fix(api): avoid loading encoder twice when using LoRAs and inversions together

api/onnx_web/diffusers/load.py

@@ -236,14 +236,16 @@ def load_pipeline(
                 list(zip(inversion_models, inversion_weights, inversion_names)),
             )
 
+            components["tokenizer"] = tokenizer
+
             # should be pretty small and should not need external data
+            if loras is None or len(loras) == 0:
                 components["text_encoder"] = OnnxRuntimeModel(
                     OnnxRuntimeModel.load_model(
                         text_encoder.SerializeToString(),
                         provider=device.ort_provider(),
                     )
                 )
-            components["tokenizer"] = tokenizer
 
         # test LoRA blending
         if loras is not None and len(loras) > 0: