Update model_sym_links to indicate it requires admin/developer mode in windows

invokeai/app/invocations/compel.py

@@ -3,9 +3,8 @@ from typing import Iterator, List, Optional, Tuple, Union
 import torch
 from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
-from transformers import CLIPTokenizer
+from transformers import CLIPTextModel, CLIPTokenizer
 
-import invokeai.backend.util.logging as logger
 from invokeai.app.invocations.fields import (
     FieldDescriptions,
     Input,
@@ -14,11 +13,9 @@ from invokeai.app.invocations.fields import (
     UIComponent,
 )
 from invokeai.app.invocations.primitives import ConditioningOutput
-from invokeai.app.services.model_records import UnknownModelException
 from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.app.util.ti_utils import extract_ti_triggers_from_prompt
+from invokeai.app.util.ti_utils import generate_ti_list
 from invokeai.backend.lora import LoRAModelRaw
-from invokeai.backend.model_manager import ModelType
 from invokeai.backend.model_patcher import ModelPatcher
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
@@ -26,7 +23,6 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     ExtraConditioningInfo,
     SDXLConditioningInfo,
 )
-from invokeai.backend.textual_inversion import TextualInversionModelRaw
 from invokeai.backend.util.devices import torch_dtype
 
 from .baseinvocation import (
@@ -70,7 +66,11 @@ class CompelInvocation(BaseInvocation):
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
         tokenizer_info = context.models.load(**self.clip.tokenizer.model_dump())
+        tokenizer_model = tokenizer_info.model
+        assert isinstance(tokenizer_model, CLIPTokenizer)
         text_encoder_info = context.models.load(**self.clip.text_encoder.model_dump())
+        text_encoder_model = text_encoder_info.model
+        assert isinstance(text_encoder_model, CLIPTextModel)
 
         def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
             for lora in self.clip.loras:
@@ -82,21 +82,10 @@ class CompelInvocation(BaseInvocation):
 
         # loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
-        ti_list = []
-        for trigger in extract_ti_triggers_from_prompt(self.prompt):
-            name = trigger[1:-1]
-            try:
-                loaded_model = context.models.load(key=name).model
-                assert isinstance(loaded_model, TextualInversionModelRaw)
-                ti_list.append((name, loaded_model))
-            except UnknownModelException:
-                # print(e)
-                # import traceback
-                # print(traceback.format_exc())
-                print(f'Warn: trigger: "{trigger}" not found')
+        ti_list = generate_ti_list(self.prompt, text_encoder_info.config.base, context)
 
         with (
-            ModelPatcher.apply_ti(tokenizer_info.model, text_encoder_info.model, ti_list) as (
+            ModelPatcher.apply_ti(tokenizer_model, text_encoder_model, ti_list) as (
                 tokenizer,
                 ti_manager,
             ),
@@ -104,8 +93,9 @@ class CompelInvocation(BaseInvocation):
             # Apply the LoRA after text_encoder has been moved to its target device for faster patching.
             ModelPatcher.apply_lora_text_encoder(text_encoder, _lora_loader()),
             # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
-            ModelPatcher.apply_clip_skip(text_encoder_info.model, self.clip.skipped_layers),
+            ModelPatcher.apply_clip_skip(text_encoder_model, self.clip.skipped_layers),
         ):
+            assert isinstance(text_encoder, CLIPTextModel)
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
@@ -155,7 +145,11 @@ class SDXLPromptInvocationBase:
         zero_on_empty: bool,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[ExtraConditioningInfo]]:
         tokenizer_info = context.models.load(**clip_field.tokenizer.model_dump())
+        tokenizer_model = tokenizer_info.model
+        assert isinstance(tokenizer_model, CLIPTokenizer)
         text_encoder_info = context.models.load(**clip_field.text_encoder.model_dump())
+        text_encoder_model = text_encoder_info.model
+        assert isinstance(text_encoder_model, CLIPTextModel)
 
         # return zero on empty
         if prompt == "" and zero_on_empty:
@@ -189,25 +183,10 @@ class SDXLPromptInvocationBase:
 
         # loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
-        ti_list = []
-        for trigger in extract_ti_triggers_from_prompt(prompt):
-            name = trigger[1:-1]
-            try:
-                ti_model = context.models.load_by_attrs(
-                    model_name=name, base_model=text_encoder_info.config.base, model_type=ModelType.TextualInversion
-                ).model
-                assert isinstance(ti_model, TextualInversionModelRaw)
-                ti_list.append((name, ti_model))
-            except UnknownModelException:
-                # print(e)
-                # import traceback
-                # print(traceback.format_exc())
-                logger.warning(f'trigger: "{trigger}" not found')
-            except ValueError:
-                logger.warning(f'trigger: "{trigger}" more than one similarly-named textual inversion models')
+        ti_list = generate_ti_list(prompt, text_encoder_info.config.base, context)
 
         with (
-            ModelPatcher.apply_ti(tokenizer_info.model, text_encoder_info.model, ti_list) as (
+            ModelPatcher.apply_ti(tokenizer_model, text_encoder_model, ti_list) as (
                 tokenizer,
                 ti_manager,
             ),
@@ -215,8 +194,9 @@ class SDXLPromptInvocationBase:
             # Apply the LoRA after text_encoder has been moved to its target device for faster patching.
             ModelPatcher.apply_lora(text_encoder, _lora_loader(), lora_prefix),
             # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
-            ModelPatcher.apply_clip_skip(text_encoder_info.model, clip_field.skipped_layers),
+            ModelPatcher.apply_clip_skip(text_encoder_model, clip_field.skipped_layers),
         ):
+            assert isinstance(text_encoder, CLIPTextModel)
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,

invokeai/app/services/config/config_default.py

@@ -197,6 +197,7 @@ class Categories(object):
     Development: JsonDict = {"category": "Development"}
     Other: JsonDict = {"category": "Other"}
     ModelCache: JsonDict = {"category": "Model Cache"}
+    ModelImport: JsonDict = {"category": "Model Import"}
     Device: JsonDict = {"category": "Device"}
     Generation: JsonDict = {"category": "Generation"}
     Queue: JsonDict = {"category": "Queue"}
@@ -286,7 +287,8 @@ class InvokeAIAppConfig(InvokeAISettings):
     node_cache_size     : int = Field(default=512, description="How many cached nodes to keep in memory", json_schema_extra=Categories.Nodes)
 
     # MODEL IMPORT
-    civitai_api_key       : Optional[str] = Field(default=os.environ.get("CIVITAI_API_KEY"), description="API key for CivitAI", json_schema_extra=Categories.Other)
+    civitai_api_key     : Optional[str] = Field(default=os.environ.get("CIVITAI_API_KEY"), description="API key for CivitAI", json_schema_extra=Categories.ModelImport)
+    model_sym_links     : bool = Field(default=False, description="If true, create symbolic links to models instead of copying them. [REQUIRES ADMIN PERMISSIONS OR DEVELOPER MODE IN WINDOWS]", json_schema_extra=Categories.ModelImport)
 
     # DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES
     always_use_cpu      : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", json_schema_extra=Categories.MemoryPerformance)

invokeai/app/services/model_install/model_install_default.py

@@ -507,6 +507,9 @@ class ModelInstallService(ModelInstallServiceBase):
         if old_path == new_path:
             return old_path
         new_path.parent.mkdir(parents=True, exist_ok=True)
+        if self.app_config.model_sym_links:
+            new_path.symlink_to(old_path, target_is_directory=old_path.is_dir())
+        else:
             if old_path.is_dir():
                 copytree(old_path, new_path)
             else:

invokeai/app/util/ti_utils.py

@@ -1,8 +1,47 @@
 import re
+from typing import List, Tuple
+
+import invokeai.backend.util.logging as logger
+from invokeai.app.services.model_records import UnknownModelException
+from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.backend.model_manager.config import BaseModelType, ModelType
+from invokeai.backend.textual_inversion import TextualInversionModelRaw
 
 
-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

invokeai/backend/model_manager/metadata/fetch/civitai.py

@@ -160,7 +160,7 @@ class CivitaiMetadataFetch(ModelMetadataFetchBase):
             nsfw=model_json["nsfw"],
             restrictions=LicenseRestrictions(
                 AllowNoCredit=model_json["allowNoCredit"],
-                AllowCommercialUse=CommercialUsage(model_json["allowCommercialUse"]),
+                AllowCommercialUse={CommercialUsage(x) for x in model_json["allowCommercialUse"]},
                 AllowDerivatives=model_json["allowDerivatives"],
                 AllowDifferentLicense=model_json["allowDifferentLicense"],
             ),

invokeai/backend/model_manager/metadata/metadata_base.py

@@ -54,8 +54,8 @@ class LicenseRestrictions(BaseModel):
     AllowDifferentLicense: bool = Field(
         description="if true, derivatives of this model be redistributed under a different license", default=False
     )
-    AllowCommercialUse: Optional[CommercialUsage] = Field(
-        description="Type of commercial use allowed or 'No' if no commercial use is allowed.", default=None
+    AllowCommercialUse: Optional[Set[CommercialUsage] | CommercialUsage] = Field(
+        description="Type of commercial use allowed if no commercial use is allowed.", default=None
     )
 
 
@@ -142,7 +142,10 @@ class CivitaiMetadata(ModelMetadataWithFiles):
         if self.restrictions.AllowCommercialUse is None:
             return False
         else:
-            return self.restrictions.AllowCommercialUse != CommercialUsage("None")
+            # accommodate schema change
+            acu = self.restrictions.AllowCommercialUse
+            commercial_usage = acu if isinstance(acu, set) else {acu}
+            return CommercialUsage.No not in commercial_usage
 
     @property
     def allow_derivatives(self) -> bool:

tests/backend/model_manager/model_metadata/test_model_metadata.py

@@ -133,7 +133,7 @@ def test_metadata_civitai_fetch(mm2_session: Session) -> None:
     assert metadata.id == 215485
     assert metadata.author == "test_author"  # note that this is not the same as the original from Civitai
     assert metadata.allow_commercial_use  # changed to make sure we are reading locally not remotely
-    assert metadata.restrictions.AllowCommercialUse == CommercialUsage("RentCivit")
+    assert CommercialUsage("RentCivit") in metadata.restrictions.AllowCommercialUse
     assert metadata.version_id == 242807
     assert metadata.tags == {"tool", "turbo", "sdxl turbo"}