diff --git a/src/diffusers/loaders/lora.py b/src/diffusers/loaders/lora.py
index 8b42f412add1..94910956d022 100644
--- a/src/diffusers/loaders/lora.py
+++ b/src/diffusers/loaders/lora.py
@@ -30,6 +30,7 @@
     _get_model_file,
     convert_state_dict_to_diffusers,
     convert_state_dict_to_peft,
+    convert_unet_state_dict_to_peft,
     delete_adapter_layers,
     get_adapter_name,
     get_peft_kwargs,
@@ -1543,6 +1544,11 @@ def load_lora_into_transformer(cls, state_dict, transformer, adapter_name=None,
         }
 
         if len(state_dict.keys()) > 0:
+            # check with first key if is not in peft format
+            first_key = next(iter(state_dict.keys()))
+            if "lora_A" not in first_key:
+                state_dict = convert_unet_state_dict_to_peft(state_dict)
+
             if adapter_name in getattr(transformer, "peft_config", {}):
                 raise ValueError(
                     f"Adapter name {adapter_name} already in use in the transformer - please select a new adapter name."
diff --git a/tests/lora/test_lora_layers_sd3.py b/tests/lora/test_lora_layers_sd3.py
index e76c65778174..2589625ec6ac 100644
--- a/tests/lora/test_lora_layers_sd3.py
+++ b/tests/lora/test_lora_layers_sd3.py
@@ -27,7 +27,7 @@
     SD3Transformer2DModel,
     StableDiffusion3Pipeline,
 )
-from diffusers.utils.testing_utils import is_peft_available, require_peft_backend, torch_device
+from diffusers.utils.testing_utils import is_peft_available, require_peft_backend, require_torch_gpu, torch_device
 
 
 if is_peft_available():
@@ -287,3 +287,24 @@ def test_simple_inference_with_transformer_fuse_unfuse(self):
         self.assertTrue(
             np.allclose(ouput_fused, output_unfused_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output"
         )
+
+    @require_torch_gpu
+    def test_sd3_lora(self):
+        """
+        Test loading the loras that are saved with the diffusers and peft formats.
+        Related PR: https://github.com/huggingface/diffusers/pull/8584
+        """
+        components = self.get_dummy_components()
+
+        pipe = self.pipeline_class(**components)
+        pipe = pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        lora_model_id = "hf-internal-testing/tiny-sd3-loras"
+
+        lora_filename = "lora_diffusers_format.safetensors"
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
+        pipe.unload_lora_weights()
+
+        lora_filename = "lora_peft_format.safetensors"
+        pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)