Extract TI loading logic into util, disallow it from ever failing a generation

invokeai/app/util/ti_utils.py

@@ -1,8 +1,44 @@
 import re
+from typing import List, Tuple
+
+from invokeai.backend.model_manager.config import BaseModelType, ModelType
+from invokeai.backend.textual_inversion import TextualInversionModelRaw
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.services.model_records import UnknownModelException
+import invokeai.backend.util.logging as logger
 
 
-def extract_ti_triggers_from_prompt(prompt: str) -> list[str]:
-    ti_triggers = []
+def extract_ti_triggers_from_prompt(prompt: str) -> List[str]:
+    ti_triggers: List[str] = []
     for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
-        ti_triggers.append(trigger)
+        ti_triggers.append(str(trigger))
     return ti_triggers
+
+def generate_ti_list(prompt: str, base: BaseModelType, context: InvocationContext) -> List[Tuple[str, TextualInversionModelRaw]]:
+    ti_list: List[Tuple[str, TextualInversionModelRaw]] = []
+    for trigger in extract_ti_triggers_from_prompt(prompt):
+        name_or_key = trigger[1:-1]
+        try:
+            loaded_model = context.models.load(key=name_or_key)
+            model = loaded_model.model
+            assert isinstance(model, TextualInversionModelRaw)
+            assert loaded_model.config.base == base
+            ti_list.append((name_or_key, model))
+        except UnknownModelException:
+            try:
+                loaded_model = context.models.load_by_attrs(
+                    model_name=name_or_key, base_model=base, model_type=ModelType.TextualInversion
+                )
+                model = loaded_model.model
+                assert isinstance(model, TextualInversionModelRaw)
+                assert loaded_model.config.base == base
+                ti_list.append((name_or_key, model))
+            except UnknownModelException:
+                pass
+        except ValueError:
+            logger.warning(f'trigger: "{trigger}" more than one similarly-named textual inversion models')
+        except AssertionError:
+            logger.warning(f'trigger: "{trigger}" not a valid textual inversion model for this graph')
+        except Exception:
+            logger.warning(f'Failed to load TI model for trigger: "{trigger}"')
+    return ti_list