Update handle single blocks on _convert_xlabs_flux_lora_to_diffusers (#9915)

* Update handle single blocks on _convert_xlabs_flux_lora_to_diffusers to fix bug on updating keys and old_state_dict


---------
Co-authored-by: raul_ar <raul.moreno.salinas@autoretouch.com>
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

src/diffusers/loaders/lora_conversion_utils.py

@@ -636,10 +636,15 @@ def _convert_xlabs_flux_lora_to_diffusers(old_state_dict):
             block_num = re.search(r"single_blocks\.(\d+)", old_key).group(1)
             new_key = f"transformer.single_transformer_blocks.{block_num}"
 
-            if "proj_lora1" in old_key or "proj_lora2" in old_key:
+            if "proj_lora" in old_key:
                 new_key += ".proj_out"
-            elif "qkv_lora1" in old_key or "qkv_lora2" in old_key:
-                new_key += ".norm.linear"
+            elif "qkv_lora" in old_key and "up" not in old_key:
+                handle_qkv(
+                    old_state_dict,
+                    new_state_dict,
+                    old_key,
+                    [f"transformer.single_transformer_blocks.{block_num}.norm.linear"],
+                )
 
             if "down" in old_key:
                 new_key += ".lora_A.weight"

tests/lora/test_lora_layers_flux.py

@@ -282,3 +282,28 @@ class FluxLoRAIntegrationTests(unittest.TestCase):
         max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), out_slice)
 
         assert max_diff < 1e-3
+
+    def test_flux_xlabs_load_lora_with_single_blocks(self):
+        self.pipeline.load_lora_weights(
+            "salinasr/test_xlabs_flux_lora_with_singleblocks", weight_name="lora.safetensors"
+        )
+        self.pipeline.fuse_lora()
+        self.pipeline.unload_lora_weights()
+        self.pipeline.enable_model_cpu_offload()
+
+        prompt = "a wizard mouse playing chess"
+
+        out = self.pipeline(
+            prompt,
+            num_inference_steps=self.num_inference_steps,
+            guidance_scale=3.5,
+            output_type="np",
+            generator=torch.manual_seed(self.seed),
+        ).images
+        out_slice = out[0, -3:, -3:, -1].flatten()
+        expected_slice = np.array(
+            [0.04882812, 0.04101562, 0.04882812, 0.03710938, 0.02929688, 0.02734375, 0.0234375, 0.01757812, 0.0390625]
+        )
+        max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), out_slice)
+
+        assert max_diff < 1e-3