diff --git a/scripts/tag_autocomplete_helper.py b/scripts/tag_autocomplete_helper.py
index 781e819..f6b9dde 100644
--- a/scripts/tag_autocomplete_helper.py
+++ b/scripts/tag_autocomplete_helper.py
@@ -13,7 +13,7 @@ import gradio as gr
 import yaml
 from fastapi import FastAPI
 from fastapi.responses import Response, FileResponse, JSONResponse
-from modules import script_callbacks, sd_hijack, shared, hashes
+from modules import script_callbacks, sd_hijack, shared, hashes, sd_models
 from pydantic import BaseModel
 
 from scripts.model_keyword_support import (get_lora_simple_hash,
@@ -41,9 +41,32 @@ except (ImportError, ValueError, sqlite3.Error) as e:
     print(f"Tag Autocomplete: Tag frequency database error - \"{e}\"")
     db = None
 
+def get_embed_db(sd_model=None):
+    """Returns the embedding database, if available."""
+    try:
+        return sd_hijack.model_hijack.embedding_db
+    except Exception:
+        try: # sd next with diffusers backend
+            sdnext_model = sd_model if sd_model is not None else shared.sd_model
+            return sdnext_model.embedding_db
+        except Exception:
+            try: # forge webui
+                forge_model = sd_model if sd_model is not None else sd_models.model_data.get_sd_model()
+                if type(forge_model).__name__ == "FakeInitialModel":
+                    return None
+                else:
+                    processer = getattr(forge_model, "text_processing_engine", getattr(forge_model, "text_processing_engine_l"))
+                    return processer.embeddings
+            except Exception:
+                return None
+
 # Attempt to get embedding load function, using the same call as api.
 try:
-    load_textual_inversion_embeddings = sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings
+    embed_db = get_embed_db()
+    if embed_db is not None:
+        load_textual_inversion_embeddings = embed_db.load_textual_inversion_embeddings
+    else:
+        load_textual_inversion_embeddings = lambda *args, **kwargs: None
 except Exception as e: # Not supported.
     load_textual_inversion_embeddings = lambda *args, **kwargs: None
     print("Tag Autocomplete: Cannot reload embeddings instantly:", e)
@@ -190,19 +213,10 @@ def get_embeddings(sd_model):
     results = []
 
     try:
-        # The sd_model embedding_db reference only exists in sd.next with diffusers backend
-        try:
-            loaded_sdnext = sd_model.embedding_db.word_embeddings
-            skipped_sdnext = sd_model.embedding_db.skipped_embeddings
-        except (NameError, AttributeError):
-            loaded_sdnext = {}
-            skipped_sdnext = {}
+        embed_db = get_embed_db(sd_model)
         
-        # Get embedding dict from sd_hijack to separate v1/v2 embeddings
-        loaded = sd_hijack.model_hijack.embedding_db.word_embeddings
-        skipped = sd_hijack.model_hijack.embedding_db.skipped_embeddings
-        loaded = loaded | loaded_sdnext
-        skipped = skipped | skipped_sdnext
+        loaded = embed_db.word_embeddings
+        skipped = embed_db.skipped_embeddings
 
         # Add embeddings to the correct list
         for key, emb in (skipped | loaded).items():
@@ -430,8 +444,11 @@ def refresh_embeddings(force: bool, *args, **kwargs):
         # Fix for SD.Next infinite refresh loop due to gradio not updating after model load on demand.
         # This will just skip embedding loading if no model is loaded yet (or there really are no embeddings).
         # Try catch is just for safety incase sd_hijack access fails for some reason.
-        loaded = sd_hijack.model_hijack.embedding_db.word_embeddings
-        skipped = sd_hijack.model_hijack.embedding_db.skipped_embeddings
+        embed_db = get_embed_db()
+        if embed_db is None:
+            return
+        loaded = embed_db.word_embeddings
+        skipped = embed_db.skipped_embeddings
         if len((loaded | skipped)) > 0:
             load_textual_inversion_embeddings(force_reload=force)
             get_embeddings(None)