Next: Allow in place local installs of models

invokeai/app/api/routers/model_manager.py

@@ -451,6 +451,7 @@ async def add_model_record(
 )
 async def install_model(
     source: str = Query(description="Model source to install, can be a local path, repo_id, or remote URL"),
+    inplace: Optional[bool] = Query(description="Whether or not to install a local model in place", default=False),
     # TODO(MM2): Can we type this?
     config: Optional[Dict[str, Any]] = Body(
         description="Dict of fields that override auto-probed values in the model config record, such as name, description and prediction_type ",
@@ -493,6 +494,7 @@ async def install_model(
             source=source,
             config=config,
             access_token=access_token,
+            inplace=bool(inplace),
         )
         logger.info(f"Started installation of {source}")
     except UnknownModelException as e:

invokeai/app/api_app.py

@@ -2,6 +2,7 @@
 # which are imported/used before parse_args() is called will get the default config values instead of the
 # values from the command line or config file.
 import sys
+from contextlib import asynccontextmanager
 
 from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
 from invokeai.version.invokeai_version import __version__
@@ -71,9 +72,25 @@ logger = InvokeAILogger.get_logger(config=app_config)
 mimetypes.add_type("application/javascript", ".js")
 mimetypes.add_type("text/css", ".css")
 
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # Add startup event to load dependencies
+    ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
+    yield
+    # Shut down threads
+    ApiDependencies.shutdown()
+
+
 # Create the app
 # TODO: create this all in a method so configuration/etc. can be passed in?
-app = FastAPI(title="Invoke - Community Edition", docs_url=None, redoc_url=None, separate_input_output_schemas=False)
+app = FastAPI(
+    title="Invoke - Community Edition",
+    docs_url=None,
+    redoc_url=None,
+    separate_input_output_schemas=False,
+    lifespan=lifespan,
+)
 
 # Add event handler
 event_handler_id: int = id(app)
@@ -96,18 +113,6 @@ app.add_middleware(
 app.add_middleware(GZipMiddleware, minimum_size=1000)
 
 
-# Add startup event to load dependencies
-@app.on_event("startup")
-async def startup_event() -> None:
-    ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
-
-
-# Shut down threads
-@app.on_event("shutdown")
-async def shutdown_event() -> None:
-    ApiDependencies.shutdown()
-
-
 # Include all routers
 app.include_router(utilities.utilities_router, prefix="/api")
 app.include_router(model_manager.model_manager_router, prefix="/api")

invokeai/app/invocations/compel.py

@@ -1,17 +1,11 @@
-from typing import Iterator, List, Optional, Tuple, Union
+from typing import Iterator, List, Optional, Tuple, Union, cast
 
 import torch
 from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
-from transformers import CLIPTextModel, CLIPTokenizer
+from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 
-from invokeai.app.invocations.fields import (
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIComponent
-    FieldDescriptions,
-    Input,
-    InputField,
-    OutputField,
-    UIComponent,
-)
 from invokeai.app.invocations.primitives import ConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.ti_utils import generate_ti_list
@@ -25,12 +19,7 @@ from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
 )
 from invokeai.backend.util.devices import torch_dtype
 
-from .baseinvocation import (
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
-    BaseInvocation,
-    BaseInvocationOutput,
-    invocation,
-    invocation_output,
-)
 from .model import ClipField
 
 # unconditioned: Optional[torch.Tensor]
@@ -149,7 +138,7 @@ class SDXLPromptInvocationBase:
         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)
+        assert isinstance(text_encoder_model, (CLIPTextModel, CLIPTextModelWithProjection))
 
         # return zero on empty
         if prompt == "" and zero_on_empty:
@@ -196,7 +185,8 @@ class SDXLPromptInvocationBase:
             # Apply CLIP Skip after LoRA to prevent LoRA application from failing on skipped layers.
             ModelPatcher.apply_clip_skip(text_encoder_model, clip_field.skipped_layers),
         ):
-            assert isinstance(text_encoder, CLIPTextModel)
+            assert isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection))
+            text_encoder = cast(CLIPTextModel, text_encoder)
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,

invokeai/app/services/model_install/model_install_default.py

@@ -181,13 +181,14 @@ class ModelInstallService(ModelInstallServiceBase):
         source: str,
         config: Optional[Dict[str, Any]] = None,
         access_token: Optional[str] = None,
+        inplace: bool = False,
     ) -> ModelInstallJob:
         variants = "|".join(ModelRepoVariant.__members__.values())
         hf_repoid_re = f"^([^/:]+/[^/:]+)(?::({variants})?(?::/?([^:]+))?)?$"
         source_obj: Optional[StringLikeSource] = None
 
         if Path(source).exists():  # A local file or directory
-            source_obj = LocalModelSource(path=Path(source))
+            source_obj = LocalModelSource(path=Path(source), inplace=inplace)
         elif match := re.match(hf_repoid_re, source):
             source_obj = HFModelSource(
                 repo_id=match.group(1),

invokeai/backend/model_manager/search.py

@@ -28,6 +28,7 @@ from typing import Callable, Optional, Set, Union
 
 from pydantic import BaseModel, Field
 
+from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.backend.util.logging import InvokeAILogger
 
 default_logger: Logger = InvokeAILogger.get_logger()
@@ -117,13 +118,10 @@ class ModelSearch(ModelSearchBase):
     """
 
     models_found: Set[Path] = Field(default_factory=set)
-    scanned_dirs: Set[Path] = Field(default_factory=set)
+    config: InvokeAIAppConfig = InvokeAIAppConfig.get_config()
-    pruned_paths: Set[Path] = Field(default_factory=set)
 
     def search_started(self) -> None:
         self.models_found = set()
-        self.scanned_dirs = set()
-        self.pruned_paths = set()
         if self.on_search_started:
             self.on_search_started(self._directory)
 
@@ -139,53 +137,53 @@ class ModelSearch(ModelSearchBase):
 
     def search(self, directory: Union[Path, str]) -> Set[Path]:
         self._directory = Path(directory)
+        if not self._directory.is_absolute():
+            self._directory = self.config.models_path / self._directory
         self.stats = SearchStats()  # zero out
         self.search_started()  # This will initialize _models_found to empty
-        self._walk_directory(directory)
+        self._walk_directory(self._directory)
         self.search_completed()
         return self.models_found
 
-    def _walk_directory(self, path: Union[Path, str]) -> None:
+    def _walk_directory(self, path: Union[Path, str], max_depth: int = 20) -> None:
-        for root, dirs, files in os.walk(path, followlinks=True):
+        absolute_path = Path(path)
-            # don't descend into directories that start with a "."
+        if (
-            # to avoid the Mac .DS_STORE issue.
+            len(absolute_path.parts) - len(self._directory.parts) > max_depth
-            if str(Path(root).name).startswith("."):
+            or not absolute_path.exists()
-                self.pruned_paths.add(Path(root))
+            or absolute_path.parent in self.models_found
-            if any(Path(root).is_relative_to(x) for x in self.pruned_paths):
+        ):
-                continue
+            return
+        entries = os.scandir(absolute_path.as_posix())
+        entries = [entry for entry in entries if not entry.name.startswith(".")]
+        dirs = [entry for entry in entries if entry.is_dir()]
+        file_names = [entry.name for entry in entries if entry.is_file()]
+        if any(
+            x in file_names
+            for x in [
+                "config.json",
+                "model_index.json",
+                "learned_embeds.bin",
+                "pytorch_lora_weights.bin",
+                "image_encoder.txt",
+            ]
+        ):
+            try:
+                self.model_found(absolute_path)
+                return
+            except KeyboardInterrupt:
+                raise
+            except Exception as e:
+                self.logger.warning(str(e))
+                return
 
-            self.stats.items_scanned += len(dirs) + len(files)
+        for n in file_names:
-            for d in dirs:
+            if n.endswith((".ckpt", ".bin", ".pth", ".safetensors", ".pt")):
-                path = Path(root) / d
+                try:
-                if path.parent in self.scanned_dirs:
+                    self.model_found(absolute_path / n)
-                    self.scanned_dirs.add(path)
+                except KeyboardInterrupt:
-                    continue
+                    raise
-                if any(
+                except Exception as e:
-                    (path / x).exists()
+                    self.logger.warning(str(e))
-                    for x in [
-                        "config.json",
-                        "model_index.json",
-                        "learned_embeds.bin",
-                        "pytorch_lora_weights.bin",
-                        "image_encoder.txt",
-                    ]
-                ):
-                    self.scanned_dirs.add(path)
-                    try:
-                        self.model_found(path)
-                    except KeyboardInterrupt:
-                        raise
-                    except Exception as e:
-                        self.logger.warning(str(e))
 
-            for f in files:
+        for d in dirs:
-                path = Path(root) / f
+            self._walk_directory(absolute_path / d)
-                if path.parent in self.scanned_dirs:
-                    continue
-                if path.suffix in {".ckpt", ".bin", ".pth", ".safetensors", ".pt"}:
-                    try:
-                        self.model_found(path)
-                    except KeyboardInterrupt:
-                        raise
-                    except Exception as e:
-                        self.logger.warning(str(e))

invokeai/backend/model_patcher.py

@@ -4,12 +4,12 @@ from __future__ import annotations
 
 import pickle
 from contextlib import contextmanager
-from typing import Any, Dict, Iterator, List, Optional, Tuple
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
 from diffusers import OnnxRuntimeModel, UNet2DConditionModel
-from transformers import CLIPTextModel, CLIPTokenizer
+from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
 
 from invokeai.app.shared.models import FreeUConfig
 from invokeai.backend.model_manager import AnyModel
@@ -168,7 +168,7 @@ class ModelPatcher:
     def apply_ti(
         cls,
         tokenizer: CLIPTokenizer,
-        text_encoder: CLIPTextModel,
+        text_encoder: Union[CLIPTextModel, CLIPTextModelWithProjection],
         ti_list: List[Tuple[str, TextualInversionModelRaw]],
     ) -> Iterator[Tuple[CLIPTokenizer, TextualInversionManager]]:
         init_tokens_count = None
@@ -265,7 +265,7 @@ class ModelPatcher:
     @contextmanager
     def apply_clip_skip(
         cls,
-        text_encoder: CLIPTextModel,
+        text_encoder: Union[CLIPTextModel, CLIPTextModelWithProjection],
         clip_skip: int,
     ) -> None:
         skipped_layers = []