Update Ruff to latest Version (#10919)

* update

* update

* update

* update

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -660,7 +660,7 @@ class StableDiffusionInpaintPipeline(
         if padding_mask_crop is not None:
             if not isinstance(image, PIL.Image.Image):
                 raise ValueError(
-                    f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(image)}."
+                    f"The image should be a PIL image when inpainting mask crop, but is of type {type(image)}."
                 )
             if not isinstance(mask_image, PIL.Image.Image):
                 raise ValueError(
@@ -668,7 +668,7 @@ class StableDiffusionInpaintPipeline(
                     f" {type(mask_image)}."
                 )
             if output_type != "pil":
-                raise ValueError(f"The output type should be PIL when inpainting mask crop, but is" f" {output_type}.")
+                raise ValueError(f"The output type should be PIL when inpainting mask crop, but is {output_type}.")
 
         if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
             raise ValueError(
@@ -1226,7 +1226,7 @@ class StableDiffusionInpaintPipeline(
                     f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
                     f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
                     f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
-                    f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
+                    f" = {num_channels_latents + num_channels_masked_image + num_channels_mask}. Please verify the config of"
                     " `pipeline.unet` or your `mask_image` or `image` input."
                 )
         elif num_channels_unet != 4:

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py

@@ -401,7 +401,7 @@ class StableDiffusionInstructPix2PixPipeline(
                 f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
                 f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
                 f" `num_channels_image`: {num_channels_image} "
-                f" = {num_channels_latents+num_channels_image}. Please verify the config of"
+                f" = {num_channels_latents + num_channels_image}. Please verify the config of"
                 " `pipeline.unet` or your `image` input."
             )
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py

@@ -600,7 +600,7 @@ class StableDiffusionLatentUpscalePipeline(DiffusionPipeline, StableDiffusionMix
                 f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
                 f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
                 f" `num_channels_image`: {num_channels_image} "
-                f" = {num_channels_latents+num_channels_image}. Please verify the config of"
+                f" = {num_channels_latents + num_channels_image}. Please verify the config of"
                 " `pipeline.unet` or your `image` input."
             )
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py

@@ -740,7 +740,7 @@ class StableDiffusionUpscalePipeline(
                 f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
                 f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
                 f" `num_channels_image`: {num_channels_image} "
-                f" = {num_channels_latents+num_channels_image}. Please verify the config of"
+                f" = {num_channels_latents + num_channels_image}. Please verify the config of"
                 " `pipeline.unet` or your `image` input."
             )
 

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py

@@ -1258,7 +1258,7 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
                     f"Incorrect configuration settings! The config of `pipeline.transformer`: {self.transformer.config} expects"
                     f" {self.transformer.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
                     f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
-                    f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
+                    f" = {num_channels_latents + num_channels_masked_image + num_channels_mask}. Please verify the config of"
                     " `pipeline.transformer` or your `mask_image` or `image` input."
                 )
         elif num_channels_transformer != 16:

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py

@@ -741,7 +741,7 @@ class StableDiffusionXLInpaintPipeline(
         if padding_mask_crop is not None:
             if not isinstance(image, PIL.Image.Image):
                 raise ValueError(
-                    f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(image)}."
+                    f"The image should be a PIL image when inpainting mask crop, but is of type {type(image)}."
                 )
             if not isinstance(mask_image, PIL.Image.Image):
                 raise ValueError(
@@ -749,7 +749,7 @@ class StableDiffusionXLInpaintPipeline(
                     f" {type(mask_image)}."
                 )
             if output_type != "pil":
-                raise ValueError(f"The output type should be PIL when inpainting mask crop, but is" f" {output_type}.")
+                raise ValueError(f"The output type should be PIL when inpainting mask crop, but is {output_type}.")
 
         if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
             raise ValueError(
@@ -1509,7 +1509,7 @@ class StableDiffusionXLInpaintPipeline(
                     f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
                     f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
                     f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
-                    f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
+                    f" = {num_channels_latents + num_channels_masked_image + num_channels_mask}. Please verify the config of"
                     " `pipeline.unet` or your `mask_image` or `image` input."
                 )
         elif num_channels_unet != 4:

src/diffusers/pipelines/wan/pipeline_wan_i2v.py

@@ -334,7 +334,7 @@ class WanImageToVideoPipeline(DiffusionPipeline, WanLoraLoaderMixin):
                 "Provide either `image` or `prompt_embeds`. Cannot leave both `image` and `image_embeds` undefined."
             )
         if image is not None and not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image):
-            raise ValueError("`image` has to be of type `torch.Tensor` or `PIL.Image.Image` but is" f" {type(image)}")
+            raise ValueError(f"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` but is {type(image)}")
         if height % 16 != 0 or width % 16 != 0:
             raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
 

src/diffusers/quantizers/base.py

@@ -215,19 +215,15 @@ class DiffusersQuantizer(ABC):
         )
 
     @abstractmethod
-    def _process_model_before_weight_loading(self, model, **kwargs):
-        ...
+    def _process_model_before_weight_loading(self, model, **kwargs): ...
 
     @abstractmethod
-    def _process_model_after_weight_loading(self, model, **kwargs):
-        ...
+    def _process_model_after_weight_loading(self, model, **kwargs): ...
 
     @property
     @abstractmethod
-    def is_serializable(self):
-        ...
+    def is_serializable(self): ...
 
     @property
     @abstractmethod
-    def is_trainable(self):
-        ...
+    def is_trainable(self): ...

src/diffusers/schedulers/scheduling_consistency_models.py

@@ -203,8 +203,7 @@ class CMStochasticIterativeScheduler(SchedulerMixin, ConfigMixin):
 
             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
-                    f"`timesteps` must start before `self.config.train_timesteps`:"
-                    f" {self.config.num_train_timesteps}."
+                    f"`timesteps` must start before `self.config.train_timesteps`: {self.config.num_train_timesteps}."
                 )
 
             timesteps = np.array(timesteps, dtype=np.int64)

src/diffusers/schedulers/scheduling_ddpm.py

@@ -279,8 +279,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
 
             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
-                    f"`timesteps` must start before `self.config.train_timesteps`:"
-                    f" {self.config.num_train_timesteps}."
+                    f"`timesteps` must start before `self.config.train_timesteps`: {self.config.num_train_timesteps}."
                 )
 
             timesteps = np.array(timesteps, dtype=np.int64)

src/diffusers/schedulers/scheduling_ddpm_parallel.py

@@ -289,8 +289,7 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
 
             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
-                    f"`timesteps` must start before `self.config.train_timesteps`:"
-                    f" {self.config.num_train_timesteps}."
+                    f"`timesteps` must start before `self.config.train_timesteps`: {self.config.num_train_timesteps}."
                 )
 
             timesteps = np.array(timesteps, dtype=np.int64)

src/diffusers/schedulers/scheduling_lcm.py

@@ -413,8 +413,7 @@ class LCMScheduler(SchedulerMixin, ConfigMixin):
 
             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
-                    f"`timesteps` must start before `self.config.train_timesteps`:"
-                    f" {self.config.num_train_timesteps}."
+                    f"`timesteps` must start before `self.config.train_timesteps`: {self.config.num_train_timesteps}."
                 )
 
             # Raise warning if timestep schedule does not start with self.config.num_train_timesteps - 1

src/diffusers/schedulers/scheduling_tcd.py

@@ -431,8 +431,7 @@ class TCDScheduler(SchedulerMixin, ConfigMixin):
 
             if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
-                    f"`timesteps` must start before `self.config.train_timesteps`:"
-                    f" {self.config.num_train_timesteps}."
+                    f"`timesteps` must start before `self.config.train_timesteps`: {self.config.num_train_timesteps}."
                 )
 
             # Raise warning if timestep schedule does not start with self.config.num_train_timesteps - 1

src/diffusers/training_utils.py

@@ -241,7 +241,7 @@ def _set_state_dict_into_text_encoder(
     """
 
     text_encoder_state_dict = {
-        f'{k.replace(prefix, "")}': v for k, v in lora_state_dict.items() if k.startswith(prefix)
+        f"{k.replace(prefix, '')}": v for k, v in lora_state_dict.items() if k.startswith(prefix)
     }
     text_encoder_state_dict = convert_state_dict_to_peft(convert_state_dict_to_diffusers(text_encoder_state_dict))
     set_peft_model_state_dict(text_encoder, text_encoder_state_dict, adapter_name="default")
@@ -583,7 +583,7 @@ class EMAModel:
         """
 
         if self.temp_stored_params is None:
-            raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`")
+            raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights to `restore()`")
         if self.foreach:
             torch._foreach_copy_(
                 [param.data for param in parameters], [c_param.data for c_param in self.temp_stored_params]

src/diffusers/utils/deprecation_utils.py

@@ -40,7 +40,7 @@ def deprecate(*args, take_from: Optional[Union[Dict, Any]] = None, standard_warn
         line_number = call_frame.lineno
         function = call_frame.function
         key, value = next(iter(deprecated_kwargs.items()))
-        raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`")
+        raise TypeError(f"{function} in {filename} line {line_number - 1} got an unexpected keyword argument `{key}`")
 
     if len(values) == 0:
         return

src/diffusers/utils/logging.py

@@ -60,8 +60,7 @@ def _get_default_logging_level() -> int:
             return log_levels[env_level_str]
         else:
             logging.getLogger().warning(
-                f"Unknown option DIFFUSERS_VERBOSITY={env_level_str}, "
-                f"has to be one of: { ', '.join(log_levels.keys()) }"
+                f"Unknown option DIFFUSERS_VERBOSITY={env_level_str}, has to be one of: {', '.join(log_levels.keys())}"
             )
     return _default_log_level
 

src/diffusers/utils/state_dict_utils.py

@@ -334,7 +334,7 @@ def convert_state_dict_to_kohya(state_dict, original_type=None, **kwargs):
         kohya_key = kohya_key.replace(peft_adapter_name, "")  # Kohya doesn't take names
         kohya_ss_state_dict[kohya_key] = weight
         if "lora_down" in kohya_key:
-            alpha_key = f'{kohya_key.split(".")[0]}.alpha'
+            alpha_key = f"{kohya_key.split('.')[0]}.alpha"
             kohya_ss_state_dict[alpha_key] = torch.tensor(len(weight))
 
     return kohya_ss_state_dict

src/diffusers/utils/testing_utils.py

@@ -882,7 +882,7 @@ def pytest_terminal_summary_main(tr, id):
             f.write("slowest durations\n")
             for i, rep in enumerate(dlist):
                 if rep.duration < durations_min:
-                    f.write(f"{len(dlist)-i} durations < {durations_min} secs were omitted")
+                    f.write(f"{len(dlist) - i} durations < {durations_min} secs were omitted")
                     break
                 f.write(f"{rep.duration:02.2f}s {rep.when:<8} {rep.nodeid}\n")
 
@@ -1027,7 +1027,7 @@ def run_test_in_subprocess(test_case, target_func, inputs=None, timeout=None):
     process.join(timeout=timeout)
 
     if results["error"] is not None:
-        test_case.fail(f'{results["error"]}')
+        test_case.fail(f"{results['error']}")
 
 
 class CaptureLogger:

tests/hooks/test_hooks.py

@@ -168,9 +168,7 @@ class HookTests(unittest.TestCase):
         registry.register_hook(MultiplyHook(2), "multiply_hook")
 
         registry_repr = repr(registry)
-        expected_repr = (
-            "HookRegistry(\n" "  (0) add_hook - AddHook\n" "  (1) multiply_hook - MultiplyHook(value=2)\n" ")"
-        )
+        expected_repr = "HookRegistry(\n  (0) add_hook - AddHook\n  (1) multiply_hook - MultiplyHook(value=2)\n)"
 
         self.assertEqual(len(registry.hooks), 2)
         self.assertEqual(registry._hook_order, ["add_hook", "multiply_hook"])
@@ -285,12 +283,7 @@ class HookTests(unittest.TestCase):
             self.model(input)
         output = cap_logger.out.replace(" ", "").replace("\n", "")
         expected_invocation_order_log = (
-            (
-                "MultiplyHook pre_forward\n"
-                "AddHook pre_forward\n"
-                "AddHook post_forward\n"
-                "MultiplyHook post_forward\n"
-            )
+            ("MultiplyHook pre_forward\nAddHook pre_forward\nAddHook post_forward\nMultiplyHook post_forward\n")
             .replace(" ", "")
             .replace("\n", "")
         )

tests/models/test_modeling_common.py

@@ -299,9 +299,9 @@ class ModelUtilsTest(unittest.TestCase):
                 )
 
             download_requests = [r.method for r in m.request_history]
-            assert (
-                download_requests.count("HEAD") == 3
-            ), "3 HEAD requests one for config, one for model, and one for shard index file."
+            assert download_requests.count("HEAD") == 3, (
+                "3 HEAD requests one for config, one for model, and one for shard index file."
+            )
             assert download_requests.count("GET") == 2, "2 GET requests one for config, one for model"
 
             with requests_mock.mock(real_http=True) as m:
@@ -313,9 +313,9 @@ class ModelUtilsTest(unittest.TestCase):
                 )
 
             cache_requests = [r.method for r in m.request_history]
-            assert (
-                "HEAD" == cache_requests[0] and len(cache_requests) == 2
-            ), "We should call only `model_info` to check for commit hash and  knowing if shard index is present."
+            assert "HEAD" == cache_requests[0] and len(cache_requests) == 2, (
+                "We should call only `model_info` to check for commit hash and  knowing if shard index is present."
+            )
 
     def test_weight_overwrite(self):
         with tempfile.TemporaryDirectory() as tmpdirname, self.assertRaises(ValueError) as error_context: