diff --git a/javascript/ext_embeddings.js b/javascript/ext_embeddings.js
index 9c7bd44..820aae4 100644
--- a/javascript/ext_embeddings.js
+++ b/javascript/ext_embeddings.js
@@ -11,12 +11,15 @@ class EmbeddingParser extends BaseTagParser {
             if (searchTerm.startsWith("v1") || searchTerm.startsWith("v2")) {
                 versionString = searchTerm.slice(0, 2);
                 searchTerm = searchTerm.slice(2);
+            } else if (searchTerm.startsWith("vxl")) {
+                versionString = searchTerm.slice(0, 3);
+                searchTerm = searchTerm.slice(3);
             }
 
             let filterCondition = x => x[0].toLowerCase().includes(searchTerm) || x[0].toLowerCase().replaceAll(" ", "_").includes(searchTerm);
 
             if (versionString)
-                tempResults = embeddings.filter(x => filterCondition(x) && x[2] && x[2] === versionString); // Filter by tagword
+                tempResults = embeddings.filter(x => filterCondition(x) && x[2] && x[2].toLowerCase() === versionString.toLowerCase()); // Filter by tagword
             else
                 tempResults = embeddings.filter(x => filterCondition(x)); // Filter by tagword
         } else {
diff --git a/scripts/tag_autocomplete_helper.py b/scripts/tag_autocomplete_helper.py
index 3e0c7ca..eca09f3 100644
--- a/scripts/tag_autocomplete_helper.py
+++ b/scripts/tag_autocomplete_helper.py
@@ -156,44 +156,30 @@ def get_embeddings(sd_model):
     # Version constants
     V1_SHAPE = 768
     V2_SHAPE = 1024
+    VXL_SHAPE = 2048
     emb_v1 = []
     emb_v2 = []
+    emb_vXL = []
     results = []
 
     try:
         # Get embedding dict from sd_hijack to separate v1/v2 embeddings
-        emb_type_a = sd_hijack.model_hijack.embedding_db.word_embeddings
-        emb_type_b = sd_hijack.model_hijack.embedding_db.skipped_embeddings
-        # Get the shape of the first item in the dict
-        emb_a_shape = -1
-        emb_b_shape = -1
-        if (len(emb_type_a) > 0):
-            emb_a_shape = next(iter(emb_type_a.items()))[1].shape
-        if (len(emb_type_b) > 0):
-            emb_b_shape = next(iter(emb_type_b.items()))[1].shape
+        loaded = sd_hijack.model_hijack.embedding_db.word_embeddings
+        skipped = sd_hijack.model_hijack.embedding_db.skipped_embeddings
 
         # Add embeddings to the correct list
-        if (emb_a_shape == V1_SHAPE):
-            emb_v1 = [(Path(v.filename), k, "v1") for (k,v) in emb_type_a.items() if v.filename is not None]
-        elif (emb_a_shape == V2_SHAPE):
-            emb_v2 = [(Path(v.filename), k, "v2") for (k,v) in emb_type_a.items() if v.filename is not None]
+        for key, emb in (loaded | skipped).items():
+            if (emb.filename is None):
+                continue
 
-        if (emb_b_shape == V1_SHAPE):
-            emb_v1 = [(Path(v.filename), k, "v1") for (k,v) in emb_type_b.items() if v.filename is not None]
-        elif (emb_b_shape == V2_SHAPE):
-            emb_v2 = [(Path(v.filename), k, "v2") for (k,v) in emb_type_b.items() if v.filename is not None]
+            if emb.shape == V1_SHAPE:
+                emb_v1.append((Path(emb.filename), key, "v1"))
+            elif emb.shape == V2_SHAPE:
+                emb_v2.append((Path(emb.filename), key, "v2"))
+            elif emb.shape == VXL_SHAPE:
+                emb_vXL.append((Path(emb.filename), key, "vXL"))
 
-        # Get shape of current model
-        #vec = sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
-        #model_shape = vec.shape[1]
-        # Show relevant entries at the top
-        #if (model_shape == V1_SHAPE):
-        #    results = [e + ",v1" for e in emb_v1] + [e + ",v2" for e in emb_v2]
-        #elif (model_shape == V2_SHAPE):
-        #    results = [e + ",v2" for e in emb_v2] + [e + ",v1" for e in emb_v1]
-        #else:
-        #    raise AttributeError # Fallback to old method
-        results = sort_models(emb_v1) + sort_models(emb_v2)
+        results = sort_models(emb_v1) + sort_models(emb_v2) + sort_models(emb_vXL)
     except AttributeError:
         print("tag_autocomplete_helper: Old webui version or unrecognized model shape, using fallback for embedding completion.")
         # Get a list of all embeddings in the folder