[lora] Remove lora docs unneeded and add " # Copied from ..." (#12824)

* remove unneeded docs on load_lora_weights().

* remove more.

* up[

* up

* up

Author: sayakpaul

src/diffusers/loaders/lora_pipeline.py

@@ -1487,7 +1487,7 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
     Load LoRA layers into [`FluxTransformer2DModel`],
     [`CLIPTextModel`](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).
 
-    Specific to [`StableDiffusion3Pipeline`].
+    Specific to [`FluxPipeline`].
     """
 
     _lora_loadable_modules = ["transformer", "text_encoder"]
@@ -1628,30 +1628,7 @@ def load_lora_weights(
         **kwargs,
     ):
         """
-        Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer` and
-        `self.text_encoder`.
-
-        All kwargs are forwarded to `self.lora_state_dict`.
-
-        See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`] for more details on how the state dict is
-        loaded.
-
-        See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_transformer`] for more details on how the state
-        dict is loaded into `self.transformer`.
-
-        Parameters:
-            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
-                See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
-            adapter_name (`str`, *optional*):
-                Adapter name to be used for referencing the loaded adapter model. If not specified, it will use
-                `default_{i}` where i is the total number of adapters being loaded.
-            low_cpu_mem_usage (`bool`, *optional*):
-                `Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
-                weights.
-            hotswap (`bool`, *optional*):
-                See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`].
-            kwargs (`dict`, *optional*):
-                See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`].
+        See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for more details.
         """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
@@ -3651,44 +3628,17 @@ class KandinskyLoraLoaderMixin(LoraBaseMixin):
 
     @classmethod
     @validate_hf_hub_args
+    # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.lora_state_dict
     def lora_state_dict(
         cls,
         pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
         **kwargs,
     ):
         r"""
-        Return state dict for lora weights and the network alphas.
-
-        Parameters:
-            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
-                Can be either:
-                    - A string, the *model id* of a pretrained model hosted on the Hub.
-                    - A path to a *directory* containing the model weights.
-                    - A [torch state
-                      dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
-
-            cache_dir (`Union[str, os.PathLike]`, *optional*):
-                Path to a directory where a downloaded pretrained model configuration is cached.
-            force_download (`bool`, *optional*, defaults to `False`):
-                Whether or not to force the (re-)download of the model weights.
-            proxies (`Dict[str, str]`, *optional*):
-                A dictionary of proxy servers to use by protocol or endpoint.
-            local_files_only (`bool`, *optional*, defaults to `False`):
-                Whether to only load local model weights and configuration files.
-            token (`str` or *bool*, *optional*):
-                The token to use as HTTP bearer authorization for remote files.
-            revision (`str`, *optional*, defaults to `"main"`):
-                The specific model version to use.
-            subfolder (`str`, *optional*, defaults to `""`):
-                The subfolder location of a model file within a larger model repository.
-            weight_name (`str`, *optional*, defaults to None):
-                Name of the serialized state dict file.
-            use_safetensors (`bool`, *optional*):
-                Whether to use safetensors for loading.
-            return_lora_metadata (`bool`, *optional*, defaults to False):
-                When enabled, additionally return the LoRA adapter metadata.
+        See [`~loaders.StableDiffusionLoraLoaderMixin.lora_state_dict`] for more details.
         """
-        # Load the main state dict first which has the LoRA layers
+        # Load the main state dict first which has the LoRA layers for either of
+        # transformer and text encoder or both.
         cache_dir = kwargs.pop("cache_dir", None)
         force_download = kwargs.pop("force_download", False)
         proxies = kwargs.pop("proxies", None)
@@ -3731,6 +3681,7 @@ def lora_state_dict(
         out = (state_dict, metadata) if return_lora_metadata else state_dict
         return out
 
+    # Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.load_lora_weights
     def load_lora_weights(
         self,
         pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
@@ -3739,26 +3690,13 @@ def load_lora_weights(
         **kwargs,
     ):
         """
-        Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into `self.transformer`
-
-        Parameters:
-            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
-                See [`~loaders.KandinskyLoraLoaderMixin.lora_state_dict`].
-            adapter_name (`str`, *optional*):
-                Adapter name to be used for referencing the loaded adapter model.
-            hotswap (`bool`, *optional*):
-                Whether to substitute an existing (LoRA) adapter with the newly loaded adapter in-place.
-            low_cpu_mem_usage (`bool`, *optional*):
-                Speed up model loading by only loading the pretrained LoRA weights and not initializing the random
-                weights.
-            kwargs (`dict`, *optional*):
-                See [`~loaders.KandinskyLoraLoaderMixin.lora_state_dict`].
+        See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for more details.
         """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
 
         low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT_LORA)
-        if low_cpu_mem_usage and not is_peft_version(">=", "0.13.1"):
+        if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
             raise ValueError(
                 "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
             )
@@ -3775,7 +3713,6 @@ def load_lora_weights(
         if not is_correct_format:
             raise ValueError("Invalid LoRA checkpoint.")
 
-        # Load LoRA into transformer
         self.load_lora_into_transformer(
             state_dict,
             transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
@@ -3787,6 +3724,7 @@ def load_lora_weights(
         )
 
     @classmethod
+    # Copied from diffusers.loaders.lora_pipeline.SD3LoraLoaderMixin.load_lora_into_transformer
     def load_lora_into_transformer(
         cls,
         state_dict,
@@ -3798,23 +3736,9 @@ def load_lora_into_transformer(
         metadata=None,
     ):
         """
-        Load the LoRA layers specified in `state_dict` into `transformer`.
-
-        Parameters:
-            state_dict (`dict`):
-                A standard state dict containing the lora layer parameters.
-            transformer (`Kandinsky5Transformer3DModel`):
-                The transformer model to load the LoRA layers into.
-            adapter_name (`str`, *optional*):
-                Adapter name to be used for referencing the loaded adapter model.
-            low_cpu_mem_usage (`bool`, *optional*):
-                Speed up model loading by only loading the pretrained LoRA weights.
-            hotswap (`bool`, *optional*):
-                See [`~loaders.KandinskyLoraLoaderMixin.load_lora_weights`].
-            metadata (`dict`):
-                Optional LoRA adapter metadata.
+        See [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_into_unet`] for more details.
         """
-        if low_cpu_mem_usage and not is_peft_version(">=", "0.13.1"):
+        if low_cpu_mem_usage and is_peft_version("<", "0.13.0"):
             raise ValueError(
                 "`low_cpu_mem_usage=True` is not compatible with this `peft` version. Please update it with `pip install -U peft`."
             )
@@ -3832,6 +3756,7 @@ def load_lora_into_transformer(
         )
 
     @classmethod
+    # Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.save_lora_weights
     def save_lora_weights(
         cls,
         save_directory: Union[str, os.PathLike],
@@ -3840,24 +3765,10 @@ def save_lora_weights(
         weight_name: str = None,
         save_function: Callable = None,
         safe_serialization: bool = True,
-        transformer_lora_adapter_metadata=None,
+        transformer_lora_adapter_metadata: Optional[dict] = None,
     ):
         r"""
-        Save the LoRA parameters corresponding to the transformer and text encoders.
-
-        Arguments:
-            save_directory (`str` or `os.PathLike`):
-                Directory to save LoRA parameters to.
-            transformer_lora_layers (`Dict[str, torch.nn.Module]` or `Dict[str, torch.Tensor]`):
-                State dict of the LoRA layers corresponding to the `transformer`.
-            is_main_process (`bool`, *optional*, defaults to `True`):
-                Whether the process calling this is the main process.
-            save_function (`Callable`):
-                The function to use to save the state dictionary.
-            safe_serialization (`bool`, *optional*, defaults to `True`):
-                Whether to save the model using `safetensors` or the traditional PyTorch way.
-            transformer_lora_adapter_metadata:
-                LoRA adapter metadata associated with the transformer.
+        See [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for more information.
         """
         lora_layers = {}
         lora_metadata = {}
@@ -3867,7 +3778,7 @@ def save_lora_weights(
             lora_metadata[cls.transformer_name] = transformer_lora_adapter_metadata
 
         if not lora_layers:
-            raise ValueError("You must pass at least one of `transformer_lora_layers`")
+            raise ValueError("You must pass at least one of `transformer_lora_layers` or `text_encoder_lora_layers`.")
 
         cls._save_lora_weights(
             save_directory=save_directory,
@@ -3879,6 +3790,7 @@ def save_lora_weights(
             safe_serialization=safe_serialization,
         )
 
+    # Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.fuse_lora
     def fuse_lora(
         self,
         components: List[str] = ["transformer"],
@@ -3888,25 +3800,7 @@ def fuse_lora(
         **kwargs,
     ):
         r"""
-        Fuses the LoRA parameters into the original parameters of the corresponding blocks.
-
-        Args:
-            components: (`List[str]`): List of LoRA-injectable components to fuse the LoRAs into.
-            lora_scale (`float`, defaults to 1.0):
-                Controls how much to influence the outputs with the LoRA parameters.
-            safe_fusing (`bool`, defaults to `False`):
-                Whether to check fused weights for NaN values before fusing.
-            adapter_names (`List[str]`, *optional*):
-                Adapter names to be used for fusing.
-
-        Example:
-        ```py
-        from diffusers import Kandinsky5T2VPipeline
-
-        pipeline = Kandinsky5T2VPipeline.from_pretrained("ai-forever/Kandinsky-5.0-T2V")
-        pipeline.load_lora_weights("path/to/lora.safetensors")
-        pipeline.fuse_lora(lora_scale=0.7)
-        ```
+        See [`~loaders.StableDiffusionLoraLoaderMixin.fuse_lora`] for more details.
         """
         super().fuse_lora(
             components=components,
@@ -3916,12 +3810,10 @@ def fuse_lora(
             **kwargs,
         )
 
+    # Copied from diffusers.loaders.lora_pipeline.CogVideoXLoraLoaderMixin.unfuse_lora
     def unfuse_lora(self, components: List[str] = ["transformer"], **kwargs):
         r"""
-        Reverses the effect of [`pipe.fuse_lora()`].
-
-        Args:
-            components (`List[str]`): List of LoRA-injectable components to unfuse LoRA from.
+        See [`~loaders.StableDiffusionLoraLoaderMixin.unfuse_lora`] for more details.
         """
         super().unfuse_lora(components=components, **kwargs)