from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
 from ..test_pipelines_common import (
     PipelineTesterMixin,
@@ -575,38 +578,15 @@ class AdapterSDXLPipelineSlowTests(unittest.TestCase):
         gc.collect()
         torch.cuda.empty_cache()
 
-    def test_canny(self):
-        adapter = T2IAdapter.from_pretrained(
-            "TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16
-        ).to("cpu")
-        pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
-        'stabilityai/stable-diffusion-xl-base-1.0', adapter=adapter, torch_dtype=torch.float16, variant="fp16", 
-        )
-        pipe.load_lora_weights("CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors")
-        pipe.enable_sequential_cpu_offload()
-        pipe.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        prompt = "toy"
-        image = load_image(
-                    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png"
-        )
-
-        images = pipe(prompt, image=image, generator=generator, output_type="np", num_inference_steps=3).images
-
-        assert images[0].shape == (768, 512, 3)
-
-        original_image = images[0, -3:, -3:, -1].flatten()
-        assert numpy_cosine_similarity_distance(original_image, expected_image) < 1e-4
-        assert np.allclose(original_image, expected_image, atol=1e-04)
-
-
     def test_canny_lora(self):
-        adapter = T2IAdapter.from_pretrained(
-            "TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16
-        ).to("cpu")
+        adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16).to(
+            "cpu"
+        )
         pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
-        'stabilityai/stable-diffusion-xl-base-1.0', adapter=adapter, torch_dtype=torch.float16, variant="fp16", 
+            "stabilityai/stable-diffusion-xl-base-1.0",
+            adapter=adapter,
+            torch_dtype=torch.float16,
+            variant="fp16",
         )
         pipe.load_lora_weights("CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors")
         pipe.enable_sequential_cpu_offload()
@@ -623,6 +603,7 @@ class AdapterSDXLPipelineSlowTests(unittest.TestCase):
         assert images[0].shape == (768, 512, 3)
 
         original_image = images[0, -3:, -3:, -1].flatten()
-        expected_image = np.array([0.50346327, 0.50708383, 0.50719553, 0.5135172,  0.5155377,  0.5066059, 0.49680984, 0.5005894,  0.48509413])
+        expected_image = np.array(
+            [0.50346327, 0.50708383, 0.50719553, 0.5135172, 0.5155377, 0.5066059, 0.49680984, 0.5005894, 0.48509413]
+        )
         assert numpy_cosine_similarity_distance(original_image, expected_image) < 1e-4
-