diff --git a/api/onnx_web/diffusers/load.py b/api/onnx_web/diffusers/load.py
index 2773922c..60ef7090 100644
--- a/api/onnx_web/diffusers/load.py
+++ b/api/onnx_web/diffusers/load.py
@@ -195,9 +195,7 @@ def load_pipeline(
         )
         components.update(encoder_components)
 
-        unet_components = load_unet(
-            server, device, model, loras, unet_type, params
-        )
+        unet_components = load_unet(server, device, model, loras, unet_type, params)
         components.update(unet_components)
 
         vae_components = load_vae(server, device, model, params)
@@ -330,23 +328,33 @@ def load_text_encoders(
 
     # should be pretty small and should not need external data
     if loras is None or len(loras) == 0:
-        # TODO: handle XL encoders
-        components["text_encoder"] = OnnxRuntimeModel(
-            OnnxRuntimeModel.load_model(
-                text_encoder.SerializeToString(),
-                provider=device.ort_provider("text-encoder"),
-                sess_options=device.sess_options(),
-            )
-        )
-
         if params.is_xl():
+            text_encoder = path.join(model, "text_encoder", ONNX_MODEL)
+            text_encoder_opts = device.sess_options(cache=False)
+            text_encoder_session = InferenceSession(
+                text_encoder.SerializeToString(),
+                providers=[device.ort_provider("text-encoder")],
+                sess_options=text_encoder_opts,
+            )
+
+            text_encoder_session._model_path = path.join(model, "text_encoder")
+
+            text_encoder_2 = path.join(model, "text_encoder_2", ONNX_MODEL)
+            text_encoder_2_opts = device.sess_options(cache=False)
             text_encoder_2_session = InferenceSession(
                 text_encoder_2.SerializeToString(),
                 providers=[device.ort_provider("text-encoder")],
                 sess_options=text_encoder_2_opts,
             )
             text_encoder_2_session._model_path = path.join(model, "text_encoder_2")
-            components["text_encoder_2_session"] = text_encoder_2_session
+        else:
+            components["text_encoder"] = OnnxRuntimeModel(
+                OnnxRuntimeModel.load_model(
+                    text_encoder.SerializeToString(),
+                    provider=device.ort_provider("text-encoder"),
+                    sess_options=device.sess_options(),
+                )
+            )
 
     else:
         # blend and load text encoder
@@ -498,9 +506,7 @@ def load_vae(server, device, model, params):
                 provider=device.ort_provider("vae"),
                 sess_options=device.sess_options(),
             )
-            components[
-                "vae_decoder_session"
-            ]._model_path = vae_decoder
+            components["vae_decoder_session"]._model_path = vae_decoder
 
             logger.debug("loading VAE encoder from %s", vae_encoder)
             components["vae_encoder_session"] = OnnxRuntimeModel.load_model(
@@ -508,9 +514,7 @@ def load_vae(server, device, model, params):
                 provider=device.ort_provider("vae"),
                 sess_options=device.sess_options(),
             )
-            components[
-                "vae_encoder_session"
-            ]._model_path = vae_encoder
+            components["vae_encoder_session"]._model_path = vae_encoder
 
         else:
             logger.debug("loading VAE decoder from %s", vae_decoder)