diff --git a/train_textual_inversion.py b/train_textual_inversion.py
index 8575698d..ad26870f 100644
--- a/train_textual_inversion.py
+++ b/train_textual_inversion.py
@@ -435,10 +435,13 @@ class TextualInversionTrainer:
         text_encoders = [accelerator.prepare(text_encoder) for text_encoder in text_encoders]
 
         index_no_updates_list = []
+        index_updates_list = []
         orig_embeds_params_list = []
         for tokenizer, token_ids, text_encoder in zip(tokenizers, token_ids_list, text_encoders):
             index_no_updates = torch.arange(len(tokenizer)) < token_ids[0]
             index_no_updates_list.append(index_no_updates)
+            index_updates = ~index_no_updates
+            index_updates_list.append(index_updates)
 
             # accelerator.print(len(index_no_updates), torch.sum(index_no_updates))
             orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.detach().clone()
@@ -634,8 +637,31 @@ class TextualInversionTrainer:
                     lr_scheduler.step()
                     optimizer.zero_grad(set_to_none=True)
 
-                    # Let's make sure we don't update any embedding weights besides the newly added token
                     with torch.no_grad():
+                        # normalize embeddings
+                        if args.clip_ti_decay:
+                            for text_encoder, index_updates in zip(
+                                text_encoders, index_updates_list
+                            ):
+                                pre_norm = (
+                                    text_encoder.get_input_embeddings()
+                                    .weight[index_updates, :]
+                                    .norm(dim=-1, keepdim=True)
+                                )
+
+                                lambda_ = min(1.0, 100 * lr_scheduler.get_last_lr()[0])
+                                text_encoder.get_input_embeddings().weight[
+                                    index_updates
+                                ] = torch.nn.functional.normalize(
+                                    text_encoder.get_input_embeddings().weight[
+                                        index_updates, :
+                                    ],
+                                    dim=-1,
+                                ) * (
+                                    pre_norm + lambda_ * (args.clip_ti_decay - pre_norm)
+                                )
+
+                        # Let's make sure we don't update any embedding weights besides the newly added token
                         for text_encoder, orig_embeds_params, index_no_updates in zip(
                             text_encoders, orig_embeds_params_list, index_no_updates_list
                         ):
@@ -818,6 +844,12 @@ def setup_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="do not use fp16/bf16 VAE in mixed precision (use float VAE) / mixed precisionでも fp16/bf16 VAEを使わずfloat VAEを使う",
     )
+    parser.add_argument(
+        "--clip_ti_decay",
+        type=float,
+        default=None,
+        help="Keep the norm of the textual inversion intact (0.4 is a good starting point)",
+    )
 
     return parser