[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/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py

@@ -115,6 +115,7 @@ class StableDiffusionInpaintPipelineLegacy(
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
+
     model_cpu_offload_seq = "text_encoder->unet->vae"
     _optional_components = ["feature_extractor"]
     _exclude_from_cpu_offload = ["safety_checker"]

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py

@@ -89,6 +89,7 @@ class StableDiffusionInstructPix2PixPipeline(DiffusionPipeline, TextualInversion
         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"]

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py

@@ -80,6 +80,7 @@ class StableDiffusionKDiffusionPipeline(DiffusionPipeline, TextualInversionLoade
         feature_extractor ([`CLIPImageProcessor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
+
     model_cpu_offload_seq = "text_encoder->unet->vae"
     _optional_components = ["safety_checker", "feature_extractor"]
     _exclude_from_cpu_offload = ["safety_checker"]

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py

@@ -79,6 +79,7 @@ class StableDiffusionLatentUpscalePipeline(DiffusionPipeline, FromSingleFileMixi
         scheduler ([`SchedulerMixin`]):
             A [`EulerDiscreteScheduler`] to be used in combination with `unet` to denoise the encoded image latents.
     """
+
     model_cpu_offload_seq = "text_encoder->unet->vae"
 
     def __init__(

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_ldm3d.py

@@ -115,6 +115,7 @@ class StableDiffusionLDM3DPipeline(
         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"]

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_model_editing.py

@@ -66,6 +66,7 @@ class StableDiffusionModelEditingPipeline(DiffusionPipeline, TextualInversionLoa
         with_augs ([`list`]):
             Textual augmentations to apply while editing the text-to-image model. Set to `[]` for no augmentations.
     """
+
     model_cpu_offload_seq = "text_encoder->unet->vae"
     _optional_components = ["safety_checker", "feature_extractor"]
     _exclude_from_cpu_offload = ["safety_checker"]

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py

@@ -85,6 +85,7 @@ class StableDiffusionPanoramaPipeline(DiffusionPipeline, TextualInversionLoaderM
         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"]

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_paradigms.py

@@ -96,6 +96,7 @@ class StableDiffusionParadigmsPipeline(
         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"]

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py

@@ -310,6 +310,7 @@ class StableDiffusionPix2PixZeroPipeline(DiffusionPipeline):
             Whether the pipeline requires a safety checker. We recommend setting it to True if you're using the
             pipeline publicly.
     """
+
     model_cpu_offload_seq = "text_encoder->unet->vae"
     _optional_components = [
         "safety_checker",

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py

@@ -124,6 +124,7 @@ class StableDiffusionSAGPipeline(DiffusionPipeline, TextualInversionLoaderMixin)
         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"]

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py

@@ -92,6 +92,7 @@ class StableDiffusionUpscalePipeline(
             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->unet->vae"
     _optional_components = ["watermarker", "safety_checker", "feature_extractor"]
     _exclude_from_cpu_offload = ["safety_checker"]

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py

@@ -140,6 +140,7 @@ class StableDiffusionXLPipeline(
             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"]
     _callback_tensor_inputs = [

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -153,6 +153,7 @@ class StableDiffusionXLImg2ImgPipeline(
             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"]
     _callback_tensor_inputs = [

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py

@@ -298,6 +298,7 @@ class StableDiffusionXLInpaintPipeline(
             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"]

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_instruct_pix2pix.py

@@ -151,6 +151,7 @@ class StableDiffusionXLInstructPix2PixPipeline(
             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"]
 

src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py

@@ -152,6 +152,7 @@ class StableDiffusionAdapterPipeline(DiffusionPipeline):
         feature_extractor ([`CLIPFeatureExtractor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
+
     model_cpu_offload_seq = "text_encoder->adapter->unet->vae"
     _optional_components = ["safety_checker", "feature_extractor"]
 

src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py

@@ -159,6 +159,7 @@ class StableDiffusionXLAdapterPipeline(
         feature_extractor ([`CLIPFeatureExtractor`]):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
+
     model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
     _optional_components = ["tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2"]
 

src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py

@@ -96,6 +96,7 @@ class TextToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lora
             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->unet->vae"
 
     def __init__(

src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py

@@ -158,6 +158,7 @@ class VideoToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lor
             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->unet->vae"
 
     def __init__(

src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py

@@ -183,6 +183,7 @@ class TextToVideoPipelineOutput(BaseOutput):
             List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or
             `None` if safety checking could not be performed.
     """
+
     images: Union[List[PIL.Image.Image], np.ndarray]
     nsfw_content_detected: Optional[List[bool]]