diff --git a/scripts/training/finetune-ldm-lora.py b/scripts/training/finetune-ldm-lora.py
index 40ad8a5..11d02e4 100644
--- a/scripts/training/finetune-ldm-lora.py
+++ b/scripts/training/finetune-ldm-lora.py
@@ -82,7 +82,7 @@ class LoadLoras(Callback[LoraLatentDiffusionTrainer]):
             model_targets: list[LoraTarget] = getattr(lora_config, f"{model_name}_targets")
             adapter = LoraAdapter[type(model)](
                 model,
-                sub_targets=[x for target in model_targets for x in lora_targets(model, target)],
+                sub_targets=lora_targets(model, model_targets),
                 rank=lora_config.rank,
             )
             for sub_adapter, _ in adapter.sub_adapters:
diff --git a/src/refiners/foundationals/latent_diffusion/lora.py b/src/refiners/foundationals/latent_diffusion/lora.py
index 910da40..d5ddd25 100644
--- a/src/refiners/foundationals/latent_diffusion/lora.py
+++ b/src/refiners/foundationals/latent_diffusion/lora.py
@@ -47,7 +47,15 @@ class LoraTarget(str, Enum):
                 return TransformerLayer
 
 
-def lora_targets(module: fl.Chain, target: LoraTarget) -> Iterator[tuple[fl.Linear, fl.Chain]]:
+def lora_targets(
+    module: fl.Chain,
+    target: LoraTarget | list[LoraTarget],
+) -> Iterator[tuple[fl.Linear, fl.Chain]]:
+    if isinstance(target, list):
+        for t in target:
+            yield from lora_targets(module, t)
+        return
+
     lookup_class = fl.Linear if target == LoraTarget.Self else target.get_class()
 
     if isinstance(module, SD1UNet):
@@ -100,7 +108,7 @@ class SD1LoraAdapter(fl.Chain, Adapter[StableDiffusion_1]):
             self.sub_adapters.append(
                 LoraAdapter[type(model)](
                     model,
-                    sub_targets=[x for target in model_targets for x in lora_targets(model, target)],
+                    sub_targets=lora_targets(model, model_targets),
                     scale=scale,
                     weights=lora_weights,
                 )