[docs] minor cleanups in the lora docs.

docs/source/en/api/loaders/lora.md

@@ -37,6 +37,10 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 </Tip>
 
+## LoraBaseMixin
+
+[[autodoc]] loaders.lora_base.LoraBaseMixin
+
 ## StableDiffusionLoraLoaderMixin
 
 [[autodoc]] loaders.lora_pipeline.StableDiffusionLoraLoaderMixin
@@ -96,10 +100,6 @@ To learn more about how to load LoRA weights, see the [LoRA](../../using-diffuse
 
 [[autodoc]] loaders.lora_pipeline.HiDreamImageLoraLoaderMixin
 
-## LoraBaseMixin
-
-[[autodoc]] loaders.lora_base.LoraBaseMixin
-
 ## WanLoraLoaderMixin
 
 [[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin

src/diffusers/loaders/lora_base.py

@@ -424,6 +424,17 @@ def _load_lora_into_text_encoder(
 
 
 def _func_optionally_disable_offloading(_pipeline):
+    """
+    Optionally removes offloading in case the pipeline has been already sequentially offloaded to CPU.
+
+    Args:
+        _pipeline (`DiffusionPipeline`):
+            The pipeline to disable offloading for.
+
+    Returns:
+        tuple:
+            A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
+    """
     is_model_cpu_offload = False
     is_sequential_cpu_offload = False
 
@@ -453,6 +464,24 @@ class LoraBaseMixin:
     _lora_loadable_modules = []
     _merged_adapters = set()
 
+    @property
+    def lora_scale(self) -> float:
+        """
+        Returns the lora scale which can be set at run time by the pipeline. # if `_lora_scale` has not been set,
+        return 1.
+        """
+        return self._lora_scale if hasattr(self, "_lora_scale") else 1.0
+
+    @property
+    def num_fused_loras(self):
+        """Returns the number of LoRAs that have been fused."""
+        return len(self._merged_adapters)
+
+    @property
+    def fused_loras(self):
+        """Returns names of the LoRAs that have been fused."""
+        return self._merged_adapters
+
     def load_lora_weights(self, **kwargs):
         raise NotImplementedError("`load_lora_weights()` is not implemented.")
 
@@ -464,33 +493,6 @@ def save_lora_weights(cls, **kwargs):
     def lora_state_dict(cls, **kwargs):
         raise NotImplementedError("`lora_state_dict()` is not implemented.")
 
-    @classmethod
-    def _optionally_disable_offloading(cls, _pipeline):
-        """
-        Optionally removes offloading in case the pipeline has been already sequentially offloaded to CPU.
-
-        Args:
-            _pipeline (`DiffusionPipeline`):
-                The pipeline to disable offloading for.
-
-        Returns:
-            tuple:
-                A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
-        """
-        return _func_optionally_disable_offloading(_pipeline=_pipeline)
-
-    @classmethod
-    def _fetch_state_dict(cls, *args, **kwargs):
-        deprecation_message = f"Using the `_fetch_state_dict()` method from {cls} has been deprecated and will be removed in a future version. Please use `from diffusers.loaders.lora_base import _fetch_state_dict`."
-        deprecate("_fetch_state_dict", "0.35.0", deprecation_message)
-        return _fetch_state_dict(*args, **kwargs)
-
-    @classmethod
-    def _best_guess_weight_name(cls, *args, **kwargs):
-        deprecation_message = f"Using the `_best_guess_weight_name()` method from {cls} has been deprecated and will be removed in a future version. Please use `from diffusers.loaders.lora_base import _best_guess_weight_name`."
-        deprecate("_best_guess_weight_name", "0.35.0", deprecation_message)
-        return _best_guess_weight_name(*args, **kwargs)
-
     def unload_lora_weights(self):
         """
         Unloads the LoRA parameters.
@@ -661,19 +663,37 @@ def unfuse_lora(self, components: List[str] = [], **kwargs):
                                     self._merged_adapters = self._merged_adapters - {adapter}
                             module.unmerge()
 
-    @property
-    def num_fused_loras(self):
-        return len(self._merged_adapters)
-
-    @property
-    def fused_loras(self):
-        return self._merged_adapters
-
     def set_adapters(
         self,
         adapter_names: Union[List[str], str],
         adapter_weights: Optional[Union[float, Dict, List[float], List[Dict]]] = None,
     ):
+        """
+        Set the currently active adapters for use in the pipeline.
+
+        Args:
+            adapter_names (`List[str]` or `str`):
+                The names of the adapters to use.
+            adapter_weights (`Union[List[float], float]`, *optional*):
+                The adapter(s) weights to use with the UNet. If `None`, the weights are set to `1.0` for all the
+                adapters.
+
+        Example:
+
+        ```py
+        from diffusers import AutoPipelineForText2Image
+        import torch
+
+        pipeline = AutoPipelineForText2Image.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights(
+            "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
+        )
+        pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
+        pipeline.set_adapters(["cinematic", "pixel"], adapter_weights=[0.5, 0.5])
+        ```
+        """
         if isinstance(adapter_weights, dict):
             components_passed = set(adapter_weights.keys())
             lora_components = set(self._lora_loadable_modules)
@@ -743,6 +763,24 @@ def set_adapters(
                 set_adapters_for_text_encoder(adapter_names, model, _component_adapter_weights[component])
 
     def disable_lora(self):
+        """
+        Disables the active LoRA layers of the pipeline.
+
+        Example:
+
+        ```py
+        from diffusers import AutoPipelineForText2Image
+        import torch
+
+        pipeline = AutoPipelineForText2Image.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights(
+            "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
+        )
+        pipeline.disable_lora()
+        ```
+        """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
 
@@ -755,6 +793,24 @@ def disable_lora(self):
                     disable_lora_for_text_encoder(model)
 
     def enable_lora(self):
+        """
+        Enables the active LoRA layers of the pipeline.
+
+        Example:
+
+        ```py
+        from diffusers import AutoPipelineForText2Image
+        import torch
+
+        pipeline = AutoPipelineForText2Image.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights(
+            "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
+        )
+        pipeline.enable_lora()
+        ```
+        """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
 
@@ -768,10 +824,26 @@ def enable_lora(self):
 
     def delete_adapters(self, adapter_names: Union[List[str], str]):
         """
+        Delete an adapter's LoRA layers from the pipeline.
+
         Args:
-        Deletes the LoRA layers of `adapter_name` for the unet and text-encoder(s).
             adapter_names (`Union[List[str], str]`):
-                The names of the adapter to delete. Can be a single string or a list of strings
+                The names of the adapters to delete.
+
+        Example:
+
+        ```py
+        from diffusers import AutoPipelineForText2Image
+        import torch
+
+        pipeline = AutoPipelineForText2Image.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        ).to("cuda")
+        pipeline.load_lora_weights(
+            "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_names="cinematic"
+        )
+        pipeline.delete_adapters("cinematic")
+        ```
         """
         if not USE_PEFT_BACKEND:
             raise ValueError("PEFT backend is required for this method.")
@@ -872,6 +944,24 @@ def set_lora_device(self, adapter_names: List[str], device: Union[torch.device,
                                         adapter_name
                                     ].to(device)
 
+    def enable_lora_hotswap(self, **kwargs) -> None:
+        """
+        Hotswap adapters without triggering recompilation of a model or if the ranks of the loaded adapters are
+        different.
+
+        Args:
+            target_rank (`int`):
+                The highest rank among all the adapters that will be loaded.
+            check_compiled (`str`, *optional*, defaults to `"error"`):
+                How to handle a model that is already compiled. The check can return the following messages:
+                  - "error" (default): raise an error
+                  - "warn": issue a warning
+                  - "ignore": do nothing
+        """
+        for key, component in self.components.items():
+            if hasattr(component, "enable_lora_hotswap") and (key in self._lora_loadable_modules):
+                component.enable_lora_hotswap(**kwargs)
+
     @staticmethod
     def pack_weights(layers, prefix):
         layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers
@@ -887,6 +977,7 @@ def write_lora_layers(
         safe_serialization: bool,
         lora_adapter_metadata: Optional[dict] = None,
     ):
+        """Writes the state dict of the LoRA layers (optionally with metadata) to disk."""
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return
@@ -927,28 +1018,18 @@ def save_function(weights, filename):
         save_function(state_dict, save_path)
         logger.info(f"Model weights saved in {save_path}")
 
-    @property
-    def lora_scale(self) -> float:
-        # property function that returns the lora scale which can be set at run time by the pipeline.
-        # if _lora_scale has not been set, return 1
-        return self._lora_scale if hasattr(self, "_lora_scale") else 1.0
-
-    def enable_lora_hotswap(self, **kwargs) -> None:
-        """Enables the possibility to hotswap LoRA adapters.
+    @classmethod
+    def _optionally_disable_offloading(cls, _pipeline):
+        return _func_optionally_disable_offloading(_pipeline=_pipeline)
 
-        Calling this method is only required when hotswapping adapters and if the model is compiled or if the ranks of
-        the loaded adapters differ.
+    @classmethod
+    def _fetch_state_dict(cls, *args, **kwargs):
+        deprecation_message = f"Using the `_fetch_state_dict()` method from {cls} has been deprecated and will be removed in a future version. Please use `from diffusers.loaders.lora_base import _fetch_state_dict`."
+        deprecate("_fetch_state_dict", "0.35.0", deprecation_message)
+        return _fetch_state_dict(*args, **kwargs)
 
-        Args:
-            target_rank (`int`):
-                The highest rank among all the adapters that will be loaded.
-            check_compiled (`str`, *optional*, defaults to `"error"`):
-                How to handle the case when the model is already compiled, which should generally be avoided. The
-                options are:
-                  - "error" (default): raise an error
-                  - "warn": issue a warning
-                  - "ignore": do nothing
-        """
-        for key, component in self.components.items():
-            if hasattr(component, "enable_lora_hotswap") and (key in self._lora_loadable_modules):
-                component.enable_lora_hotswap(**kwargs)
+    @classmethod
+    def _best_guess_weight_name(cls, *args, **kwargs):
+        deprecation_message = f"Using the `_best_guess_weight_name()` method from {cls} has been deprecated and will be removed in a future version. Please use `from diffusers.loaders.lora_base import _best_guess_weight_name`."
+        deprecate("_best_guess_weight_name", "0.35.0", deprecation_message)
+        return _best_guess_weight_name(*args, **kwargs)

src/diffusers/loaders/peft.py

@@ -85,17 +85,6 @@ class PeftAdapterMixin:
     @classmethod
     # Copied from diffusers.loaders.lora_base.LoraBaseMixin._optionally_disable_offloading
     def _optionally_disable_offloading(cls, _pipeline):
-        """
-        Optionally removes offloading in case the pipeline has been already sequentially offloaded to CPU.
-
-        Args:
-            _pipeline (`DiffusionPipeline`):
-                The pipeline to disable offloading for.
-
-        Returns:
-            tuple:
-                A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
-        """
         return _func_optionally_disable_offloading(_pipeline=_pipeline)
 
     def load_lora_adapter(
@@ -444,7 +433,7 @@ def set_adapters(
         weights: Optional[Union[float, Dict, List[float], List[Dict], List[None]]] = None,
     ):
         """
-        Set the currently active adapters for use in the UNet.
+        Set the currently active adapters for use in the diffusion network (e.g. unet, transformer, etc.).
 
         Args:
             adapter_names (`List[str]` or `str`):
@@ -466,7 +455,7 @@ def set_adapters(
             "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
         )
         pipeline.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel")
-        pipeline.set_adapters(["cinematic", "pixel"], adapter_weights=[0.5, 0.5])
+        pipeline.unet.set_adapters(["cinematic", "pixel"], adapter_weights=[0.5, 0.5])
         ```
         """
         if not USE_PEFT_BACKEND:
@@ -714,7 +703,7 @@ def disable_lora(self):
         pipeline.load_lora_weights(
             "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
         )
-        pipeline.disable_lora()
+        pipeline.unet.disable_lora()
         ```
         """
         if not USE_PEFT_BACKEND:
@@ -737,7 +726,7 @@ def enable_lora(self):
         pipeline.load_lora_weights(
             "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_name="cinematic"
         )
-        pipeline.enable_lora()
+        pipeline.unet.enable_lora()
         ```
         """
         if not USE_PEFT_BACKEND:
@@ -764,7 +753,7 @@ def delete_adapters(self, adapter_names: Union[List[str], str]):
         pipeline.load_lora_weights(
             "jbilcke-hf/sdxl-cinematic-1", weight_name="pytorch_lora_weights.safetensors", adapter_names="cinematic"
         )
-        pipeline.delete_adapters("cinematic")
+        pipeline.unet.delete_adapters("cinematic")
         ```
         """
         if not USE_PEFT_BACKEND:

src/diffusers/loaders/unet.py

@@ -394,17 +394,6 @@ def _process_lora(
     @classmethod
     # Copied from diffusers.loaders.lora_base.LoraBaseMixin._optionally_disable_offloading
     def _optionally_disable_offloading(cls, _pipeline):
-        """
-        Optionally removes offloading in case the pipeline has been already sequentially offloaded to CPU.
-
-        Args:
-            _pipeline (`DiffusionPipeline`):
-                The pipeline to disable offloading for.
-
-        Returns:
-            tuple:
-                A tuple indicating if `is_model_cpu_offload` or `is_sequential_cpu_offload` is True.
-        """
         return _func_optionally_disable_offloading(_pipeline=_pipeline)
 
     def save_attn_procs(