sd

src/diffusers/loaders/lora_pipeline.py

@@ -457,6 +457,8 @@ def save_lora_weights(
         weight_name: str = None,
         save_function: Callable = None,
         safe_serialization: bool = True,
+        unet_lora_adapter_metadata=None,
+        text_encoder_lora_adapter_metadata=None,
     ):
         r"""
         Save the LoRA parameters corresponding to the UNet and text encoder.
@@ -479,8 +481,11 @@ def save_lora_weights(
                 `DIFFUSERS_SAVE_MODE`.
             safe_serialization (`bool`, *optional*, defaults to `True`):
                 Whether to save the model using `safetensors` or the traditional PyTorch way with `pickle`.
+            unet_lora_adapter_metadata: TODO
+            text_encoder_lora_adapter_metadata: TODO
         """
         state_dict = {}
+        lora_adapter_metadata = {}
 
         if not (unet_lora_layers or text_encoder_lora_layers):
             raise ValueError("You must pass at least one of `unet_lora_layers` and `text_encoder_lora_layers`.")
@@ -491,6 +496,12 @@ def save_lora_weights(
         if text_encoder_lora_layers:
             state_dict.update(cls.pack_weights(text_encoder_lora_layers, cls.text_encoder_name))
 
+        if unet_lora_adapter_metadata is not None:
+            lora_adapter_metadata.update(cls.pack_weights(unet_lora_adapter_metadata, cls.unet_name))
+
+        if text_encoder_lora_adapter_metadata:
+            lora_adapter_metadata.update(cls.pack_weights(text_encoder_lora_adapter_metadata, cls.text_encoder_name))
+
         # Save the model
         cls.write_lora_layers(
             state_dict=state_dict,
@@ -499,6 +510,7 @@ def save_lora_weights(
             weight_name=weight_name,
             save_function=save_function,
             safe_serialization=safe_serialization,
+            lora_adapter_metadata=lora_adapter_metadata,
         )
 
     def fuse_lora(