Run fix-copies to match code style

Author: gapatron

src/diffusers/loaders/lora_pipeline.py

@@ -1463,18 +1463,6 @@ def load_lora_into_text_encoder(
     @classmethod
     # Copied from diffusers.loaders.lora_pipeline.StableDiffusionXLLoraLoaderMixin.save_lora_weights with unet->transformer
     def save_lora_weights(
-        cls,
-        save_directory: Union[str, os.PathLike],
-        transformer_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
-        text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
-        text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
-        is_main_process: bool = True,
-        weight_name: str = None,
-        save_function: Callable = None,
-        safe_serialization: bool = True,
-    ):
-        r"""
-        Save the LoRA parameters corresponding to the UNet and text encoder.
 
         Arguments:
             save_directory (`str` or `os.PathLike`):
@@ -1525,15 +1513,6 @@ def save_lora_weights(
 
     # Copied from diffusers.loaders.lora_pipeline.StableDiffusionXLLoraLoaderMixin.fuse_lora with unet->transformer
     def fuse_lora(
-        self,
-        components: List[str] = ["transformer", "text_encoder", "text_encoder_2"],
-        lora_scale: float = 1.0,
-        safe_fusing: bool = False,
-        adapter_names: Optional[List[str]] = None,
-        **kwargs,
-    ):
-        r"""
-        Fuses the LoRA parameters into the original parameters of the corresponding blocks.
 
         <Tip warning={true}>
 
@@ -1573,9 +1552,6 @@ def fuse_lora(
 
     # Copied from diffusers.loaders.lora_pipeline.StableDiffusionXLLoraLoaderMixin.unfuse_lora with unet->transformer
     def unfuse_lora(self, components: List[str] = ["transformer", "text_encoder", "text_encoder_2"], **kwargs):
-        r"""
-        Reverses the effect of
-        [`pipe.fuse_lora()`](https://huggingface.co/docs/diffusers/main/en/api/loaders#diffusers.loaders.LoraBaseMixin.fuse_lora).
 
         <Tip warning={true}>
 
@@ -2083,17 +2059,6 @@ def load_lora_into_text_encoder(
     @classmethod
     # Copied from diffusers.loaders.lora_pipeline.StableDiffusionLoraLoaderMixin.save_lora_weights with unet->transformer
     def save_lora_weights(
-        cls,
-        save_directory: Union[str, os.PathLike],
-        transformer_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
-        text_encoder_lora_layers: Dict[str, torch.nn.Module] = None,
-        is_main_process: bool = True,
-        weight_name: str = None,
-        save_function: Callable = None,
-        safe_serialization: bool = True,
-    ):
-        r"""
-        Save the LoRA parameters corresponding to the UNet and text encoder.
 
         Arguments:
             save_directory (`str` or `os.PathLike`):
@@ -2496,17 +2461,6 @@ class AmusedLoraLoaderMixin(StableDiffusionLoraLoaderMixin):
     @classmethod
     # Copied from diffusers.loaders.lora_pipeline.FluxLoraLoaderMixin.load_lora_into_transformer with FluxTransformer2DModel->UVit2DModel
     def load_lora_into_transformer(
-        cls,
-        state_dict,
-        network_alphas,
-        transformer,
-        adapter_name=None,
-        _pipeline=None,
-        low_cpu_mem_usage=False,
-        hotswap: bool = False,
-    ):
-        """
-        This will load the LoRA layers specified in `state_dict` into `transformer`.
 
         Parameters:
             state_dict (`dict`):
@@ -2854,10 +2808,6 @@ def load_lora_weights(
     @classmethod
     # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.load_lora_into_transformer with SD3Transformer2DModel->CogVideoXTransformer3DModel
     def load_lora_into_transformer(
-        cls, state_dict, transformer, adapter_name=None, _pipeline=None, low_cpu_mem_usage=False, hotswap: bool = False
-    ):
-        """
-        This will load the LoRA layers specified in `state_dict` into `transformer`.
 
         Parameters:
             state_dict (`dict`):
@@ -3185,10 +3135,6 @@ def load_lora_weights(
     @classmethod
     # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.load_lora_into_transformer with SD3Transformer2DModel->MochiTransformer3DModel
     def load_lora_into_transformer(
-        cls, state_dict, transformer, adapter_name=None, _pipeline=None, low_cpu_mem_usage=False, hotswap: bool = False
-    ):
-        """
-        This will load the LoRA layers specified in `state_dict` into `transformer`.
 
         Parameters:
             state_dict (`dict`):
@@ -3518,10 +3464,6 @@ def load_lora_weights(
     @classmethod
     # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.load_lora_into_transformer with SD3Transformer2DModel->LTXVideoTransformer3DModel
     def load_lora_into_transformer(
-        cls, state_dict, transformer, adapter_name=None, _pipeline=None, low_cpu_mem_usage=False, hotswap: bool = False
-    ):
-        """
-        This will load the LoRA layers specified in `state_dict` into `transformer`.
 
         Parameters:
             state_dict (`dict`):
@@ -3851,10 +3793,6 @@ def load_lora_weights(
     @classmethod
     # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.load_lora_into_transformer with SD3Transformer2DModel->SanaTransformer2DModel
     def load_lora_into_transformer(
-        cls, state_dict, transformer, adapter_name=None, _pipeline=None, low_cpu_mem_usage=False, hotswap: bool = False
-    ):
-        """
-        This will load the LoRA layers specified in `state_dict` into `transformer`.
 
         Parameters:
             state_dict (`dict`):
@@ -4187,10 +4125,6 @@ def load_lora_weights(
     @classmethod
     # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.load_lora_into_transformer with SD3Transformer2DModel->HunyuanVideoTransformer3DModel
     def load_lora_into_transformer(
-        cls, state_dict, transformer, adapter_name=None, _pipeline=None, low_cpu_mem_usage=False, hotswap: bool = False
-    ):
-        """
-        This will load the LoRA layers specified in `state_dict` into `transformer`.
 
         Parameters:
             state_dict (`dict`):
@@ -4524,10 +4458,6 @@ def load_lora_weights(
     @classmethod
     # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.load_lora_into_transformer with SD3Transformer2DModel->Lumina2Transformer2DModel
     def load_lora_into_transformer(
-        cls, state_dict, transformer, adapter_name=None, _pipeline=None, low_cpu_mem_usage=False, hotswap: bool = False
-    ):
-        """
-        This will load the LoRA layers specified in `state_dict` into `transformer`.
 
         Parameters:
             state_dict (`dict`):
@@ -4890,10 +4820,6 @@ def load_lora_weights(
     @classmethod
     # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.load_lora_into_transformer with SD3Transformer2DModel->WanTransformer3DModel
     def load_lora_into_transformer(
-        cls, state_dict, transformer, adapter_name=None, _pipeline=None, low_cpu_mem_usage=False, hotswap: bool = False
-    ):
-        """
-        This will load the LoRA layers specified in `state_dict` into `transformer`.
 
         Parameters:
             state_dict (`dict`):
@@ -5223,10 +5149,6 @@ def load_lora_weights(
     @classmethod
     # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.load_lora_into_transformer with SD3Transformer2DModel->CogView4Transformer2DModel
     def load_lora_into_transformer(
-        cls, state_dict, transformer, adapter_name=None, _pipeline=None, low_cpu_mem_usage=False, hotswap: bool = False
-    ):
-        """
-        This will load the LoRA layers specified in `state_dict` into `transformer`.
 
         Parameters:
             state_dict (`dict`):

src/diffusers/models/controlnets/multicontrolnet_union.py

@@ -85,16 +85,6 @@ def forward(
 
     # Copied from diffusers.models.controlnets.multicontrolnet.MultiControlNetModel.save_pretrained with ControlNet->ControlNetUnion
     def save_pretrained(
-        self,
-        save_directory: Union[str, os.PathLike],
-        is_main_process: bool = True,
-        save_function: Callable = None,
-        safe_serialization: bool = True,
-        variant: Optional[str] = None,
-    ):
-        """
-        Save a model and its configuration file to a directory, so that it can be re-loaded using the
-        `[`~models.controlnets.multicontrolnet.MultiControlNetUnionModel.from_pretrained`]` class method.
 
         Arguments:
             save_directory (`str` or `os.PathLike`):
@@ -125,8 +115,6 @@ def save_pretrained(
     @classmethod
     # Copied from diffusers.models.controlnets.multicontrolnet.MultiControlNetModel.from_pretrained with ControlNet->ControlNetUnion
     def from_pretrained(cls, pretrained_model_path: Optional[Union[str, os.PathLike]], **kwargs):
-        r"""
-        Instantiate a pretrained MultiControlNetUnion model from multiple pre-trained controlnet models.
 
         The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
         the model, you should first set it back in training mode with `model.train()`.

src/diffusers/models/transformers/auraflow_transformer_2d.py

@@ -406,9 +406,6 @@ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
 
     # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedAuraFlowAttnProcessor2_0
     def fuse_qkv_projections(self):
-        """
-        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
-        are fused. For cross-attention modules, key and value projection matrices are fused.
 
         <Tip warning={true}>
 

src/diffusers/models/transformers/cogvideox_transformer_3d.py

@@ -393,9 +393,6 @@ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
 
     # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedCogVideoXAttnProcessor2_0
     def fuse_qkv_projections(self):
-        """
-        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
-        are fused. For cross-attention modules, key and value projection matrices are fused.
 
         <Tip warning={true}>
 

src/diffusers/models/transformers/hunyuan_transformer_2d.py

@@ -320,9 +320,6 @@ def __init__(
 
     # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedHunyuanAttnProcessor2_0
     def fuse_qkv_projections(self):
-        """
-        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
-        are fused. For cross-attention modules, key and value projection matrices are fused.
 
         <Tip warning={true}>
 

src/diffusers/models/transformers/stable_audio_transformer.py

@@ -341,7 +341,7 @@ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
 
     # Copied from diffusers.models.transformers.hunyuan_transformer_2d.HunyuanDiT2DModel.set_default_attn_processor with Hunyuan->StableAudio
     def set_default_attn_processor(self):
-        """
+       """
         Disables custom attention processors and sets the default attention implementation.
         """
         self.set_attn_processor(StableAudioAttnProcessor2_0())

src/diffusers/models/transformers/transformer_flux.py

@@ -348,9 +348,6 @@ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
 
     # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedFluxAttnProcessor2_0
     def fuse_qkv_projections(self):
-        """
-        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
-        are fused. For cross-attention modules, key and value projection matrices are fused.
 
         <Tip warning={true}>
 

src/diffusers/models/transformers/transformer_sd3.py

@@ -276,9 +276,6 @@ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
 
     # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedJointAttnProcessor2_0
     def fuse_qkv_projections(self):
-        """
-        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
-        are fused. For cross-attention modules, key and value projection matrices are fused.
 
         <Tip warning={true}>
 

src/diffusers/pipelines/allegro/pipeline_allegro.py

@@ -213,22 +213,6 @@ def __init__(
 
     # Copied from diffusers.pipelines.pixart_alpha.pipeline_pixart_alpha.PixArtAlphaPipeline.encode_prompt with 120->512, num_images_per_prompt->num_videos_per_prompt
     def encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        do_classifier_free_guidance: bool = True,
-        negative_prompt: str = "",
-        num_videos_per_prompt: int = 1,
-        device: Optional[torch.device] = None,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        prompt_attention_mask: Optional[torch.Tensor] = None,
-        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
-        clean_caption: bool = False,
-        max_sequence_length: int = 512,
-        **kwargs,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
 
         Args:
             prompt (`str` or `List[str]`, *optional*):

src/diffusers/pipelines/animatediff/pipeline_animatediff.py

@@ -154,19 +154,6 @@ def __init__(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
     def encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        lora_scale: Optional[float] = None,
-        clip_skip: Optional[int] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
 
         Args:
             prompt (`str` or `List[str]`, *optional*):

src/diffusers/pipelines/animatediff/pipeline_animatediff_controlnet.py

@@ -197,19 +197,6 @@ def __init__(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
     def encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        lora_scale: Optional[float] = None,
-        clip_skip: Optional[int] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
 
         Args:
             prompt (`str` or `List[str]`, *optional*):

src/diffusers/pipelines/animatediff/pipeline_animatediff_sdxl.py

@@ -327,23 +327,6 @@ def __init__(
 
     # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt with num_images_per_prompt->num_videos_per_prompt
     def encode_prompt(
-        self,
-        prompt: str,
-        prompt_2: Optional[str] = None,
-        device: Optional[torch.device] = None,
-        num_videos_per_prompt: int = 1,
-        do_classifier_free_guidance: bool = True,
-        negative_prompt: Optional[str] = None,
-        negative_prompt_2: Optional[str] = None,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        pooled_prompt_embeds: Optional[torch.Tensor] = None,
-        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
-        lora_scale: Optional[float] = None,
-        clip_skip: Optional[int] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
 
         Args:
             prompt (`str` or `List[str]`, *optional*):

src/diffusers/pipelines/animatediff/pipeline_animatediff_sparsectrl.py

@@ -206,19 +206,6 @@ def __init__(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
     def encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        lora_scale: Optional[float] = None,
-        clip_skip: Optional[int] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
 
         Args:
             prompt (`str` or `List[str]`, *optional*):

src/diffusers/pipelines/audioldm/pipeline_audioldm.py

@@ -335,7 +335,7 @@ def check_inputs(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents with width->self.vocoder.config.model_in_dim
     def prepare_latents(self, batch_size, num_channels_latents, height, dtype, device, generator, latents=None):
-        shape = (
+       shape = (
             batch_size,
             num_channels_latents,
             int(height) // self.vae_scale_factor,

src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py

@@ -815,7 +815,7 @@ def check_inputs(
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents with width->self.vocoder.config.model_in_dim
     def prepare_latents(self, batch_size, num_channels_latents, height, dtype, device, generator, latents=None):
-        shape = (
+       shape = (
             batch_size,
             num_channels_latents,
             int(height) // self.vae_scale_factor,

src/diffusers/pipelines/cogview3/pipeline_cogview3plus.py

@@ -166,7 +166,7 @@ def __init__(
 
     # Copied from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline._get_t5_prompt_embeds with num_videos_per_prompt->num_images_per_prompt
     def _get_t5_prompt_embeds(
-        self,
+       self,
         prompt: Union[str, List[str]] = None,
         num_images_per_prompt: int = 1,
         max_sequence_length: int = 226,

src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py

@@ -639,7 +639,7 @@ def check_inputs(
 
     # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.preprocess_image with preprocess_image -> preprocess_original_image
     def preprocess_original_image(self, image: PIL.Image.Image) -> torch.Tensor:
-        if not isinstance(image, list):
+       if not isinstance(image, list):
             image = [image]
 
         def numpy_to_pt(images):