[Styling] stylify using ruff (#5841)

* ruff format

* not need to use doc-builder's black styling as the doc is styled in ruff

* make fix-copies

* comment

* use run_ruff

src/diffusers/pipelines/auto_pipeline.py

@@ -181,6 +181,7 @@ class AutoPipelineForText2Image(ConfigMixin):
           diffusion pipeline's components.
 
     """
+
     config_name = "model_index.json"
 
     def __init__(self, *args, **kwargs):
@@ -451,6 +452,7 @@ class AutoPipelineForImage2Image(ConfigMixin):
           diffusion pipeline's components.
 
     """
+
     config_name = "model_index.json"
 
     def __init__(self, *args, **kwargs):
@@ -726,6 +728,7 @@ class AutoPipelineForInpainting(ConfigMixin):
           diffusion pipeline's components.
 
     """
+
     config_name = "model_index.json"
 
     def __init__(self, *args, **kwargs):

src/diffusers/pipelines/consistency_models/pipeline_consistency_models.py

@@ -74,6 +74,7 @@ class ConsistencyModelPipeline(DiffusionPipeline):
             A scheduler to be used in combination with `unet` to denoise the encoded image latents. Currently only
             compatible with [`CMStochasticIterativeScheduler`].
     """
+
     model_cpu_offload_seq = "unet"
 
     def __init__(self, unet: UNet2DModel, scheduler: CMStochasticIterativeScheduler) -> None:

src/diffusers/pipelines/controlnet/pipeline_controlnet.py

@@ -126,6 +126,7 @@ class StableDiffusionControlNetPipeline(
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
+
     model_cpu_offload_seq = "text_encoder->unet->vae"
     _optional_components = ["safety_checker", "feature_extractor"]
     _exclude_from_cpu_offload = ["safety_checker"]
@@ -886,9 +887,10 @@ class StableDiffusionControlNetPipeline(
             control_guidance_end = len(control_guidance_start) * [control_guidance_end]
         elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
             mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
-            control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [
-                control_guidance_end
-            ]
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
 
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(

src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py

@@ -160,6 +160,7 @@ class StableDiffusionControlNetImg2ImgPipeline(
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
+
     model_cpu_offload_seq = "text_encoder->unet->vae"
     _optional_components = ["safety_checker", "feature_extractor"]
     _exclude_from_cpu_offload = ["safety_checker"]
@@ -933,9 +934,10 @@ class StableDiffusionControlNetImg2ImgPipeline(
             control_guidance_end = len(control_guidance_start) * [control_guidance_end]
         elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
             mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
-            control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [
-                control_guidance_end
-            ]
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
 
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py

@@ -282,6 +282,7 @@ class StableDiffusionControlNetInpaintPipeline(
         feature_extractor ([`~transformers.CLIPImageProcessor`]):
             A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
     """
+
     model_cpu_offload_seq = "text_encoder->unet->vae"
     _optional_components = ["safety_checker", "feature_extractor"]
     _exclude_from_cpu_offload = ["safety_checker"]
@@ -1142,9 +1143,10 @@ class StableDiffusionControlNetInpaintPipeline(
             control_guidance_end = len(control_guidance_start) * [control_guidance_end]
         elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
             mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
-            control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [
-                control_guidance_end
-            ]
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
 
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py

@@ -164,6 +164,7 @@ class StableDiffusionXLControlNetInpaintPipeline(
             A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
             [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
     """
+
     model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
     _optional_components = ["tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2"]
 
@@ -1198,9 +1199,10 @@ class StableDiffusionXLControlNetInpaintPipeline(
             control_guidance_end = len(control_guidance_start) * [control_guidance_end]
         elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
             mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
-            control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [
-                control_guidance_end
-            ]
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
 
         # # 0.0 Default height and width to unet
         # height = height or self.unet.config.sample_size * self.vae_scale_factor
@@ -1213,9 +1215,10 @@ class StableDiffusionXLControlNetInpaintPipeline(
             control_guidance_end = len(control_guidance_start) * [control_guidance_end]
         elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
             mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
-            control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [
-                control_guidance_end
-            ]
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
 
         # 1. Check inputs
         self.check_inputs(

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py

@@ -139,6 +139,7 @@ class StableDiffusionXLControlNetPipeline(
             watermark output images. If not defined, it defaults to `True` if the package is installed; otherwise no
             watermarker is used.
     """
+
     # leave controlnet out on purpose because it iterates with unet
     model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
     _optional_components = ["tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2"]
@@ -1005,9 +1006,10 @@ class StableDiffusionXLControlNetPipeline(
             control_guidance_end = len(control_guidance_start) * [control_guidance_end]
         elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
             mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
-            control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [
-                control_guidance_end
-            ]
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
 
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(

src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl_img2img.py

@@ -192,6 +192,7 @@ class StableDiffusionXLControlNetImg2ImgPipeline(
             watermark output images. If not defined, it will default to True if the package is installed, otherwise no
             watermarker will be used.
     """
+
     model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
     _optional_components = ["tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2"]
 
@@ -1154,9 +1155,10 @@ class StableDiffusionXLControlNetImg2ImgPipeline(
             control_guidance_end = len(control_guidance_start) * [control_guidance_end]
         elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
             mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
-            control_guidance_start, control_guidance_end = mult * [control_guidance_start], mult * [
-                control_guidance_end
-            ]
+            control_guidance_start, control_guidance_end = (
+                mult * [control_guidance_start],
+                mult * [control_guidance_end],
+            )
 
         # 1. Check inputs. Raise error if not correct
         self.check_inputs(

src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py

@@ -39,6 +39,7 @@ class DanceDiffusionPipeline(DiffusionPipeline):
             A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of
             [`IPNDMScheduler`].
     """
+
     model_cpu_offload_seq = "unet"
 
     def __init__(self, unet, scheduler):

src/diffusers/pipelines/ddim/pipeline_ddim.py

@@ -35,6 +35,7 @@ class DDIMPipeline(DiffusionPipeline):
             A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
             [`DDPMScheduler`], or [`DDIMScheduler`].
     """
+
     model_cpu_offload_seq = "unet"
 
     def __init__(self, unet, scheduler):

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -35,6 +35,7 @@ class DDPMPipeline(DiffusionPipeline):
             A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
             [`DDPMScheduler`], or [`DDIMScheduler`].
     """
+
     model_cpu_offload_seq = "unet"
 
     def __init__(self, unet, scheduler):

src/diffusers/pipelines/deepfloyd_if/pipeline_if.py

@@ -98,7 +98,19 @@ class IFPipeline(DiffusionPipeline, LoraLoaderMixin):
     watermarker: Optional[IFWatermarker]
 
     bad_punct_regex = re.compile(
-        r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
+        r"["
+        + "#®•©™&@·º½¾¿¡§~"
+        + r"\)"
+        + r"\("
+        + r"\]"
+        + r"\["
+        + r"\}"
+        + r"\{"
+        + r"\|"
+        + "\\"
+        + r"\/"
+        + r"\*"
+        + r"]{1,}"
     )  # noqa
 
     _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"]

src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py

@@ -122,7 +122,19 @@ class IFImg2ImgPipeline(DiffusionPipeline, LoraLoaderMixin):
     watermarker: Optional[IFWatermarker]
 
     bad_punct_regex = re.compile(
-        r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
+        r"["
+        + "#®•©™&@·º½¾¿¡§~"
+        + r"\)"
+        + r"\("
+        + r"\]"
+        + r"\["
+        + r"\}"
+        + r"\{"
+        + r"\|"
+        + "\\"
+        + r"\/"
+        + r"\*"
+        + r"]{1,}"
     )  # noqa
 
     _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"]

src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py

@@ -126,7 +126,19 @@ class IFImg2ImgSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
     watermarker: Optional[IFWatermarker]
 
     bad_punct_regex = re.compile(
-        r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
+        r"["
+        + "#®•©™&@·º½¾¿¡§~"
+        + r"\)"
+        + r"\("
+        + r"\]"
+        + r"\["
+        + r"\}"
+        + r"\{"
+        + r"\|"
+        + "\\"
+        + r"\/"
+        + r"\*"
+        + r"]{1,}"
     )  # noqa
 
     _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor"]

src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting.py

@@ -125,7 +125,19 @@ class IFInpaintingPipeline(DiffusionPipeline, LoraLoaderMixin):
     watermarker: Optional[IFWatermarker]
 
     bad_punct_regex = re.compile(
-        r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
+        r"["
+        + "#®•©™&@·º½¾¿¡§~"
+        + r"\)"
+        + r"\("
+        + r"\]"
+        + r"\["
+        + r"\}"
+        + r"\{"
+        + r"\|"
+        + "\\"
+        + r"\/"
+        + r"\*"
+        + r"]{1,}"
     )  # noqa
 
     _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"]

src/diffusers/pipelines/deepfloyd_if/pipeline_if_inpainting_superresolution.py

@@ -128,7 +128,19 @@ class IFInpaintingSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
     watermarker: Optional[IFWatermarker]
 
     bad_punct_regex = re.compile(
-        r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
+        r"["
+        + "#®•©™&@·º½¾¿¡§~"
+        + r"\)"
+        + r"\("
+        + r"\]"
+        + r"\["
+        + r"\}"
+        + r"\{"
+        + r"\|"
+        + "\\"
+        + r"\/"
+        + r"\*"
+        + r"]{1,}"
     )  # noqa
 
     model_cpu_offload_seq = "text_encoder->unet"

src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py

@@ -84,7 +84,19 @@ class IFSuperResolutionPipeline(DiffusionPipeline, LoraLoaderMixin):
     watermarker: Optional[IFWatermarker]
 
     bad_punct_regex = re.compile(
-        r"[" + "#®•©™&@·º½¾¿¡§~" + "\)" + "\(" + "\]" + "\[" + "\}" + "\{" + "\|" + "\\" + "\/" + "\*" + r"]{1,}"
+        r"["
+        + "#®•©™&@·º½¾¿¡§~"
+        + r"\)"
+        + r"\("
+        + r"\]"
+        + r"\["
+        + r"\}"
+        + r"\{"
+        + r"\|"
+        + "\\"
+        + r"\/"
+        + r"\*"
+        + r"]{1,}"
     )  # noqa
 
     _optional_components = ["tokenizer", "text_encoder", "safety_checker", "feature_extractor", "watermarker"]

src/diffusers/pipelines/dit/pipeline_dit.py

@@ -43,6 +43,7 @@ class DiTPipeline(DiffusionPipeline):
         scheduler ([`DDIMScheduler`]):
             A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
     """
+
     model_cpu_offload_seq = "transformer->vae"
 
     def __init__(

src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_img2img.py

@@ -115,6 +115,7 @@ class LatentConsistencyModelImg2ImgPipeline(
         requires_safety_checker (`bool`, *optional*, defaults to `True`):
             Whether the pipeline requires a safety checker component.
     """
+
     model_cpu_offload_seq = "text_encoder->unet->vae"
     _optional_components = ["safety_checker", "feature_extractor"]
     _exclude_from_cpu_offload = ["safety_checker"]

src/diffusers/pipelines/latent_consistency_models/pipeline_latent_consistency_text2img.py

@@ -97,6 +97,7 @@ class LatentConsistencyModelPipeline(
         requires_safety_checker (`bool`, *optional*, defaults to `True`):
             Whether the pipeline requires a safety checker component.
     """
+
     model_cpu_offload_seq = "text_encoder->unet->vae"
     _optional_components = ["safety_checker", "feature_extractor"]
     _exclude_from_cpu_offload = ["safety_checker"]