Replace deepcopy with a pickle roundtrip in apply_ti(...) to improve speed.

Author: RyanJDick

invokeai/backend/model_management/lora.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-import copy
+import pickle
 from contextlib import contextmanager
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple, Union
@@ -165,7 +165,13 @@ def apply_ti(
         new_tokens_added = None
 
         try:
-            ti_tokenizer = copy.deepcopy(tokenizer)
+            # HACK: The CLIPTokenizer API does not include a way to remove tokens after calling add_tokens(...). As a
+            # workaround, we create a full copy of `tokenizer` so that its original behavior can be restored after
+            # exiting this `apply_ti(...)` context manager.
+            #
+            # In a previous implementation, the deep copy was obtained with `ti_tokenizer = copy.deepcopy(tokenizer)`,
+            # but a pickle roundtrip was found to be much faster (1 sec vs. 0.05 secs).
+            ti_tokenizer = pickle.loads(pickle.dumps(tokenizer))
             ti_manager = TextualInversionManager(ti_tokenizer)
             init_tokens_count = text_encoder.resize_token_embeddings(None).num_embeddings
 
@@ -439,7 +445,13 @@ def apply_ti(
         orig_embeddings = None
 
         try:
-            ti_tokenizer = copy.deepcopy(tokenizer)
+            # HACK: The CLIPTokenizer API does not include a way to remove tokens after calling add_tokens(...). As a
+            # workaround, we create a full copy of `tokenizer` so that its original behavior can be restored after
+            # exiting this `apply_ti(...)` context manager.
+            #
+            # In a previous implementation, the deep copy was obtained with `ti_tokenizer = copy.deepcopy(tokenizer)`,
+            # but a pickle roundtrip was found to be much faster (1 sec vs. 0.05 secs).
+            ti_tokenizer = pickle.loads(pickle.dumps(tokenizer))
             ti_manager = TextualInversionManager(ti_tokenizer)
 
             def _get_trigger(ti_name, index):