diff --git a/api/onnx_web/diffusers/load.py b/api/onnx_web/diffusers/load.py
index 1198d85d..eeea47d4 100644
--- a/api/onnx_web/diffusers/load.py
+++ b/api/onnx_web/diffusers/load.py
@@ -201,11 +201,6 @@ def load_pipeline(
         vae_components = load_vae(server, device, model, params)
         components.update(vae_components)
 
-        # additional options for panorama pipeline
-        if params.is_panorama():
-            components["window"] = params.tiles // 8
-            components["stride"] = params.stride // 8
-
         pipeline_class = available_pipelines.get(pipeline, OnnxStableDiffusionPipeline)
         logger.debug("loading pretrained SD pipeline for %s", pipeline_class.__name__)
         pipe = pipeline_class.from_pretrained(
@@ -228,8 +223,15 @@ def load_pipeline(
                 components["text_encoder_2_session"], pipe
             )
 
+        if "tokenizer" in components:
+            pipe.tokenizer = components["tokenizer"]
+
+        if "tokenizer_2" in components:
+            pipe.tokenizer_2 = components["tokenizer_2"]
+
         if "unet_session" in components:
             # unload old UNet
+            logger.debug("unloading previous Unet")
             pipe.unet = None
             run_gc([device])
 
@@ -300,20 +302,25 @@ def load_text_encoders(
     torch_dtype,
     params: ImageParams,
 ):
+    text_encoder = load_model(path.join(model, "text_encoder", ONNX_MODEL))
     tokenizer = CLIPTokenizer.from_pretrained(
         model,
         subfolder="tokenizer",
         torch_dtype=torch_dtype,
     )
 
-    components = {}
-    components["tokenizer"] = tokenizer
-
-    text_encoder = load_model(path.join(model, "text_encoder", ONNX_MODEL))
-    text_encoder_2 = None
+    components = {
+        "tokenizer": tokenizer,
+    }
 
     if params.is_xl():
         text_encoder_2 = load_model(path.join(model, "text_encoder_2", ONNX_MODEL))
+        tokenizer_2 = CLIPTokenizer.from_pretrained(
+            model,
+            subfolder="tokenizer_2",
+            torch_dtype=torch_dtype,
+        )
+        components["tokenizer_2"] = tokenizer_2
 
     # blend embeddings, if any
     if embeddings is not None and len(embeddings) > 0:
@@ -339,6 +346,23 @@ def load_text_encoders(
                 )
             ),
         )
+        components["tokenizer"] = tokenizer
+
+        if params.is_xl():
+            text_encoder_2, tokenizer_2 = blend_textual_inversions(
+                server,
+                text_encoder_2,
+                tokenizer_2,
+                list(
+                    zip(
+                        embedding_models,
+                        embedding_weights,
+                        embedding_names,
+                        [None] * len(embedding_models),
+                    )
+                ),
+            )
+            components["tokenizer_2"] = tokenizer_2
 
     # blend LoRAs, if any
     if loras is not None and len(loras) > 0: