fix style/quality for code

examples/community/composable_stable_diffusion.py

@@ -481,13 +481,13 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline):
         # corresponds to doing no classifier free guidance.
         do_classifier_free_guidance = guidance_scale > 1.0
 
-        if '|' in prompt:
-            prompt = [x.strip() for x in prompt.split('|')]
+        if "|" in prompt:
+            prompt = [x.strip() for x in prompt.split("|")]
             print(f"composing {prompt}...")
 
             if not weights:
                 # specify weights for prompts (excluding the unconditional score)
-                print('using equal positive weights (conjunction) for all prompts...')
+                print("using equal positive weights (conjunction) for all prompts...")
                 weights = torch.tensor([guidance_scale] * len(prompt), device=self.device).reshape(-1, 1, 1, 1)
             else:
                 # set prompt weight for each
@@ -546,7 +546,9 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline):
                 # perform guidance
                 if do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + (weights * (noise_pred_text - noise_pred_uncond)).sum(dim=0, keepdims=True)
+                    noise_pred = noise_pred_uncond + (weights * (noise_pred_text - noise_pred_uncond)).sum(
+                        dim=0, keepdims=True
+                    )
 
                 # compute the previous noisy sample x_t -> x_t-1
                 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
@@ -570,4 +572,4 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline):
         if not return_dict:
             return (image, has_nsfw_concept)
 
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

examples/textual_inversion/textual_inversion.py

@@ -336,7 +336,10 @@ class TextualInversionDataset(Dataset):
 
         if self.center_crop:
             crop = min(img.shape[0], img.shape[1])
-            h, w, = (
+            (
+                h,
+                w,
+            ) = (
                 img.shape[0],
                 img.shape[1],
             )

examples/textual_inversion/textual_inversion_flax.py

@@ -306,7 +306,10 @@ class TextualInversionDataset(Dataset):
 
         if self.center_crop:
             crop = min(img.shape[0], img.shape[1])
-            h, w, = (
+            (
+                h,
+                w,
+            ) = (
                 img.shape[0],
                 img.shape[1],
             )

src/diffusers/modeling_flax_pytorch_utils.py

@@ -37,6 +37,7 @@ def rename_key(key):
 # PyTorch => Flax #
 #####################
 
+
 # Adapted from https://github.com/huggingface/transformers/blob/c603c80f46881ae18b2ca50770ef65fa4033eacd/src/transformers/modeling_flax_pytorch_utils.py#L69
 # and https://github.com/patil-suraj/stable-diffusion-jax/blob/main/stable_diffusion_jax/convert_diffusers_to_jax.py
 def rename_key_and_reshape_tensor(pt_tuple_key, pt_tensor, random_flax_state_dict):

src/diffusers/models/attention.py

@@ -288,8 +288,10 @@ class AttentionBlock(nn.Module):
     def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool):
         if not is_xformers_available():
             raise ModuleNotFoundError(
-                "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
-                " xformers",
+                (
+                    "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
+                    " xformers"
+                ),
                 name="xformers",
             )
         elif not torch.cuda.is_available():
@@ -450,8 +452,10 @@ class BasicTransformerBlock(nn.Module):
         if not is_xformers_available():
             print("Here is how to install it")
             raise ModuleNotFoundError(
-                "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
-                " xformers",
+                (
+                    "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
+                    " xformers"
+                ),
                 name="xformers",
             )
         elif not torch.cuda.is_available():

src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py

@@ -189,9 +189,11 @@ class EulerAncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
             or isinstance(timestep, torch.LongTensor)
         ):
             raise ValueError(
-                "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
-                " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
-                " one of the `scheduler.timesteps` as a timestep.",
+                (
+                    "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
+                    " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
+                    " one of the `scheduler.timesteps` as a timestep."
+                ),
             )
 
         if not self.is_scale_input_called:

src/diffusers/schedulers/scheduling_euler_discrete.py

@@ -198,9 +198,11 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
             or isinstance(timestep, torch.LongTensor)
         ):
             raise ValueError(
-                "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
-                " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
-                " one of the `scheduler.timesteps` as a timestep.",
+                (
+                    "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
+                    " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
+                    " one of the `scheduler.timesteps` as a timestep."
+                ),
             )
 
         if not self.is_scale_input_called:

tests/test_scheduler.py

@@ -535,8 +535,10 @@ class SchedulerCommonTest(unittest.TestCase):
                 )
                 self.assertTrue(
                     hasattr(scheduler, "scale_model_input"),
-                    f"{scheduler_class} does not implement a required class method `scale_model_input(sample,"
-                    " timestep)`",
+                    (
+                        f"{scheduler_class} does not implement a required class method `scale_model_input(sample,"
+                        " timestep)`"
+                    ),
                 )
             self.assertTrue(
                 hasattr(scheduler, "step"),

utils/custom_init_isort.py

@@ -96,6 +96,7 @@ def ignore_underscore(key):
 
 def sort_objects(objects, key=None):
     "Sort a list of `objects` following the rules of isort. `key` optionally maps an object to a str."
+
     # If no key is provided, we use a noop.
     def noop(x):
         return x
@@ -117,6 +118,7 @@ def sort_objects_in_import(import_statement):
     """
     Return the same `import_statement` but with objects properly sorted.
     """
+
     # This inner function sort imports between [ ].
     def _replace(match):
         imports = match.groups()[0]