Update Ruff to latest Version (#10919)

* update

* update

* update

* update

tests/models/transformers/test_models_transformer_sd3.py

@@ -92,9 +92,9 @@ class SD3TransformerTests(ModelTesterMixin, unittest.TestCase):
 
         model.enable_xformers_memory_efficient_attention()
 
-        assert (
-            model.transformer_blocks[0].attn.processor.__class__.__name__ == "XFormersJointAttnProcessor"
-        ), "xformers is not enabled"
+        assert model.transformer_blocks[0].attn.processor.__class__.__name__ == "XFormersJointAttnProcessor", (
+            "xformers is not enabled"
+        )
 
     @unittest.skip("SD3Transformer2DModel uses a dedicated attention processor. This test doesn't apply")
     def test_set_attn_processor_for_determinism(self):
@@ -167,9 +167,9 @@ class SD35TransformerTests(ModelTesterMixin, unittest.TestCase):
 
         model.enable_xformers_memory_efficient_attention()
 
-        assert (
-            model.transformer_blocks[0].attn.processor.__class__.__name__ == "XFormersJointAttnProcessor"
-        ), "xformers is not enabled"
+        assert model.transformer_blocks[0].attn.processor.__class__.__name__ == "XFormersJointAttnProcessor", (
+            "xformers is not enabled"
+        )
 
     @unittest.skip("SD3Transformer2DModel uses a dedicated attention processor. This test doesn't apply")
     def test_set_attn_processor_for_determinism(self):

tests/models/unets/test_models_unet_2d_condition.py

@@ -654,22 +654,22 @@ class UNet2DConditionModelTests(ModelTesterMixin, UNetTesterMixin, unittest.Test
 
             keepall_mask = torch.ones(*cond.shape[:-1], device=cond.device, dtype=mask_dtype)
             full_cond_keepallmask_out = model(**{**inputs_dict, "encoder_attention_mask": keepall_mask}).sample
-            assert full_cond_keepallmask_out.allclose(
-                full_cond_out, rtol=1e-05, atol=1e-05
-            ), "a 'keep all' mask should give the same result as no mask"
+            assert full_cond_keepallmask_out.allclose(full_cond_out, rtol=1e-05, atol=1e-05), (
+                "a 'keep all' mask should give the same result as no mask"
+            )
 
             trunc_cond = cond[:, :-1, :]
             trunc_cond_out = model(**{**inputs_dict, "encoder_hidden_states": trunc_cond}).sample
-            assert not trunc_cond_out.allclose(
-                full_cond_out, rtol=1e-05, atol=1e-05
-            ), "discarding the last token from our cond should change the result"
+            assert not trunc_cond_out.allclose(full_cond_out, rtol=1e-05, atol=1e-05), (
+                "discarding the last token from our cond should change the result"
+            )
 
             batch, tokens, _ = cond.shape
             mask_last = (torch.arange(tokens) < tokens - 1).expand(batch, -1).to(cond.device, mask_dtype)
             masked_cond_out = model(**{**inputs_dict, "encoder_attention_mask": mask_last}).sample
-            assert masked_cond_out.allclose(
-                trunc_cond_out, rtol=1e-05, atol=1e-05
-            ), "masking the last token from our cond should be equivalent to truncating that token out of the condition"
+            assert masked_cond_out.allclose(trunc_cond_out, rtol=1e-05, atol=1e-05), (
+                "masking the last token from our cond should be equivalent to truncating that token out of the condition"
+            )
 
     # see diffusers.models.attention_processor::Attention#prepare_attention_mask
     # note: we may not need to fix mask padding to work for stable-diffusion cross-attn masks.
@@ -697,9 +697,9 @@ class UNet2DConditionModelTests(ModelTesterMixin, UNetTesterMixin, unittest.Test
 
             trunc_mask = torch.zeros(batch, tokens - 1, device=cond.device, dtype=torch.bool)
             trunc_mask_out = model(**{**inputs_dict, "encoder_attention_mask": trunc_mask}).sample
-            assert trunc_mask_out.allclose(
-                keeplast_out
-            ), "a mask with fewer tokens than condition, will be padded with 'keep' tokens. a 'discard-all' mask missing the final token is thus equivalent to a 'keep last' mask."
+            assert trunc_mask_out.allclose(keeplast_out), (
+                "a mask with fewer tokens than condition, will be padded with 'keep' tokens. a 'discard-all' mask missing the final token is thus equivalent to a 'keep last' mask."
+            )
 
     def test_custom_diffusion_processors(self):
         # enable deterministic behavior for gradient checkpointing
@@ -1114,12 +1114,12 @@ class UNet2DConditionModelTests(ModelTesterMixin, UNetTesterMixin, unittest.Test
             with torch.no_grad():
                 lora_sample_2 = model(**inputs_dict).sample
 
-        assert not torch.allclose(
-            non_lora_sample, lora_sample_1, atol=1e-4, rtol=1e-4
-        ), "LoRA injected UNet should produce different results."
-        assert torch.allclose(
-            lora_sample_1, lora_sample_2, atol=1e-4, rtol=1e-4
-        ), "Loading from a saved checkpoint should produce identical results."
+        assert not torch.allclose(non_lora_sample, lora_sample_1, atol=1e-4, rtol=1e-4), (
+            "LoRA injected UNet should produce different results."
+        )
+        assert torch.allclose(lora_sample_1, lora_sample_2, atol=1e-4, rtol=1e-4), (
+            "Loading from a saved checkpoint should produce identical results."
+        )
 
     @require_peft_backend
     def test_save_attn_procs_raise_warning(self):

tests/others/test_image_processor.py

@@ -65,9 +65,9 @@ class ImageProcessorTest(unittest.TestCase):
             )
             out_np = self.to_np(out)
             in_np = (input_np * 255).round() if output_type == "pil" else input_np
-            assert (
-                np.abs(in_np - out_np).max() < 1e-6
-            ), f"decoded output does not match input for output_type {output_type}"
+            assert np.abs(in_np - out_np).max() < 1e-6, (
+                f"decoded output does not match input for output_type {output_type}"
+            )
 
     def test_vae_image_processor_np(self):
         image_processor = VaeImageProcessor(do_resize=False, do_normalize=True)
@@ -78,9 +78,9 @@ class ImageProcessorTest(unittest.TestCase):
 
             out_np = self.to_np(out)
             in_np = (input_np * 255).round() if output_type == "pil" else input_np
-            assert (
-                np.abs(in_np - out_np).max() < 1e-6
-            ), f"decoded output does not match input for output_type {output_type}"
+            assert np.abs(in_np - out_np).max() < 1e-6, (
+                f"decoded output does not match input for output_type {output_type}"
+            )
 
     def test_vae_image_processor_pil(self):
         image_processor = VaeImageProcessor(do_resize=False, do_normalize=True)
@@ -93,9 +93,9 @@ class ImageProcessorTest(unittest.TestCase):
             for i, o in zip(input_pil, out):
                 in_np = np.array(i)
                 out_np = self.to_np(out) if output_type == "pil" else (self.to_np(out) * 255).round()
-                assert (
-                    np.abs(in_np - out_np).max() < 1e-6
-                ), f"decoded output does not match input for output_type {output_type}"
+                assert np.abs(in_np - out_np).max() < 1e-6, (
+                    f"decoded output does not match input for output_type {output_type}"
+                )
 
     def test_preprocess_input_3d(self):
         image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
@@ -293,9 +293,9 @@ class ImageProcessorTest(unittest.TestCase):
         scale = 2
         out_pt = image_processor.resize(image=input_pt, height=h // scale, width=w // scale)
         exp_pt_shape = (b, c, h // scale, w // scale)
-        assert (
-            out_pt.shape == exp_pt_shape
-        ), f"resized image output shape '{out_pt.shape}' didn't match expected shape '{exp_pt_shape}'."
+        assert out_pt.shape == exp_pt_shape, (
+            f"resized image output shape '{out_pt.shape}' didn't match expected shape '{exp_pt_shape}'."
+        )
 
     def test_vae_image_processor_resize_np(self):
         image_processor = VaeImageProcessor(do_resize=True, vae_scale_factor=1)
@@ -305,6 +305,6 @@ class ImageProcessorTest(unittest.TestCase):
         input_np = self.to_np(input_pt)
         out_np = image_processor.resize(image=input_np, height=h // scale, width=w // scale)
         exp_np_shape = (b, h // scale, w // scale, c)
-        assert (
-            out_np.shape == exp_np_shape
-        ), f"resized image output shape '{out_np.shape}' didn't match expected shape '{exp_np_shape}'."
+        assert out_np.shape == exp_np_shape, (
+            f"resized image output shape '{out_np.shape}' didn't match expected shape '{exp_np_shape}'."
+        )

tests/pipelines/amused/test_amused.py

@@ -126,8 +126,7 @@ class AmusedPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         self._test_inference_batch_consistent(batch_sizes=batch_sizes, batch_generator=False)
 
     @unittest.skip("aMUSEd does not support lists of generators")
-    def test_inference_batch_single_identical(self):
-        ...
+    def test_inference_batch_single_identical(self): ...
 
 
 @slow

tests/pipelines/amused/test_amused_img2img.py

@@ -126,8 +126,7 @@ class AmusedImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         self._test_inference_batch_consistent(batch_sizes=batch_sizes, batch_generator=False)
 
     @unittest.skip("aMUSEd does not support lists of generators")
-    def test_inference_batch_single_identical(self):
-        ...
+    def test_inference_batch_single_identical(self): ...
 
 
 @slow

tests/pipelines/amused/test_amused_inpaint.py

@@ -130,8 +130,7 @@ class AmusedInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         self._test_inference_batch_consistent(batch_sizes=batch_sizes, batch_generator=False)
 
     @unittest.skip("aMUSEd does not support lists of generators")
-    def test_inference_batch_single_identical(self):
-        ...
+    def test_inference_batch_single_identical(self): ...
 
 
 @slow

tests/pipelines/aura_flow/test_pipeline_aura_flow.py

@@ -106,9 +106,9 @@ class AuraFlowPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
         # TODO (sayakpaul): will refactor this once `fuse_qkv_projections()` has been added
         # to the pipeline level.
         pipe.transformer.fuse_qkv_projections()
-        assert check_qkv_fusion_processors_exist(
-            pipe.transformer
-        ), "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        assert check_qkv_fusion_processors_exist(pipe.transformer), (
+            "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        )
         assert check_qkv_fusion_matches_attn_procs_length(
             pipe.transformer, pipe.transformer.original_attn_processors
         ), "Something wrong with the attention processors concerning the fused QKV projections."
@@ -122,15 +122,15 @@ class AuraFlowPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
         image = pipe(**inputs).images
         image_slice_disabled = image[0, -3:, -3:, -1]
 
-        assert np.allclose(
-            original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3
-        ), "Fusion of QKV projections shouldn't affect the outputs."
-        assert np.allclose(
-            image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3
-        ), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
-        assert np.allclose(
-            original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
-        ), "Original outputs should match when fused QKV projections are disabled."
+        assert np.allclose(original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3), (
+            "Fusion of QKV projections shouldn't affect the outputs."
+        )
+        assert np.allclose(image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3), (
+            "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
+        )
+        assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2), (
+            "Original outputs should match when fused QKV projections are disabled."
+        )
 
     @unittest.skip("xformers attention processor does not exist for AuraFlow")
     def test_xformers_attention_forwardGenerator_pass(self):

tests/pipelines/blipdiffusion/test_blipdiffusion.py

@@ -195,9 +195,9 @@ class BlipDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             [0.5329548, 0.8372512, 0.33269387, 0.82096875, 0.43657133, 0.3783, 0.5953028, 0.51934963, 0.42142007]
         )
 
-        assert (
-            np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        ), f" expected_slice {image_slice.flatten()}, but got {image_slice.flatten()}"
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2, (
+            f" expected_slice {image_slice.flatten()}, but got {image_slice.flatten()}"
+        )
 
     @unittest.skip("Test not supported because of complexities in deriving query_embeds.")
     def test_encode_prompt_works_in_isolation(self):

tests/pipelines/cogvideo/test_cogvideox.py

@@ -299,9 +299,9 @@ class CogVideoXPipelineFastTests(
         original_image_slice = frames[0, -2:, -1, -3:, -3:]
 
         pipe.fuse_qkv_projections()
-        assert check_qkv_fusion_processors_exist(
-            pipe.transformer
-        ), "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        assert check_qkv_fusion_processors_exist(pipe.transformer), (
+            "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        )
         assert check_qkv_fusion_matches_attn_procs_length(
             pipe.transformer, pipe.transformer.original_attn_processors
         ), "Something wrong with the attention processors concerning the fused QKV projections."
@@ -315,15 +315,15 @@ class CogVideoXPipelineFastTests(
         frames = pipe(**inputs).frames
         image_slice_disabled = frames[0, -2:, -1, -3:, -3:]
 
-        assert np.allclose(
-            original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3
-        ), "Fusion of QKV projections shouldn't affect the outputs."
-        assert np.allclose(
-            image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3
-        ), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
-        assert np.allclose(
-            original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
-        ), "Original outputs should match when fused QKV projections are disabled."
+        assert np.allclose(original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3), (
+            "Fusion of QKV projections shouldn't affect the outputs."
+        )
+        assert np.allclose(image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3), (
+            "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
+        )
+        assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2), (
+            "Original outputs should match when fused QKV projections are disabled."
+        )
 
 
 @slow

tests/pipelines/cogvideo/test_cogvideox_fun_control.py

@@ -299,9 +299,9 @@ class CogVideoXFunControlPipelineFastTests(PipelineTesterMixin, unittest.TestCas
         original_image_slice = frames[0, -2:, -1, -3:, -3:]
 
         pipe.fuse_qkv_projections()
-        assert check_qkv_fusion_processors_exist(
-            pipe.transformer
-        ), "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        assert check_qkv_fusion_processors_exist(pipe.transformer), (
+            "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        )
         assert check_qkv_fusion_matches_attn_procs_length(
             pipe.transformer, pipe.transformer.original_attn_processors
         ), "Something wrong with the attention processors concerning the fused QKV projections."
@@ -315,12 +315,12 @@ class CogVideoXFunControlPipelineFastTests(PipelineTesterMixin, unittest.TestCas
         frames = pipe(**inputs).frames
         image_slice_disabled = frames[0, -2:, -1, -3:, -3:]
 
-        assert np.allclose(
-            original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3
-        ), "Fusion of QKV projections shouldn't affect the outputs."
-        assert np.allclose(
-            image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3
-        ), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
-        assert np.allclose(
-            original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
-        ), "Original outputs should match when fused QKV projections are disabled."
+        assert np.allclose(original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3), (
+            "Fusion of QKV projections shouldn't affect the outputs."
+        )
+        assert np.allclose(image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3), (
+            "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
+        )
+        assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2), (
+            "Original outputs should match when fused QKV projections are disabled."
+        )

tests/pipelines/cogvideo/test_cogvideox_image2video.py

@@ -317,9 +317,9 @@ class CogVideoXImageToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestC
         original_image_slice = frames[0, -2:, -1, -3:, -3:]
 
         pipe.fuse_qkv_projections()
-        assert check_qkv_fusion_processors_exist(
-            pipe.transformer
-        ), "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        assert check_qkv_fusion_processors_exist(pipe.transformer), (
+            "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        )
         assert check_qkv_fusion_matches_attn_procs_length(
             pipe.transformer, pipe.transformer.original_attn_processors
         ), "Something wrong with the attention processors concerning the fused QKV projections."
@@ -333,15 +333,15 @@ class CogVideoXImageToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestC
         frames = pipe(**inputs).frames
         image_slice_disabled = frames[0, -2:, -1, -3:, -3:]
 
-        assert np.allclose(
-            original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3
-        ), "Fusion of QKV projections shouldn't affect the outputs."
-        assert np.allclose(
-            image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3
-        ), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
-        assert np.allclose(
-            original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
-        ), "Original outputs should match when fused QKV projections are disabled."
+        assert np.allclose(original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3), (
+            "Fusion of QKV projections shouldn't affect the outputs."
+        )
+        assert np.allclose(image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3), (
+            "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
+        )
+        assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2), (
+            "Original outputs should match when fused QKV projections are disabled."
+        )
 
 
 @slow

tests/pipelines/cogvideo/test_cogvideox_video2video.py

@@ -298,9 +298,9 @@ class CogVideoXVideoToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestC
         original_image_slice = frames[0, -2:, -1, -3:, -3:]
 
         pipe.fuse_qkv_projections()
-        assert check_qkv_fusion_processors_exist(
-            pipe.transformer
-        ), "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        assert check_qkv_fusion_processors_exist(pipe.transformer), (
+            "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        )
         assert check_qkv_fusion_matches_attn_procs_length(
             pipe.transformer, pipe.transformer.original_attn_processors
         ), "Something wrong with the attention processors concerning the fused QKV projections."
@@ -314,12 +314,12 @@ class CogVideoXVideoToVideoPipelineFastTests(PipelineTesterMixin, unittest.TestC
         frames = pipe(**inputs).frames
         image_slice_disabled = frames[0, -2:, -1, -3:, -3:]
 
-        assert np.allclose(
-            original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3
-        ), "Fusion of QKV projections shouldn't affect the outputs."
-        assert np.allclose(
-            image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3
-        ), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
-        assert np.allclose(
-            original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
-        ), "Original outputs should match when fused QKV projections are disabled."
+        assert np.allclose(original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3), (
+            "Fusion of QKV projections shouldn't affect the outputs."
+        )
+        assert np.allclose(image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3), (
+            "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
+        )
+        assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2), (
+            "Original outputs should match when fused QKV projections are disabled."
+        )

tests/pipelines/controlnet/test_controlnet_blip_diffusion.py

@@ -219,9 +219,9 @@ class BlipDiffusionControlNetPipelineFastTests(PipelineTesterMixin, unittest.Tes
         assert image.shape == (1, 16, 16, 4)
         expected_slice = np.array([0.7953, 0.7136, 0.6597, 0.4779, 0.7389, 0.4111, 0.5826, 0.4150, 0.8422])
 
-        assert (
-            np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2, (
+            f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
+        )
 
     @unittest.skip("Test not supported because of complexities in deriving query_embeds.")
     def test_encode_prompt_works_in_isolation(self):

tests/pipelines/controlnet_flux/test_controlnet_flux.py

@@ -178,9 +178,9 @@ class FluxControlNetPipelineFastTests(unittest.TestCase, PipelineTesterMixin, Fl
             [0.47387695, 0.63134766, 0.5605469, 0.61621094, 0.7207031, 0.7089844, 0.70410156, 0.6113281, 0.64160156]
         )
 
-        assert (
-            np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        ), f"Expected: {expected_slice}, got: {image_slice.flatten()}"
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2, (
+            f"Expected: {expected_slice}, got: {image_slice.flatten()}"
+        )
 
     @unittest.skip("xFormersAttnProcessor does not work with SD3 Joint Attention")
     def test_xformers_attention_forwardGenerator_pass(self):

tests/pipelines/controlnet_flux/test_controlnet_flux_img2img.py

@@ -170,9 +170,9 @@ class FluxControlNetImg2ImgPipelineFastTests(unittest.TestCase, PipelineTesterMi
         original_image_slice = image[0, -3:, -3:, -1]
 
         pipe.transformer.fuse_qkv_projections()
-        assert check_qkv_fusion_processors_exist(
-            pipe.transformer
-        ), "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        assert check_qkv_fusion_processors_exist(pipe.transformer), (
+            "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        )
         assert check_qkv_fusion_matches_attn_procs_length(
             pipe.transformer, pipe.transformer.original_attn_processors
         ), "Something wrong with the attention processors concerning the fused QKV projections."
@@ -186,15 +186,15 @@ class FluxControlNetImg2ImgPipelineFastTests(unittest.TestCase, PipelineTesterMi
         image = pipe(**inputs).images
         image_slice_disabled = image[0, -3:, -3:, -1]
 
-        assert np.allclose(
-            original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3
-        ), "Fusion of QKV projections shouldn't affect the outputs."
-        assert np.allclose(
-            image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3
-        ), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
-        assert np.allclose(
-            original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
-        ), "Original outputs should match when fused QKV projections are disabled."
+        assert np.allclose(original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3), (
+            "Fusion of QKV projections shouldn't affect the outputs."
+        )
+        assert np.allclose(image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3), (
+            "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
+        )
+        assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2), (
+            "Original outputs should match when fused QKV projections are disabled."
+        )
 
     def test_flux_image_output_shape(self):
         pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)

tests/pipelines/controlnet_hunyuandit/test_controlnet_hunyuandit.py

@@ -162,9 +162,9 @@ class HunyuanDiTControlNetPipelineFastTests(unittest.TestCase, PipelineTesterMix
                 [0.6953125, 0.89208984, 0.59375, 0.5078125, 0.5786133, 0.6035156, 0.5839844, 0.53564453, 0.52246094]
             )
 
-        assert (
-            np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        ), f"Expected: {expected_slice}, got: {image_slice.flatten()}"
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2, (
+            f"Expected: {expected_slice}, got: {image_slice.flatten()}"
+        )
 
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(

tests/pipelines/controlnet_sd3/test_controlnet_inpaint_sd3.py

@@ -194,9 +194,9 @@ class StableDiffusion3ControlInpaintNetPipelineFastTests(unittest.TestCase, Pipe
             [0.51708984, 0.7421875, 0.4580078, 0.6435547, 0.65625, 0.43603516, 0.5151367, 0.65722656, 0.60839844]
         )
 
-        assert (
-            np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        ), f"Expected: {expected_slice}, got: {image_slice.flatten()}"
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2, (
+            f"Expected: {expected_slice}, got: {image_slice.flatten()}"
+        )
 
     @unittest.skip("xFormersAttnProcessor does not work with SD3 Joint Attention")
     def test_xformers_attention_forwardGenerator_pass(self):

tests/pipelines/controlnet_sd3/test_controlnet_sd3.py

@@ -202,9 +202,9 @@ class StableDiffusion3ControlNetPipelineFastTests(unittest.TestCase, PipelineTes
         else:
             expected_slice = np.array([1.0000, 0.9072, 0.4209, 0.2744, 0.5737, 0.3840, 0.6113, 0.6250, 0.6328])
 
-        assert (
-            np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        ), f"Expected: {expected_slice}, got: {image_slice.flatten()}"
+        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2, (
+            f"Expected: {expected_slice}, got: {image_slice.flatten()}"
+        )
 
     def test_controlnet_sd3(self):
         components = self.get_dummy_components()

tests/pipelines/dit/test_dit.py

@@ -149,8 +149,7 @@ class DiTPipelineIntegrationTests(unittest.TestCase):
 
         for word, image in zip(words, images):
             expected_image = load_numpy(
-                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-                f"/dit/{word}_512.npy"
+                f"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}_512.npy"
             )
 
             assert np.abs((expected_image - image).max()) < 1e-1

tests/pipelines/flux/test_pipeline_flux.py

@@ -170,9 +170,9 @@ class FluxPipelineFastTests(
         # TODO (sayakpaul): will refactor this once `fuse_qkv_projections()` has been added
         # to the pipeline level.
         pipe.transformer.fuse_qkv_projections()
-        assert check_qkv_fusion_processors_exist(
-            pipe.transformer
-        ), "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        assert check_qkv_fusion_processors_exist(pipe.transformer), (
+            "Something wrong with the fused attention processors. Expected all the attention processors to be fused."
+        )
         assert check_qkv_fusion_matches_attn_procs_length(
             pipe.transformer, pipe.transformer.original_attn_processors
         ), "Something wrong with the attention processors concerning the fused QKV projections."
@@ -186,15 +186,15 @@ class FluxPipelineFastTests(
         image = pipe(**inputs).images
         image_slice_disabled = image[0, -3:, -3:, -1]
 
-        assert np.allclose(
-            original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3
-        ), "Fusion of QKV projections shouldn't affect the outputs."
-        assert np.allclose(
-            image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3
-        ), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
-        assert np.allclose(
-            original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
-        ), "Original outputs should match when fused QKV projections are disabled."
+        assert np.allclose(original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3), (
+            "Fusion of QKV projections shouldn't affect the outputs."
+        )
+        assert np.allclose(image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3), (
+            "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
+        )
+        assert np.allclose(original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2), (
+            "Original outputs should match when fused QKV projections are disabled."
+        )
 
     def test_flux_image_output_shape(self):
         pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)