fix use_dora

Author: SahilCarterr

examples/dreambooth/train_dreambooth_lora_sdxl.py

@@ -68,6 +68,7 @@
     convert_state_dict_to_kohya,
     convert_unet_state_dict_to_peft,
     is_wandb_available,
+    is_peft_version
 )
 from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
 from diffusers.utils.import_utils import is_xformers_available
@@ -1183,25 +1184,39 @@ def main(args):
             text_encoder_one.gradient_checkpointing_enable()
             text_encoder_two.gradient_checkpointing_enable()
 
+    def get_lora_config(rank, use_dora, target_modules):
+        base_config = {
+            "r": rank,
+            "lora_alpha": rank,
+            "init_lora_weights": "gaussian",
+            "target_modules": target_modules,
+        }
+        if use_dora and is_peft_version("<", "0.9.0"):
+            raise ValueError(
+                        "You need `peft` 0.9.0 at least to use DoRA-enabled LoRAs. Please upgrade your installation of `peft`."
+                    )
+        else:
+            base_config["use_dora"] = True
+        
+        return LoraConfig(**base_config)
+        
     # now we will add new LoRA weights to the attention layers
-    unet_lora_config = LoraConfig(
-        r=args.rank,
+    unet_target_modules = ["to_k", "to_q", "to_v", "to_out.0"]
+    unet_lora_config = get_lora_config(
+        rank=args.rank,
         use_dora=args.use_dora,
-        lora_alpha=args.rank,
-        init_lora_weights="gaussian",
-        target_modules=["to_k", "to_q", "to_v", "to_out.0"],
+        target_modules=unet_target_modules
     )
     unet.add_adapter(unet_lora_config)
 
     # The text encoder comes from 🤗 transformers, so we cannot directly modify it.
     # So, instead, we monkey-patch the forward calls of its attention-blocks.
     if args.train_text_encoder:
-        text_lora_config = LoraConfig(
-            r=args.rank,
+        text_target_modules = ["q_proj", "k_proj", "v_proj", "out_proj"]
+        text_lora_config = get_lora_config(
+            rank=args.rank,
             use_dora=args.use_dora,
-            lora_alpha=args.rank,
-            init_lora_weights="gaussian",
-            target_modules=["q_proj", "k_proj", "v_proj", "out_proj"],
+            target_modules=text_target_modules
         )
         text_encoder_one.add_adapter(text_lora_config)
         text_encoder_two.add_adapter(text_lora_config)