It's currently trying to load with the name as the key, which should never be the case.

docs/nodes/INVOCATION_API.md

@@ -1,45 +0,0 @@
-# Invocation API
-
-Each invocation's `invoke` method is provided a single arg - the Invocation
-Context.
-
-This object provides access to various methods, used to interact with the
-application. Loading and saving images, logging messages, etc.
-
-!!! warning ""
-
-    This API may shift slightly until the release of v4.0.0 as we work through a few final updates to the Model Manager.
-
-```py
-class MyInvocation(BaseInvocation):
-  ...
-  def invoke(self, context: InvocationContext) -> ImageOutput:
-      image_pil = context.images.get_pil(image_name)
-      # Do something to the image
-      image_dto = context.images.save(image_pil)
-      # Log a message
-      context.logger.info(f"Did something cool, image saved!")
-      ...
-```
-
-<!-- prettier-ignore-start -->
-::: invokeai.app.services.shared.invocation_context.InvocationContext
-    options:
-        members: false
-
-::: invokeai.app.services.shared.invocation_context.ImagesInterface
-
-::: invokeai.app.services.shared.invocation_context.TensorsInterface
-
-::: invokeai.app.services.shared.invocation_context.ConditioningInterface
-
-::: invokeai.app.services.shared.invocation_context.ModelsInterface
-
-::: invokeai.app.services.shared.invocation_context.LoggerInterface
-
-::: invokeai.app.services.shared.invocation_context.ConfigInterface
-
-::: invokeai.app.services.shared.invocation_context.UtilInterface
-
-::: invokeai.app.services.shared.invocation_context.BoardsInterface
-<!-- prettier-ignore-end -->

docs/nodes/NODES_MIGRATION_V3_V4.md

@@ -1,148 +0,0 @@
-# Invoke v4.0.0 Nodes API Migration guide
-
-Invoke v4.0.0 is versioned as such due to breaking changes to the API utilized
-by nodes, both core and custom.
-
-## Motivation
-
-Prior to v4.0.0, the `invokeai` python package has not be set up to be utilized
-as a library. That is to say, it didn't have any explicitly public API, and node
-authors had to work with the unstable internal application API.
-
-v4.0.0 introduces a stable public API for nodes.
-
-## Changes
-
-There are two node-author-facing changes:
-
-1. Import Paths
-1. Invocation Context API
-
-### Import Paths
-
-All public objects are now exported from `invokeai.invocation_api`:
-
-```py
-# Old
-from invokeai.app.invocations.baseinvocation import (
-    BaseInvocation,
-    InputField,
-    InvocationContext,
-    invocation,
-)
-from invokeai.app.invocations.primitives import ImageField
-
-# New
-from invokeai.invocation_api import (
-    BaseInvocation,
-    ImageField,
-    InputField,
-    InvocationContext,
-    invocation,
-)
-```
-
-It's possible that we've missed some classes you need in your node. Please let
-us know if that's the case.
-
-### Invocation Context API
-
-Most nodes utilize the Invocation Context, an object that is passed to the
-`invoke` that provides access to data and services a node may need.
-
-Until now, that object and the services it exposed were internal. Exposing them
-to nodes means that changes to our internal implementation could break nodes.
-The methods on the services are also often fairly complicated and allowed nodes
-to footgun.
-
-In v4.0.0, this object has been refactored to be much simpler.
-
-See [INVOCATION_API](./INVOCATION_API.md) for full details of the API.
-
-!!! warning ""
-
-    This API may shift slightly until the release of v4.0.0 as we work through a few final updates to the Model Manager.
-
-#### Improved Service Methods
-
-The biggest offender was the image save method:
-
-```py
-# Old
-image_dto = context.services.images.create(
-    image=image,
-    image_origin=ResourceOrigin.INTERNAL,
-    image_category=ImageCategory.GENERAL,
-    node_id=self.id,
-    session_id=context.graph_execution_state_id,
-    is_intermediate=self.is_intermediate,
-    metadata=self.metadata,
-    workflow=context.workflow,
-)
-
-# New
-image_dto = context.images.save(image=image)
-```
-
-Other methods are simplified, or enhanced with additional functionality:
-
-```py
-# Old
-image = context.services.images.get_pil_image(image_name)
-
-# New
-image = context.images.get_pil(image_name)
-image_cmyk = context.images.get_pil(image_name, "CMYK")
-```
-
-We also had some typing issues around tensors:
-
-```py
-# Old
-# `latents` typed as `torch.Tensor`, but could be `ConditioningFieldData`
-latents = context.services.latents.get(self.latents.latents_name)
-# `data` typed as `torch.Tenssor,` but could be `ConditioningFieldData`
-context.services.latents.save(latents_name, data)
-
-# New - separate methods for tensors and conditioning data w/ correct typing
-# Also, the service generates the names
-tensor_name = context.tensors.save(tensor)
-tensor = context.tensors.load(tensor_name)
-# For conditioning
-cond_name = context.conditioning.save(cond_data)
-cond_data = context.conditioning.load(cond_name)
-```
-
-#### Output Construction
-
-Core Outputs have builder functions right on them - no need to manually
-construct these objects, or use an extra utility:
-
-```py
-# Old
-image_output = ImageOutput(
-    image=ImageField(image_name=image_dto.image_name),
-    width=image_dto.width,
-    height=image_dto.height,
-)
-latents_output = build_latents_output(latents_name=name, latents=latents, seed=None)
-noise_output = NoiseOutput(
-    noise=LatentsField(latents_name=latents_name, seed=seed),
-    width=latents.size()[3] * 8,
-    height=latents.size()[2] * 8,
-)
-cond_output = ConditioningOutput(
-    conditioning=ConditioningField(
-        conditioning_name=conditioning_name,
-    ),
-)
-
-# New
-image_output = ImageOutput.build(image_dto)
-latents_output = LatentsOutput.build(latents_name=name, latents=noise, seed=self.seed)
-noise_output = NoiseOutput.build(latents_name=name, latents=noise, seed=self.seed)
-cond_output = ConditioningOutput.build(conditioning_name)
-```
-
-You can still create the objects using constructors if you want, but we suggest
-using the builder methods.

docs/requirements-mkdocs.txt

@@ -0,0 +1,5 @@
+mkdocs
+mkdocs-material>=8, <9
+mkdocs-git-revision-date-localized-plugin
+mkdocs-redirects==1.2.0
+

docs/stylesheets/extra.css

@@ -0,0 +1,5 @@
+:root {
+    --md-primary-fg-color:        #35A4DB;
+    --md-primary-fg-color--light: #35A4DB;
+    --md-primary-fg-color--dark:  #35A4DB;
+  }

invokeai/app/api_app.py

@@ -2,7 +2,6 @@
 # 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__
@@ -72,25 +71,9 @@ 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,
-    lifespan=lifespan,
-)
+app = FastAPI(title="Invoke - Community Edition", docs_url=None, redoc_url=None, separate_input_output_schemas=False)
 
 # Add event handler
 event_handler_id: int = id(app)
@@ -113,6 +96,18 @@ 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

@@ -1,15 +1,24 @@
-from typing import Iterator, List, Optional, Tuple, Union, cast
+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 CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
+from transformers import CLIPTokenizer
 
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIComponent
+import invokeai.backend.util.logging as logger
+from invokeai.app.invocations.fields import (
+    FieldDescriptions,
+    Input,
+    InputField,
+    OutputField,
+    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 generate_ti_list
+from invokeai.app.util.ti_utils import extract_ti_triggers_from_prompt
 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,
@@ -17,9 +26,15 @@ 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 BaseInvocation, BaseInvocationOutput, invocation, invocation_output
+from .baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    invocation,
+    invocation_output,
+)
 from .model import ClipField
 
 # unconditioned: Optional[torch.Tensor]
@@ -55,11 +70,7 @@ 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:
@@ -71,10 +82,23 @@ class CompelInvocation(BaseInvocation):
 
         # loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
-        ti_list = generate_ti_list(self.prompt, text_encoder_info.config.base, context)
+        ti_list = []
+        for trigger in extract_ti_triggers_from_prompt(self.prompt):
+            name = trigger[1:-1]
+            try:
+                loaded_model = context.models.load_by_attrs(
+                    model_name=name, base_model=text_encoder_info.config.base, model_type=ModelType.TextualInversion
+                ).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')
 
         with (
-            ModelPatcher.apply_ti(tokenizer_model, text_encoder_model, ti_list) as (
+            ModelPatcher.apply_ti(tokenizer_info.model, text_encoder_info.model, ti_list) as (
                 tokenizer,
                 ti_manager,
             ),
@@ -82,9 +106,8 @@ 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_model, self.clip.skipped_layers),
+            ModelPatcher.apply_clip_skip(text_encoder_info.model, self.clip.skipped_layers),
         ):
-            assert isinstance(text_encoder, CLIPTextModel)
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,
@@ -134,11 +157,7 @@ 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, CLIPTextModelWithProjection))
 
         # return zero on empty
         if prompt == "" and zero_on_empty:
@@ -172,10 +191,25 @@ class SDXLPromptInvocationBase:
 
         # loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
 
-        ti_list = generate_ti_list(prompt, text_encoder_info.config.base, context)
+        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')
 
         with (
-            ModelPatcher.apply_ti(tokenizer_model, text_encoder_model, ti_list) as (
+            ModelPatcher.apply_ti(tokenizer_info.model, text_encoder_info.model, ti_list) as (
                 tokenizer,
                 ti_manager,
             ),
@@ -183,10 +217,8 @@ 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_model, clip_field.skipped_layers),
+            ModelPatcher.apply_clip_skip(text_encoder_info.model, clip_field.skipped_layers),
         ):
-            assert isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection))
-            text_encoder = cast(CLIPTextModel, text_encoder)
             compel = Compel(
                 tokenizer=tokenizer,
                 text_encoder=text_encoder,

invokeai/app/invocations/controlnet_image_processors.py

@@ -576,7 +576,7 @@ DEPTH_ANYTHING_MODEL_SIZES = Literal["large", "base", "small"]
     title="Depth Anything Processor",
     tags=["controlnet", "depth", "depth anything"],
     category="controlnet",
-    version="1.0.1",
+    version="1.0.0",
 )
 class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a depth map based on the Depth Anything algorithm"""
@@ -585,12 +585,13 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
         default="small", description="The size of the depth model to use"
     )
     resolution: int = InputField(default=512, ge=64, multiple_of=64, description=FieldDescriptions.image_res)
+    offload: bool = InputField(default=False)
 
     def run_processor(self, image: Image.Image):
         depth_anything_detector = DepthAnythingDetector()
         depth_anything_detector.load_model(model_size=self.model_size)
 
-        processed_image = depth_anything_detector(image=image, resolution=self.resolution)
+        processed_image = depth_anything_detector(image=image, resolution=self.resolution, offload=self.offload)
         return processed_image
 
 

invokeai/app/invocations/ip_adapter.py

@@ -93,7 +93,7 @@ class IPAdapterInvocation(BaseInvocation):
         image_encoder_model_id = ip_adapter_info.image_encoder_model_id
         image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
         image_encoder_models = context.models.search_by_attrs(
-            name=image_encoder_model_name, base=BaseModelType.Any, type=ModelType.CLIPVision
+            model_name=image_encoder_model_name, base_model=BaseModelType.Any, model_type=ModelType.CLIPVision
         )
         assert len(image_encoder_models) == 1
         image_encoder_model = CLIPVisionModelField(key=image_encoder_models[0].key)

invokeai/app/services/shared/invocation_context.py

@@ -65,86 +65,75 @@ class InvocationContextInterface:
 
 class BoardsInterface(InvocationContextInterface):
     def create(self, board_name: str) -> BoardDTO:
-        """Creates a board.
+        """
+        Creates a board.
 
-        Args:
-            board_name: The name of the board to create.
-
-        Returns:
-            The created board DTO.
+        :param board_name: The name of the board to create.
         """
         return self._services.boards.create(board_name)
 
     def get_dto(self, board_id: str) -> BoardDTO:
-        """Gets a board DTO.
+        """
+        Gets a board DTO.
 
-        Args:
-            board_id: The ID of the board to get.
-
-        Returns:
-            The board DTO.
+        :param board_id: The ID of the board to get.
         """
         return self._services.boards.get_dto(board_id)
 
     def get_all(self) -> list[BoardDTO]:
-        """Gets all boards.
-
-        Returns:
-            A list of all boards.
+        """
+        Gets all boards.
         """
         return self._services.boards.get_all()
 
     def add_image_to_board(self, board_id: str, image_name: str) -> None:
-        """Adds an image to a board.
+        """
+        Adds an image to a board.
 
-        Args:
-            board_id: The ID of the board to add the image to.
-            image_name: The name of the image to add to the board.
+        :param board_id: The ID of the board to add the image to.
+        :param image_name: The name of the image to add to the board.
         """
         return self._services.board_images.add_image_to_board(board_id, image_name)
 
     def get_all_image_names_for_board(self, board_id: str) -> list[str]:
-        """Gets all image names for a board.
+        """
+        Gets all image names for a board.
 
-        Args:
-            board_id: The ID of the board to get the image names for.
-
-        Returns:
-            A list of all image names for the board.
+        :param board_id: The ID of the board to get the image names for.
         """
         return self._services.board_images.get_all_board_image_names_for_board(board_id)
 
 
 class LoggerInterface(InvocationContextInterface):
     def debug(self, message: str) -> None:
-        """Logs a debug message.
+        """
+        Logs a debug message.
 
-        Args:
-            message: The message to log.
+        :param message: The message to log.
         """
         self._services.logger.debug(message)
 
     def info(self, message: str) -> None:
-        """Logs an info message.
+        """
+        Logs an info message.
 
-        Args:
-            message: The message to log.
+        :param message: The message to log.
         """
         self._services.logger.info(message)
 
     def warning(self, message: str) -> None:
-        """Logs a warning message.
+        """
+        Logs a warning message.
 
-        Args:
-            message: The message to log.
+        :param message: The message to log.
         """
         self._services.logger.warning(message)
 
     def error(self, message: str) -> None:
-        """Logs an error message.
+        """
+        Logs an error message.
 
-        Args:
-            message: The message to log.
+        :param message: The message to log.
         """
         self._services.logger.error(message)
 
@@ -157,23 +146,20 @@ class ImagesInterface(InvocationContextInterface):
         image_category: ImageCategory = ImageCategory.GENERAL,
         metadata: Optional[MetadataField] = None,
     ) -> ImageDTO:
-        """Saves an image, returning its DTO.
+        """
+        Saves an image, returning its DTO.
 
         If the current queue item has a workflow or metadata, it is automatically saved with the image.
 
-        Args:
-            image: The image to save, as a PIL image.
-            board_id: The board ID to add the image to, if it should be added. It the invocation \
+        :param image: The image to save, as a PIL image.
+        :param board_id: The board ID to add the image to, if it should be added. It the invocation \
             inherits from `WithBoard`, that board will be used automatically. **Use this only if \
             you want to override or provide a board manually!**
-            image_category: The category of the image. Only the GENERAL category is added \
+        :param image_category: The category of the image. Only the GENERAL category is added \
             to the gallery.
-            metadata: The metadata to save with the image, if it should have any. If the \
+        :param metadata: The metadata to save with the image, if it should have any. If the \
             invocation inherits from `WithMetadata`, that metadata will be used automatically. \
             **Use this only if you want to override or provide metadata manually!**
-
-        Returns:
-            The saved image DTO.
         """
 
         # If `metadata` is provided directly, use that. Else, use the metadata provided by `WithMetadata`, falling back to None.
@@ -203,14 +189,11 @@ class ImagesInterface(InvocationContextInterface):
         )
 
     def get_pil(self, image_name: str, mode: IMAGE_MODES | None = None) -> Image:
-        """Gets an image as a PIL Image object.
+        """
+        Gets an image as a PIL Image object.
 
-        Args:
-            image_name: The name of the image to get.
-            mode: The color mode to convert the image to. If None, the original mode is used.
-
-        Returns:
-            The image as a PIL Image object.
+        :param image_name: The name of the image to get.
+        :param mode: The color mode to convert the image to. If None, the original mode is used.
         """
         image = self._services.images.get_pil_image(image_name)
         if mode and mode != image.mode:
@@ -223,76 +206,58 @@ class ImagesInterface(InvocationContextInterface):
         return image
 
     def get_metadata(self, image_name: str) -> Optional[MetadataField]:
-        """Gets an image's metadata, if it has any.
+        """
+        Gets an image's metadata, if it has any.
 
-        Args:
-            image_name: The name of the image to get the metadata for.
-
-        Returns:
-            The image's metadata, if it has any.
+        :param image_name: The name of the image to get the metadata for.
         """
         return self._services.images.get_metadata(image_name)
 
     def get_dto(self, image_name: str) -> ImageDTO:
-        """Gets an image as an ImageDTO object.
+        """
+        Gets an image as an ImageDTO object.
 
-        Args:
-            image_name: The name of the image to get.
-
-        Returns:
-            The image as an ImageDTO object.
+        :param image_name: The name of the image to get.
         """
         return self._services.images.get_dto(image_name)
 
 
 class TensorsInterface(InvocationContextInterface):
     def save(self, tensor: Tensor) -> str:
-        """Saves a tensor, returning its name.
+        """
+        Saves a tensor, returning its name.
 
-        Args:
-            tensor: The tensor to save.
-
-        Returns:
-            The name of the saved tensor.
+        :param tensor: The tensor to save.
         """
 
         name = self._services.tensors.save(obj=tensor)
         return name
 
     def load(self, name: str) -> Tensor:
-        """Loads a tensor by name.
+        """
+        Loads a tensor by name.
 
-        Args:
-            name: The name of the tensor to load.
-
-        Returns:
-            The loaded tensor.
+        :param name: The name of the tensor to load.
         """
         return self._services.tensors.load(name)
 
 
 class ConditioningInterface(InvocationContextInterface):
     def save(self, conditioning_data: ConditioningFieldData) -> str:
-        """Saves a conditioning data object, returning its name.
+        """
+        Saves a conditioning data object, returning its name.
 
-        Args:
-            conditioning_data: The conditioning data to save.
-
-        Returns:
-            The name of the saved conditioning data.
+        :param conditioning_data: The conditioning data to save.
         """
 
         name = self._services.conditioning.save(obj=conditioning_data)
         return name
 
     def load(self, name: str) -> ConditioningFieldData:
-        """Loads conditioning data by name.
+        """
+        Loads conditioning data by name.
 
-        Args:
-            name: The name of the conditioning data to load.
-
-        Returns:
-            The loaded conditioning data.
+        :param name: The name of the conditioning data to load.
         """
 
         return self._services.conditioning.load(name)
@@ -300,25 +265,20 @@ class ConditioningInterface(InvocationContextInterface):
 
 class ModelsInterface(InvocationContextInterface):
     def exists(self, key: str) -> bool:
-        """Checks if a model exists.
+        """
+        Checks if a model exists.
 
-        Args:
-            key: The key of the model.
-
-        Returns:
-            True if the model exists, False if not.
+        :param key: The key of the model.
         """
         return self._services.model_manager.store.exists(key)
 
     def load(self, key: str, submodel_type: Optional[SubModelType] = None) -> LoadedModel:
-        """Loads a model.
+        """
+        Loads a model.
 
-        Args:
-            key: The key of the model.
-            submodel_type: The submodel of the model to get.
-
-        Returns:
-            An object representing the loaded model.
+        :param key: The key of the model.
+        :param submodel_type: The submodel of the model to get.
+        :returns: An object representing the loaded model.
         """
 
         # The model manager emits events as it loads the model. It needs the context data to build
@@ -329,95 +289,75 @@ class ModelsInterface(InvocationContextInterface):
         )
 
     def load_by_attrs(
-        self, name: str, base: BaseModelType, type: ModelType, submodel_type: Optional[SubModelType] = None
+        self, model_name: str, base_model: BaseModelType, model_type: ModelType, submodel: Optional[SubModelType] = None
     ) -> LoadedModel:
-        """Loads a model by its attributes.
+        """
+        Loads a model by its attributes.
 
-        Args:
-            name: Name of the model.
-            base: The models' base type, e.g. `BaseModelType.StableDiffusion1`, `BaseModelType.StableDiffusionXL`, etc.
-            type: Type of the model, e.g. `ModelType.Main`, `ModelType.Vae`, etc.
-            submodel_type: The type of submodel to load, e.g. `SubModelType.UNet`, `SubModelType.TextEncoder`, etc. Only main
-            models have submodels.
-
-        Returns:
-            An object representing the loaded model.
+        :param model_name: Name of to be fetched.
+        :param base_model: Base model
+        :param model_type: Type of the model
+        :param submodel: For main (pipeline models), the submodel to fetch
         """
         return self._services.model_manager.load_model_by_attr(
-            model_name=name,
-            base_model=base,
-            model_type=type,
-            submodel=submodel_type,
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+            submodel=submodel,
             context_data=self._data,
         )
 
     def get_config(self, key: str) -> AnyModelConfig:
-        """Gets a model's config.
+        """
+        Gets a model's info, an dict-like object.
 
-        Args:
-            key: The key of the model.
-
-        Returns:
-            The model's config.
+        :param key: The key of the model.
         """
         return self._services.model_manager.store.get_model(key=key)
 
     def get_metadata(self, key: str) -> Optional[AnyModelRepoMetadata]:
-        """Gets a model's metadata, if it has any.
+        """
+        Gets a model's metadata, if it has any.
 
-        Args:
-            key: The key of the model.
-
-        Returns:
-            The model's metadata, if it has any.
+        :param key: The key of the model.
         """
         return self._services.model_manager.store.get_metadata(key=key)
 
     def search_by_path(self, path: Path) -> list[AnyModelConfig]:
-        """Searches for models by path.
+        """
+        Searches for models by path.
 
-        Args:
-            path: The path to search for.
-
-        Returns:
-            A list of models that match the path.
+        :param path: The path to search for.
         """
         return self._services.model_manager.store.search_by_path(path)
 
     def search_by_attrs(
         self,
-        name: Optional[str] = None,
-        base: Optional[BaseModelType] = None,
-        type: Optional[ModelType] = None,
-        format: Optional[ModelFormat] = None,
+        model_name: Optional[str] = None,
+        base_model: Optional[BaseModelType] = None,
+        model_type: Optional[ModelType] = None,
+        model_format: Optional[ModelFormat] = None,
     ) -> list[AnyModelConfig]:
-        """Searches for models by attributes.
+        """
+        Searches for models by attributes.
 
-        Args:
-            name: The name to search for (exact match).
-            base: The base to search for, e.g. `BaseModelType.StableDiffusion1`, `BaseModelType.StableDiffusionXL`, etc.
-            type: Type type of model to search for, e.g. `ModelType.Main`, `ModelType.Vae`, etc.
-            format: The format of model to search for, e.g. `ModelFormat.Checkpoint`, `ModelFormat.Diffusers`, etc.
-
-        Returns:
-            A list of models that match the attributes.
+        :param model_name: Name of to be fetched.
+        :param base_model: Base model
+        :param model_type: Type of the model
+        :param submodel: For main (pipeline models), the submodel to fetch
         """
 
         return self._services.model_manager.store.search_by_attr(
-            model_name=name,
-            base_model=base,
-            model_type=type,
-            model_format=format,
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+            model_format=model_format,
         )
 
 
 class ConfigInterface(InvocationContextInterface):
     def get(self) -> InvokeAIAppConfig:
-        """Gets the app's config.
-
-        Returns:
-            The app's config.
-        """
+        """Gets the app's config."""
 
         return self._services.configuration.get_config()
 
@@ -430,11 +370,7 @@ class UtilInterface(InvocationContextInterface):
         self._cancel_event = cancel_event
 
     def is_canceled(self) -> bool:
-        """Checks if the current session has been canceled.
-
-        Returns:
-            True if the current session has been canceled, False if not.
-        """
+        """Checks if the current invocation has been canceled."""
         return self._cancel_event.is_set()
 
     def sd_step_callback(self, intermediate_state: PipelineIntermediateState, base_model: BaseModelType) -> None:
@@ -444,9 +380,8 @@ class UtilInterface(InvocationContextInterface):
 
         This should be called after each denoising step.
 
-        Args:
-            intermediate_state: The intermediate state of the diffusion pipeline.
-            base_model: The base model for the current denoising step.
+        :param intermediate_state: The intermediate state of the diffusion pipeline.
+        :param base_model: The base model for the current denoising step.
         """
 
         stable_diffusion_step_callback(
@@ -459,17 +394,8 @@ class UtilInterface(InvocationContextInterface):
 
 
 class InvocationContext:
-    """Provides access to various services and data for the current invocation.
-
-    Attributes:
-        images (ImagesInterface): Methods to save, get and update images and their metadata.
-        tensors (TensorsInterface): Methods to save and get tensors, including image, noise, masks, and masked images.
-        conditioning (ConditioningInterface): Methods to save and get conditioning data.
-        models (ModelsInterface): Methods to check if a model exists, get a model, and get a model's info.
-        logger (LoggerInterface): The app logger.
-        config (ConfigInterface): The app config.
-        util (UtilInterface): Utility methods, including a method to check if an invocation was canceled and step callbacks.
-        boards (BoardsInterface): Methods to interact with boards.
+    """
+    The `InvocationContext` provides access to various services and data for the current invocation.
     """
 
     def __init__(
@@ -512,14 +438,11 @@ def build_invocation_context(
     data: InvocationContextData,
     cancel_event: threading.Event,
 ) -> InvocationContext:
-    """Builds the invocation context for a specific invocation execution.
+    """
+    Builds the invocation context for a specific invocation execution.
 
-    Args:
-        services: The invocation services to wrap.
-        data: The invocation context data.
-
-    Returns:
-        The invocation context.
+    :param services: The invocation services to wrap.
+    :param data: The invocation context data.
     """
 
     logger = LoggerInterface(services=services, data=data)

invokeai/app/util/ti_utils.py

@@ -1,11 +1,6 @@
 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
+from typing import List
 
 
 def extract_ti_triggers_from_prompt(prompt: str) -> List[str]:
@@ -13,35 +8,3 @@ def extract_ti_triggers_from_prompt(prompt: str) -> List[str]:
     for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
         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(
-                    name=name_or_key, base=base, 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/image_util/depth_anything/__init__.py

@@ -17,8 +17,6 @@ from invokeai.backend.util.util import download_with_progress_bar
 
 config = InvokeAIAppConfig.get_config()
 
-DEPTH_ANYTHING_MODEL_SIZES = Literal["large", "base", "small"]
-
 DEPTH_ANYTHING_MODELS = {
     "large": {
         "url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitl14.pth?download=true",
@@ -55,9 +53,9 @@ transform = Compose(
 class DepthAnythingDetector:
     def __init__(self) -> None:
         self.model = None
-        self.model_size: Union[DEPTH_ANYTHING_MODEL_SIZES, None] = None
+        self.model_size: Union[Literal["large", "base", "small"], None] = None
 
-    def load_model(self, model_size: DEPTH_ANYTHING_MODEL_SIZES = "small"):
+    def load_model(self, model_size=Literal["large", "base", "small"]):
         DEPTH_ANYTHING_MODEL_PATH = pathlib.Path(config.models_path / DEPTH_ANYTHING_MODELS[model_size]["local"])
         if not DEPTH_ANYTHING_MODEL_PATH.exists():
             download_with_progress_bar(DEPTH_ANYTHING_MODELS[model_size]["url"], DEPTH_ANYTHING_MODEL_PATH)
@@ -86,19 +84,16 @@ class DepthAnythingDetector:
         self.model.to(device)
         return self
 
-    def __call__(self, image: Image.Image, resolution: int = 512):
-        if self.model is None:
-            raise Exception("Depth Anything Model not loaded")
+    def __call__(self, image, resolution=512, offload=False):
+        image = np.array(image, dtype=np.uint8)
+        image = image[:, :, ::-1] / 255.0
 
-        np_image = np.array(image, dtype=np.uint8)
-        np_image = np_image[:, :, ::-1] / 255.0
-
-        image_height, image_width = np_image.shape[:2]
-        np_image = transform({"image": image})["image"]
-        tensor_image = torch.from_numpy(np_image).unsqueeze(0).to(choose_torch_device())
+        image_height, image_width = image.shape[:2]
+        image = transform({"image": image})["image"]
+        image = torch.from_numpy(image).unsqueeze(0).to(choose_torch_device())
 
         with torch.no_grad():
-            depth = self.model(tensor_image)
+            depth = self.model(image)
             depth = F.interpolate(depth[None], (image_height, image_width), mode="bilinear", align_corners=False)[0, 0]
             depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
 
@@ -108,4 +103,7 @@ class DepthAnythingDetector:
         new_height = int(image_height * (resolution / image_width))
         depth_map = depth_map.resize((resolution, new_height))
 
+        if offload:
+            del self.model
+
         return depth_map

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(x) for x in model_json["allowCommercialUse"]},
+                AllowCommercialUse=CommercialUsage(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[Set[CommercialUsage] | CommercialUsage] = Field(
-        description="Type of commercial use allowed if no commercial use is allowed.", default=None
+    AllowCommercialUse: Optional[CommercialUsage] = Field(
+        description="Type of commercial use allowed or 'No' if no commercial use is allowed.", default=None
     )
 
 
@@ -142,10 +142,7 @@ class CivitaiMetadata(ModelMetadataWithFiles):
         if self.restrictions.AllowCommercialUse is None:
             return False
         else:
-            # accommodate schema change
-            acu = self.restrictions.AllowCommercialUse
-            commercial_usage = acu if isinstance(acu, set) else {acu}
-            return CommercialUsage.No not in commercial_usage
+            return self.restrictions.AllowCommercialUse != CommercialUsage("None")
 
     @property
     def allow_derivatives(self) -> bool:

invokeai/backend/model_manager/search.py

@@ -28,7 +28,6 @@ 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()
@@ -118,10 +117,13 @@ class ModelSearch(ModelSearchBase):
     """
 
     models_found: Set[Path] = Field(default_factory=set)
-    config: InvokeAIAppConfig = InvokeAIAppConfig.get_config()
+    scanned_dirs: Set[Path] = Field(default_factory=set)
+    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)
 
@@ -137,53 +139,53 @@ 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(self._directory)
+        self._walk_directory(directory)
         self.search_completed()
         return self.models_found
 
-    def _walk_directory(self, path: Union[Path, str], max_depth: int = 20) -> None:
-        absolute_path = Path(path)
-        if (
-            len(absolute_path.parts) - len(self._directory.parts) > max_depth
-            or not absolute_path.exists()
-            or absolute_path.parent in self.models_found
-        ):
-            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
+    def _walk_directory(self, path: Union[Path, str]) -> None:
+        for root, dirs, files in os.walk(path, followlinks=True):
+            # don't descend into directories that start with a "."
+            # to avoid the Mac .DS_STORE issue.
+            if str(Path(root).name).startswith("."):
+                self.pruned_paths.add(Path(root))
+            if any(Path(root).is_relative_to(x) for x in self.pruned_paths):
+                continue
 
-        for n in file_names:
-            if n.endswith((".ckpt", ".bin", ".pth", ".safetensors", ".pt")):
-                try:
-                    self.model_found(absolute_path / n)
-                except KeyboardInterrupt:
-                    raise
-                except Exception as e:
-                    self.logger.warning(str(e))
+            self.stats.items_scanned += len(dirs) + len(files)
+            for d in dirs:
+                path = Path(root) / d
+                if path.parent in self.scanned_dirs:
+                    self.scanned_dirs.add(path)
+                    continue
+                if any(
+                    (path / x).exists()
+                    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 d in dirs:
-            self._walk_directory(absolute_path / d)
+            for f in files:
+                path = Path(root) / f
+                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, Union
+from typing import Any, Dict, Iterator, List, Optional, Tuple
 
 import numpy as np
 import torch
 from diffusers import OnnxRuntimeModel, UNet2DConditionModel
-from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
+from transformers import CLIPTextModel, 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: Union[CLIPTextModel, CLIPTextModelWithProjection],
+        text_encoder: CLIPTextModel,
         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: Union[CLIPTextModel, CLIPTextModelWithProjection],
+        text_encoder: CLIPTextModel,
         clip_skip: int,
     ) -> None:
         skipped_layers = []

mkdocs.yml

@@ -15,7 +15,7 @@ edit_uri: edit/main/docs/
 copyright: Copyright © 2023 InvokeAI Team
 
 extra_css:
-  - stylesheets/extra.css
+    - stylesheets/extra.css
 
 # Configuration
 theme:
@@ -25,7 +25,18 @@ theme:
     edit: material/file-document-edit-outline
   favicon: img/favicon.ico
   palette:
-    scheme: slate
+    # - media: '(prefers-color-scheme: light)'
+    #   scheme: default
+    #   primary: custom
+    #   toggle:
+    #     icon: material/lightbulb
+    #     name: Switch to dark mode
+    - media: '(prefers-color-scheme: dark)'
+      scheme: slate
+      primary: custom
+      # toggle:
+      #   icon: material/lightbulb-outline
+      #   name: Switch to light mode
   features:
     - navigation.instant
     - navigation.tabs
@@ -37,6 +48,7 @@ theme:
     - search.suggest
     - toc.integrate
 
+
 # Extensions
 markdown_extensions:
   - abbr
@@ -54,8 +66,8 @@ markdown_extensions:
   - pymdownx.caret
   - pymdownx.details
   - pymdownx.emoji:
-      emoji_index: !!python/name:material.extensions.emoji.twemoji
-      emoji_generator: !!python/name:material.extensions.emoji.to_svg
+      emoji_index: !!python/name:materialx.emoji.twemoji
+      emoji_generator: !!python/name:materialx.emoji.to_svg
   - pymdownx.highlight:
       anchor_linenums: true
   - pymdownx.inlinehilite
@@ -85,25 +97,12 @@ plugins:
       enable_creation_date: true
   - redirects:
       redirect_maps:
-        'installation/INSTALL_AUTOMATED.md': 'installation/010_INSTALL_AUTOMATED.md'
-        'installation/INSTALL_MANUAL.md': 'installation/020_INSTALL_MANUAL.md'
-        'installation/INSTALL_SOURCE.md': 'installation/020_INSTALL_MANUAL.md'
-        'installation/INSTALL_DOCKER.md': 'installation/040_INSTALL_DOCKER.md'
-        'installation/INSTALLING_MODELS.md': 'installation/050_INSTALLING_MODELS.md'
-        'installation/INSTALL_PATCHMATCH.md': 'installation/060_INSTALL_PATCHMATCH.md'
-  - mkdocstrings:
-      handlers:
-        python:
-          options:
-            separate_signature: true
-            show_signature_annotations: true
-            parameter_headings: false
-            signature_crossrefs: true
-            show_source: false
-            summary: true
-            show_root_heading: true
-            show_root_full_path: false
-            show_bases: false
+          'installation/INSTALL_AUTOMATED.md': 'installation/010_INSTALL_AUTOMATED.md'
+          'installation/INSTALL_MANUAL.md': 'installation/020_INSTALL_MANUAL.md'
+          'installation/INSTALL_SOURCE.md': 'installation/020_INSTALL_MANUAL.md'
+          'installation/INSTALL_DOCKER.md': 'installation/040_INSTALL_DOCKER.md'
+          'installation/INSTALLING_MODELS.md': 'installation/050_INSTALLING_MODELS.md'
+          'installation/INSTALL_PATCHMATCH.md': 'installation/060_INSTALL_PATCHMATCH.md'
 
 extra_javascript:
   - https://unpkg.com/tablesort@5.3.0/dist/tablesort.min.js
@@ -116,8 +115,8 @@ extra:
     property: G-2X4JR4S4FB
 
 nav:
-  - Home: 'index.md'
-  - Installation:
+    - Home: 'index.md'
+    - Installation:
       - Overview: 'installation/INSTALLATION.md'
       - Installing with the Automated Installer: 'installation/010_INSTALL_AUTOMATED.md'
       - Installing Manually: 'installation/020_INSTALL_MANUAL.md'
@@ -128,14 +127,14 @@ nav:
       - Installing xFormers: 'installation/070_INSTALL_XFORMERS.md'
       - Developers Documentation: 'installation/Developers_documentation/BUILDING_BINARY_INSTALLERS.md'
       - Deprecated Documentation:
-          - Binary Installer: 'installation/deprecated_documentation/INSTALL_BINARY.md'
-          - Runninng InvokeAI on Google Colab: 'installation/deprecated_documentation/INSTALL_JUPYTER.md'
-          - Manual Installation on Linux: 'installation/deprecated_documentation/INSTALL_LINUX.md'
-          - Manual Installation on macOS: 'installation/deprecated_documentation/INSTALL_MAC.md'
-          - Manual Installation on Windows: 'installation/deprecated_documentation/INSTALL_WINDOWS.md'
-          - Installing Invoke with pip: 'installation/deprecated_documentation/INSTALL_PCP.md'
-          - Source Installer: 'installation/deprecated_documentation/INSTALL_SOURCE.md'
-  - Workflows & Nodes:
+        - Binary Installer: 'installation/deprecated_documentation/INSTALL_BINARY.md'
+        - Runninng InvokeAI on Google Colab: 'installation/deprecated_documentation/INSTALL_JUPYTER.md'
+        - Manual Installation on Linux: 'installation/deprecated_documentation/INSTALL_LINUX.md'
+        - Manual Installation on macOS: 'installation/deprecated_documentation/INSTALL_MAC.md'
+        - Manual Installation on Windows: 'installation/deprecated_documentation/INSTALL_WINDOWS.md'
+        - Installing Invoke with pip: 'installation/deprecated_documentation/INSTALL_PCP.md'
+        - Source Installer: 'installation/deprecated_documentation/INSTALL_SOURCE.md'
+    - Workflows & Nodes:
       - Nodes Overview: 'nodes/overview.md'
       - Workflow Editor Basics: 'nodes/NODES.md'
       - List of Default Nodes: 'nodes/defaultNodes.md'
@@ -143,10 +142,8 @@ nav:
       - ComfyUI to InvokeAI: 'nodes/comfyToInvoke.md'
       - Facetool Node: 'nodes/detailedNodes/faceTools.md'
       - Contributing Nodes: 'nodes/contributingNodes.md'
-      - Migrating from v3 to v4: 'nodes/NODES_MIGRATION_V3_V4.md'
-      - Invocation API: 'nodes/INVOCATION_API.md'
-  - Community Nodes: 'nodes/communityNodes.md'
-  - Features:
+    - Community Nodes: 'nodes/communityNodes.md'
+    - Features:
       - Overview: 'features/index.md'
       - New to InvokeAI?: 'help/gettingStartedWithAI.md'
       - Configuration: 'features/CONFIGURATION.md'
@@ -155,35 +152,35 @@ nav:
       - Controlling Logging: 'features/LOGGING.md'
       - LoRAs & LCM-LoRAs: 'features/LORAS.md'
       - Model Merging: 'features/MODEL_MERGING.md'
-      - Workflows &  Nodes: 'nodes/overview'
+      - Workflows &  Nodes : 'nodes/overview'
       - NSFW Checker: 'features/WATERMARK+NSFW.md'
       - Postprocessing: 'features/POSTPROCESS.md'
       - Prompting Features: 'features/PROMPTS.md'
       - Textual Inversions:
-          - Textual Inversions: 'features/TEXTUAL_INVERSIONS.md'
-          - Textual Inversion Training: 'features/TRAINING.md'
+        - Textual Inversions: 'features/TEXTUAL_INVERSIONS.md'
+        - Textual Inversion Training: 'features/TRAINING.md'
       - Unified Canvas: 'features/UNIFIED_CANVAS.md'
       - InvokeAI Web Server: 'features/WEB.md'
-      - WebUI Hotkeys: 'features/WEBUIHOTKEYS.md'
-      - Maintenance Utilities: 'features/UTILITIES.md'
+      - WebUI Hotkeys: "features/WEBUIHOTKEYS.md"
+      - Maintenance Utilities: "features/UTILITIES.md"
       - Other: 'features/OTHER.md'
-  - Contributing:
+    - Contributing:
       - How to Contribute: 'contributing/CONTRIBUTING.md'
       - InvokeAI Code of Conduct: 'CODE_OF_CONDUCT.md'
       - Development:
-          - Overview: 'contributing/contribution_guides/development.md'
-          - New Contributors: 'contributing/contribution_guides/newContributorChecklist.md'
-          - Model Manager v2: 'contributing/MODEL_MANAGER.md'
-          - Frontend Documentation: 'contributing/contribution_guides/contributingToFrontend.md'
-          - Local Development: 'contributing/LOCAL_DEVELOPMENT.md'
-          - Testing: 'contributing/TESTS.md'
+        - Overview: 'contributing/contribution_guides/development.md'
+        - New Contributors: 'contributing/contribution_guides/newContributorChecklist.md'
+        - Model Manager v2: 'contributing/MODEL_MANAGER.md'
+        - Frontend Documentation: 'contributing/contribution_guides/contributingToFrontend.md'
+        - Local Development: 'contributing/LOCAL_DEVELOPMENT.md'
+        - Testing: 'contributing/TESTS.md'
       - Documentation: 'contributing/contribution_guides/documentation.md'
       - Nodes: 'contributing/INVOCATIONS.md'
       - Model Manager: 'contributing/MODEL_MANAGER.md'
       - Download Queue: 'contributing/DOWNLOAD_QUEUE.md'
       - Translation: 'contributing/contribution_guides/translation.md'
       - Tutorials: 'contributing/contribution_guides/tutorials.md'
-  - Deprecated:
+    - Deprecated:
       - InvokeAI 2.3: 'deprecated/2to3.md'
       - Command Line Interface: 'deprecated/CLI.md'
       - Changelog: 'CHANGELOG.md'
@@ -193,11 +190,11 @@ nav:
       - Inpainting: 'deprecated/INPAINTING.md'
       - Outpainting: 'deprecated/OUTPAINTING.md'
       - Troubleshooting: 'help/deprecated/TROUBLESHOOT.md'
-  - Help:
+    - Help:
       - Getting Started: 'help/gettingStartedWithAI.md'
       - FAQs: 'help/FAQ.md'
       - Diffusion Overview: 'help/diffusion.md'
       - Sampler Convergence: 'help/SAMPLER_CONVERGENCE.md'
-  - Other:
+    - Other:
       - Contributors: 'other/CONTRIBUTORS.md'
       - CompViz-README: 'other/README-CompViz.md'

pyproject.toml

@@ -105,10 +105,9 @@ dependencies = [
 "onnx-directml" = ["onnxruntime-directml"]
 "dist" = ["pip-tools", "pipdeptree", "twine"]
 "docs" = [
-  "mkdocs-material",
+  "mkdocs-material<9.0",
   "mkdocs-git-revision-date-localized-plugin",
-  "mkdocs-redirects",
-  "mkdocstrings[python]",
+  "mkdocs-redirects==1.2.0",
 ]
 "dev" = ["jurigged", "pudb", "snakeviz", "gprof2dot"]
 "test" = [

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 CommercialUsage("RentCivit") in metadata.restrictions.AllowCommercialUse
+    assert metadata.restrictions.AllowCommercialUse == CommercialUsage("RentCivit")
     assert metadata.version_id == 242807
     assert metadata.tags == {"tool", "turbo", "sdxl turbo"}