docstrings

src/diffusers/loaders.py

@@ -838,6 +838,26 @@ class LoraLoaderMixin:
     unet_name = UNET_NAME
 
     def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
+        """
+        Load LoRA weights specified in `pretrained_model_name_or_path_or_dict` into self.unet and self.text_encoder.
+
+        All kwargs are forwarded to `self.lora_state_dict`.
+
+        See [`~loaders.LoraLoaderMixin.lora_state_dict`] for more details on how the state dict is loaded.
+
+        See [`~loaders.LoraLoaderMixin.load_lora_into_unet`] for more details on how the state dict is loaded into
+        `self.unet`.
+
+        See [`~loaders.LoraLoaderMixin.load_lora_into_text_encoder`] for more details on how the state dict is loaded
+        into `self.text_encoder`.
+
+        Parameters:
+            pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
+                See [`~loaders.LoraLoaderMixin.lora_state_dict`].
+
+            kwargs:
+                See [`~loaders.LoraLoaderMixin.lora_state_dict`].
+        """
         state_dict, network_alpha = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)
         self.load_lora_into_unet(state_dict, network_alpha=network_alpha, unet=self.unet)
         self.load_lora_into_text_encoder(
@@ -990,6 +1010,20 @@ def lora_state_dict(
 
     @classmethod
     def load_lora_into_unet(cls, state_dict, network_alpha, unet):
+        """
+        This will load the LoRA layers specified in `state_dict` into `unet`
+
+        Parameters:
+            state_dict (`dict`):
+                A standard state dict containing the lora layer parameters. The keys can either be indexed directly
+                into the unet or prefixed with an additional `unet` which can be used to distinguish between text
+                encoder lora layers.
+            network_alpha (`float`):
+                See `LoRALinearLayer` for more details.
+            unet (`UNet2DConditionModel`):
+                The UNet model to load the LoRA layers into.
+        """
+
         # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
         # then the `state_dict` keys should have `self.unet_name` and/or `self.text_encoder_name` as
         # their prefixes.
@@ -1014,6 +1048,22 @@ def load_lora_into_unet(cls, state_dict, network_alpha, unet):
 
     @classmethod
     def load_lora_into_text_encoder(cls, state_dict, network_alpha, text_encoder, lora_scale=1.0):
+        """
+        This will load the LoRA layers specified in `state_dict` into `text_encoder`
+
+        Parameters:
+            state_dict (`dict`):
+                A standard state dict containing the lora layer parameters. The key shoult be prefixed with an
+                additional `text_encoder` to distinguish between unet lora layers.
+            network_alpha (`float`):
+                See `LoRALinearLayer` for more details.
+            text_encoder (`CLIPTextModel`):
+                The text encoder model to load the LoRA layers into.
+            lora_scale (`float`):
+                How much to scale the output of the lora linear layer before it is added with the output of the regular
+                lora layer.
+        """
+
         # If the serialization format is new (introduced in https://github.com/huggingface/diffusers/pull/2918),
         # then the `state_dict` keys should have `self.unet_name` and/or `self.text_encoder_name` as
         # their prefixes.