recover gracefully from VRAM out of memory errors

Currently translated at 80.0% (1176 of 1470 strings)

Co-authored-by: Alexander Eichhorn <pfannkuchensack@einfach-doof.de>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/de/
Translation: InvokeAI/Web UI

docs/contributing/INVOCATIONS.md

@@ -9,15 +9,11 @@ complex functionality.
 
 ## Invocations Directory
 
-InvokeAI Nodes can be found in the `invokeai/app/invocations` directory. These
-can be used as examples to create your own nodes.
+InvokeAI Nodes can be found in the `invokeai/app/invocations` directory. These can be used as examples to create your own nodes.
 
-New nodes should be added to a subfolder in `nodes` direction found at the root
-level of the InvokeAI installation location. Nodes added to this folder will be
-able to be used upon application startup.
-
-Example `nodes` subfolder structure:
+New nodes should be added to a subfolder in `nodes` direction found at the root level of the InvokeAI installation location. Nodes added to this folder will be able to be used upon application startup. 
 
+Example `nodes`  subfolder structure:
 ```py
 ├── __init__.py # Invoke-managed custom node loader
 │
@@ -34,14 +30,14 @@ Example `nodes` subfolder structure:
         └── fancy_node.py
 ```
 
-Each node folder must have an `__init__.py` file that imports its nodes. Only
-nodes imported in the `__init__.py` file are loaded. See the README in the nodes
-folder for more examples:
+Each node folder must have an `__init__.py` file that imports its nodes. Only nodes imported in the `__init__.py` file are loaded.
+ See the README in the nodes folder for more examples: 
 
 ```py
 from .cool_node import CoolInvocation
 ```
 
+
 ## Creating A New Invocation
 
 In order to understand the process of creating a new Invocation, let us actually
@@ -135,6 +131,7 @@ from invokeai.app.invocations.primitives import ImageField
 class ResizeInvocation(BaseInvocation):
     '''Resizes an image'''
 
+    # Inputs
     image: ImageField = InputField(description="The input image")
     width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
     height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
@@ -170,6 +167,7 @@ from invokeai.app.invocations.primitives import ImageField
 class ResizeInvocation(BaseInvocation):
     '''Resizes an image'''
 
+    # Inputs
     image: ImageField = InputField(description="The input image")
     width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
     height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
@@ -199,6 +197,7 @@ from invokeai.app.invocations.image import ImageOutput
 class ResizeInvocation(BaseInvocation):
     '''Resizes an image'''
 
+    # Inputs
     image: ImageField = InputField(description="The input image")
     width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
     height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
@@ -230,17 +229,30 @@ class ResizeInvocation(BaseInvocation):
     height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        # Load the input image as a PIL image
-        image = context.images.get_pil(self.image.image_name)
+        # Load the image using InvokeAI's predefined Image Service. Returns the PIL image.
+        image = context.services.images.get_pil_image(self.image.image_name)
 
-        # Resize the image
+        # Resizing the image
         resized_image = image.resize((self.width, self.height))
 
-        # Save the image
-        image_dto = context.images.save(image=resized_image)
+        # Save the image using InvokeAI's predefined Image Service. Returns the prepared PIL image.
+        output_image = context.services.images.create(
+            image=resized_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,
+        )
 
-        # Return an ImageOutput
-        return ImageOutput.build(image_dto)
+        # Returning the Image
+        return ImageOutput(
+            image=ImageField(
+                image_name=output_image.image_name,
+            ),
+            width=output_image.width,
+            height=output_image.height,
+        )
 ```
 
 **Note:** Do not be overwhelmed by the `ImageOutput` process. InvokeAI has a
@@ -331,25 +343,27 @@ class ImageColorStringOutput(BaseInvocationOutput):
 
 That's all there is to it.
 
+<!-- TODO: DANGER - we probably do not want people to create their own field types, because this requires a lot of work on the frontend to accomodate.
+
 ### Custom Input Fields
 
 Now that you know how to create your own Invocations, let us dive into slightly
 more advanced topics.
 
 While creating your own Invocations, you might run into a scenario where the
-existing fields in InvokeAI do not meet your requirements. In such cases, you
-can create your own fields.
+existing input types in InvokeAI do not meet your requirements. In such cases,
+you can create your own input types.
 
 Let us create one as an example. Let us say we want to create a color input
 field that represents a color code. But before we start on that here are some
 general good practices to keep in mind.
 
-### Best Practices
+**Good Practices**
 
 - There is no naming convention for input fields but we highly recommend that
   you name it something appropriate like `ColorField`.
 - It is not mandatory but it is heavily recommended to add a relevant
-  `docstring` to describe your field.
+  `docstring` to describe your input field.
 - Keep your field in the same file as the Invocation that it is made for or in
   another file where it is relevant.
 
@@ -364,13 +378,10 @@ class ColorField(BaseModel):
     pass
 ```
 
-Perfect. Now let us create the properties for our field. This is similar to how
-you created input fields for your Invocation. All the same rules apply. Let us
-create four fields representing the _red(r)_, _blue(b)_, _green(g)_ and
-_alpha(a)_ channel of the color.
-
-> Technically, the properties are _also_ called fields - but in this case, it
-> refers to a `pydantic` field.
+Perfect. Now let us create our custom inputs for our field. This is exactly
+similar how you created input fields for your Invocation. All the same rules
+apply. Let us create four fields representing the _red(r)_, _blue(b)_,
+_green(g)_ and _alpha(a)_ channel of the color.
 
 ```python
 class ColorField(BaseModel):
@@ -385,11 +396,25 @@ That's it. We now have a new input field type that we can use in our Invocations
 like this.
 
 ```python
-color: ColorField = InputField(default=ColorField(r=0, g=0, b=0, a=0), description='Background color of an image')
+color: ColorField = Field(default=ColorField(r=0, g=0, b=0, a=0), description='Background color of an image')
 ```
 
-### Using the custom field
+### Custom Components For Frontend
 
-When you start the UI, your custom field will be automatically recognized.
+Every backend input type should have a corresponding frontend component so the
+UI knows what to render when you use a particular field type.
 
-Custom fields only support connection inputs in the Workflow Editor.
+If you are using existing field types, we already have components for those. So
+you don't have to worry about creating anything new. But this might not always
+be the case. Sometimes you might want to create new field types and have the
+frontend UI deal with it in a different way.
+
+This is where we venture into the world of React and Javascript and create our
+own new components for our Invocations. Do not fear the world of JS. It's
+actually pretty straightforward.
+
+Let us create a new component for our custom color field we created above. When
+we use a color field, let us say we want the UI to display a color picker for
+the user to pick from rather than entering values. That is what we will build
+now.
+-->

docs/contributing/MODEL_MANAGER.md

@@ -28,7 +28,7 @@ model. These are the:
   Hugging Face, as well as discriminating among model versions in
   Civitai, but can be used for arbitrary content.
   
-  * _ModelLoadServiceBase_
+  * _ModelLoadServiceBase_ (**CURRENTLY UNDER DEVELOPMENT - NOT IMPLEMENTED**)
   Responsible for loading a model from disk
   into RAM and VRAM and getting it ready for inference.
 
@@ -41,10 +41,10 @@ The four main services can be found in
 * `invokeai/app/services/model_records/`
 * `invokeai/app/services/model_install/`
 * `invokeai/app/services/downloads/`
-* `invokeai/app/services/model_load/`
+* `invokeai/app/services/model_loader/` (**under development**)
 
 Code related to the FastAPI web API can be found in
-`invokeai/app/api/routers/model_manager_v2.py`.
+`invokeai/app/api/routers/model_records.py`.
 
 ***
 
@@ -84,10 +84,10 @@ diffusers model. When this happens, `original_hash` is unchanged, but
 `ModelType`, `ModelFormat` and `BaseModelType` are string enums that
 are defined in `invokeai.backend.model_manager.config`. They are also
 imported by, and can be reexported from,
-`invokeai.app.services.model_manager.model_records`:
+`invokeai.app.services.model_record_service`:
 
 ```
-from invokeai.app.services.model_records import ModelType, ModelFormat, BaseModelType
+from invokeai.app.services.model_record_service import ModelType, ModelFormat, BaseModelType
 ```
 
 The `path` field can be absolute or relative. If relative, it is taken
@@ -123,7 +123,7 @@ taken to be the `models_dir` directory.
 
 `variant` is an enumerated string class with values `normal`,
 `inpaint` and `depth`. If needed, it can be imported if needed from
-either `invokeai.app.services.model_records` or
+either `invokeai.app.services.model_record_service` or
 `invokeai.backend.model_manager.config`.
 
 ### ONNXSD2Config
@@ -134,7 +134,7 @@ either `invokeai.app.services.model_records` or
 | `upcast_attention`  | bool | Model requires its attention module to be upcast |
 
 The `SchedulerPredictionType` enum can be imported from either
-`invokeai.app.services.model_records` or
+`invokeai.app.services.model_record_service` or
 `invokeai.backend.model_manager.config`.
 
 ### Other config classes
@@ -157,6 +157,15 @@ indicates that the model is compatible with any of the base
 models. This works OK for some models, such as the IP Adapter image
 encoders, but is an all-or-nothing proposition.
 
+Another issue is that the config class hierarchy is paralleled to some
+extent by a `ModelBase` class hierarchy defined in
+`invokeai.backend.model_manager.models.base` and its subclasses. These
+are classes representing the models after they are loaded into RAM and
+include runtime information such as load status and bytes used. Some
+of the fields, including `name`, `model_type` and `base_model`, are
+shared between `ModelConfigBase` and `ModelBase`, and this is a
+potential source of confusion.
+
 ## Reading and Writing Model Configuration Records
 
 The `ModelRecordService` provides the ability to retrieve model
@@ -168,11 +177,11 @@ initialization and can be retrieved within an invocation from the
 `InvocationContext` object:
 
 ```
-store = context.services.model_manager.store
+store = context.services.model_record_store
 ```
 
 or from elsewhere in the code by accessing
-`ApiDependencies.invoker.services.model_manager.store`.
+`ApiDependencies.invoker.services.model_record_store`.
 
 ### Creating a `ModelRecordService` 
 
@@ -181,7 +190,7 @@ you can directly create either a `ModelRecordServiceSQL` or a
 `ModelRecordServiceFile` object:
 
 ```
-from invokeai.app.services.model_records import ModelRecordServiceSQL, ModelRecordServiceFile
+from invokeai.app.services.model_record_service import ModelRecordServiceSQL, ModelRecordServiceFile
 
 store = ModelRecordServiceSQL.from_connection(connection, lock)
 store = ModelRecordServiceSQL.from_db_file('/path/to/sqlite_database.db')
@@ -243,7 +252,7 @@ So a typical startup pattern would be:
 ```
 import sqlite3
 from invokeai.app.services.thread import lock
-from invokeai.app.services.model_records import ModelRecordServiceBase
+from invokeai.app.services.model_record_service import ModelRecordServiceBase
 from invokeai.app.services.config import InvokeAIAppConfig
 
 config = InvokeAIAppConfig.get_config()
@@ -251,6 +260,19 @@ db_conn = sqlite3.connect(config.db_path.as_posix(), check_same_thread=False)
 store = ModelRecordServiceBase.open(config, db_conn, lock)
 ```
 
+_A note on simultaneous access to `invokeai.db`_: The current InvokeAI
+service architecture for the image and graph databases is careful to
+use a shared sqlite3 connection and a thread lock to ensure that two
+threads don't attempt to access the database simultaneously. However,
+the default `sqlite3` library used by Python reports using
+**Serialized** mode, which allows multiple threads to access the
+database simultaneously using multiple database connections (see
+https://www.sqlite.org/threadsafe.html and
+https://ricardoanderegg.com/posts/python-sqlite-thread-safety/). Therefore
+it should be safe to allow the record service to open its own SQLite
+database connection. Opening a model record service should then be as
+simple as `ModelRecordServiceBase.open(config)`.
+
 ### Fetching a Model's Configuration from `ModelRecordServiceBase`
 
 Configurations can be retrieved in several ways.
@@ -446,44 +468,6 @@ required parameters:
 
 Once initialized, the installer will provide the following methods:
 
-#### install_job = installer.heuristic_import(source, [config], [access_token])
-
-This is a simplified interface to the installer which takes a source
-string, an optional model configuration dictionary and an optional
-access token.
-
-The `source` is a string that can be any of these forms
-
-1. A path on the local filesystem (`C:\\users\\fred\\model.safetensors`)
-2. A Url pointing to a single downloadable model file (`https://civitai.com/models/58390/detail-tweaker-lora-lora`)
-3. A HuggingFace repo_id with any of the following formats:
-   - `model/name` -- entire model
-   - `model/name:fp32` -- entire model, using the fp32 variant
-   - `model/name:fp16:vae` -- vae submodel, using the fp16 variant
-   - `model/name::vae` -- vae submodel, using default precision
-   - `model/name:fp16:path/to/model.safetensors` -- an individual model file, fp16 variant
-   - `model/name::path/to/model.safetensors` -- an individual model file, default variant
-
-Note that by specifying a relative path to the top of the HuggingFace
-repo, you can download and install arbitrary models files. 
-
-The variant, if not provided, will be automatically filled in with
-`fp32` if the user has requested full precision, and `fp16`
-otherwise. If a variant that does not exist is requested, then the
-method will install whatever HuggingFace returns as its default
-revision.
-
-`config` is an optional dict of values that will override the
-autoprobed values for model type, base, scheduler prediction type, and
-so forth. See [Model configuration and
-probing](#Model-configuration-and-probing) for details.
-
-`access_token` is an optional access token for accessing resources
-that need authentication.
-
-The method will return a `ModelInstallJob`. This object is discussed
-at length in the following section.
-
 #### install_job = installer.import_model()
 
 The `import_model()` method is the core of the installer. The
@@ -502,10 +486,9 @@ source2 = LocalModelSource(path='/opt/models/sushi_diffusers')     # a local dif
 source3 = HFModelSource(repo_id='runwayml/stable-diffusion-v1-5')  # a repo_id
 source4 = HFModelSource(repo_id='runwayml/stable-diffusion-v1-5', subfolder='vae')  # a subfolder within a repo_id
 source5 = HFModelSource(repo_id='runwayml/stable-diffusion-v1-5', variant='fp16')   # a named variant of a HF model
-source6 = HFModelSource(repo_id='runwayml/stable-diffusion-v1-5', subfolder='OrangeMix/OrangeMix1.ckpt')   # path to an individual model file
 
-source7 = URLModelSource(url='https://civitai.com/api/download/models/63006')       # model located at a URL
-source8 = URLModelSource(url='https://civitai.com/api/download/models/63006', access_token='letmein') # with an access token
+source6 = URLModelSource(url='https://civitai.com/api/download/models/63006')       # model located at a URL
+source7 = URLModelSource(url='https://civitai.com/api/download/models/63006', access_token='letmein') # with an access token
 
 for source in [source1, source2, source3, source4, source5, source6, source7]:
    install_job = installer.install_model(source)
@@ -561,6 +544,7 @@ can be passed to `import_model()`.
 attributes returned by the model prober. See the section below for
 details.
 
+
 #### LocalModelSource
 
 This is used for a model that is located on a locally-accessible Posix
@@ -753,7 +737,7 @@ and `cancelled`, as well as `in_terminal_state`. The last will return
 True if the job is in the complete, errored or cancelled states.
 
 
-#### Model configuration and probing
+#### Model confguration and probing
 
 The install service uses the `invokeai.backend.model_manager.probe`
 module during import to determine the model's type, base type, and
@@ -792,14 +776,6 @@ returns a list of completed jobs. The optional `timeout` argument will
 return from the call if jobs aren't completed in the specified
 time. An argument of 0 (the default) will block indefinitely.
 
-#### jobs = installer.wait_for_job(job, [timeout])
-
-Like `wait_for_installs()`, but block until a specific job has
-completed or errored, and then return the job.  The optional `timeout`
-argument will return from the call if the job doesn't complete in the
-specified time. An argument of 0 (the default) will block
-indefinitely.
-
 #### jobs = installer.list_jobs()
 
 Return a list of all active and complete `ModelInstallJobs`.
@@ -862,31 +838,6 @@ This method is similar to `unregister()`, but also unconditionally
 deletes the corresponding model weights file(s), regardless of whether
 they are inside or outside the InvokeAI models hierarchy.
 
-
-#### path = installer.download_and_cache(remote_source, [access_token], [timeout])
-
-This utility routine will download the model file located at source,
-cache it, and return the path to the cached file. It does not attempt
-to determine the model type, probe its configuration values, or
-register it with the models database.
-
-You may provide an access token if the remote source requires
-authorization. The call will block indefinitely until the file is
-completely downloaded, cancelled or raises an error of some sort. If
-you provide a timeout (in seconds), the call will raise a
-`TimeoutError` exception if the download hasn't completed in the
-specified period.
-
-You may use this mechanism to request any type of file, not just a
-model. The file will be stored in a subdirectory of
-`INVOKEAI_ROOT/models/.cache`. If the requested file is found in the
-cache, its path will be returned without redownloading it.
-
-Be aware that the models cache is cleared of infrequently-used files
-and directories at regular intervals when the size of the cache
-exceeds the value specified in Invoke's `convert_cache` configuration
-variable.
-
 #### List[str]=installer.scan_directory(scan_dir: Path, install: bool)
 
 This method will recursively scan the directory indicated in
@@ -1177,7 +1128,7 @@ job = queue.create_download_job(
 		 event_handlers=[my_handler1, my_handler2], # if desired
 		 start=True,
 	)
-```
+ ```
  
 The `filename` argument forces the downloader to use the specified
 name for the file rather than the name provided by the remote source,
@@ -1220,13 +1171,6 @@ queue or was not created by this queue.
 This method will block until all the active jobs in the queue have
 reached a terminal state (completed, errored or cancelled).
 
-#### queue.wait_for_job(job, [timeout])
-
-This method will block until the indicated job has reached a terminal
-state (completed, errored or cancelled). If the optional timeout is
-provided, the call will block for at most timeout seconds, and raise a
-TimeoutError otherwise.
-
 #### jobs = queue.list_jobs()
 
 This will return a list of all jobs, including ones that have not yet
@@ -1505,9 +1449,9 @@ set of keys to the corresponding model config objects.
 Find all model metadata records that have the given author and return
 a set of keys to the corresponding model config objects.
 
-***
+# The remainder of this documentation is provisional, pending implementation of the Load service
 
-## The Lowdown on the ModelLoadService
+## Let's get loaded, the lowdown on ModelLoadService
 
 The `ModelLoadService` is responsible for loading a named model into
 memory so that it can be used for inference. Despite the fact that it
@@ -1521,7 +1465,7 @@ create alternative instances if you wish.
 ### Creating a ModelLoadService object
 
 The class is defined in
-`invokeai.app.services.model_load`. It is initialized with
+`invokeai.app.services.model_loader_service`. It is initialized with
 an InvokeAIAppConfig object, from which it gets configuration
 information such as the user's desired GPU and precision, and with a
 previously-created `ModelRecordServiceBase` object, from which it
@@ -1531,29 +1475,26 @@ Here is a typical initialization pattern:
 
 ```
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.app.services.model_load import ModelLoadService, ModelLoaderRegistry
+from invokeai.app.services.model_record_service import ModelRecordServiceBase
+from invokeai.app.services.model_loader_service import ModelLoadService
 
 config = InvokeAIAppConfig.get_config()
-ram_cache = ModelCache(
-	max_cache_size=config.ram_cache_size, max_vram_cache_size=config.vram_cache_size, logger=logger
-)
-convert_cache = ModelConvertCache(
-	cache_path=config.models_convert_cache_path, max_size=config.convert_cache_size
-)
-loader = ModelLoadService(
-	app_config=config,
-	ram_cache=ram_cache,
-	convert_cache=convert_cache,
-	registry=ModelLoaderRegistry
-)
+store = ModelRecordServiceBase.open(config)
+loader = ModelLoadService(config, store)
 ```
 
-### load_model(model_config, [submodel_type], [context]) -> LoadedModel
+Note that we are relying on the contents of the application
+configuration to choose the implementation of
+`ModelRecordServiceBase`.
 
-The `load_model()` method takes an `AnyModelConfig` returned by
-`ModelRecordService.get_model()` and returns the corresponding loaded
+### get_model(key, [submodel_type], [context]) -> ModelInfo:
+
+*** TO DO: change to get_model(key, context=None, **kwargs)
+
+The `get_model()` method, like its similarly-named cousin in
+`ModelRecordService`, receives the unique key that identifies the
 model.  It loads the model into memory, gets the model ready for use,
-and returns a `LoadedModel` object.
+and returns a `ModelInfo` object. 
 
 The optional second argument, `subtype` is a `SubModelType` string
 enum, such as "vae". It is mandatory when used with a main model, and
@@ -1563,45 +1504,46 @@ The optional third argument, `context` can be provided by
 an invocation to trigger model load event reporting. See below for
 details.
 
-The returned `LoadedModel` object contains a copy of the configuration
-record returned by the model record `get_model()` method, as well as
-the in-memory loaded model:
+The returned `ModelInfo` object shares some fields in common with
+`ModelConfigBase`, but is otherwise a completely different beast:
 
-
-| **Attribute Name** | **Type**        |  **Description** |
+| **Field Name** | **Type**        |  **Description** |
 |----------------|-----------------|------------------|
-| `config`       | AnyModelConfig         | A copy of the model's configuration record for retrieving base type, etc. |
-| `model`        | AnyModel               | The instantiated model (details below) |
-| `locker`       | ModelLockerBase        | A context manager that mediates the movement of the model into VRAM |
+| `key`          | str                    | The model key derived from the ModelRecordService database |
+| `name`         | str                    | Name of this model |
+| `base_model`   | BaseModelType          | Base model for this model |
+| `type`         | ModelType or SubModelType   | Either the model type (non-main) or the submodel type (main models)|
+| `location`     | Path or str            | Location of the model on the filesystem |
+| `precision`    | torch.dtype            | The torch.precision to use for inference |
+| `context`      | ModelCache.ModelLocker | A context class used to lock the model in VRAM while in use |
 
-Because the loader can return multiple model types, it is typed to
-return `AnyModel`, a Union `ModelMixin`, `torch.nn.Module`,
-`IAIOnnxRuntimeModel`, `IPAdapter`, `IPAdapterPlus`, and
-`EmbeddingModelRaw`. `ModelMixin` is the base class of all diffusers
-models, `EmbeddingModelRaw` is used for LoRA and TextualInversion
-models. The others are obvious.
+The types for `ModelInfo` and `SubModelType` can be imported from
+`invokeai.app.services.model_loader_service`.
 
-
-`LoadedModel` acts as a context manager. The context loads the model
-into the execution device (e.g. VRAM on CUDA systems), locks the model
-in the execution device for the duration of the context, and returns
-the model. Use it like this:
+To use the model, you use the `ModelInfo` as a context manager using
+the following pattern:
 
 ```
-model_info = loader.get_model_by_key('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
+model_info = loader.get_model('f13dd932c0c35c22dcb8d6cda4203764', SubModelType('vae'))
 with model_info as vae:
 	image = vae.decode(latents)[0]
 ```
 
-`get_model_by_key()` may raise any of the following exceptions:
+The `vae` model will stay locked in the GPU during the period of time
+it is in the context manager's scope.
 
-- `UnknownModelException`   -- key not in database
-- `ModelNotFoundException`  -- key in database but model not found at path
-- `NotImplementedException` -- the loader doesn't know how to load this type of model
+`get_model()` may raise any of the following exceptions:
+
+- `UnknownModelException`  -- key not in database
+- `ModelNotFoundException` -- key in database but model not found at path
+- `InvalidModelException`  -- the model is guilty of a variety of sins
   
+** TO DO: ** Resolve discrepancy between ModelInfo.location and
+ModelConfig.path.
+
 ### Emitting model loading events
 
-When the `context` argument is passed to `load_model_*()`, it will
+When the `context` argument is passed to `get_model()`, it will
 retrieve the invocation event bus from the passed `InvocationContext`
 object to emit events on the invocation bus. The two events are
 "model_load_started" and "model_load_completed". Both carry the
@@ -1614,174 +1556,10 @@ payload=dict(
 	queue_batch_id=queue_batch_id,
 	graph_execution_state_id=graph_execution_state_id,
 	model_key=model_key,
-	submodel_type=submodel,
+	submodel=submodel,
 	hash=model_info.hash,
 	location=str(model_info.location),
 	precision=str(model_info.precision),
 )
 ```
 
-### Adding Model Loaders
-
-Model loaders are small classes that inherit from the `ModelLoader`
-base class. They typically implement one method `_load_model()` whose
-signature is:
-
-```
-def _load_model(
-    self,
-    model_path: Path,
-    model_variant: Optional[ModelRepoVariant] = None,
-    submodel_type: Optional[SubModelType] = None,
-) -> AnyModel:
-```
-
-`_load_model()` will be passed the path to the model on disk, an
-optional repository variant (used by the diffusers loaders to select,
-e.g.  the `fp16` variant, and an optional submodel_type for main and
-onnx models.
-
-To install a new loader, place it in
-`invokeai/backend/model_manager/load/model_loaders`. Inherit from
-`ModelLoader` and use the `@ModelLoaderRegistry.register()` decorator to
-indicate what type of models the loader can handle.
-
-Here is a complete example from `generic_diffusers.py`, which is able
-to load several different diffusers types:
-
-```
-from pathlib import Path
-from typing import Optional
-
-from invokeai.backend.model_manager import (
-    AnyModel,
-    BaseModelType,
-    ModelFormat,
-    ModelRepoVariant,
-    ModelType,
-    SubModelType,
-)
-from .. import ModelLoader, ModelLoaderRegistry
-
-
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.CLIPVision, format=ModelFormat.Diffusers)
-@ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.T2IAdapter, format=ModelFormat.Diffusers)
-class GenericDiffusersLoader(ModelLoader):
-    """Class to load simple diffusers models."""
-
-    def _load_model(
-        self,
-        model_path: Path,
-        model_variant: Optional[ModelRepoVariant] = None,
-        submodel_type: Optional[SubModelType] = None,
-    ) -> AnyModel:
-        model_class = self._get_hf_load_class(model_path)
-        if submodel_type is not None:
-            raise Exception(f"There are no submodels in models of type {model_class}")
-        variant = model_variant.value if model_variant else None
-        result: AnyModel = model_class.from_pretrained(model_path, torch_dtype=self._torch_dtype, variant=variant)  # type: ignore
-        return result
-```
-
-Note that a loader can register itself to handle several different
-model types. An exception will be raised if more than one loader tries
-to register the same model type.
-
-#### Conversion
-
-Some models require conversion to diffusers format before they can be
-loaded. These loaders should override two additional methods:
-
-```
-_needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool
-_convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
-```
-
-The first method accepts the model configuration, the path to where
-the unmodified model is currently installed, and a proposed
-destination for the converted model. This method returns True if the
-model needs to be converted. It typically does this by comparing the
-last modification time of the original model file to the modification
-time of the converted model. In some cases you will also want to check
-the modification date of the configuration record, in the event that
-the user has changed something like the scheduler prediction type that
-will require the model to be re-converted. See `controlnet.py` for an
-example of this logic.
-
-The second method accepts the model configuration, the path to the
-original model on disk, and the desired output path for the converted
-model. It does whatever it needs to do to get the model into diffusers
-format, and returns the Path of the resulting model. (The path should
-ordinarily be the same as `output_path`.)
-
-## The ModelManagerService object
-
-For convenience, the API provides a `ModelManagerService` object which
-gives a single point of access to the major model manager
-services. This object is created at initialization time and can be
-found in the global `ApiDependencies.invoker.services.model_manager`
-object, or in `context.services.model_manager` from within an
-invocation.
-
-In the examples below, we have retrieved the manager using:
-```
-mm = ApiDependencies.invoker.services.model_manager
-```
-
-The following properties and methods will be available:
-
-### mm.store
-
-This retrieves the `ModelRecordService` associated with the
-manager. Example:
-
-```
-configs = mm.store.get_model_by_attr(name='stable-diffusion-v1-5')
-```
-
-### mm.install
-
-This retrieves the `ModelInstallService` associated with the manager.
-Example:
-
-```
-job = mm.install.heuristic_import(`https://civitai.com/models/58390/detail-tweaker-lora-lora`)
-```
-
-### mm.load
-
-This retrieves the `ModelLoaderService` associated with the manager. Example:
-
-```
-configs = mm.store.get_model_by_attr(name='stable-diffusion-v1-5')
-assert len(configs) > 0
-
-loaded_model = mm.load.load_model(configs[0])
-```
-
-The model manager also offers a few convenience shortcuts for loading
-models:
-
-### mm.load_model_by_config(model_config, [submodel], [context]) -> LoadedModel
-
-Same as `mm.load.load_model()`.
-
-### mm.load_model_by_attr(model_name, base_model, model_type, [submodel], [context]) -> LoadedModel
-
-This accepts the combination of the model's name, type and base, which
-it passes to the model record config store for retrieval. If a unique
-model config is found, this method returns a `LoadedModel`. It can
-raise the following exceptions:
-
-```
-UnknownModelException -- model with these attributes not known
-NotImplementedException -- the loader doesn't know how to load this type of model
-ValueError -- more than one model matches this combination of base/type/name
-```
-
-### mm.load_model_by_key(key, [submodel], [context]) -> LoadedModel
-
-This method takes a model key, looks it up using the
-`ModelRecordServiceBase` object in `mm.store`, and passes the returned
-model configuration to `load_model_by_config()`.  It may raise a
-`NotImplementedException`.

docs/nodes/communityNodes.md

@@ -32,6 +32,7 @@ To use a community workflow, download the the `.json` node graph file and load i
     + [Image to Character Art Image Nodes](#image-to-character-art-image-nodes)
     + [Image Picker](#image-picker)
     + [Image Resize Plus](#image-resize-plus)
+    + [Latent Upscale](#latent-upscale)
     + [Load Video Frame](#load-video-frame)
     + [Make 3D](#make-3d)
     + [Mask Operations](#mask-operations)
@@ -290,6 +291,13 @@ View:
 </br><img src="https://raw.githubusercontent.com/VeyDlin/image-resize-plus-node/master/.readme/node.png" width="500" />
 
 
+--------------------------------
+### Latent Upscale
+
+**Description:** This node uses a small (~2.4mb) model to upscale the latents used in a Stable Diffusion 1.5 or Stable Diffusion XL image generation, rather than the typical interpolation method, avoiding the traditional downsides of the latent upscale technique.
+
+**Node Link:** [https://github.com/gogurtenjoyer/latent-upscale](https://github.com/gogurtenjoyer/latent-upscale)
+
 --------------------------------
 ### Load Video Frame
 

invokeai/app/api/dependencies.py

@@ -2,13 +2,9 @@
 
 from logging import Logger
 
-import torch
-
 from invokeai.app.services.item_storage.item_storage_memory import ItemStorageMemory
-from invokeai.app.services.object_serializer.object_serializer_disk import ObjectSerializerDisk
-from invokeai.app.services.object_serializer.object_serializer_forward_cache import ObjectSerializerForwardCache
 from invokeai.app.services.shared.sqlite.sqlite_util import init_db
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
+from invokeai.backend.model_manager.metadata import ModelMetadataStore
 from invokeai.backend.util.logging import InvokeAILogger
 from invokeai.version.invokeai_version import __version__
 
@@ -27,8 +23,10 @@ from ..services.invocation_queue.invocation_queue_memory import MemoryInvocation
 from ..services.invocation_services import InvocationServices
 from ..services.invocation_stats.invocation_stats_default import InvocationStatsService
 from ..services.invoker import Invoker
+from ..services.latents_storage.latents_storage_disk import DiskLatentsStorage
+from ..services.latents_storage.latents_storage_forward_cache import ForwardCacheLatentsStorage
+from ..services.model_install import ModelInstallService
 from ..services.model_manager.model_manager_default import ModelManagerService
-from ..services.model_metadata import ModelMetadataStoreSQL
 from ..services.model_records import ModelRecordServiceSQL
 from ..services.names.names_default import SimpleNameService
 from ..services.session_processor.session_processor_default import DefaultSessionProcessor
@@ -70,9 +68,6 @@ class ApiDependencies:
         logger.debug(f"Internet connectivity is {config.internet_available}")
 
         output_folder = config.output_path
-        if output_folder is None:
-            raise ValueError("Output folder is not set")
-
         image_files = DiskImageFileStorage(f"{output_folder}/images")
 
         db = init_db(config=config, logger=logger, image_files=image_files)
@@ -89,19 +84,17 @@ class ApiDependencies:
         image_records = SqliteImageRecordStorage(db=db)
         images = ImageService()
         invocation_cache = MemoryInvocationCache(max_cache_size=config.node_cache_size)
-        tensors = ObjectSerializerForwardCache(
-            ObjectSerializerDisk[torch.Tensor](output_folder / "tensors", ephemeral=True)
-        )
-        conditioning = ObjectSerializerForwardCache(
-            ObjectSerializerDisk[ConditioningFieldData](output_folder / "conditioning", ephemeral=True)
-        )
+        latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
+        model_manager = ModelManagerService(config, logger)
+        model_record_service = ModelRecordServiceSQL(db=db)
         download_queue_service = DownloadQueueService(event_bus=events)
-        model_metadata_service = ModelMetadataStoreSQL(db=db)
-        model_manager = ModelManagerService.build_model_manager(
-            app_config=configuration,
-            model_record_service=ModelRecordServiceSQL(db=db, metadata_store=model_metadata_service),
+        metadata_store = ModelMetadataStore(db=db)
+        model_install_service = ModelInstallService(
+            app_config=config,
+            record_store=model_record_service,
             download_queue=download_queue_service,
-            events=events,
+            metadata_store=metadata_store,
+            event_bus=events,
         )
         names = SimpleNameService()
         performance_statistics = InvocationStatsService()
@@ -124,9 +117,12 @@ class ApiDependencies:
             image_records=image_records,
             images=images,
             invocation_cache=invocation_cache,
+            latents=latents,
             logger=logger,
             model_manager=model_manager,
+            model_records=model_record_service,
             download_queue=download_queue_service,
+            model_install=model_install_service,
             names=names,
             performance_statistics=performance_statistics,
             processor=processor,
@@ -135,8 +131,6 @@ class ApiDependencies:
             session_queue=session_queue,
             urls=urls,
             workflow_records=workflow_records,
-            tensors=tensors,
-            conditioning=conditioning,
         )
 
         ApiDependencies.invoker = Invoker(services)

invokeai/app/api/routers/download_queue.py

@@ -36,7 +36,7 @@ async def list_downloads() -> List[DownloadJob]:
         400: {"description": "Bad request"},
     },
 )
-async def prune_downloads() -> Response:
+async def prune_downloads():
     """Prune completed and errored jobs."""
     queue = ApiDependencies.invoker.services.download_queue
     queue.prune_jobs()
@@ -55,7 +55,7 @@ async def download(
 ) -> DownloadJob:
     """Download the source URL to the file or directory indicted in dest."""
     queue = ApiDependencies.invoker.services.download_queue
-    return queue.download(source, Path(dest), priority, access_token)
+    return queue.download(source, dest, priority, access_token)
 
 
 @download_queue_router.get(
@@ -87,7 +87,7 @@ async def get_download_job(
 )
 async def cancel_download_job(
     id: int = Path(description="ID of the download job to cancel."),
-) -> Response:
+):
     """Cancel a download job using its ID."""
     try:
         queue = ApiDependencies.invoker.services.download_queue
@@ -105,7 +105,7 @@ async def cancel_download_job(
         204: {"description": "Download jobs have been cancelled"},
     },
 )
-async def cancel_all_download_jobs() -> Response:
+async def cancel_all_download_jobs():
     """Cancel all download jobs."""
     ApiDependencies.invoker.services.download_queue.cancel_all_jobs()
     return Response(status_code=204)

invokeai/app/api/routers/images.py

@@ -8,7 +8,7 @@ from fastapi.routing import APIRouter
 from PIL import Image
 from pydantic import BaseModel, Field, ValidationError
 
-from invokeai.app.invocations.fields import MetadataField, MetadataFieldValidator
+from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ImageRecordChanges, ResourceOrigin
 from invokeai.app.services.images.images_common import ImageDTO, ImageUrlsDTO
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults

invokeai/app/api/routers/model_manager.py

@@ -1,759 +0,0 @@
-# Copyright (c) 2023 Lincoln D. Stein
-"""FastAPI route for model configuration records."""
-
-import pathlib
-import shutil
-from hashlib import sha1
-from random import randbytes
-from typing import Any, Dict, List, Optional, Set
-
-from fastapi import Body, Path, Query, Response
-from fastapi.routing import APIRouter
-from pydantic import BaseModel, ConfigDict
-from starlette.exceptions import HTTPException
-from typing_extensions import Annotated
-
-from invokeai.app.services.model_install import ModelInstallJob, ModelSource
-from invokeai.app.services.model_records import (
-    DuplicateModelException,
-    InvalidModelException,
-    ModelRecordOrderBy,
-    ModelSummary,
-    UnknownModelException,
-)
-from invokeai.app.services.shared.pagination import PaginatedResults
-from invokeai.backend.model_manager.config import (
-    AnyModelConfig,
-    BaseModelType,
-    MainCheckpointConfig,
-    ModelFormat,
-    ModelType,
-    SubModelType,
-)
-from invokeai.backend.model_manager.merge import MergeInterpolationMethod, ModelMerger
-from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
-
-from ..dependencies import ApiDependencies
-
-model_manager_router = APIRouter(prefix="/v2/models", tags=["model_manager"])
-
-
-class ModelsList(BaseModel):
-    """Return list of configs."""
-
-    models: List[AnyModelConfig]
-
-    model_config = ConfigDict(use_enum_values=True)
-
-
-class ModelTagSet(BaseModel):
-    """Return tags for a set of models."""
-
-    key: str
-    name: str
-    author: str
-    tags: Set[str]
-
-
-##############################################################################
-# These are example inputs and outputs that are used in places where Swagger
-# is unable to generate a correct example.
-##############################################################################
-example_model_config = {
-    "path": "string",
-    "name": "string",
-    "base": "sd-1",
-    "type": "main",
-    "format": "checkpoint",
-    "config": "string",
-    "key": "string",
-    "original_hash": "string",
-    "current_hash": "string",
-    "description": "string",
-    "source": "string",
-    "last_modified": 0,
-    "vae": "string",
-    "variant": "normal",
-    "prediction_type": "epsilon",
-    "repo_variant": "fp16",
-    "upcast_attention": False,
-    "ztsnr_training": False,
-}
-
-example_model_input = {
-    "path": "/path/to/model",
-    "name": "model_name",
-    "base": "sd-1",
-    "type": "main",
-    "format": "checkpoint",
-    "config": "configs/stable-diffusion/v1-inference.yaml",
-    "description": "Model description",
-    "vae": None,
-    "variant": "normal",
-}
-
-example_model_metadata = {
-    "name": "ip_adapter_sd_image_encoder",
-    "author": "InvokeAI",
-    "tags": [
-        "transformers",
-        "safetensors",
-        "clip_vision_model",
-        "endpoints_compatible",
-        "region:us",
-        "has_space",
-        "license:apache-2.0",
-    ],
-    "files": [
-        {
-            "url": "https://huggingface.co/InvokeAI/ip_adapter_sd_image_encoder/resolve/main/README.md",
-            "path": "ip_adapter_sd_image_encoder/README.md",
-            "size": 628,
-            "sha256": None,
-        },
-        {
-            "url": "https://huggingface.co/InvokeAI/ip_adapter_sd_image_encoder/resolve/main/config.json",
-            "path": "ip_adapter_sd_image_encoder/config.json",
-            "size": 560,
-            "sha256": None,
-        },
-        {
-            "url": "https://huggingface.co/InvokeAI/ip_adapter_sd_image_encoder/resolve/main/model.safetensors",
-            "path": "ip_adapter_sd_image_encoder/model.safetensors",
-            "size": 2528373448,
-            "sha256": "6ca9667da1ca9e0b0f75e46bb030f7e011f44f86cbfb8d5a36590fcd7507b030",
-        },
-    ],
-    "type": "huggingface",
-    "id": "InvokeAI/ip_adapter_sd_image_encoder",
-    "tag_dict": {"license": "apache-2.0"},
-    "last_modified": "2023-09-23T17:33:25Z",
-}
-
-##############################################################################
-# ROUTES
-##############################################################################
-
-
-@model_manager_router.get(
-    "/",
-    operation_id="list_model_records",
-)
-async def list_model_records(
-    base_models: Optional[List[BaseModelType]] = Query(default=None, description="Base models to include"),
-    model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
-    model_name: Optional[str] = Query(default=None, description="Exact match on the name of the model"),
-    model_format: Optional[ModelFormat] = Query(
-        default=None, description="Exact match on the format of the model (e.g. 'diffusers')"
-    ),
-) -> ModelsList:
-    """Get a list of models."""
-    record_store = ApiDependencies.invoker.services.model_manager.store
-    found_models: list[AnyModelConfig] = []
-    if base_models:
-        for base_model in base_models:
-            found_models.extend(
-                record_store.search_by_attr(
-                    base_model=base_model, model_type=model_type, model_name=model_name, model_format=model_format
-                )
-            )
-    else:
-        found_models.extend(
-            record_store.search_by_attr(model_type=model_type, model_name=model_name, model_format=model_format)
-        )
-    return ModelsList(models=found_models)
-
-
-@model_manager_router.get(
-    "/i/{key}",
-    operation_id="get_model_record",
-    responses={
-        200: {
-            "description": "The model configuration was retrieved successfully",
-            "content": {"application/json": {"example": example_model_config}},
-        },
-        400: {"description": "Bad request"},
-        404: {"description": "The model could not be found"},
-    },
-)
-async def get_model_record(
-    key: str = Path(description="Key of the model record to fetch."),
-) -> AnyModelConfig:
-    """Get a model record"""
-    record_store = ApiDependencies.invoker.services.model_manager.store
-    try:
-        config: AnyModelConfig = record_store.get_model(key)
-        return config
-    except UnknownModelException as e:
-        raise HTTPException(status_code=404, detail=str(e))
-
-
-@model_manager_router.get("/summary", operation_id="list_model_summary")
-async def list_model_summary(
-    page: int = Query(default=0, description="The page to get"),
-    per_page: int = Query(default=10, description="The number of models per page"),
-    order_by: ModelRecordOrderBy = Query(default=ModelRecordOrderBy.Default, description="The attribute to order by"),
-) -> PaginatedResults[ModelSummary]:
-    """Gets a page of model summary data."""
-    record_store = ApiDependencies.invoker.services.model_manager.store
-    results: PaginatedResults[ModelSummary] = record_store.list_models(page=page, per_page=per_page, order_by=order_by)
-    return results
-
-
-@model_manager_router.get(
-    "/meta/i/{key}",
-    operation_id="get_model_metadata",
-    responses={
-        200: {
-            "description": "The model metadata was retrieved successfully",
-            "content": {"application/json": {"example": example_model_metadata}},
-        },
-        400: {"description": "Bad request"},
-        404: {"description": "No metadata available"},
-    },
-)
-async def get_model_metadata(
-    key: str = Path(description="Key of the model repo metadata to fetch."),
-) -> Optional[AnyModelRepoMetadata]:
-    """Get a model metadata object."""
-    record_store = ApiDependencies.invoker.services.model_manager.store
-    result: Optional[AnyModelRepoMetadata] = record_store.get_metadata(key)
-    if not result:
-        raise HTTPException(status_code=404, detail="No metadata for a model with this key")
-    return result
-
-
-@model_manager_router.get(
-    "/tags",
-    operation_id="list_tags",
-)
-async def list_tags() -> Set[str]:
-    """Get a unique set of all the model tags."""
-    record_store = ApiDependencies.invoker.services.model_manager.store
-    result: Set[str] = record_store.list_tags()
-    return result
-
-
-@model_manager_router.get(
-    "/tags/search",
-    operation_id="search_by_metadata_tags",
-)
-async def search_by_metadata_tags(
-    tags: Set[str] = Query(default=None, description="Tags to search for"),
-) -> ModelsList:
-    """Get a list of models."""
-    record_store = ApiDependencies.invoker.services.model_manager.store
-    results = record_store.search_by_metadata_tag(tags)
-    return ModelsList(models=results)
-
-
-@model_manager_router.patch(
-    "/i/{key}",
-    operation_id="update_model_record",
-    responses={
-        200: {
-            "description": "The model was updated successfully",
-            "content": {"application/json": {"example": example_model_config}},
-        },
-        400: {"description": "Bad request"},
-        404: {"description": "The model could not be found"},
-        409: {"description": "There is already a model corresponding to the new name"},
-    },
-    status_code=200,
-)
-async def update_model_record(
-    key: Annotated[str, Path(description="Unique key of model")],
-    info: Annotated[
-        AnyModelConfig, Body(description="Model config", discriminator="type", example=example_model_input)
-    ],
-) -> AnyModelConfig:
-    """Update model contents with a new config. If the model name or base fields are changed, then the model is renamed."""
-    logger = ApiDependencies.invoker.services.logger
-    record_store = ApiDependencies.invoker.services.model_manager.store
-    try:
-        model_response: AnyModelConfig = record_store.update_model(key, config=info)
-        logger.info(f"Updated model: {key}")
-    except UnknownModelException as e:
-        raise HTTPException(status_code=404, detail=str(e))
-    except ValueError as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=409, detail=str(e))
-    return model_response
-
-
-@model_manager_router.delete(
-    "/i/{key}",
-    operation_id="del_model_record",
-    responses={
-        204: {"description": "Model deleted successfully"},
-        404: {"description": "Model not found"},
-    },
-    status_code=204,
-)
-async def del_model_record(
-    key: str = Path(description="Unique key of model to remove from model registry."),
-) -> Response:
-    """
-    Delete model record from database.
-
-    The configuration record will be removed. The corresponding weights files will be
-    deleted as well if they reside within the InvokeAI "models" directory.
-    """
-    logger = ApiDependencies.invoker.services.logger
-
-    try:
-        installer = ApiDependencies.invoker.services.model_manager.install
-        installer.delete(key)
-        logger.info(f"Deleted model: {key}")
-        return Response(status_code=204)
-    except UnknownModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=404, detail=str(e))
-
-
-@model_manager_router.post(
-    "/i/",
-    operation_id="add_model_record",
-    responses={
-        201: {
-            "description": "The model added successfully",
-            "content": {"application/json": {"example": example_model_config}},
-        },
-        409: {"description": "There is already a model corresponding to this path or repo_id"},
-        415: {"description": "Unrecognized file/folder format"},
-    },
-    status_code=201,
-)
-async def add_model_record(
-    config: Annotated[
-        AnyModelConfig, Body(description="Model config", discriminator="type", example=example_model_input)
-    ],
-) -> AnyModelConfig:
-    """Add a model using the configuration information appropriate for its type."""
-    logger = ApiDependencies.invoker.services.logger
-    record_store = ApiDependencies.invoker.services.model_manager.store
-    if config.key == "<NOKEY>":
-        config.key = sha1(randbytes(100)).hexdigest()
-        logger.info(f"Created model {config.key} for {config.name}")
-    try:
-        record_store.add_model(config.key, config)
-    except DuplicateModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=409, detail=str(e))
-    except InvalidModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=415)
-
-    # now fetch it out
-    result: AnyModelConfig = record_store.get_model(config.key)
-    return result
-
-
-@model_manager_router.post(
-    "/heuristic_import",
-    operation_id="heuristic_import_model",
-    responses={
-        201: {"description": "The model imported successfully"},
-        415: {"description": "Unrecognized file/folder format"},
-        424: {"description": "The model appeared to import successfully, but could not be found in the model manager"},
-        409: {"description": "There is already a model corresponding to this path or repo_id"},
-    },
-    status_code=201,
-)
-async def heuristic_import(
-    source: str,
-    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 ",
-        default=None,
-        example={"name": "modelT", "description": "antique cars"},
-    ),
-    access_token: Optional[str] = None,
-) -> ModelInstallJob:
-    """Install a model using a string identifier.
-
-    `source` can be any of the following.
-
-    1. A path on the local filesystem ('C:\\users\\fred\\model.safetensors')
-    2. A Url pointing to a single downloadable model file
-    3. A HuggingFace repo_id with any of the following formats:
-       - model/name
-       - model/name:fp16:vae
-       - model/name::vae          -- use default precision
-       - model/name:fp16:path/to/model.safetensors
-       - model/name::path/to/model.safetensors
-
-    `config` is an optional dict containing model configuration values that will override
-    the ones that are probed automatically.
-
-    `access_token` is an optional access token for use with Urls that require
-    authentication.
-
-    Models will be downloaded, probed, configured and installed in a
-    series of background threads. The return object has `status` attribute
-    that can be used to monitor progress.
-
-    See the documentation for `import_model_record` for more information on
-    interpreting the job information returned by this route.
-    """
-    logger = ApiDependencies.invoker.services.logger
-
-    try:
-        installer = ApiDependencies.invoker.services.model_manager.install
-        result: ModelInstallJob = installer.heuristic_import(
-            source=source,
-            config=config,
-        )
-        logger.info(f"Started installation of {source}")
-    except UnknownModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=424, detail=str(e))
-    except InvalidModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=415)
-    except ValueError as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=409, detail=str(e))
-    return result
-
-
-@model_manager_router.post(
-    "/install",
-    operation_id="import_model",
-    responses={
-        201: {"description": "The model imported successfully"},
-        415: {"description": "Unrecognized file/folder format"},
-        424: {"description": "The model appeared to import successfully, but could not be found in the model manager"},
-        409: {"description": "There is already a model corresponding to this path or repo_id"},
-    },
-    status_code=201,
-)
-async def import_model(
-    source: ModelSource,
-    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 ",
-        default=None,
-    ),
-) -> ModelInstallJob:
-    """Install a model using its local path, repo_id, or remote URL.
-
-    Models will be downloaded, probed, configured and installed in a
-    series of background threads. The return object has `status` attribute
-    that can be used to monitor progress.
-
-    The source object is a discriminated Union of LocalModelSource,
-    HFModelSource and URLModelSource. Set the "type" field to the
-    appropriate value:
-
-    * To install a local path using LocalModelSource, pass a source of form:
-      ```
-      {
-        "type": "local",
-        "path": "/path/to/model",
-        "inplace": false
-      }
-      ```
-      The "inplace" flag, if true, will register the model in place in its
-      current filesystem location. Otherwise, the model will be copied
-      into the InvokeAI models directory.
-
-    * To install a HuggingFace repo_id using HFModelSource, pass a source of form:
-      ```
-      {
-        "type": "hf",
-        "repo_id": "stabilityai/stable-diffusion-2.0",
-        "variant": "fp16",
-        "subfolder": "vae",
-        "access_token": "f5820a918aaf01"
-      }
-      ```
-      The `variant`, `subfolder` and `access_token` fields are optional.
-
-    * To install a remote model using an arbitrary URL, pass:
-      ```
-      {
-        "type": "url",
-        "url": "http://www.civitai.com/models/123456",
-        "access_token": "f5820a918aaf01"
-      }
-      ```
-      The `access_token` field is optonal
-
-    The model's configuration record will be probed and filled in
-    automatically.  To override the default guesses, pass "metadata"
-    with a Dict containing the attributes you wish to override.
-
-    Installation occurs in the background. Either use list_model_install_jobs()
-    to poll for completion, or listen on the event bus for the following events:
-
-      * "model_install_running"
-      * "model_install_completed"
-      * "model_install_error"
-
-    On successful completion, the event's payload will contain the field "key"
-    containing the installed ID of the model. On an error, the event's payload
-    will contain the fields "error_type" and "error" describing the nature of the
-    error and its traceback, respectively.
-
-    """
-    logger = ApiDependencies.invoker.services.logger
-
-    try:
-        installer = ApiDependencies.invoker.services.model_manager.install
-        result: ModelInstallJob = installer.import_model(
-            source=source,
-            config=config,
-        )
-        logger.info(f"Started installation of {source}")
-    except UnknownModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=424, detail=str(e))
-    except InvalidModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=415)
-    except ValueError as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=409, detail=str(e))
-    return result
-
-
-@model_manager_router.get(
-    "/import",
-    operation_id="list_model_install_jobs",
-)
-async def list_model_install_jobs() -> List[ModelInstallJob]:
-    """Return the list of model install jobs.
-
-    Install jobs have a numeric `id`, a `status`, and other fields that provide information on
-    the nature of the job and its progress. The `status` is one of:
-
-    * "waiting" -- Job is waiting in the queue to run
-    * "downloading" -- Model file(s) are downloading
-    * "running" -- Model has downloaded and the model probing and registration process is running
-    * "completed" -- Installation completed successfully
-    * "error" -- An error occurred. Details will be in the "error_type" and "error" fields.
-    * "cancelled" -- Job was cancelled before completion.
-
-    Once completed, information about the model such as its size, base
-    model, type, and metadata can be retrieved from the `config_out`
-    field. For multi-file models such as diffusers, information on individual files
-    can be retrieved from `download_parts`.
-
-    See the example and schema below for more information.
-    """
-    jobs: List[ModelInstallJob] = ApiDependencies.invoker.services.model_manager.install.list_jobs()
-    return jobs
-
-
-@model_manager_router.get(
-    "/import/{id}",
-    operation_id="get_model_install_job",
-    responses={
-        200: {"description": "Success"},
-        404: {"description": "No such job"},
-    },
-)
-async def get_model_install_job(id: int = Path(description="Model install id")) -> ModelInstallJob:
-    """
-    Return model install job corresponding to the given source. See the documentation for 'List Model Install Jobs'
-    for information on the format of the return value.
-    """
-    try:
-        result: ModelInstallJob = ApiDependencies.invoker.services.model_manager.install.get_job_by_id(id)
-        return result
-    except ValueError as e:
-        raise HTTPException(status_code=404, detail=str(e))
-
-
-@model_manager_router.delete(
-    "/import/{id}",
-    operation_id="cancel_model_install_job",
-    responses={
-        201: {"description": "The job was cancelled successfully"},
-        415: {"description": "No such job"},
-    },
-    status_code=201,
-)
-async def cancel_model_install_job(id: int = Path(description="Model install job ID")) -> None:
-    """Cancel the model install job(s) corresponding to the given job ID."""
-    installer = ApiDependencies.invoker.services.model_manager.install
-    try:
-        job = installer.get_job_by_id(id)
-    except ValueError as e:
-        raise HTTPException(status_code=415, detail=str(e))
-    installer.cancel_job(job)
-
-
-@model_manager_router.patch(
-    "/import",
-    operation_id="prune_model_install_jobs",
-    responses={
-        204: {"description": "All completed and errored jobs have been pruned"},
-        400: {"description": "Bad request"},
-    },
-)
-async def prune_model_install_jobs() -> Response:
-    """Prune all completed and errored jobs from the install job list."""
-    ApiDependencies.invoker.services.model_manager.install.prune_jobs()
-    return Response(status_code=204)
-
-
-@model_manager_router.patch(
-    "/sync",
-    operation_id="sync_models_to_config",
-    responses={
-        204: {"description": "Model config record database resynced with files on disk"},
-        400: {"description": "Bad request"},
-    },
-)
-async def sync_models_to_config() -> Response:
-    """
-    Traverse the models and autoimport directories.
-
-    Model files without a corresponding
-    record in the database are added. Orphan records without a models file are deleted.
-    """
-    ApiDependencies.invoker.services.model_manager.install.sync_to_config()
-    return Response(status_code=204)
-
-
-@model_manager_router.put(
-    "/convert/{key}",
-    operation_id="convert_model",
-    responses={
-        200: {
-            "description": "Model converted successfully",
-            "content": {"application/json": {"example": example_model_config}},
-        },
-        400: {"description": "Bad request"},
-        404: {"description": "Model not found"},
-        409: {"description": "There is already a model registered at this location"},
-    },
-)
-async def convert_model(
-    key: str = Path(description="Unique key of the safetensors main model to convert to diffusers format."),
-) -> AnyModelConfig:
-    """
-    Permanently convert a model into diffusers format, replacing the safetensors version.
-    Note that during the conversion process the key and model hash will change.
-    The return value is the model configuration for the converted model.
-    """
-    logger = ApiDependencies.invoker.services.logger
-    loader = ApiDependencies.invoker.services.model_manager.load
-    store = ApiDependencies.invoker.services.model_manager.store
-    installer = ApiDependencies.invoker.services.model_manager.install
-
-    try:
-        model_config = store.get_model(key)
-    except UnknownModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=424, detail=str(e))
-
-    if not isinstance(model_config, MainCheckpointConfig):
-        logger.error(f"The model with key {key} is not a main checkpoint model.")
-        raise HTTPException(400, f"The model with key {key} is not a main checkpoint model.")
-
-    # loading the model will convert it into a cached diffusers file
-    loader.load_model_by_config(model_config, submodel_type=SubModelType.Scheduler)
-
-    # Get the path of the converted model from the loader
-    cache_path = loader.convert_cache.cache_path(key)
-    assert cache_path.exists()
-
-    # temporarily rename the original safetensors file so that there is no naming conflict
-    original_name = model_config.name
-    model_config.name = f"{original_name}.DELETE"
-    store.update_model(key, config=model_config)
-
-    # install the diffusers
-    try:
-        new_key = installer.install_path(
-            cache_path,
-            config={
-                "name": original_name,
-                "description": model_config.description,
-                "original_hash": model_config.original_hash,
-                "source": model_config.source,
-            },
-        )
-    except DuplicateModelException as e:
-        logger.error(str(e))
-        raise HTTPException(status_code=409, detail=str(e))
-
-    # get the original metadata
-    if orig_metadata := store.get_metadata(key):
-        store.metadata_store.add_metadata(new_key, orig_metadata)
-
-    # delete the original safetensors file
-    installer.delete(key)
-
-    # delete the cached version
-    shutil.rmtree(cache_path)
-
-    # return the config record for the new diffusers directory
-    new_config: AnyModelConfig = store.get_model(new_key)
-    return new_config
-
-
-@model_manager_router.put(
-    "/merge",
-    operation_id="merge",
-    responses={
-        200: {
-            "description": "Model converted successfully",
-            "content": {"application/json": {"example": example_model_config}},
-        },
-        400: {"description": "Bad request"},
-        404: {"description": "Model not found"},
-        409: {"description": "There is already a model registered at this location"},
-    },
-)
-async def merge(
-    keys: List[str] = Body(description="Keys for two to three models to merge", min_length=2, max_length=3),
-    merged_model_name: Optional[str] = Body(description="Name of destination model", default=None),
-    alpha: float = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
-    force: bool = Body(
-        description="Force merging of models created with different versions of diffusers",
-        default=False,
-    ),
-    interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method", default=None),
-    merge_dest_directory: Optional[str] = Body(
-        description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
-        default=None,
-    ),
-) -> AnyModelConfig:
-    """
-    Merge diffusers models. The process is controlled by a set parameters provided in the body of the request.
-    ```
-    Argument                Description [default]
-    --------               ----------------------
-    keys                   List of 2-3 model keys to merge together. All models must use the same base type.
-    merged_model_name      Name for the merged model [Concat model names]
-    alpha                  Alpha value (0.0-1.0). Higher values give more weight to the second model [0.5]
-    force                  If true, force the merge even if the models were generated by different versions of the diffusers library [False]
-    interp                 Interpolation method. One of "weighted_sum", "sigmoid", "inv_sigmoid" or "add_difference" [weighted_sum]
-    merge_dest_directory   Specify a directory to store the merged model in [models directory]
-    ```
-    """
-    logger = ApiDependencies.invoker.services.logger
-    try:
-        logger.info(f"Merging models: {keys} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")
-        dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None
-        installer = ApiDependencies.invoker.services.model_manager.install
-        merger = ModelMerger(installer)
-        model_names = [installer.record_store.get_model(x).name for x in keys]
-        response = merger.merge_diffusion_models_and_save(
-            model_keys=keys,
-            merged_model_name=merged_model_name or "+".join(model_names),
-            alpha=alpha,
-            interp=interp,
-            force=force,
-            merge_dest_directory=dest,
-        )
-    except UnknownModelException:
-        raise HTTPException(
-            status_code=404,
-            detail=f"One or more of the models '{keys}' not found",
-        )
-    except ValueError as e:
-        raise HTTPException(status_code=400, detail=str(e))
-    return response

invokeai/app/api/routers/model_records.py

@@ -0,0 +1,472 @@
+# Copyright (c) 2023 Lincoln D. Stein
+"""FastAPI route for model configuration records."""
+
+import pathlib
+from hashlib import sha1
+from random import randbytes
+from typing import Any, Dict, List, Optional, Set
+
+from fastapi import Body, Path, Query, Response
+from fastapi.routing import APIRouter
+from pydantic import BaseModel, ConfigDict
+from starlette.exceptions import HTTPException
+from typing_extensions import Annotated
+
+from invokeai.app.services.model_install import ModelInstallJob, ModelSource
+from invokeai.app.services.model_records import (
+    DuplicateModelException,
+    InvalidModelException,
+    ModelRecordOrderBy,
+    ModelSummary,
+    UnknownModelException,
+)
+from invokeai.app.services.shared.pagination import PaginatedResults
+from invokeai.backend.model_manager.config import (
+    AnyModelConfig,
+    BaseModelType,
+    ModelFormat,
+    ModelType,
+)
+from invokeai.backend.model_manager.merge import MergeInterpolationMethod, ModelMerger
+from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
+
+from ..dependencies import ApiDependencies
+
+model_records_router = APIRouter(prefix="/v1/model/record", tags=["model_manager_v2_unstable"])
+
+
+class ModelsList(BaseModel):
+    """Return list of configs."""
+
+    models: List[AnyModelConfig]
+
+    model_config = ConfigDict(use_enum_values=True)
+
+
+class ModelTagSet(BaseModel):
+    """Return tags for a set of models."""
+
+    key: str
+    name: str
+    author: str
+    tags: Set[str]
+
+
+@model_records_router.get(
+    "/",
+    operation_id="list_model_records",
+)
+async def list_model_records(
+    base_models: Optional[List[BaseModelType]] = Query(default=None, description="Base models to include"),
+    model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
+    model_name: Optional[str] = Query(default=None, description="Exact match on the name of the model"),
+    model_format: Optional[ModelFormat] = Query(
+        default=None, description="Exact match on the format of the model (e.g. 'diffusers')"
+    ),
+) -> ModelsList:
+    """Get a list of models."""
+    record_store = ApiDependencies.invoker.services.model_records
+    found_models: list[AnyModelConfig] = []
+    if base_models:
+        for base_model in base_models:
+            found_models.extend(
+                record_store.search_by_attr(
+                    base_model=base_model, model_type=model_type, model_name=model_name, model_format=model_format
+                )
+            )
+    else:
+        found_models.extend(
+            record_store.search_by_attr(model_type=model_type, model_name=model_name, model_format=model_format)
+        )
+    return ModelsList(models=found_models)
+
+
+@model_records_router.get(
+    "/i/{key}",
+    operation_id="get_model_record",
+    responses={
+        200: {"description": "Success"},
+        400: {"description": "Bad request"},
+        404: {"description": "The model could not be found"},
+    },
+)
+async def get_model_record(
+    key: str = Path(description="Key of the model record to fetch."),
+) -> AnyModelConfig:
+    """Get a model record"""
+    record_store = ApiDependencies.invoker.services.model_records
+    try:
+        return record_store.get_model(key)
+    except UnknownModelException as e:
+        raise HTTPException(status_code=404, detail=str(e))
+
+
+@model_records_router.get("/meta", operation_id="list_model_summary")
+async def list_model_summary(
+    page: int = Query(default=0, description="The page to get"),
+    per_page: int = Query(default=10, description="The number of models per page"),
+    order_by: ModelRecordOrderBy = Query(default=ModelRecordOrderBy.Default, description="The attribute to order by"),
+) -> PaginatedResults[ModelSummary]:
+    """Gets a page of model summary data."""
+    return ApiDependencies.invoker.services.model_records.list_models(page=page, per_page=per_page, order_by=order_by)
+
+
+@model_records_router.get(
+    "/meta/i/{key}",
+    operation_id="get_model_metadata",
+    responses={
+        200: {"description": "Success"},
+        400: {"description": "Bad request"},
+        404: {"description": "No metadata available"},
+    },
+)
+async def get_model_metadata(
+    key: str = Path(description="Key of the model repo metadata to fetch."),
+) -> Optional[AnyModelRepoMetadata]:
+    """Get a model metadata object."""
+    record_store = ApiDependencies.invoker.services.model_records
+    result = record_store.get_metadata(key)
+    if not result:
+        raise HTTPException(status_code=404, detail="No metadata for a model with this key")
+    return result
+
+
+@model_records_router.get(
+    "/tags",
+    operation_id="list_tags",
+)
+async def list_tags() -> Set[str]:
+    """Get a unique set of all the model tags."""
+    record_store = ApiDependencies.invoker.services.model_records
+    return record_store.list_tags()
+
+
+@model_records_router.get(
+    "/tags/search",
+    operation_id="search_by_metadata_tags",
+)
+async def search_by_metadata_tags(
+    tags: Set[str] = Query(default=None, description="Tags to search for"),
+) -> ModelsList:
+    """Get a list of models."""
+    record_store = ApiDependencies.invoker.services.model_records
+    results = record_store.search_by_metadata_tag(tags)
+    return ModelsList(models=results)
+
+
+@model_records_router.patch(
+    "/i/{key}",
+    operation_id="update_model_record",
+    responses={
+        200: {"description": "The model was updated successfully"},
+        400: {"description": "Bad request"},
+        404: {"description": "The model could not be found"},
+        409: {"description": "There is already a model corresponding to the new name"},
+    },
+    status_code=200,
+    response_model=AnyModelConfig,
+)
+async def update_model_record(
+    key: Annotated[str, Path(description="Unique key of model")],
+    info: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")],
+) -> AnyModelConfig:
+    """Update model contents with a new config. If the model name or base fields are changed, then the model is renamed."""
+    logger = ApiDependencies.invoker.services.logger
+    record_store = ApiDependencies.invoker.services.model_records
+    try:
+        model_response = record_store.update_model(key, config=info)
+        logger.info(f"Updated model: {key}")
+    except UnknownModelException as e:
+        raise HTTPException(status_code=404, detail=str(e))
+    except ValueError as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=409, detail=str(e))
+    return model_response
+
+
+@model_records_router.delete(
+    "/i/{key}",
+    operation_id="del_model_record",
+    responses={
+        204: {"description": "Model deleted successfully"},
+        404: {"description": "Model not found"},
+    },
+    status_code=204,
+)
+async def del_model_record(
+    key: str = Path(description="Unique key of model to remove from model registry."),
+) -> Response:
+    """
+    Delete model record from database.
+
+    The configuration record will be removed. The corresponding weights files will be
+    deleted as well if they reside within the InvokeAI "models" directory.
+    """
+    logger = ApiDependencies.invoker.services.logger
+
+    try:
+        installer = ApiDependencies.invoker.services.model_install
+        installer.delete(key)
+        logger.info(f"Deleted model: {key}")
+        return Response(status_code=204)
+    except UnknownModelException as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=404, detail=str(e))
+
+
+@model_records_router.post(
+    "/i/",
+    operation_id="add_model_record",
+    responses={
+        201: {"description": "The model added successfully"},
+        409: {"description": "There is already a model corresponding to this path or repo_id"},
+        415: {"description": "Unrecognized file/folder format"},
+    },
+    status_code=201,
+)
+async def add_model_record(
+    config: Annotated[AnyModelConfig, Body(description="Model config", discriminator="type")],
+) -> AnyModelConfig:
+    """Add a model using the configuration information appropriate for its type."""
+    logger = ApiDependencies.invoker.services.logger
+    record_store = ApiDependencies.invoker.services.model_records
+    if config.key == "<NOKEY>":
+        config.key = sha1(randbytes(100)).hexdigest()
+        logger.info(f"Created model {config.key} for {config.name}")
+    try:
+        record_store.add_model(config.key, config)
+    except DuplicateModelException as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=409, detail=str(e))
+    except InvalidModelException as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=415)
+
+    # now fetch it out
+    return record_store.get_model(config.key)
+
+
+@model_records_router.post(
+    "/import",
+    operation_id="import_model_record",
+    responses={
+        201: {"description": "The model imported successfully"},
+        415: {"description": "Unrecognized file/folder format"},
+        424: {"description": "The model appeared to import successfully, but could not be found in the model manager"},
+        409: {"description": "There is already a model corresponding to this path or repo_id"},
+    },
+    status_code=201,
+)
+async def import_model(
+    source: ModelSource,
+    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 ",
+        default=None,
+    ),
+) -> ModelInstallJob:
+    """Add a model using its local path, repo_id, or remote URL.
+
+    Models will be downloaded, probed, configured and installed in a
+    series of background threads. The return object has `status` attribute
+    that can be used to monitor progress.
+
+    The source object is a discriminated Union of LocalModelSource,
+    HFModelSource and URLModelSource. Set the "type" field to the
+    appropriate value:
+
+    * To install a local path using LocalModelSource, pass a source of form:
+      `{
+        "type": "local",
+        "path": "/path/to/model",
+        "inplace": false
+      }`
+       The "inplace" flag, if true, will register the model in place in its
+       current filesystem location. Otherwise, the model will be copied
+       into the InvokeAI models directory.
+
+    * To install a HuggingFace repo_id using HFModelSource, pass a source of form:
+      `{
+        "type": "hf",
+        "repo_id": "stabilityai/stable-diffusion-2.0",
+        "variant": "fp16",
+        "subfolder": "vae",
+        "access_token": "f5820a918aaf01"
+      }`
+     The `variant`, `subfolder` and `access_token` fields are optional.
+
+    * To install a remote model using an arbitrary URL, pass:
+      `{
+        "type": "url",
+        "url": "http://www.civitai.com/models/123456",
+        "access_token": "f5820a918aaf01"
+      }`
+    The `access_token` field is optonal
+
+    The model's configuration record will be probed and filled in
+    automatically.  To override the default guesses, pass "metadata"
+    with a Dict containing the attributes you wish to override.
+
+    Installation occurs in the background. Either use list_model_install_jobs()
+    to poll for completion, or listen on the event bus for the following events:
+
+      "model_install_running"
+      "model_install_completed"
+      "model_install_error"
+
+    On successful completion, the event's payload will contain the field "key"
+    containing the installed ID of the model. On an error, the event's payload
+    will contain the fields "error_type" and "error" describing the nature of the
+    error and its traceback, respectively.
+
+    """
+    logger = ApiDependencies.invoker.services.logger
+
+    try:
+        installer = ApiDependencies.invoker.services.model_install
+        result: ModelInstallJob = installer.import_model(
+            source=source,
+            config=config,
+        )
+        logger.info(f"Started installation of {source}")
+    except UnknownModelException as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=424, detail=str(e))
+    except InvalidModelException as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=415)
+    except ValueError as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=409, detail=str(e))
+    return result
+
+
+@model_records_router.get(
+    "/import",
+    operation_id="list_model_install_jobs",
+)
+async def list_model_install_jobs() -> List[ModelInstallJob]:
+    """Return list of model install jobs."""
+    jobs: List[ModelInstallJob] = ApiDependencies.invoker.services.model_install.list_jobs()
+    return jobs
+
+
+@model_records_router.get(
+    "/import/{id}",
+    operation_id="get_model_install_job",
+    responses={
+        200: {"description": "Success"},
+        404: {"description": "No such job"},
+    },
+)
+async def get_model_install_job(id: int = Path(description="Model install id")) -> ModelInstallJob:
+    """Return model install job corresponding to the given source."""
+    try:
+        return ApiDependencies.invoker.services.model_install.get_job_by_id(id)
+    except ValueError as e:
+        raise HTTPException(status_code=404, detail=str(e))
+
+
+@model_records_router.delete(
+    "/import/{id}",
+    operation_id="cancel_model_install_job",
+    responses={
+        201: {"description": "The job was cancelled successfully"},
+        415: {"description": "No such job"},
+    },
+    status_code=201,
+)
+async def cancel_model_install_job(id: int = Path(description="Model install job ID")) -> None:
+    """Cancel the model install job(s) corresponding to the given job ID."""
+    installer = ApiDependencies.invoker.services.model_install
+    try:
+        job = installer.get_job_by_id(id)
+    except ValueError as e:
+        raise HTTPException(status_code=415, detail=str(e))
+    installer.cancel_job(job)
+
+
+@model_records_router.patch(
+    "/import",
+    operation_id="prune_model_install_jobs",
+    responses={
+        204: {"description": "All completed and errored jobs have been pruned"},
+        400: {"description": "Bad request"},
+    },
+)
+async def prune_model_install_jobs() -> Response:
+    """Prune all completed and errored jobs from the install job list."""
+    ApiDependencies.invoker.services.model_install.prune_jobs()
+    return Response(status_code=204)
+
+
+@model_records_router.patch(
+    "/sync",
+    operation_id="sync_models_to_config",
+    responses={
+        204: {"description": "Model config record database resynced with files on disk"},
+        400: {"description": "Bad request"},
+    },
+)
+async def sync_models_to_config() -> Response:
+    """
+    Traverse the models and autoimport directories.
+
+    Model files without a corresponding
+    record in the database are added. Orphan records without a models file are deleted.
+    """
+    ApiDependencies.invoker.services.model_install.sync_to_config()
+    return Response(status_code=204)
+
+
+@model_records_router.put(
+    "/merge",
+    operation_id="merge",
+)
+async def merge(
+    keys: List[str] = Body(description="Keys for two to three models to merge", min_length=2, max_length=3),
+    merged_model_name: Optional[str] = Body(description="Name of destination model", default=None),
+    alpha: float = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
+    force: bool = Body(
+        description="Force merging of models created with different versions of diffusers",
+        default=False,
+    ),
+    interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method", default=None),
+    merge_dest_directory: Optional[str] = Body(
+        description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
+        default=None,
+    ),
+) -> AnyModelConfig:
+    """
+    Merge diffusers models.
+
+        keys: List of 2-3 model keys to merge together. All models must use the same base type.
+        merged_model_name: Name for the merged model [Concat model names]
+        alpha: Alpha value (0.0-1.0). Higher values give more weight to the second model [0.5]
+        force: If true, force the merge even if the models were generated by different versions of the diffusers library [False]
+        interp: Interpolation method. One of "weighted_sum", "sigmoid", "inv_sigmoid" or "add_difference" [weighted_sum]
+        merge_dest_directory: Specify a directory to store the merged model in [models directory]
+    """
+    print(f"here i am, keys={keys}")
+    logger = ApiDependencies.invoker.services.logger
+    try:
+        logger.info(f"Merging models: {keys} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")
+        dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None
+        installer = ApiDependencies.invoker.services.model_install
+        merger = ModelMerger(installer)
+        model_names = [installer.record_store.get_model(x).name for x in keys]
+        response = merger.merge_diffusion_models_and_save(
+            model_keys=keys,
+            merged_model_name=merged_model_name or "+".join(model_names),
+            alpha=alpha,
+            interp=interp,
+            force=force,
+            merge_dest_directory=dest,
+        )
+    except UnknownModelException:
+        raise HTTPException(
+            status_code=404,
+            detail=f"One or more of the models '{keys}' not found",
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    return response

invokeai/app/api/routers/models.py

@@ -0,0 +1,427 @@
+# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654), 2023 Kent Keirsey (https://github.com/hipsterusername), 2023 Lincoln D. Stein
+
+import pathlib
+from typing import Annotated, List, Literal, Optional, Union
+
+from fastapi import Body, Path, Query, Response
+from fastapi.routing import APIRouter
+from pydantic import BaseModel, ConfigDict, Field, TypeAdapter
+from starlette.exceptions import HTTPException
+
+from invokeai.backend import BaseModelType, ModelType
+from invokeai.backend.model_management import MergeInterpolationMethod
+from invokeai.backend.model_management.models import (
+    OPENAPI_MODEL_CONFIGS,
+    InvalidModelException,
+    ModelNotFoundException,
+    SchedulerPredictionType,
+)
+
+from ..dependencies import ApiDependencies
+
+models_router = APIRouter(prefix="/v1/models", tags=["models"])
+
+UpdateModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
+UpdateModelResponseValidator = TypeAdapter(UpdateModelResponse)
+
+ImportModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
+ImportModelResponseValidator = TypeAdapter(ImportModelResponse)
+
+ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
+ConvertModelResponseValidator = TypeAdapter(ConvertModelResponse)
+
+MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
+ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
+
+
+class ModelsList(BaseModel):
+    models: list[Union[tuple(OPENAPI_MODEL_CONFIGS)]]
+
+    model_config = ConfigDict(use_enum_values=True)
+
+
+ModelsListValidator = TypeAdapter(ModelsList)
+
+
+@models_router.get(
+    "/",
+    operation_id="list_models",
+    responses={200: {"model": ModelsList}},
+)
+async def list_models(
+    base_models: Optional[List[BaseModelType]] = Query(default=None, description="Base models to include"),
+    model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
+) -> ModelsList:
+    """Gets a list of models"""
+    if base_models and len(base_models) > 0:
+        models_raw = []
+        for base_model in base_models:
+            models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
+    else:
+        models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type)
+    models = ModelsListValidator.validate_python({"models": models_raw})
+    return models
+
+
+@models_router.patch(
+    "/{base_model}/{model_type}/{model_name}",
+    operation_id="update_model",
+    responses={
+        200: {"description": "The model was updated successfully"},
+        400: {"description": "Bad request"},
+        404: {"description": "The model could not be found"},
+        409: {"description": "There is already a model corresponding to the new name"},
+    },
+    status_code=200,
+    response_model=UpdateModelResponse,
+)
+async def update_model(
+    base_model: BaseModelType = Path(description="Base model"),
+    model_type: ModelType = Path(description="The type of model"),
+    model_name: str = Path(description="model name"),
+    info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
+) -> UpdateModelResponse:
+    """Update model contents with a new config. If the model name or base fields are changed, then the model is renamed."""
+    logger = ApiDependencies.invoker.services.logger
+
+    try:
+        previous_info = ApiDependencies.invoker.services.model_manager.list_model(
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+        )
+
+        # rename operation requested
+        if info.model_name != model_name or info.base_model != base_model:
+            ApiDependencies.invoker.services.model_manager.rename_model(
+                base_model=base_model,
+                model_type=model_type,
+                model_name=model_name,
+                new_name=info.model_name,
+                new_base=info.base_model,
+            )
+            logger.info(f"Successfully renamed {base_model.value}/{model_name}=>{info.base_model}/{info.model_name}")
+            # update information to support an update of attributes
+            model_name = info.model_name
+            base_model = info.base_model
+            new_info = ApiDependencies.invoker.services.model_manager.list_model(
+                model_name=model_name,
+                base_model=base_model,
+                model_type=model_type,
+            )
+            if new_info.get("path") != previous_info.get(
+                "path"
+            ):  # model manager moved model path during rename - don't overwrite it
+                info.path = new_info.get("path")
+
+        # replace empty string values with None/null to avoid phenomenon of vae: ''
+        info_dict = info.model_dump()
+        info_dict = {x: info_dict[x] if info_dict[x] else None for x in info_dict.keys()}
+
+        ApiDependencies.invoker.services.model_manager.update_model(
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+            model_attributes=info_dict,
+        )
+
+        model_raw = ApiDependencies.invoker.services.model_manager.list_model(
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+        )
+        model_response = UpdateModelResponseValidator.validate_python(model_raw)
+    except ModelNotFoundException as e:
+        raise HTTPException(status_code=404, detail=str(e))
+    except ValueError as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=409, detail=str(e))
+    except Exception as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=400, detail=str(e))
+
+    return model_response
+
+
+@models_router.post(
+    "/import",
+    operation_id="import_model",
+    responses={
+        201: {"description": "The model imported successfully"},
+        404: {"description": "The model could not be found"},
+        415: {"description": "Unrecognized file/folder format"},
+        424: {"description": "The model appeared to import successfully, but could not be found in the model manager"},
+        409: {"description": "There is already a model corresponding to this path or repo_id"},
+    },
+    status_code=201,
+    response_model=ImportModelResponse,
+)
+async def import_model(
+    location: str = Body(description="A model path, repo_id or URL to import"),
+    prediction_type: Optional[Literal["v_prediction", "epsilon", "sample"]] = Body(
+        description="Prediction type for SDv2 checkpoints and rare SDv1 checkpoints",
+        default=None,
+    ),
+) -> ImportModelResponse:
+    """Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically"""
+
+    location = location.strip("\"' ")
+    items_to_import = {location}
+    prediction_types = {x.value: x for x in SchedulerPredictionType}
+    logger = ApiDependencies.invoker.services.logger
+
+    try:
+        installed_models = ApiDependencies.invoker.services.model_manager.heuristic_import(
+            items_to_import=items_to_import,
+            prediction_type_helper=lambda x: prediction_types.get(prediction_type),
+        )
+        info = installed_models.get(location)
+
+        if not info:
+            logger.error("Import failed")
+            raise HTTPException(status_code=415)
+
+        logger.info(f"Successfully imported {location}, got {info}")
+        model_raw = ApiDependencies.invoker.services.model_manager.list_model(
+            model_name=info.name, base_model=info.base_model, model_type=info.model_type
+        )
+        return ImportModelResponseValidator.validate_python(model_raw)
+
+    except ModelNotFoundException as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=404, detail=str(e))
+    except InvalidModelException as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=415)
+    except ValueError as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=409, detail=str(e))
+
+
+@models_router.post(
+    "/add",
+    operation_id="add_model",
+    responses={
+        201: {"description": "The model added successfully"},
+        404: {"description": "The model could not be found"},
+        424: {"description": "The model appeared to add successfully, but could not be found in the model manager"},
+        409: {"description": "There is already a model corresponding to this path or repo_id"},
+    },
+    status_code=201,
+    response_model=ImportModelResponse,
+)
+async def add_model(
+    info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
+) -> ImportModelResponse:
+    """Add a model using the configuration information appropriate for its type. Only local models can be added by path"""
+
+    logger = ApiDependencies.invoker.services.logger
+
+    try:
+        ApiDependencies.invoker.services.model_manager.add_model(
+            info.model_name,
+            info.base_model,
+            info.model_type,
+            model_attributes=info.model_dump(),
+        )
+        logger.info(f"Successfully added {info.model_name}")
+        model_raw = ApiDependencies.invoker.services.model_manager.list_model(
+            model_name=info.model_name,
+            base_model=info.base_model,
+            model_type=info.model_type,
+        )
+        return ImportModelResponseValidator.validate_python(model_raw)
+    except ModelNotFoundException as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=404, detail=str(e))
+    except ValueError as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=409, detail=str(e))
+
+
+@models_router.delete(
+    "/{base_model}/{model_type}/{model_name}",
+    operation_id="del_model",
+    responses={
+        204: {"description": "Model deleted successfully"},
+        404: {"description": "Model not found"},
+    },
+    status_code=204,
+    response_model=None,
+)
+async def delete_model(
+    base_model: BaseModelType = Path(description="Base model"),
+    model_type: ModelType = Path(description="The type of model"),
+    model_name: str = Path(description="model name"),
+) -> Response:
+    """Delete Model"""
+    logger = ApiDependencies.invoker.services.logger
+
+    try:
+        ApiDependencies.invoker.services.model_manager.del_model(
+            model_name, base_model=base_model, model_type=model_type
+        )
+        logger.info(f"Deleted model: {model_name}")
+        return Response(status_code=204)
+    except ModelNotFoundException as e:
+        logger.error(str(e))
+        raise HTTPException(status_code=404, detail=str(e))
+
+
+@models_router.put(
+    "/convert/{base_model}/{model_type}/{model_name}",
+    operation_id="convert_model",
+    responses={
+        200: {"description": "Model converted successfully"},
+        400: {"description": "Bad request"},
+        404: {"description": "Model not found"},
+    },
+    status_code=200,
+    response_model=ConvertModelResponse,
+)
+async def convert_model(
+    base_model: BaseModelType = Path(description="Base model"),
+    model_type: ModelType = Path(description="The type of model"),
+    model_name: str = Path(description="model name"),
+    convert_dest_directory: Optional[str] = Query(
+        default=None, description="Save the converted model to the designated directory"
+    ),
+) -> ConvertModelResponse:
+    """Convert a checkpoint model into a diffusers model, optionally saving to the indicated destination directory, or `models` if none."""
+    logger = ApiDependencies.invoker.services.logger
+    try:
+        logger.info(f"Converting model: {model_name}")
+        dest = pathlib.Path(convert_dest_directory) if convert_dest_directory else None
+        ApiDependencies.invoker.services.model_manager.convert_model(
+            model_name,
+            base_model=base_model,
+            model_type=model_type,
+            convert_dest_directory=dest,
+        )
+        model_raw = ApiDependencies.invoker.services.model_manager.list_model(
+            model_name, base_model=base_model, model_type=model_type
+        )
+        response = ConvertModelResponseValidator.validate_python(model_raw)
+    except ModelNotFoundException as e:
+        raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}")
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    return response
+
+
+@models_router.get(
+    "/search",
+    operation_id="search_for_models",
+    responses={
+        200: {"description": "Directory searched successfully"},
+        404: {"description": "Invalid directory path"},
+    },
+    status_code=200,
+    response_model=List[pathlib.Path],
+)
+async def search_for_models(
+    search_path: pathlib.Path = Query(description="Directory path to search for models"),
+) -> List[pathlib.Path]:
+    if not search_path.is_dir():
+        raise HTTPException(
+            status_code=404,
+            detail=f"The search path '{search_path}' does not exist or is not directory",
+        )
+    return ApiDependencies.invoker.services.model_manager.search_for_models(search_path)
+
+
+@models_router.get(
+    "/ckpt_confs",
+    operation_id="list_ckpt_configs",
+    responses={
+        200: {"description": "paths retrieved successfully"},
+    },
+    status_code=200,
+    response_model=List[pathlib.Path],
+)
+async def list_ckpt_configs() -> List[pathlib.Path]:
+    """Return a list of the legacy checkpoint configuration files stored in `ROOT/configs/stable-diffusion`, relative to ROOT."""
+    return ApiDependencies.invoker.services.model_manager.list_checkpoint_configs()
+
+
+@models_router.post(
+    "/sync",
+    operation_id="sync_to_config",
+    responses={
+        201: {"description": "synchronization successful"},
+    },
+    status_code=201,
+    response_model=bool,
+)
+async def sync_to_config() -> bool:
+    """Call after making changes to models.yaml, autoimport directories or models directory to synchronize
+    in-memory data structures with disk data structures."""
+    ApiDependencies.invoker.services.model_manager.sync_to_config()
+    return True
+
+
+# There's some weird pydantic-fastapi behaviour that requires this to be a separate class
+# TODO: After a few updates, see if it works inside the route operation handler?
+class MergeModelsBody(BaseModel):
+    model_names: List[str] = Field(description="model name", min_length=2, max_length=3)
+    merged_model_name: Optional[str] = Field(description="Name of destination model")
+    alpha: Optional[float] = Field(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5)
+    interp: Optional[MergeInterpolationMethod] = Field(description="Interpolation method")
+    force: Optional[bool] = Field(
+        description="Force merging of models created with different versions of diffusers",
+        default=False,
+    )
+
+    merge_dest_directory: Optional[str] = Field(
+        description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
+        default=None,
+    )
+
+    model_config = ConfigDict(protected_namespaces=())
+
+
+@models_router.put(
+    "/merge/{base_model}",
+    operation_id="merge_models",
+    responses={
+        200: {"description": "Model converted successfully"},
+        400: {"description": "Incompatible models"},
+        404: {"description": "One or more models not found"},
+    },
+    status_code=200,
+    response_model=MergeModelResponse,
+)
+async def merge_models(
+    body: Annotated[MergeModelsBody, Body(description="Model configuration", embed=True)],
+    base_model: BaseModelType = Path(description="Base model"),
+) -> MergeModelResponse:
+    """Convert a checkpoint model into a diffusers model"""
+    logger = ApiDependencies.invoker.services.logger
+    try:
+        logger.info(
+            f"Merging models: {body.model_names} into {body.merge_dest_directory or '<MODELS>'}/{body.merged_model_name}"
+        )
+        dest = pathlib.Path(body.merge_dest_directory) if body.merge_dest_directory else None
+        result = ApiDependencies.invoker.services.model_manager.merge_models(
+            model_names=body.model_names,
+            base_model=base_model,
+            merged_model_name=body.merged_model_name or "+".join(body.model_names),
+            alpha=body.alpha,
+            interp=body.interp,
+            force=body.force,
+            merge_dest_directory=dest,
+        )
+        model_raw = ApiDependencies.invoker.services.model_manager.list_model(
+            result.name,
+            base_model=base_model,
+            model_type=ModelType.Main,
+        )
+        response = ConvertModelResponseValidator.validate_python(model_raw)
+    except ModelNotFoundException:
+        raise HTTPException(
+            status_code=404,
+            detail=f"One or more of the models '{body.model_names}' not found",
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    return response

invokeai/app/api_app.py

@@ -6,7 +6,6 @@ import sys
 from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
 from invokeai.version.invokeai_version import __version__
 
-from .invocations.fields import InputFieldJSONSchemaExtra, OutputFieldJSONSchemaExtra
 from .services.config import InvokeAIAppConfig
 
 app_config = InvokeAIAppConfig.get_config()
@@ -48,7 +47,8 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
         boards,
         download_queue,
         images,
-        model_manager,
+        model_records,
+        models,
         session_queue,
         sessions,
         utilities,
@@ -57,6 +57,8 @@ if True:  # hack to make flake8 happy with imports coming after setting up the c
     from .api.sockets import SocketIO
     from .invocations.baseinvocation import (
         BaseInvocation,
+        InputFieldJSONSchemaExtra,
+        OutputFieldJSONSchemaExtra,
         UIConfigBase,
     )
 
@@ -113,7 +115,8 @@ async def shutdown_event() -> None:
 app.include_router(sessions.session_router, prefix="/api")
 
 app.include_router(utilities.utilities_router, prefix="/api")
-app.include_router(model_manager.model_manager_router, prefix="/api")
+app.include_router(models.models_router, prefix="/api")
+app.include_router(model_records.model_records_router, prefix="/api")
 app.include_router(download_queue.download_queue_router, prefix="/api")
 app.include_router(images.images_router, prefix="/api")
 app.include_router(boards.boards_router, prefix="/api")
@@ -175,23 +178,21 @@ def custom_openapi() -> dict[str, Any]:
         invoker_schema["class"] = "invocation"
         openapi_schema["components"]["schemas"][f"{output_type_title}"]["class"] = "output"
 
-    # This code no longer seems to be necessary?
-    # Leave it here just in case
-    #
-    # from invokeai.backend.model_manager import get_model_config_formats
-    # formats = get_model_config_formats()
-    # for model_config_name, enum_set in formats.items():
+    from invokeai.backend.model_management.models import get_model_config_enums
 
-    #     if model_config_name in openapi_schema["components"]["schemas"]:
-    #         # print(f"Config with name {name} already defined")
-    #         continue
+    for model_config_format_enum in set(get_model_config_enums()):
+        name = model_config_format_enum.__qualname__
 
-    #     openapi_schema["components"]["schemas"][model_config_name] = {
-    #         "title": model_config_name,
-    #         "description": "An enumeration.",
-    #         "type": "string",
-    #         "enum": [v.value for v in enum_set],
-    #     }
+        if name in openapi_schema["components"]["schemas"]:
+            # print(f"Config with name {name} already defined")
+            continue
+
+        openapi_schema["components"]["schemas"][name] = {
+            "title": name,
+            "description": "An enumeration.",
+            "type": "string",
+            "enum": [v.value for v in model_config_format_enum],
+        }
 
     app.openapi_schema = openapi_schema
     return app.openapi_schema

invokeai/app/invocations/baseinvocation.py

@@ -12,16 +12,13 @@ from types import UnionType
 from typing import TYPE_CHECKING, Any, Callable, ClassVar, Iterable, Literal, Optional, Type, TypeVar, Union, cast
 
 import semver
-from pydantic import BaseModel, ConfigDict, Field, create_model
-from pydantic.fields import FieldInfo
+from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter, create_model
+from pydantic.fields import FieldInfo, _Unset
 from pydantic_core import PydanticUndefined
 
-from invokeai.app.invocations.fields import (
-    FieldKind,
-    Input,
-)
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
-from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
+from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.app.util.metaenum import MetaEnum
 from invokeai.app.util.misc import uuid_string
 from invokeai.backend.util.logging import InvokeAILogger
@@ -55,6 +52,393 @@ class Classification(str, Enum, metaclass=MetaEnum):
     Prototype = "prototype"
 
 
+class Input(str, Enum, metaclass=MetaEnum):
+    """
+    The type of input a field accepts.
+    - `Input.Direct`: The field must have its value provided directly, when the invocation and field \
+      are instantiated.
+    - `Input.Connection`: The field must have its value provided by a connection.
+    - `Input.Any`: The field may have its value provided either directly or by a connection.
+    """
+
+    Connection = "connection"
+    Direct = "direct"
+    Any = "any"
+
+
+class FieldKind(str, Enum, metaclass=MetaEnum):
+    """
+    The kind of field.
+    - `Input`: An input field on a node.
+    - `Output`: An output field on a node.
+    - `Internal`: A field which is treated as an input, but cannot be used in node definitions. Metadata is
+    one example. It is provided to nodes via the WithMetadata class, and we want to reserve the field name
+    "metadata" for this on all nodes. `FieldKind` is used to short-circuit the field name validation logic,
+    allowing "metadata" for that field.
+    - `NodeAttribute`: The field is a node attribute. These are fields which are not inputs or outputs,
+    but which are used to store information about the node. For example, the `id` and `type` fields are node
+    attributes.
+
+    The presence of this in `json_schema_extra["field_kind"]` is used when initializing node schemas on app
+    startup, and when generating the OpenAPI schema for the workflow editor.
+    """
+
+    Input = "input"
+    Output = "output"
+    Internal = "internal"
+    NodeAttribute = "node_attribute"
+
+
+class UIType(str, Enum, metaclass=MetaEnum):
+    """
+    Type hints for the UI for situations in which the field type is not enough to infer the correct UI type.
+
+    - Model Fields
+    The most common node-author-facing use will be for model fields. Internally, there is no difference
+    between SD-1, SD-2 and SDXL model fields - they all use the class `MainModelField`. To ensure the
+    base-model-specific UI is rendered, use e.g. `ui_type=UIType.SDXLMainModelField` to indicate that
+    the field is an SDXL main model field.
+
+    - Any Field
+    We cannot infer the usage of `typing.Any` via schema parsing, so you *must* use `ui_type=UIType.Any` to
+    indicate that the field accepts any type. Use with caution. This cannot be used on outputs.
+
+    - Scheduler Field
+    Special handling in the UI is needed for this field, which otherwise would be parsed as a plain enum field.
+
+    - Internal Fields
+    Similar to the Any Field, the `collect` and `iterate` nodes make use of `typing.Any`. To facilitate
+    handling these types in the client, we use `UIType._Collection` and `UIType._CollectionItem`. These
+    should not be used by node authors.
+
+    - DEPRECATED Fields
+    These types are deprecated and should not be used by node authors. A warning will be logged if one is
+    used, and the type will be ignored. They are included here for backwards compatibility.
+    """
+
+    # region Model Field Types
+    SDXLMainModel = "SDXLMainModelField"
+    SDXLRefinerModel = "SDXLRefinerModelField"
+    ONNXModel = "ONNXModelField"
+    VaeModel = "VAEModelField"
+    LoRAModel = "LoRAModelField"
+    ControlNetModel = "ControlNetModelField"
+    IPAdapterModel = "IPAdapterModelField"
+    # endregion
+
+    # region Misc Field Types
+    Scheduler = "SchedulerField"
+    Any = "AnyField"
+    # endregion
+
+    # region Internal Field Types
+    _Collection = "CollectionField"
+    _CollectionItem = "CollectionItemField"
+    # endregion
+
+    # region DEPRECATED
+    Boolean = "DEPRECATED_Boolean"
+    Color = "DEPRECATED_Color"
+    Conditioning = "DEPRECATED_Conditioning"
+    Control = "DEPRECATED_Control"
+    Float = "DEPRECATED_Float"
+    Image = "DEPRECATED_Image"
+    Integer = "DEPRECATED_Integer"
+    Latents = "DEPRECATED_Latents"
+    String = "DEPRECATED_String"
+    BooleanCollection = "DEPRECATED_BooleanCollection"
+    ColorCollection = "DEPRECATED_ColorCollection"
+    ConditioningCollection = "DEPRECATED_ConditioningCollection"
+    ControlCollection = "DEPRECATED_ControlCollection"
+    FloatCollection = "DEPRECATED_FloatCollection"
+    ImageCollection = "DEPRECATED_ImageCollection"
+    IntegerCollection = "DEPRECATED_IntegerCollection"
+    LatentsCollection = "DEPRECATED_LatentsCollection"
+    StringCollection = "DEPRECATED_StringCollection"
+    BooleanPolymorphic = "DEPRECATED_BooleanPolymorphic"
+    ColorPolymorphic = "DEPRECATED_ColorPolymorphic"
+    ConditioningPolymorphic = "DEPRECATED_ConditioningPolymorphic"
+    ControlPolymorphic = "DEPRECATED_ControlPolymorphic"
+    FloatPolymorphic = "DEPRECATED_FloatPolymorphic"
+    ImagePolymorphic = "DEPRECATED_ImagePolymorphic"
+    IntegerPolymorphic = "DEPRECATED_IntegerPolymorphic"
+    LatentsPolymorphic = "DEPRECATED_LatentsPolymorphic"
+    StringPolymorphic = "DEPRECATED_StringPolymorphic"
+    MainModel = "DEPRECATED_MainModel"
+    UNet = "DEPRECATED_UNet"
+    Vae = "DEPRECATED_Vae"
+    CLIP = "DEPRECATED_CLIP"
+    Collection = "DEPRECATED_Collection"
+    CollectionItem = "DEPRECATED_CollectionItem"
+    Enum = "DEPRECATED_Enum"
+    WorkflowField = "DEPRECATED_WorkflowField"
+    IsIntermediate = "DEPRECATED_IsIntermediate"
+    BoardField = "DEPRECATED_BoardField"
+    MetadataItem = "DEPRECATED_MetadataItem"
+    MetadataItemCollection = "DEPRECATED_MetadataItemCollection"
+    MetadataItemPolymorphic = "DEPRECATED_MetadataItemPolymorphic"
+    MetadataDict = "DEPRECATED_MetadataDict"
+    # endregion
+
+
+class UIComponent(str, Enum, metaclass=MetaEnum):
+    """
+    The type of UI component to use for a field, used to override the default components, which are
+    inferred from the field type.
+    """
+
+    None_ = "none"
+    Textarea = "textarea"
+    Slider = "slider"
+
+
+class InputFieldJSONSchemaExtra(BaseModel):
+    """
+    Extra attributes to be added to input fields and their OpenAPI schema. Used during graph execution,
+    and by the workflow editor during schema parsing and UI rendering.
+    """
+
+    input: Input
+    orig_required: bool
+    field_kind: FieldKind
+    default: Optional[Any] = None
+    orig_default: Optional[Any] = None
+    ui_hidden: bool = False
+    ui_type: Optional[UIType] = None
+    ui_component: Optional[UIComponent] = None
+    ui_order: Optional[int] = None
+    ui_choice_labels: Optional[dict[str, str]] = None
+
+    model_config = ConfigDict(
+        validate_assignment=True,
+        json_schema_serialization_defaults_required=True,
+    )
+
+
+class OutputFieldJSONSchemaExtra(BaseModel):
+    """
+    Extra attributes to be added to input fields and their OpenAPI schema. Used by the workflow editor
+    during schema parsing and UI rendering.
+    """
+
+    field_kind: FieldKind
+    ui_hidden: bool
+    ui_type: Optional[UIType]
+    ui_order: Optional[int]
+
+    model_config = ConfigDict(
+        validate_assignment=True,
+        json_schema_serialization_defaults_required=True,
+    )
+
+
+def InputField(
+    # copied from pydantic's Field
+    # TODO: Can we support default_factory?
+    default: Any = _Unset,
+    default_factory: Callable[[], Any] | None = _Unset,
+    title: str | None = _Unset,
+    description: str | None = _Unset,
+    pattern: str | None = _Unset,
+    strict: bool | None = _Unset,
+    gt: float | None = _Unset,
+    ge: float | None = _Unset,
+    lt: float | None = _Unset,
+    le: float | None = _Unset,
+    multiple_of: float | None = _Unset,
+    allow_inf_nan: bool | None = _Unset,
+    max_digits: int | None = _Unset,
+    decimal_places: int | None = _Unset,
+    min_length: int | None = _Unset,
+    max_length: int | None = _Unset,
+    # custom
+    input: Input = Input.Any,
+    ui_type: Optional[UIType] = None,
+    ui_component: Optional[UIComponent] = None,
+    ui_hidden: bool = False,
+    ui_order: Optional[int] = None,
+    ui_choice_labels: Optional[dict[str, str]] = None,
+) -> Any:
+    """
+    Creates an input field for an invocation.
+
+    This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/latest/api/fields/#pydantic.fields.Field) \
+    that adds a few extra parameters to support graph execution and the node editor UI.
+
+    :param Input input: [Input.Any] The kind of input this field requires. \
+      `Input.Direct` means a value must be provided on instantiation. \
+      `Input.Connection` means the value must be provided by a connection. \
+      `Input.Any` means either will do.
+
+    :param UIType ui_type: [None] Optionally provides an extra type hint for the UI. \
+      In some situations, the field's type is not enough to infer the correct UI type. \
+      For example, model selection fields should render a dropdown UI component to select a model. \
+      Internally, there is no difference between SD-1, SD-2 and SDXL model fields, they all use \
+      `MainModelField`. So to ensure the base-model-specific UI is rendered, you can use \
+      `UIType.SDXLMainModelField` to indicate that the field is an SDXL main model field.
+
+    :param UIComponent ui_component: [None] Optionally specifies a specific component to use in the UI. \
+      The UI will always render a suitable component, but sometimes you want something different than the default. \
+      For example, a `string` field will default to a single-line input, but you may want a multi-line textarea instead. \
+      For this case, you could provide `UIComponent.Textarea`.
+
+    :param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI.
+
+    :param int ui_order: [None] Specifies the order in which this field should be rendered in the UI.
+
+    :param dict[str, str] ui_choice_labels: [None] Specifies the labels to use for the choices in an enum field.
+    """
+
+    json_schema_extra_ = InputFieldJSONSchemaExtra(
+        input=input,
+        ui_type=ui_type,
+        ui_component=ui_component,
+        ui_hidden=ui_hidden,
+        ui_order=ui_order,
+        ui_choice_labels=ui_choice_labels,
+        field_kind=FieldKind.Input,
+        orig_required=True,
+    )
+
+    """
+    There is a conflict between the typing of invocation definitions and the typing of an invocation's
+    `invoke()` function.
+
+    On instantiation of a node, the invocation definition is used to create the python class. At this time,
+    any number of fields may be optional, because they may be provided by connections.
+
+    On calling of `invoke()`, however, those fields may be required.
+
+    For example, consider an ResizeImageInvocation with an `image: ImageField` field.
+
+    `image` is required during the call to `invoke()`, but when the python class is instantiated,
+    the field may not be present. This is fine, because that image field will be provided by a
+    connection from an ancestor node, which outputs an image.
+
+    This means we want to type the `image` field as optional for the node class definition, but required
+    for the `invoke()` function.
+
+    If we use `typing.Optional` in the node class definition, the field will be typed as optional in the
+    `invoke()` method, and we'll have to do a lot of runtime checks to ensure the field is present - or
+    any static type analysis tools will complain.
+
+    To get around this, in node class definitions, we type all fields correctly for the `invoke()` function,
+    but secretly make them optional in `InputField()`. We also store the original required bool and/or default
+    value. When we call `invoke()`, we use this stored information to do an additional check on the class.
+    """
+
+    if default_factory is not _Unset and default_factory is not None:
+        default = default_factory()
+        logger.warn('"default_factory" is not supported, calling it now to set "default"')
+
+    # These are the args we may wish pass to the pydantic `Field()` function
+    field_args = {
+        "default": default,
+        "title": title,
+        "description": description,
+        "pattern": pattern,
+        "strict": strict,
+        "gt": gt,
+        "ge": ge,
+        "lt": lt,
+        "le": le,
+        "multiple_of": multiple_of,
+        "allow_inf_nan": allow_inf_nan,
+        "max_digits": max_digits,
+        "decimal_places": decimal_places,
+        "min_length": min_length,
+        "max_length": max_length,
+    }
+
+    # We only want to pass the args that were provided, otherwise the `Field()`` function won't work as expected
+    provided_args = {k: v for (k, v) in field_args.items() if v is not PydanticUndefined}
+
+    # Because we are manually making fields optional, we need to store the original required bool for reference later
+    json_schema_extra_.orig_required = default is PydanticUndefined
+
+    # Make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
+    if input is Input.Any or input is Input.Connection:
+        default_ = None if default is PydanticUndefined else default
+        provided_args.update({"default": default_})
+        if default is not PydanticUndefined:
+            # Before invoking, we'll check for the original default value and set it on the field if the field has no value
+            json_schema_extra_.default = default
+            json_schema_extra_.orig_default = default
+    elif default is not PydanticUndefined:
+        default_ = default
+        provided_args.update({"default": default_})
+        json_schema_extra_.orig_default = default_
+
+    return Field(
+        **provided_args,
+        json_schema_extra=json_schema_extra_.model_dump(exclude_none=True),
+    )
+
+
+def OutputField(
+    # copied from pydantic's Field
+    default: Any = _Unset,
+    title: str | None = _Unset,
+    description: str | None = _Unset,
+    pattern: str | None = _Unset,
+    strict: bool | None = _Unset,
+    gt: float | None = _Unset,
+    ge: float | None = _Unset,
+    lt: float | None = _Unset,
+    le: float | None = _Unset,
+    multiple_of: float | None = _Unset,
+    allow_inf_nan: bool | None = _Unset,
+    max_digits: int | None = _Unset,
+    decimal_places: int | None = _Unset,
+    min_length: int | None = _Unset,
+    max_length: int | None = _Unset,
+    # custom
+    ui_type: Optional[UIType] = None,
+    ui_hidden: bool = False,
+    ui_order: Optional[int] = None,
+) -> Any:
+    """
+    Creates an output field for an invocation output.
+
+    This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/1.10/usage/schema/#field-customization) \
+    that adds a few extra parameters to support graph execution and the node editor UI.
+
+    :param UIType ui_type: [None] Optionally provides an extra type hint for the UI. \
+      In some situations, the field's type is not enough to infer the correct UI type. \
+      For example, model selection fields should render a dropdown UI component to select a model. \
+      Internally, there is no difference between SD-1, SD-2 and SDXL model fields, they all use \
+      `MainModelField`. So to ensure the base-model-specific UI is rendered, you can use \
+      `UIType.SDXLMainModelField` to indicate that the field is an SDXL main model field.
+
+    :param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI. \
+
+    :param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
+    """
+    return Field(
+        default=default,
+        title=title,
+        description=description,
+        pattern=pattern,
+        strict=strict,
+        gt=gt,
+        ge=ge,
+        lt=lt,
+        le=le,
+        multiple_of=multiple_of,
+        allow_inf_nan=allow_inf_nan,
+        max_digits=max_digits,
+        decimal_places=decimal_places,
+        min_length=min_length,
+        max_length=max_length,
+        json_schema_extra=OutputFieldJSONSchemaExtra(
+            ui_type=ui_type,
+            ui_hidden=ui_hidden,
+            ui_order=ui_order,
+            field_kind=FieldKind.Output,
+        ).model_dump(exclude_none=True),
+    )
+
+
 class UIConfigBase(BaseModel):
     """
     Provides additional node configuration to the UI.
@@ -76,6 +460,33 @@ class UIConfigBase(BaseModel):
     )
 
 
+class InvocationContext:
+    """Initialized and provided to on execution of invocations."""
+
+    services: InvocationServices
+    graph_execution_state_id: str
+    queue_id: str
+    queue_item_id: int
+    queue_batch_id: str
+    workflow: Optional[WorkflowWithoutID]
+
+    def __init__(
+        self,
+        services: InvocationServices,
+        queue_id: str,
+        queue_item_id: int,
+        queue_batch_id: str,
+        graph_execution_state_id: str,
+        workflow: Optional[WorkflowWithoutID],
+    ):
+        self.services = services
+        self.graph_execution_state_id = graph_execution_state_id
+        self.queue_id = queue_id
+        self.queue_item_id = queue_item_id
+        self.queue_batch_id = queue_batch_id
+        self.workflow = workflow
+
+
 class BaseInvocationOutput(BaseModel):
     """
     Base class for all invocation outputs.
@@ -221,7 +632,7 @@ class BaseInvocation(ABC, BaseModel):
         """Invoke with provided context and return outputs."""
         pass
 
-    def invoke_internal(self, context: InvocationContext, services: "InvocationServices") -> BaseInvocationOutput:
+    def invoke_internal(self, context: InvocationContext) -> BaseInvocationOutput:
         """
         Internal invoke method, calls `invoke()` after some prep.
         Handles optional fields that are required to call `invoke()` and invocation cache.
@@ -246,23 +657,23 @@ class BaseInvocation(ABC, BaseModel):
                     raise MissingInputException(self.model_fields["type"].default, field_name)
 
         # skip node cache codepath if it's disabled
-        if services.configuration.node_cache_size == 0:
+        if context.services.configuration.node_cache_size == 0:
             return self.invoke(context)
 
         output: BaseInvocationOutput
         if self.use_cache:
-            key = services.invocation_cache.create_key(self)
-            cached_value = services.invocation_cache.get(key)
+            key = context.services.invocation_cache.create_key(self)
+            cached_value = context.services.invocation_cache.get(key)
             if cached_value is None:
-                services.logger.debug(f'Invocation cache miss for type "{self.get_type()}": {self.id}')
+                context.services.logger.debug(f'Invocation cache miss for type "{self.get_type()}": {self.id}')
                 output = self.invoke(context)
-                services.invocation_cache.save(key, output)
+                context.services.invocation_cache.save(key, output)
                 return output
             else:
-                services.logger.debug(f'Invocation cache hit for type "{self.get_type()}": {self.id}')
+                context.services.logger.debug(f'Invocation cache hit for type "{self.get_type()}": {self.id}')
                 return cached_value
         else:
-            services.logger.debug(f'Skipping invocation cache for "{self.get_type()}": {self.id}')
+            context.services.logger.debug(f'Skipping invocation cache for "{self.get_type()}": {self.id}')
             return self.invoke(context)
 
     id: str = Field(
@@ -303,7 +714,9 @@ RESERVED_NODE_ATTRIBUTE_FIELD_NAMES = {
     "workflow",
 }
 
-RESERVED_INPUT_FIELD_NAMES = {"metadata", "board"}
+RESERVED_INPUT_FIELD_NAMES = {
+    "metadata",
+}
 
 RESERVED_OUTPUT_FIELD_NAMES = {"type"}
 
@@ -513,3 +926,37 @@ def invocation_output(
         return cls
 
     return wrapper
+
+
+class MetadataField(RootModel):
+    """
+    Pydantic model for metadata with custom root of type dict[str, Any].
+    Metadata is stored without a strict schema.
+    """
+
+    root: dict[str, Any] = Field(description="The metadata")
+
+
+MetadataFieldValidator = TypeAdapter(MetadataField)
+
+
+class WithMetadata(BaseModel):
+    metadata: Optional[MetadataField] = Field(
+        default=None,
+        description=FieldDescriptions.metadata,
+        json_schema_extra=InputFieldJSONSchemaExtra(
+            field_kind=FieldKind.Internal,
+            input=Input.Connection,
+            orig_required=False,
+        ).model_dump(exclude_none=True),
+    )
+
+
+class WithWorkflow:
+    workflow = None
+
+    def __init_subclass__(cls) -> None:
+        logger.warn(
+            f"{cls.__module__.split('.')[0]}.{cls.__name__}: WithWorkflow is deprecated. Use `context.workflow` to access the workflow."
+        )
+        super().__init_subclass__()

invokeai/app/invocations/collections.py

@@ -5,11 +5,9 @@ import numpy as np
 from pydantic import ValidationInfo, field_validator
 
 from invokeai.app.invocations.primitives import IntegerCollectionOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.misc import SEED_MAX
 
-from .baseinvocation import BaseInvocation, invocation
-from .fields import InputField
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
 
 
 @invocation(

invokeai/app/invocations/compel.py

@@ -1,43 +1,40 @@
-from typing import Iterator, List, Optional, Tuple, Union
+from dataclasses import dataclass
+from typing import List, Optional, Union
 
 import torch
 from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
-from transformers import CLIPTokenizer
 
-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 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.app.invocations.primitives import ConditioningField, ConditioningOutput
+from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
-    ConditioningFieldData,
     ExtraConditioningInfo,
     SDXLConditioningInfo,
 )
-from invokeai.backend.textual_inversion import TextualInversionModelRaw
-from invokeai.backend.util.devices import torch_dtype
 
+from ...backend.model_management.lora import ModelPatcher
+from ...backend.model_management.models import ModelNotFoundException, ModelType
+from ...backend.util.devices import torch_dtype
+from ..util.ti_utils import extract_ti_triggers_from_prompt
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
+    UIComponent,
     invocation,
     invocation_output,
 )
 from .model import ClipField
 
-# unconditioned: Optional[torch.Tensor]
+
+@dataclass
+class ConditioningFieldData:
+    conditionings: List[BasicConditioningInfo]
+    # unconditioned: Optional[torch.Tensor]
 
 
 # class ConditioningAlgo(str, Enum):
@@ -51,7 +48,7 @@ from .model import ClipField
     title="Prompt",
     tags=["prompt", "compel"],
     category="conditioning",
-    version="1.0.1",
+    version="1.0.0",
 )
 class CompelInvocation(BaseInvocation):
     """Parse prompt using compel package to conditioning."""
@@ -69,34 +66,49 @@ class CompelInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
-        tokenizer_info = context.models.load(**self.clip.tokenizer.model_dump())
-        text_encoder_info = context.models.load(**self.clip.text_encoder.model_dump())
+        tokenizer_info = context.services.model_manager.get_model(
+            **self.clip.tokenizer.model_dump(),
+            context=context,
+        )
+        text_encoder_info = context.services.model_manager.get_model(
+            **self.clip.text_encoder.model_dump(),
+            context=context,
+        )
 
-        def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
+        def _lora_loader():
             for lora in self.clip.loras:
-                lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
-                assert isinstance(lora_info.model, LoRAModelRaw)
-                yield (lora_info.model, lora.weight)
+                lora_info = context.services.model_manager.get_model(
+                    **lora.model_dump(exclude={"weight"}), context=context
+                )
+                yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
 
-        # loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
+        # loras = [(context.services.model_manager.get_model(**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(**self.clip.text_encoder.model_dump()).model
-                assert isinstance(loaded_model, TextualInversionModelRaw)
-                ti_list.append((name, loaded_model))
-            except UnknownModelException:
+                ti_list.append(
+                    (
+                        name,
+                        context.services.model_manager.get_model(
+                            model_name=name,
+                            base_model=self.clip.text_encoder.base_model,
+                            model_type=ModelType.TextualInversion,
+                            context=context,
+                        ).context.model,
+                    )
+                )
+            except ModelNotFoundException:
                 # print(e)
                 # import traceback
                 # print(traceback.format_exc())
                 print(f'Warn: trigger: "{trigger}" not found')
 
         with (
-            ModelPatcher.apply_ti(tokenizer_info.model, text_encoder_info.model, ti_list) as (
+            ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
                 tokenizer,
                 ti_manager,
             ),
@@ -104,7 +116,7 @@ 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_info.context.model, self.clip.skipped_layers),
         ):
             compel = Compel(
                 tokenizer=tokenizer,
@@ -116,7 +128,7 @@ class CompelInvocation(BaseInvocation):
 
             conjunction = Compel.parse_prompt_string(self.prompt)
 
-            if context.config.get().log_tokenization:
+            if context.services.configuration.log_tokenization:
                 log_tokenization_for_conjunction(conjunction, tokenizer)
 
             c, options = compel.build_conditioning_tensor_for_conjunction(conjunction)
@@ -137,14 +149,17 @@ class CompelInvocation(BaseInvocation):
             ]
         )
 
-        conditioning_name = context.conditioning.save(conditioning_data)
+        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+        context.services.latents.save(conditioning_name, conditioning_data)
 
-        return ConditioningOutput.build(conditioning_name)
+        return ConditioningOutput(
+            conditioning=ConditioningField(
+                conditioning_name=conditioning_name,
+            ),
+        )
 
 
 class SDXLPromptInvocationBase:
-    """Prompt processor for SDXL models."""
-
     def run_clip_compel(
         self,
         context: InvocationContext,
@@ -153,21 +168,26 @@ class SDXLPromptInvocationBase:
         get_pooled: bool,
         lora_prefix: str,
         zero_on_empty: bool,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[ExtraConditioningInfo]]:
-        tokenizer_info = context.models.load(**clip_field.tokenizer.model_dump())
-        text_encoder_info = context.models.load(**clip_field.text_encoder.model_dump())
+    ):
+        tokenizer_info = context.services.model_manager.get_model(
+            **clip_field.tokenizer.model_dump(),
+            context=context,
+        )
+        text_encoder_info = context.services.model_manager.get_model(
+            **clip_field.text_encoder.model_dump(),
+            context=context,
+        )
 
         # return zero on empty
         if prompt == "" and zero_on_empty:
-            cpu_text_encoder = text_encoder_info.model
-            assert isinstance(cpu_text_encoder, torch.nn.Module)
+            cpu_text_encoder = text_encoder_info.context.model
             c = torch.zeros(
                 (
                     1,
                     cpu_text_encoder.config.max_position_embeddings,
                     cpu_text_encoder.config.hidden_size,
                 ),
-                dtype=cpu_text_encoder.dtype,
+                dtype=text_encoder_info.context.cache.precision,
             )
             if get_pooled:
                 c_pooled = torch.zeros(
@@ -178,36 +198,40 @@ class SDXLPromptInvocationBase:
                 c_pooled = None
             return c, c_pooled, None
 
-        def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
+        def _lora_loader():
             for lora in clip_field.loras:
-                lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
-                lora_model = lora_info.model
-                assert isinstance(lora_model, LoRAModelRaw)
-                yield (lora_model, lora.weight)
+                lora_info = context.services.model_manager.get_model(
+                    **lora.model_dump(exclude={"weight"}), context=context
+                )
+                yield (lora_info.context.model, lora.weight)
                 del lora_info
             return
 
-        # loras = [(context.models.get(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
+        # loras = [(context.services.model_manager.get_model(**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:
+                ti_list.append(
+                    (
+                        name,
+                        context.services.model_manager.get_model(
+                            model_name=name,
+                            base_model=clip_field.text_encoder.base_model,
+                            model_type=ModelType.TextualInversion,
+                            context=context,
+                        ).context.model,
+                    )
+                )
+            except ModelNotFoundException:
                 # 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')
+                print(f'Warn: trigger: "{trigger}" not found')
 
         with (
-            ModelPatcher.apply_ti(tokenizer_info.model, text_encoder_info.model, ti_list) as (
+            ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
                 tokenizer,
                 ti_manager,
             ),
@@ -215,7 +239,7 @@ 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_info.context.model, clip_field.skipped_layers),
         ):
             compel = Compel(
                 tokenizer=tokenizer,
@@ -229,7 +253,7 @@ class SDXLPromptInvocationBase:
 
             conjunction = Compel.parse_prompt_string(prompt)
 
-            if context.config.get().log_tokenization:
+            if context.services.configuration.log_tokenization:
                 # TODO: better logging for and syntax
                 log_tokenization_for_conjunction(conjunction, tokenizer)
 
@@ -262,7 +286,7 @@ class SDXLPromptInvocationBase:
     title="SDXL Prompt",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
-    version="1.0.1",
+    version="1.0.0",
 )
 class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
@@ -333,7 +357,6 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
                 dim=1,
             )
 
-        assert c2_pooled is not None
         conditioning_data = ConditioningFieldData(
             conditionings=[
                 SDXLConditioningInfo(
@@ -345,9 +368,14 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
             ]
         )
 
-        conditioning_name = context.conditioning.save(conditioning_data)
+        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+        context.services.latents.save(conditioning_name, conditioning_data)
 
-        return ConditioningOutput.build(conditioning_name)
+        return ConditioningOutput(
+            conditioning=ConditioningField(
+                conditioning_name=conditioning_name,
+            ),
+        )
 
 
 @invocation(
@@ -355,7 +383,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     title="SDXL Refiner Prompt",
     tags=["sdxl", "compel", "prompt"],
     category="conditioning",
-    version="1.0.1",
+    version="1.0.0",
 )
 class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
     """Parse prompt using compel package to conditioning."""
@@ -382,7 +410,6 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
 
         add_time_ids = torch.tensor([original_size + crop_coords + (self.aesthetic_score,)])
 
-        assert c2_pooled is not None
         conditioning_data = ConditioningFieldData(
             conditionings=[
                 SDXLConditioningInfo(
@@ -394,9 +421,14 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
             ]
         )
 
-        conditioning_name = context.conditioning.save(conditioning_data)
+        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+        context.services.latents.save(conditioning_name, conditioning_data)
 
-        return ConditioningOutput.build(conditioning_name)
+        return ConditioningOutput(
+            conditioning=ConditioningField(
+                conditioning_name=conditioning_name,
+            ),
+        )
 
 
 @invocation_output("clip_skip_output")
@@ -427,9 +459,9 @@ class ClipSkipInvocation(BaseInvocation):
 
 
 def get_max_token_count(
-    tokenizer: CLIPTokenizer,
+    tokenizer,
     prompt: Union[FlattenedPrompt, Blend, Conjunction],
-    truncate_if_too_long: bool = False,
+    truncate_if_too_long=False,
 ) -> int:
     if type(prompt) is Blend:
         blend: Blend = prompt
@@ -441,9 +473,7 @@ def get_max_token_count(
         return len(get_tokens_for_prompt_object(tokenizer, prompt, truncate_if_too_long))
 
 
-def get_tokens_for_prompt_object(
-    tokenizer: CLIPTokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long: bool = True
-) -> List[str]:
+def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True) -> List[str]:
     if type(parsed_prompt) is Blend:
         raise ValueError("Blend is not supported here - you need to get tokens for each of its .children")
 
@@ -456,29 +486,24 @@ def get_tokens_for_prompt_object(
         for x in parsed_prompt.children
     ]
     text = " ".join(text_fragments)
-    tokens: List[str] = tokenizer.tokenize(text)
+    tokens = tokenizer.tokenize(text)
     if truncate_if_too_long:
         max_tokens_length = tokenizer.model_max_length - 2  # typically 75
         tokens = tokens[0:max_tokens_length]
     return tokens
 
 
-def log_tokenization_for_conjunction(
-    c: Conjunction, tokenizer: CLIPTokenizer, display_label_prefix: Optional[str] = None
-) -> None:
+def log_tokenization_for_conjunction(c: Conjunction, tokenizer, display_label_prefix=None):
     display_label_prefix = display_label_prefix or ""
     for i, p in enumerate(c.prompts):
         if len(c.prompts) > 1:
             this_display_label_prefix = f"{display_label_prefix}(conjunction part {i + 1}, weight={c.weights[i]})"
         else:
-            assert display_label_prefix is not None
             this_display_label_prefix = display_label_prefix
         log_tokenization_for_prompt_object(p, tokenizer, display_label_prefix=this_display_label_prefix)
 
 
-def log_tokenization_for_prompt_object(
-    p: Union[Blend, FlattenedPrompt], tokenizer: CLIPTokenizer, display_label_prefix: Optional[str] = None
-) -> None:
+def log_tokenization_for_prompt_object(p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None):
     display_label_prefix = display_label_prefix or ""
     if type(p) is Blend:
         blend: Blend = p
@@ -518,12 +543,7 @@ def log_tokenization_for_prompt_object(
             log_tokenization_for_text(text, tokenizer, display_label=display_label_prefix)
 
 
-def log_tokenization_for_text(
-    text: str,
-    tokenizer: CLIPTokenizer,
-    display_label: Optional[str] = None,
-    truncate_if_too_long: Optional[bool] = False,
-) -> None:
+def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_too_long=False):
     """shows how the prompt is tokenized
     # usually tokens have '</w>' to indicate end-of-word,
     # but for readability it has been replaced with ' '

invokeai/app/invocations/constants.py

@@ -1,17 +0,0 @@
-from typing import Literal
-
-from invokeai.backend.stable_diffusion.schedulers import SCHEDULER_MAP
-
-LATENT_SCALE_FACTOR = 8
-"""
-HACK: Many nodes are currently hard-coded to use a fixed latent scale factor of 8. This is fragile, and will need to
-be addressed if future models use a different latent scale factor. Also, note that there may be places where the scale
-factor is hard-coded to a literal '8' rather than using this constant.
-The ratio of image:latent dimensions is LATENT_SCALE_FACTOR:1, or 8:1.
-"""
-
-SCHEDULER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
-"""A literal type representing the valid scheduler names."""
-
-IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
-"""A literal type for PIL image modes supported by Invoke"""

invokeai/app/invocations/controlnet_image_processors.py

@@ -23,24 +23,27 @@ from controlnet_aux import (
 )
 from controlnet_aux.util import HWC3, ade_palette
 from PIL import Image
-from pydantic import BaseModel, Field, field_validator, model_validator
+from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
 
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    ImageField,
-    Input,
-    InputField,
-    OutputField,
-    WithBoard,
-    WithMetadata,
-)
-from invokeai.app.invocations.primitives import ImageOutput
+from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
-from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
 from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
 
-from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
+from ...backend.model_management import BaseModelType
+from .baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
+    WithMetadata,
+    invocation,
+    invocation_output,
+)
 
 CONTROLNET_MODE_VALUES = Literal["balanced", "more_prompt", "more_control", "unbalanced"]
 CONTROLNET_RESIZE_VALUES = Literal[
@@ -54,7 +57,10 @@ CONTROLNET_RESIZE_VALUES = Literal[
 class ControlNetModelField(BaseModel):
     """ControlNet model field"""
 
-    key: str = Field(description="Model config record key for the ControlNet model")
+    model_name: str = Field(description="Name of the ControlNet model")
+    base_model: BaseModelType = Field(description="Base model")
+
+    model_config = ConfigDict(protected_namespaces=())
 
 
 class ControlField(BaseModel):
@@ -134,7 +140,7 @@ class ControlNetInvocation(BaseInvocation):
 
 
 # This invocation exists for other invocations to subclass it - do not register with @invocation!
-class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
+class ImageProcessorInvocation(BaseInvocation, WithMetadata):
     """Base class for invocations that preprocess images for ControlNet"""
 
     image: ImageField = InputField(description="The image to process")
@@ -143,18 +149,23 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
         # superclass just passes through image without processing
         return image
 
-    def load_image(self, context: InvocationContext) -> Image.Image:
-        # allows override for any special formatting specific to the preprocessor
-        return context.images.get_pil(self.image.image_name, "RGB")
-
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        raw_image = self.load_image(context)
+        raw_image = context.services.images.get_pil_image(self.image.image_name)
         # image type should be PIL.PngImagePlugin.PngImageFile ?
         processed_image = self.run_processor(raw_image)
 
         # currently can't see processed image in node UI without a showImage node,
         #    so for now setting image_type to RESULT instead of INTERMEDIATE so will get saved in gallery
-        image_dto = context.images.save(image=processed_image)
+        image_dto = context.services.images.create(
+            image=processed_image,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.CONTROL,
+            session_id=context.graph_execution_state_id,
+            node_id=self.id,
+            is_intermediate=self.is_intermediate,
+            metadata=self.metadata,
+            workflow=context.workflow,
+        )
 
         """Builds an ImageOutput and its ImageField"""
         processed_image_field = ImageField(image_name=image_dto.image_name)
@@ -173,7 +184,7 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
     title="Canny Processor",
     tags=["controlnet", "canny"],
     category="controlnet",
-    version="1.2.1",
+    version="1.2.0",
 )
 class CannyImageProcessorInvocation(ImageProcessorInvocation):
     """Canny edge detection for ControlNet"""
@@ -185,10 +196,6 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
         default=200, ge=0, le=255, description="The high threshold of the Canny pixel gradient (0-255)"
     )
 
-    def load_image(self, context: InvocationContext) -> Image.Image:
-        # Keep alpha channel for Canny processing to detect edges of transparent areas
-        return context.images.get_pil(self.image.image_name, "RGBA")
-
     def run_processor(self, image):
         canny_processor = CannyDetector()
         processed_image = canny_processor(image, self.low_threshold, self.high_threshold)
@@ -200,7 +207,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
     title="HED (softedge) Processor",
     tags=["controlnet", "hed", "softedge"],
     category="controlnet",
-    version="1.2.1",
+    version="1.2.0",
 )
 class HedImageProcessorInvocation(ImageProcessorInvocation):
     """Applies HED edge detection to image"""
@@ -229,7 +236,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Processor",
     tags=["controlnet", "lineart"],
     category="controlnet",
-    version="1.2.1",
+    version="1.2.0",
 )
 class LineartImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art processing to image"""
@@ -251,7 +258,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
     title="Lineart Anime Processor",
     tags=["controlnet", "lineart", "anime"],
     category="controlnet",
-    version="1.2.1",
+    version="1.2.0",
 )
 class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies line art anime processing to image"""
@@ -274,7 +281,7 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
     title="Midas Depth Processor",
     tags=["controlnet", "midas"],
     category="controlnet",
-    version="1.2.1",
+    version="1.2.0",
 )
 class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Midas depth processing to image"""
@@ -301,7 +308,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Normal BAE Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.1",
+    version="1.2.0",
 )
 class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
     """Applies NormalBae processing to image"""
@@ -318,7 +325,7 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.1"
+    "mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.0"
 )
 class MlsdImageProcessorInvocation(ImageProcessorInvocation):
     """Applies MLSD processing to image"""
@@ -341,7 +348,7 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
 
 
 @invocation(
-    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.1"
+    "pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.0"
 )
 class PidiImageProcessorInvocation(ImageProcessorInvocation):
     """Applies PIDI processing to image"""
@@ -368,7 +375,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
     title="Content Shuffle Processor",
     tags=["controlnet", "contentshuffle"],
     category="controlnet",
-    version="1.2.1",
+    version="1.2.0",
 )
 class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     """Applies content shuffle processing to image"""
@@ -398,7 +405,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
     title="Zoe (Depth) Processor",
     tags=["controlnet", "zoe", "depth"],
     category="controlnet",
-    version="1.2.1",
+    version="1.2.0",
 )
 class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     """Applies Zoe depth processing to image"""
@@ -414,7 +421,7 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
     title="Mediapipe Face Processor",
     tags=["controlnet", "mediapipe", "face"],
     category="controlnet",
-    version="1.2.1",
+    version="1.2.0",
 )
 class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     """Applies mediapipe face processing to image"""
@@ -423,6 +430,10 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     min_confidence: float = InputField(default=0.5, ge=0, le=1, description="Minimum confidence for face detection")
 
     def run_processor(self, image):
+        # MediaPipeFaceDetector throws an error if image has alpha channel
+        #     so convert to RGB if needed
+        if image.mode == "RGBA":
+            image = image.convert("RGB")
         mediapipe_face_processor = MediapipeFaceDetector()
         processed_image = mediapipe_face_processor(image, max_faces=self.max_faces, min_confidence=self.min_confidence)
         return processed_image
@@ -433,7 +444,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
     title="Leres (Depth) Processor",
     tags=["controlnet", "leres", "depth"],
     category="controlnet",
-    version="1.2.1",
+    version="1.2.0",
 )
 class LeresImageProcessorInvocation(ImageProcessorInvocation):
     """Applies leres processing to image"""
@@ -462,7 +473,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
     title="Tile Resample Processor",
     tags=["controlnet", "tile"],
     category="controlnet",
-    version="1.2.1",
+    version="1.2.0",
 )
 class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     """Tile resampler processor"""
@@ -502,7 +513,7 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
     title="Segment Anything Processor",
     tags=["controlnet", "segmentanything"],
     category="controlnet",
-    version="1.2.1",
+    version="1.2.0",
 )
 class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
     """Applies segment anything processing to image"""
@@ -544,7 +555,7 @@ class SamDetectorReproducibleColors(SamDetector):
     title="Color Map Processor",
     tags=["controlnet"],
     category="controlnet",
-    version="1.2.1",
+    version="1.2.0",
 )
 class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
     """Generates a color map from the provided image"""
@@ -552,6 +563,7 @@ class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
     color_map_tile_size: int = InputField(default=64, ge=0, description=FieldDescriptions.tile_size)
 
     def run_processor(self, image: Image.Image):
+        image = image.convert("RGB")
         np_image = np.array(image, dtype=np.uint8)
         height, width = np_image.shape[:2]
 
@@ -591,6 +603,9 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
         depth_anything_detector = DepthAnythingDetector()
         depth_anything_detector.load_model(model_size=self.model_size)
 
+        if image.mode == "RGBA":
+            image = image.convert("RGB")
+
         processed_image = depth_anything_detector(image=image, resolution=self.resolution, offload=self.offload)
         return processed_image
 
@@ -610,7 +625,7 @@ class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
     draw_hands: bool = InputField(default=False)
     image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
 
-    def run_processor(self, image: Image.Image):
+    def run_processor(self, image):
         dw_openpose = DWOpenposeDetector()
         processed_image = dw_openpose(
             image,

invokeai/app/invocations/cv.py

@@ -5,24 +5,22 @@ import cv2 as cv
 import numpy
 from PIL import Image, ImageOps
 
-from invokeai.app.invocations.fields import ImageField
-from invokeai.app.invocations.primitives import ImageOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.invocations.primitives import ImageField, ImageOutput
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 
-from .baseinvocation import BaseInvocation, invocation
-from .fields import InputField, WithBoard, WithMetadata
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
 
 
-@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.2.1")
-class CvInpaintInvocation(BaseInvocation, WithMetadata, WithBoard):
+@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.2.0")
+class CvInpaintInvocation(BaseInvocation, WithMetadata):
     """Simple inpaint using opencv."""
 
     image: ImageField = InputField(description="The image to inpaint")
     mask: ImageField = InputField(description="The mask to use when inpainting")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name)
-        mask = context.images.get_pil(self.mask.image_name)
+        image = context.services.images.get_pil_image(self.image.image_name)
+        mask = context.services.images.get_pil_image(self.mask.image_name)
 
         # Convert to cv image/mask
         # TODO: consider making these utility functions
@@ -36,6 +34,18 @@ class CvInpaintInvocation(BaseInvocation, WithMetadata, WithBoard):
         # TODO: consider making a utility function
         image_inpainted = Image.fromarray(cv.cvtColor(cv_inpainted, cv.COLOR_BGR2RGB))
 
-        image_dto = context.images.save(image=image_inpainted)
+        image_dto = context.services.images.create(
+            image=image_inpainted,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.GENERAL,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+            workflow=context.workflow,
+        )
 
-        return ImageOutput.build(image_dto)
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )

invokeai/app/invocations/facetools.py

@@ -13,13 +13,15 @@ from pydantic import field_validator
 import invokeai.assets.fonts as font_assets
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
+    InputField,
+    InvocationContext,
+    OutputField,
+    WithMetadata,
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.fields import ImageField, InputField, OutputField, WithBoard, WithMetadata
-from invokeai.app.invocations.primitives import ImageOutput
-from invokeai.app.services.image_records.image_records_common import ImageCategory
-from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.invocations.primitives import ImageField, ImageOutput
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 
 
 @invocation_output("face_mask_output")
@@ -304,37 +306,37 @@ def extract_face(
 
     # Adjust the crop boundaries to stay within the original image's dimensions
     if x_min < 0:
-        context.logger.warning("FaceTools --> -X-axis padding reached image edge.")
+        context.services.logger.warning("FaceTools --> -X-axis padding reached image edge.")
         x_max -= x_min
         x_min = 0
     elif x_max > mask.width:
-        context.logger.warning("FaceTools --> +X-axis padding reached image edge.")
+        context.services.logger.warning("FaceTools --> +X-axis padding reached image edge.")
         x_min -= x_max - mask.width
         x_max = mask.width
 
     if y_min < 0:
-        context.logger.warning("FaceTools --> +Y-axis padding reached image edge.")
+        context.services.logger.warning("FaceTools --> +Y-axis padding reached image edge.")
         y_max -= y_min
         y_min = 0
     elif y_max > mask.height:
-        context.logger.warning("FaceTools --> -Y-axis padding reached image edge.")
+        context.services.logger.warning("FaceTools --> -Y-axis padding reached image edge.")
         y_min -= y_max - mask.height
         y_max = mask.height
 
     # Ensure the crop is square and adjust the boundaries if needed
     if x_max - x_min != crop_size:
-        context.logger.warning("FaceTools --> Limiting x-axis padding to constrain bounding box to a square.")
+        context.services.logger.warning("FaceTools --> Limiting x-axis padding to constrain bounding box to a square.")
         diff = crop_size - (x_max - x_min)
         x_min -= diff // 2
         x_max += diff - diff // 2
 
     if y_max - y_min != crop_size:
-        context.logger.warning("FaceTools --> Limiting y-axis padding to constrain bounding box to a square.")
+        context.services.logger.warning("FaceTools --> Limiting y-axis padding to constrain bounding box to a square.")
         diff = crop_size - (y_max - y_min)
         y_min -= diff // 2
         y_max += diff - diff // 2
 
-    context.logger.info(f"FaceTools --> Calculated bounding box (8 multiple): {crop_size}")
+    context.services.logger.info(f"FaceTools --> Calculated bounding box (8 multiple): {crop_size}")
 
     # Crop the output image to the specified size with the center of the face mesh as the center.
     mask = mask.crop((x_min, y_min, x_max, y_max))
@@ -366,7 +368,7 @@ def get_faces_list(
 
     # Generate the face box mask and get the center of the face.
     if not should_chunk:
-        context.logger.info("FaceTools --> Attempting full image face detection.")
+        context.services.logger.info("FaceTools --> Attempting full image face detection.")
         result = generate_face_box_mask(
             context=context,
             minimum_confidence=minimum_confidence,
@@ -378,7 +380,7 @@ def get_faces_list(
             draw_mesh=draw_mesh,
         )
     if should_chunk or len(result) == 0:
-        context.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).")
+        context.services.logger.info("FaceTools --> Chunking image (chunk toggled on, or no face found in full image).")
         width, height = image.size
         image_chunks = []
         x_offsets = []
@@ -397,7 +399,7 @@ def get_faces_list(
                 x_offsets.append(x)
                 y_offsets.append(0)
                 fx += increment
-                context.logger.info(f"FaceTools --> Chunk starting at x = {x}")
+                context.services.logger.info(f"FaceTools --> Chunk starting at x = {x}")
         elif height > width:
             # Portrait - slice the image vertically
             fy = 0.0
@@ -409,10 +411,10 @@ def get_faces_list(
                 x_offsets.append(0)
                 y_offsets.append(y)
                 fy += increment
-                context.logger.info(f"FaceTools --> Chunk starting at y = {y}")
+                context.services.logger.info(f"FaceTools --> Chunk starting at y = {y}")
 
         for idx in range(len(image_chunks)):
-            context.logger.info(f"FaceTools --> Evaluating faces in chunk {idx}")
+            context.services.logger.info(f"FaceTools --> Evaluating faces in chunk {idx}")
             result = result + generate_face_box_mask(
                 context=context,
                 minimum_confidence=minimum_confidence,
@@ -426,7 +428,7 @@ def get_faces_list(
 
         if len(result) == 0:
             # Give up
-            context.logger.warning(
+            context.services.logger.warning(
                 "FaceTools --> No face detected in chunked input image. Passing through original image."
             )
 
@@ -435,7 +437,7 @@ def get_faces_list(
     return all_faces
 
 
-@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.2.1")
+@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.2.0")
 class FaceOffInvocation(BaseInvocation, WithMetadata):
     """Bound, extract, and mask a face from an image using MediaPipe detection"""
 
@@ -468,11 +470,11 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
         )
 
         if len(all_faces) == 0:
-            context.logger.warning("FaceOff --> No faces detected. Passing through original image.")
+            context.services.logger.warning("FaceOff --> No faces detected. Passing through original image.")
             return None
 
         if self.face_id > len(all_faces) - 1:
-            context.logger.warning(
+            context.services.logger.warning(
                 f"FaceOff --> Face ID {self.face_id} is outside of the number of faces detected ({len(all_faces)}). Passing through original image."
             )
             return None
@@ -484,7 +486,7 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
         return face_data
 
     def invoke(self, context: InvocationContext) -> FaceOffOutput:
-        image = context.images.get_pil(self.image.image_name)
+        image = context.services.images.get_pil_image(self.image.image_name)
         result = self.faceoff(context=context, image=image)
 
         if result is None:
@@ -498,9 +500,24 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
             x = result["x_min"]
             y = result["y_min"]
 
-        image_dto = context.images.save(image=result_image)
+        image_dto = context.services.images.create(
+            image=result_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,
+            workflow=context.workflow,
+        )
 
-        mask_dto = context.images.save(image=result_mask, image_category=ImageCategory.MASK)
+        mask_dto = context.services.images.create(
+            image=result_mask,
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.MASK,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+        )
 
         output = FaceOffOutput(
             image=ImageField(image_name=image_dto.image_name),
@@ -514,7 +531,7 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
         return output
 
 
-@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.2.1")
+@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.2.0")
 class FaceMaskInvocation(BaseInvocation, WithMetadata):
     """Face mask creation using mediapipe face detection"""
 
@@ -563,7 +580,7 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
 
             if len(intersected_face_ids) == 0:
                 id_range_str = ",".join([str(id) for id in id_range])
-                context.logger.warning(
+                context.services.logger.warning(
                     f"Face IDs must be in range of detected faces - requested {self.face_ids}, detected {id_range_str}. Passing through original image."
                 )
                 return FaceMaskResult(
@@ -599,12 +616,27 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
         )
 
     def invoke(self, context: InvocationContext) -> FaceMaskOutput:
-        image = context.images.get_pil(self.image.image_name)
+        image = context.services.images.get_pil_image(self.image.image_name)
         result = self.facemask(context=context, image=image)
 
-        image_dto = context.images.save(image=result["image"])
+        image_dto = context.services.images.create(
+            image=result["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,
+            workflow=context.workflow,
+        )
 
-        mask_dto = context.images.save(image=result["mask"], image_category=ImageCategory.MASK)
+        mask_dto = context.services.images.create(
+            image=result["mask"],
+            image_origin=ResourceOrigin.INTERNAL,
+            image_category=ImageCategory.MASK,
+            node_id=self.id,
+            session_id=context.graph_execution_state_id,
+            is_intermediate=self.is_intermediate,
+        )
 
         output = FaceMaskOutput(
             image=ImageField(image_name=image_dto.image_name),
@@ -617,9 +649,9 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
 
 
 @invocation(
-    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.2.1"
+    "face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.2.0"
 )
-class FaceIdentifierInvocation(BaseInvocation, WithMetadata, WithBoard):
+class FaceIdentifierInvocation(BaseInvocation, WithMetadata):
     """Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""
 
     image: ImageField = InputField(description="Image to face detect")
@@ -673,9 +705,21 @@ class FaceIdentifierInvocation(BaseInvocation, WithMetadata, WithBoard):
         return image
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name)
+        image = context.services.images.get_pil_image(self.image.image_name)
         result_image = self.faceidentifier(context=context, image=image)
 
-        image_dto = context.images.save(image=result_image)
+        image_dto = context.services.images.create(
+            image=result_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,
+            workflow=context.workflow,
+        )
 
-        return ImageOutput.build(image_dto)
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )

invokeai/app/invocations/fields.py

@@ -1,565 +0,0 @@
-from enum import Enum
-from typing import Any, Callable, Optional, Tuple
-
-from pydantic import BaseModel, ConfigDict, Field, RootModel, TypeAdapter
-from pydantic.fields import _Unset
-from pydantic_core import PydanticUndefined
-
-from invokeai.app.util.metaenum import MetaEnum
-from invokeai.backend.util.logging import InvokeAILogger
-
-logger = InvokeAILogger.get_logger()
-
-
-class UIType(str, Enum, metaclass=MetaEnum):
-    """
-    Type hints for the UI for situations in which the field type is not enough to infer the correct UI type.
-
-    - Model Fields
-    The most common node-author-facing use will be for model fields. Internally, there is no difference
-    between SD-1, SD-2 and SDXL model fields - they all use the class `MainModelField`. To ensure the
-    base-model-specific UI is rendered, use e.g. `ui_type=UIType.SDXLMainModelField` to indicate that
-    the field is an SDXL main model field.
-
-    - Any Field
-    We cannot infer the usage of `typing.Any` via schema parsing, so you *must* use `ui_type=UIType.Any` to
-    indicate that the field accepts any type. Use with caution. This cannot be used on outputs.
-
-    - Scheduler Field
-    Special handling in the UI is needed for this field, which otherwise would be parsed as a plain enum field.
-
-    - Internal Fields
-    Similar to the Any Field, the `collect` and `iterate` nodes make use of `typing.Any`. To facilitate
-    handling these types in the client, we use `UIType._Collection` and `UIType._CollectionItem`. These
-    should not be used by node authors.
-
-    - DEPRECATED Fields
-    These types are deprecated and should not be used by node authors. A warning will be logged if one is
-    used, and the type will be ignored. They are included here for backwards compatibility.
-    """
-
-    # region Model Field Types
-    SDXLMainModel = "SDXLMainModelField"
-    SDXLRefinerModel = "SDXLRefinerModelField"
-    ONNXModel = "ONNXModelField"
-    VaeModel = "VAEModelField"
-    LoRAModel = "LoRAModelField"
-    ControlNetModel = "ControlNetModelField"
-    IPAdapterModel = "IPAdapterModelField"
-    # endregion
-
-    # region Misc Field Types
-    Scheduler = "SchedulerField"
-    Any = "AnyField"
-    # endregion
-
-    # region Internal Field Types
-    _Collection = "CollectionField"
-    _CollectionItem = "CollectionItemField"
-    # endregion
-
-    # region DEPRECATED
-    Boolean = "DEPRECATED_Boolean"
-    Color = "DEPRECATED_Color"
-    Conditioning = "DEPRECATED_Conditioning"
-    Control = "DEPRECATED_Control"
-    Float = "DEPRECATED_Float"
-    Image = "DEPRECATED_Image"
-    Integer = "DEPRECATED_Integer"
-    Latents = "DEPRECATED_Latents"
-    String = "DEPRECATED_String"
-    BooleanCollection = "DEPRECATED_BooleanCollection"
-    ColorCollection = "DEPRECATED_ColorCollection"
-    ConditioningCollection = "DEPRECATED_ConditioningCollection"
-    ControlCollection = "DEPRECATED_ControlCollection"
-    FloatCollection = "DEPRECATED_FloatCollection"
-    ImageCollection = "DEPRECATED_ImageCollection"
-    IntegerCollection = "DEPRECATED_IntegerCollection"
-    LatentsCollection = "DEPRECATED_LatentsCollection"
-    StringCollection = "DEPRECATED_StringCollection"
-    BooleanPolymorphic = "DEPRECATED_BooleanPolymorphic"
-    ColorPolymorphic = "DEPRECATED_ColorPolymorphic"
-    ConditioningPolymorphic = "DEPRECATED_ConditioningPolymorphic"
-    ControlPolymorphic = "DEPRECATED_ControlPolymorphic"
-    FloatPolymorphic = "DEPRECATED_FloatPolymorphic"
-    ImagePolymorphic = "DEPRECATED_ImagePolymorphic"
-    IntegerPolymorphic = "DEPRECATED_IntegerPolymorphic"
-    LatentsPolymorphic = "DEPRECATED_LatentsPolymorphic"
-    StringPolymorphic = "DEPRECATED_StringPolymorphic"
-    MainModel = "DEPRECATED_MainModel"
-    UNet = "DEPRECATED_UNet"
-    Vae = "DEPRECATED_Vae"
-    CLIP = "DEPRECATED_CLIP"
-    Collection = "DEPRECATED_Collection"
-    CollectionItem = "DEPRECATED_CollectionItem"
-    Enum = "DEPRECATED_Enum"
-    WorkflowField = "DEPRECATED_WorkflowField"
-    IsIntermediate = "DEPRECATED_IsIntermediate"
-    BoardField = "DEPRECATED_BoardField"
-    MetadataItem = "DEPRECATED_MetadataItem"
-    MetadataItemCollection = "DEPRECATED_MetadataItemCollection"
-    MetadataItemPolymorphic = "DEPRECATED_MetadataItemPolymorphic"
-    MetadataDict = "DEPRECATED_MetadataDict"
-
-
-class UIComponent(str, Enum, metaclass=MetaEnum):
-    """
-    The type of UI component to use for a field, used to override the default components, which are
-    inferred from the field type.
-    """
-
-    None_ = "none"
-    Textarea = "textarea"
-    Slider = "slider"
-
-
-class FieldDescriptions:
-    denoising_start = "When to start denoising, expressed a percentage of total steps"
-    denoising_end = "When to stop denoising, expressed a percentage of total steps"
-    cfg_scale = "Classifier-Free Guidance scale"
-    cfg_rescale_multiplier = "Rescale multiplier for CFG guidance, used for models trained with zero-terminal SNR"
-    scheduler = "Scheduler to use during inference"
-    positive_cond = "Positive conditioning tensor"
-    negative_cond = "Negative conditioning tensor"
-    noise = "Noise tensor"
-    clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
-    unet = "UNet (scheduler, LoRAs)"
-    vae = "VAE"
-    cond = "Conditioning tensor"
-    controlnet_model = "ControlNet model to load"
-    vae_model = "VAE model to load"
-    lora_model = "LoRA model to load"
-    main_model = "Main model (UNet, VAE, CLIP) to load"
-    sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
-    sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
-    onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
-    lora_weight = "The weight at which the LoRA is applied to each model"
-    compel_prompt = "Prompt to be parsed by Compel to create a conditioning tensor"
-    raw_prompt = "Raw prompt text (no parsing)"
-    sdxl_aesthetic = "The aesthetic score to apply to the conditioning tensor"
-    skipped_layers = "Number of layers to skip in text encoder"
-    seed = "Seed for random number generation"
-    steps = "Number of steps to run"
-    width = "Width of output (px)"
-    height = "Height of output (px)"
-    control = "ControlNet(s) to apply"
-    ip_adapter = "IP-Adapter to apply"
-    t2i_adapter = "T2I-Adapter(s) to apply"
-    denoised_latents = "Denoised latents tensor"
-    latents = "Latents tensor"
-    strength = "Strength of denoising (proportional to steps)"
-    metadata = "Optional metadata to be saved with the image"
-    metadata_collection = "Collection of Metadata"
-    metadata_item_polymorphic = "A single metadata item or collection of metadata items"
-    metadata_item_label = "Label for this metadata item"
-    metadata_item_value = "The value for this metadata item (may be any type)"
-    workflow = "Optional workflow to be saved with the image"
-    interp_mode = "Interpolation mode"
-    torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
-    fp32 = "Whether or not to use full float32 precision"
-    precision = "Precision to use"
-    tiled = "Processing using overlapping tiles (reduce memory consumption)"
-    detect_res = "Pixel resolution for detection"
-    image_res = "Pixel resolution for output image"
-    safe_mode = "Whether or not to use safe mode"
-    scribble_mode = "Whether or not to use scribble mode"
-    scale_factor = "The factor by which to scale"
-    blend_alpha = (
-        "Blending factor. 0.0 = use input A only, 1.0 = use input B only, 0.5 = 50% mix of input A and input B."
-    )
-    num_1 = "The first number"
-    num_2 = "The second number"
-    mask = "The mask to use for the operation"
-    board = "The board to save the image to"
-    image = "The image to process"
-    tile_size = "Tile size"
-    inclusive_low = "The inclusive low value"
-    exclusive_high = "The exclusive high value"
-    decimal_places = "The number of decimal places to round to"
-    freeu_s1 = 'Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
-    freeu_s2 = 'Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
-    freeu_b1 = "Scaling factor for stage 1 to amplify the contributions of backbone features."
-    freeu_b2 = "Scaling factor for stage 2 to amplify the contributions of backbone features."
-
-
-class ImageField(BaseModel):
-    """An image primitive field"""
-
-    image_name: str = Field(description="The name of the image")
-
-
-class BoardField(BaseModel):
-    """A board primitive field"""
-
-    board_id: str = Field(description="The id of the board")
-
-
-class DenoiseMaskField(BaseModel):
-    """An inpaint mask field"""
-
-    mask_name: str = Field(description="The name of the mask image")
-    masked_latents_name: Optional[str] = Field(default=None, description="The name of the masked image latents")
-
-
-class LatentsField(BaseModel):
-    """A latents tensor primitive field"""
-
-    latents_name: str = Field(description="The name of the latents")
-    seed: Optional[int] = Field(default=None, description="Seed used to generate this latents")
-
-
-class ColorField(BaseModel):
-    """A color primitive field"""
-
-    r: int = Field(ge=0, le=255, description="The red component")
-    g: int = Field(ge=0, le=255, description="The green component")
-    b: int = Field(ge=0, le=255, description="The blue component")
-    a: int = Field(ge=0, le=255, description="The alpha component")
-
-    def tuple(self) -> Tuple[int, int, int, int]:
-        return (self.r, self.g, self.b, self.a)
-
-
-class ConditioningField(BaseModel):
-    """A conditioning tensor primitive value"""
-
-    conditioning_name: str = Field(description="The name of conditioning tensor")
-    # endregion
-
-
-class MetadataField(RootModel):
-    """
-    Pydantic model for metadata with custom root of type dict[str, Any].
-    Metadata is stored without a strict schema.
-    """
-
-    root: dict[str, Any] = Field(description="The metadata")
-
-
-MetadataFieldValidator = TypeAdapter(MetadataField)
-
-
-class Input(str, Enum, metaclass=MetaEnum):
-    """
-    The type of input a field accepts.
-    - `Input.Direct`: The field must have its value provided directly, when the invocation and field \
-      are instantiated.
-    - `Input.Connection`: The field must have its value provided by a connection.
-    - `Input.Any`: The field may have its value provided either directly or by a connection.
-    """
-
-    Connection = "connection"
-    Direct = "direct"
-    Any = "any"
-
-
-class FieldKind(str, Enum, metaclass=MetaEnum):
-    """
-    The kind of field.
-    - `Input`: An input field on a node.
-    - `Output`: An output field on a node.
-    - `Internal`: A field which is treated as an input, but cannot be used in node definitions. Metadata is
-    one example. It is provided to nodes via the WithMetadata class, and we want to reserve the field name
-    "metadata" for this on all nodes. `FieldKind` is used to short-circuit the field name validation logic,
-    allowing "metadata" for that field.
-    - `NodeAttribute`: The field is a node attribute. These are fields which are not inputs or outputs,
-    but which are used to store information about the node. For example, the `id` and `type` fields are node
-    attributes.
-
-    The presence of this in `json_schema_extra["field_kind"]` is used when initializing node schemas on app
-    startup, and when generating the OpenAPI schema for the workflow editor.
-    """
-
-    Input = "input"
-    Output = "output"
-    Internal = "internal"
-    NodeAttribute = "node_attribute"
-
-
-class InputFieldJSONSchemaExtra(BaseModel):
-    """
-    Extra attributes to be added to input fields and their OpenAPI schema. Used during graph execution,
-    and by the workflow editor during schema parsing and UI rendering.
-    """
-
-    input: Input
-    orig_required: bool
-    field_kind: FieldKind
-    default: Optional[Any] = None
-    orig_default: Optional[Any] = None
-    ui_hidden: bool = False
-    ui_type: Optional[UIType] = None
-    ui_component: Optional[UIComponent] = None
-    ui_order: Optional[int] = None
-    ui_choice_labels: Optional[dict[str, str]] = None
-
-    model_config = ConfigDict(
-        validate_assignment=True,
-        json_schema_serialization_defaults_required=True,
-    )
-
-
-class WithMetadata(BaseModel):
-    """
-    Inherit from this class if your node needs a metadata input field.
-    """
-
-    metadata: Optional[MetadataField] = Field(
-        default=None,
-        description=FieldDescriptions.metadata,
-        json_schema_extra=InputFieldJSONSchemaExtra(
-            field_kind=FieldKind.Internal,
-            input=Input.Connection,
-            orig_required=False,
-        ).model_dump(exclude_none=True),
-    )
-
-
-class WithWorkflow:
-    workflow = None
-
-    def __init_subclass__(cls) -> None:
-        logger.warn(
-            f"{cls.__module__.split('.')[0]}.{cls.__name__}: WithWorkflow is deprecated. Use `context.workflow` to access the workflow."
-        )
-        super().__init_subclass__()
-
-
-class WithBoard(BaseModel):
-    """
-    Inherit from this class if your node needs a board input field.
-    """
-
-    board: Optional[BoardField] = Field(
-        default=None,
-        description=FieldDescriptions.board,
-        json_schema_extra=InputFieldJSONSchemaExtra(
-            field_kind=FieldKind.Internal,
-            input=Input.Direct,
-            orig_required=False,
-        ).model_dump(exclude_none=True),
-    )
-
-
-class OutputFieldJSONSchemaExtra(BaseModel):
-    """
-    Extra attributes to be added to input fields and their OpenAPI schema. Used by the workflow editor
-    during schema parsing and UI rendering.
-    """
-
-    field_kind: FieldKind
-    ui_hidden: bool
-    ui_type: Optional[UIType]
-    ui_order: Optional[int]
-
-    model_config = ConfigDict(
-        validate_assignment=True,
-        json_schema_serialization_defaults_required=True,
-    )
-
-
-def InputField(
-    # copied from pydantic's Field
-    # TODO: Can we support default_factory?
-    default: Any = _Unset,
-    default_factory: Callable[[], Any] | None = _Unset,
-    title: str | None = _Unset,
-    description: str | None = _Unset,
-    pattern: str | None = _Unset,
-    strict: bool | None = _Unset,
-    gt: float | None = _Unset,
-    ge: float | None = _Unset,
-    lt: float | None = _Unset,
-    le: float | None = _Unset,
-    multiple_of: float | None = _Unset,
-    allow_inf_nan: bool | None = _Unset,
-    max_digits: int | None = _Unset,
-    decimal_places: int | None = _Unset,
-    min_length: int | None = _Unset,
-    max_length: int | None = _Unset,
-    # custom
-    input: Input = Input.Any,
-    ui_type: Optional[UIType] = None,
-    ui_component: Optional[UIComponent] = None,
-    ui_hidden: bool = False,
-    ui_order: Optional[int] = None,
-    ui_choice_labels: Optional[dict[str, str]] = None,
-) -> Any:
-    """
-    Creates an input field for an invocation.
-
-    This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/latest/api/fields/#pydantic.fields.Field) \
-    that adds a few extra parameters to support graph execution and the node editor UI.
-
-    :param Input input: [Input.Any] The kind of input this field requires. \
-      `Input.Direct` means a value must be provided on instantiation. \
-      `Input.Connection` means the value must be provided by a connection. \
-      `Input.Any` means either will do.
-
-    :param UIType ui_type: [None] Optionally provides an extra type hint for the UI. \
-      In some situations, the field's type is not enough to infer the correct UI type. \
-      For example, model selection fields should render a dropdown UI component to select a model. \
-      Internally, there is no difference between SD-1, SD-2 and SDXL model fields, they all use \
-      `MainModelField`. So to ensure the base-model-specific UI is rendered, you can use \
-      `UIType.SDXLMainModelField` to indicate that the field is an SDXL main model field.
-
-    :param UIComponent ui_component: [None] Optionally specifies a specific component to use in the UI. \
-      The UI will always render a suitable component, but sometimes you want something different than the default. \
-      For example, a `string` field will default to a single-line input, but you may want a multi-line textarea instead. \
-      For this case, you could provide `UIComponent.Textarea`.
-
-    :param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI.
-
-    :param int ui_order: [None] Specifies the order in which this field should be rendered in the UI.
-
-    :param dict[str, str] ui_choice_labels: [None] Specifies the labels to use for the choices in an enum field.
-    """
-
-    json_schema_extra_ = InputFieldJSONSchemaExtra(
-        input=input,
-        ui_type=ui_type,
-        ui_component=ui_component,
-        ui_hidden=ui_hidden,
-        ui_order=ui_order,
-        ui_choice_labels=ui_choice_labels,
-        field_kind=FieldKind.Input,
-        orig_required=True,
-    )
-
-    """
-    There is a conflict between the typing of invocation definitions and the typing of an invocation's
-    `invoke()` function.
-
-    On instantiation of a node, the invocation definition is used to create the python class. At this time,
-    any number of fields may be optional, because they may be provided by connections.
-
-    On calling of `invoke()`, however, those fields may be required.
-
-    For example, consider an ResizeImageInvocation with an `image: ImageField` field.
-
-    `image` is required during the call to `invoke()`, but when the python class is instantiated,
-    the field may not be present. This is fine, because that image field will be provided by a
-    connection from an ancestor node, which outputs an image.
-
-    This means we want to type the `image` field as optional for the node class definition, but required
-    for the `invoke()` function.
-
-    If we use `typing.Optional` in the node class definition, the field will be typed as optional in the
-    `invoke()` method, and we'll have to do a lot of runtime checks to ensure the field is present - or
-    any static type analysis tools will complain.
-
-    To get around this, in node class definitions, we type all fields correctly for the `invoke()` function,
-    but secretly make them optional in `InputField()`. We also store the original required bool and/or default
-    value. When we call `invoke()`, we use this stored information to do an additional check on the class.
-    """
-
-    if default_factory is not _Unset and default_factory is not None:
-        default = default_factory()
-        logger.warn('"default_factory" is not supported, calling it now to set "default"')
-
-    # These are the args we may wish pass to the pydantic `Field()` function
-    field_args = {
-        "default": default,
-        "title": title,
-        "description": description,
-        "pattern": pattern,
-        "strict": strict,
-        "gt": gt,
-        "ge": ge,
-        "lt": lt,
-        "le": le,
-        "multiple_of": multiple_of,
-        "allow_inf_nan": allow_inf_nan,
-        "max_digits": max_digits,
-        "decimal_places": decimal_places,
-        "min_length": min_length,
-        "max_length": max_length,
-    }
-
-    # We only want to pass the args that were provided, otherwise the `Field()`` function won't work as expected
-    provided_args = {k: v for (k, v) in field_args.items() if v is not PydanticUndefined}
-
-    # Because we are manually making fields optional, we need to store the original required bool for reference later
-    json_schema_extra_.orig_required = default is PydanticUndefined
-
-    # Make Input.Any and Input.Connection fields optional, providing None as a default if the field doesn't already have one
-    if input is Input.Any or input is Input.Connection:
-        default_ = None if default is PydanticUndefined else default
-        provided_args.update({"default": default_})
-        if default is not PydanticUndefined:
-            # Before invoking, we'll check for the original default value and set it on the field if the field has no value
-            json_schema_extra_.default = default
-            json_schema_extra_.orig_default = default
-    elif default is not PydanticUndefined:
-        default_ = default
-        provided_args.update({"default": default_})
-        json_schema_extra_.orig_default = default_
-
-    return Field(
-        **provided_args,
-        json_schema_extra=json_schema_extra_.model_dump(exclude_none=True),
-    )
-
-
-def OutputField(
-    # copied from pydantic's Field
-    default: Any = _Unset,
-    title: str | None = _Unset,
-    description: str | None = _Unset,
-    pattern: str | None = _Unset,
-    strict: bool | None = _Unset,
-    gt: float | None = _Unset,
-    ge: float | None = _Unset,
-    lt: float | None = _Unset,
-    le: float | None = _Unset,
-    multiple_of: float | None = _Unset,
-    allow_inf_nan: bool | None = _Unset,
-    max_digits: int | None = _Unset,
-    decimal_places: int | None = _Unset,
-    min_length: int | None = _Unset,
-    max_length: int | None = _Unset,
-    # custom
-    ui_type: Optional[UIType] = None,
-    ui_hidden: bool = False,
-    ui_order: Optional[int] = None,
-) -> Any:
-    """
-    Creates an output field for an invocation output.
-
-    This is a wrapper for Pydantic's [Field](https://docs.pydantic.dev/1.10/usage/schema/#field-customization) \
-    that adds a few extra parameters to support graph execution and the node editor UI.
-
-    :param UIType ui_type: [None] Optionally provides an extra type hint for the UI. \
-      In some situations, the field's type is not enough to infer the correct UI type. \
-      For example, model selection fields should render a dropdown UI component to select a model. \
-      Internally, there is no difference between SD-1, SD-2 and SDXL model fields, they all use \
-      `MainModelField`. So to ensure the base-model-specific UI is rendered, you can use \
-      `UIType.SDXLMainModelField` to indicate that the field is an SDXL main model field.
-
-    :param bool ui_hidden: [False] Specifies whether or not this field should be hidden in the UI. \
-
-    :param int ui_order: [None] Specifies the order in which this field should be rendered in the UI. \
-    """
-    return Field(
-        default=default,
-        title=title,
-        description=description,
-        pattern=pattern,
-        strict=strict,
-        gt=gt,
-        ge=ge,
-        lt=lt,
-        le=le,
-        multiple_of=multiple_of,
-        allow_inf_nan=allow_inf_nan,
-        max_digits=max_digits,
-        decimal_places=decimal_places,
-        min_length=min_length,
-        max_length=max_length,
-        json_schema_extra=OutputFieldJSONSchemaExtra(
-            ui_type=ui_type,
-            ui_hidden=ui_hidden,
-            ui_order=ui_order,
-            field_kind=FieldKind.Output,
-        ).model_dump(exclude_none=True),
-    )

invokeai/app/invocations/infill.py

@@ -6,16 +6,14 @@ from typing import Literal, Optional, get_args
 import numpy as np
 from PIL import Image, ImageOps
 
-from invokeai.app.invocations.fields import ColorField, ImageField
-from invokeai.app.invocations.primitives import ImageOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.app.util.misc import SEED_MAX
 from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
 from invokeai.backend.image_util.lama import LaMA
 from invokeai.backend.image_util.patchmatch import PatchMatch
 
-from .baseinvocation import BaseInvocation, invocation
-from .fields import InputField, WithBoard, WithMetadata
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
 from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
 
 
@@ -120,8 +118,8 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
     return si
 
 
-@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
-class InfillColorInvocation(BaseInvocation, WithMetadata, WithBoard):
+@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
+class InfillColorInvocation(BaseInvocation, WithMetadata):
     """Infills transparent areas of an image with a solid color"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -131,20 +129,33 @@ class InfillColorInvocation(BaseInvocation, WithMetadata, WithBoard):
     )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name)
+        image = context.services.images.get_pil_image(self.image.image_name)
 
         solid_bg = Image.new("RGBA", image.size, self.color.tuple())
         infilled = Image.alpha_composite(solid_bg, image.convert("RGBA"))
 
         infilled.paste(image, (0, 0), image.split()[-1])
 
-        image_dto = context.images.save(image=infilled)
+        image_dto = context.services.images.create(
+            image=infilled,
+            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,
+        )
 
-        return ImageOutput.build(image_dto)
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )
 
 
-@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.2")
-class InfillTileInvocation(BaseInvocation, WithMetadata, WithBoard):
+@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
+class InfillTileInvocation(BaseInvocation, WithMetadata):
     """Infills transparent areas of an image with tiles of the image"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -157,20 +168,33 @@ class InfillTileInvocation(BaseInvocation, WithMetadata, WithBoard):
     )
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name)
+        image = context.services.images.get_pil_image(self.image.image_name)
 
         infilled = tile_fill_missing(image.copy(), seed=self.seed, tile_size=self.tile_size)
         infilled.paste(image, (0, 0), image.split()[-1])
 
-        image_dto = context.images.save(image=infilled)
+        image_dto = context.services.images.create(
+            image=infilled,
+            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,
+        )
 
-        return ImageOutput.build(image_dto)
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )
 
 
 @invocation(
-    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1"
+    "infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0"
 )
-class InfillPatchMatchInvocation(BaseInvocation, WithMetadata, WithBoard):
+class InfillPatchMatchInvocation(BaseInvocation, WithMetadata):
     """Infills transparent areas of an image using the PatchMatch algorithm"""
 
     image: ImageField = InputField(description="The image to infill")
@@ -178,7 +202,7 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata, WithBoard):
     resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name).convert("RGBA")
+        image = context.services.images.get_pil_image(self.image.image_name).convert("RGBA")
 
         resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
 
@@ -203,38 +227,77 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata, WithBoard):
         infilled.paste(image, (0, 0), mask=image.split()[-1])
         # image.paste(infilled, (0, 0), mask=image.split()[-1])
 
-        image_dto = context.images.save(image=infilled)
+        image_dto = context.services.images.create(
+            image=infilled,
+            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,
+        )
 
-        return ImageOutput.build(image_dto)
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )
 
 
-@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
-class LaMaInfillInvocation(BaseInvocation, WithMetadata, WithBoard):
+@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
+class LaMaInfillInvocation(BaseInvocation, WithMetadata):
     """Infills transparent areas of an image using the LaMa model"""
 
     image: ImageField = InputField(description="The image to infill")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name)
+        image = context.services.images.get_pil_image(self.image.image_name)
 
         infilled = infill_lama(image.copy())
 
-        image_dto = context.images.save(image=infilled)
+        image_dto = context.services.images.create(
+            image=infilled,
+            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,
+        )
 
-        return ImageOutput.build(image_dto)
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )
 
 
-@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
-class CV2InfillInvocation(BaseInvocation, WithMetadata, WithBoard):
+@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.0")
+class CV2InfillInvocation(BaseInvocation, WithMetadata):
     """Infills transparent areas of an image using OpenCV Inpainting"""
 
     image: ImageField = InputField(description="The image to infill")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name)
+        image = context.services.images.get_pil_image(self.image.image_name)
 
         infilled = infill_cv2(image.copy())
 
-        image_dto = context.images.save(image=infilled)
+        image_dto = context.services.images.create(
+            image=infilled,
+            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,
+        )
 
-        return ImageOutput.build(image_dto)
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )

invokeai/app/invocations/ip_adapter.py

@@ -1,29 +1,38 @@
+import os
 from builtins import float
 from typing import List, Union
 
-from pydantic import BaseModel, Field, field_validator, model_validator
-from typing_extensions import Self
+from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
 
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
 from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager.config import BaseModelType, ModelType
+from invokeai.app.shared.fields import FieldDescriptions
+from invokeai.backend.model_management.models.base import BaseModelType, ModelType
+from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
 
 
-# LS: Consider moving these two classes into model.py
 class IPAdapterModelField(BaseModel):
-    key: str = Field(description="Key to the IP-Adapter model")
+    model_name: str = Field(description="Name of the IP-Adapter model")
+    base_model: BaseModelType = Field(description="Base model")
+
+    model_config = ConfigDict(protected_namespaces=())
 
 
 class CLIPVisionModelField(BaseModel):
-    key: str = Field(description="Key to the CLIP Vision image encoder model")
+    model_name: str = Field(description="Name of the CLIP Vision image encoder model")
+    base_model: BaseModelType = Field(description="Base model (usually 'Any')")
+
+    model_config = ConfigDict(protected_namespaces=())
 
 
 class IPAdapterField(BaseModel):
@@ -40,12 +49,12 @@ class IPAdapterField(BaseModel):
 
     @field_validator("weight")
     @classmethod
-    def validate_ip_adapter_weight(cls, v: float) -> float:
+    def validate_ip_adapter_weight(cls, v):
         validate_weights(v)
         return v
 
     @model_validator(mode="after")
-    def validate_begin_end_step_percent(self) -> Self:
+    def validate_begin_end_step_percent(self):
         validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
         return self
 
@@ -56,7 +65,7 @@ class IPAdapterOutput(BaseInvocationOutput):
     ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
 
 
-@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.2")
+@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.1")
 class IPAdapterInvocation(BaseInvocation):
     """Collects IP-Adapter info to pass to other nodes."""
 
@@ -78,25 +87,33 @@ class IPAdapterInvocation(BaseInvocation):
 
     @field_validator("weight")
     @classmethod
-    def validate_ip_adapter_weight(cls, v: float) -> float:
+    def validate_ip_adapter_weight(cls, v):
         validate_weights(v)
         return v
 
     @model_validator(mode="after")
-    def validate_begin_end_step_percent(self) -> Self:
+    def validate_begin_end_step_percent(self):
         validate_begin_end_step(self.begin_step_percent, self.end_step_percent)
         return self
 
     def invoke(self, context: InvocationContext) -> IPAdapterOutput:
         # Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
-        ip_adapter_info = context.models.get_config(self.ip_adapter_model.key)
-        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(
-            model_name=image_encoder_model_name, base_model=BaseModelType.Any, model_type=ModelType.CLIPVision
+        ip_adapter_info = context.services.model_manager.model_info(
+            self.ip_adapter_model.model_name, self.ip_adapter_model.base_model, ModelType.IPAdapter
+        )
+        # HACK(ryand): This is bad for a couple of reasons: 1) we are bypassing the model manager to read the model
+        # directly, and 2) we are reading from disk every time this invocation is called without caching the result.
+        # A better solution would be to store the image encoder model reference in the IP-Adapter model info, but this
+        # is currently messy due to differences between how the model info is generated when installing a model from
+        # disk vs. downloading the model.
+        image_encoder_model_id = get_ip_adapter_image_encoder_model_id(
+            os.path.join(context.services.configuration.get_config().models_path, ip_adapter_info["path"])
+        )
+        image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
+        image_encoder_model = CLIPVisionModelField(
+            model_name=image_encoder_model_name,
+            base_model=BaseModelType.Any,
         )
-        assert len(image_encoder_models) == 1
-        image_encoder_model = CLIPVisionModelField(key=image_encoder_models[0].key)
         return IPAdapterOutput(
             ip_adapter=IPAdapterField(
                 image=self.image,

invokeai/app/invocations/latent.py

@@ -3,15 +3,13 @@
 import math
 from contextlib import ExitStack
 from functools import singledispatchmethod
-from typing import Any, Iterator, List, Literal, Optional, Tuple, Union
+from typing import List, Literal, Optional, Union
 
 import einops
 import numpy as np
-import numpy.typing as npt
 import torch
 import torchvision.transforms as T
 from diffusers import AutoencoderKL, AutoencoderTiny
-from diffusers.configuration_utils import ConfigMixin
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.models.adapter import T2IAdapter
 from diffusers.models.attention_processor import (
@@ -20,44 +18,34 @@ from diffusers.models.attention_processor import (
     LoRAXFormersAttnProcessor,
     XFormersAttnProcessor,
 )
-from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
 from diffusers.schedulers import DPMSolverSDEScheduler
 from diffusers.schedulers import SchedulerMixin as Scheduler
-from PIL import Image
 from pydantic import field_validator
 from torchvision.transforms.functional import resize as tv_resize
 
-from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR, SCHEDULER_NAME_VALUES
-from invokeai.app.invocations.fields import (
-    ConditioningField,
-    DenoiseMaskField,
-    FieldDescriptions,
-    ImageField,
-    Input,
-    InputField,
-    LatentsField,
-    OutputField,
-    UIType,
-    WithBoard,
-    WithMetadata,
-)
 from invokeai.app.invocations.ip_adapter import IPAdapterField
 from invokeai.app.invocations.primitives import (
+    DenoiseMaskField,
     DenoiseMaskOutput,
+    ImageField,
     ImageOutput,
+    LatentsField,
     LatentsOutput,
+    build_latents_output,
 )
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField
-from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.app.util.controlnet_utils import prepare_control_image
+from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
-from invokeai.backend.lora import LoRAModelRaw
-from invokeai.backend.model_manager import BaseModelType, LoadedModel
-from invokeai.backend.model_patcher import ModelPatcher
-from invokeai.backend.stable_diffusion import PipelineIntermediateState, set_seamless
+from invokeai.backend.model_management.models import ModelType, SilenceWarnings
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData, IPAdapterConditioningInfo
-from invokeai.backend.util.silence_warnings import SilenceWarnings
 
+from ...backend.model_management.lora import ModelPatcher
+from ...backend.model_management.models import BaseModelType
+from ...backend.model_management.seamless import set_seamless
+from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.stable_diffusion.diffusers_pipeline import (
     ControlNetData,
     IPAdapterData,
@@ -71,9 +59,16 @@ from ...backend.util.devices import choose_precision, choose_torch_device
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
+    UIType,
+    WithMetadata,
     invocation,
     invocation_output,
 )
+from .compel import ConditioningField
 from .controlnet_image_processors import ControlField
 from .model import ModelInfo, UNetField, VaeField
 
@@ -82,10 +77,18 @@ if choose_torch_device() == torch.device("mps"):
 
 DEFAULT_PRECISION = choose_precision(choose_torch_device())
 
+SAMPLER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
+
+# HACK: Many nodes are currently hard-coded to use a fixed latent scale factor of 8. This is fragile, and will need to
+# be addressed if future models use a different latent scale factor. Also, note that there may be places where the scale
+# factor is hard-coded to a literal '8' rather than using this constant.
+# The ratio of image:latent dimensions is LATENT_SCALE_FACTOR:1, or 8:1.
+LATENT_SCALE_FACTOR = 8
+
 
 @invocation_output("scheduler_output")
 class SchedulerOutput(BaseInvocationOutput):
-    scheduler: SCHEDULER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler)
+    scheduler: SAMPLER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler)
 
 
 @invocation(
@@ -98,7 +101,7 @@ class SchedulerOutput(BaseInvocationOutput):
 class SchedulerInvocation(BaseInvocation):
     """Selects a scheduler."""
 
-    scheduler: SCHEDULER_NAME_VALUES = InputField(
+    scheduler: SAMPLER_NAME_VALUES = InputField(
         default="euler",
         description=FieldDescriptions.scheduler,
         ui_type=UIType.Scheduler,
@@ -113,7 +116,7 @@ class SchedulerInvocation(BaseInvocation):
     title="Create Denoise Mask",
     tags=["mask", "denoise"],
     category="latents",
-    version="1.0.1",
+    version="1.0.0",
 )
 class CreateDenoiseMaskInvocation(BaseInvocation):
     """Creates mask for denoising model run."""
@@ -128,10 +131,10 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
         ui_order=4,
     )
 
-    def prep_mask_tensor(self, mask_image: Image) -> torch.Tensor:
+    def prep_mask_tensor(self, mask_image):
         if mask_image.mode != "L":
             mask_image = mask_image.convert("L")
-        mask_tensor: torch.Tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
+        mask_tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
         if mask_tensor.dim() == 3:
             mask_tensor = mask_tensor.unsqueeze(0)
         # if shape is not None:
@@ -141,34 +144,41 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> DenoiseMaskOutput:
         if self.image is not None:
-            image = context.images.get_pil(self.image.image_name)
-            image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
-            if image_tensor.dim() == 3:
-                image_tensor = image_tensor.unsqueeze(0)
+            image = context.services.images.get_pil_image(self.image.image_name)
+            image = image_resized_to_grid_as_tensor(image.convert("RGB"))
+            if image.dim() == 3:
+                image = image.unsqueeze(0)
         else:
-            image_tensor = None
+            image = None
 
         mask = self.prep_mask_tensor(
-            context.images.get_pil(self.mask.image_name),
+            context.services.images.get_pil_image(self.mask.image_name),
         )
 
-        if image_tensor is not None:
-            vae_info = context.models.load(**self.vae.vae.model_dump())
+        if image is not None:
+            vae_info = context.services.model_manager.get_model(
+                **self.vae.vae.model_dump(),
+                context=context,
+            )
 
-            img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
-            masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
+            img_mask = tv_resize(mask, image.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
+            masked_image = image * torch.where(img_mask < 0.5, 0.0, 1.0)
             # TODO:
             masked_latents = ImageToLatentsInvocation.vae_encode(vae_info, self.fp32, self.tiled, masked_image.clone())
 
-            masked_latents_name = context.tensors.save(tensor=masked_latents)
+            masked_latents_name = f"{context.graph_execution_state_id}__{self.id}_masked_latents"
+            context.services.latents.save(masked_latents_name, masked_latents)
         else:
             masked_latents_name = None
 
-        mask_name = context.tensors.save(tensor=mask)
+        mask_name = f"{context.graph_execution_state_id}__{self.id}_mask"
+        context.services.latents.save(mask_name, mask)
 
-        return DenoiseMaskOutput.build(
-            mask_name=mask_name,
-            masked_latents_name=masked_latents_name,
+        return DenoiseMaskOutput(
+            denoise_mask=DenoiseMaskField(
+                mask_name=mask_name,
+                masked_latents_name=masked_latents_name,
+            ),
         )
 
 
@@ -179,7 +189,10 @@ def get_scheduler(
     seed: int,
 ) -> Scheduler:
     scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
-    orig_scheduler_info = context.models.load(**scheduler_info.model_dump())
+    orig_scheduler_info = context.services.model_manager.get_model(
+        **scheduler_info.model_dump(),
+        context=context,
+    )
     with orig_scheduler_info as orig_scheduler:
         scheduler_config = orig_scheduler.config
 
@@ -187,7 +200,7 @@ def get_scheduler(
         scheduler_config = scheduler_config["_backup"]
     scheduler_config = {
         **scheduler_config,
-        **scheduler_extra_config,  # FIXME
+        **scheduler_extra_config,
         "_backup": scheduler_config,
     }
 
@@ -200,7 +213,6 @@ def get_scheduler(
     # hack copied over from generate.py
     if not hasattr(scheduler, "uses_inpainting_model"):
         scheduler.uses_inpainting_model = lambda: False
-    assert isinstance(scheduler, Scheduler)
     return scheduler
 
 
@@ -209,7 +221,7 @@ def get_scheduler(
     title="Denoise Latents",
     tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
     category="latents",
-    version="1.5.2",
+    version="1.5.1",
 )
 class DenoiseLatentsInvocation(BaseInvocation):
     """Denoises noisy latents to decodable images"""
@@ -237,7 +249,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
         description=FieldDescriptions.denoising_start,
     )
     denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
-    scheduler: SCHEDULER_NAME_VALUES = InputField(
+    scheduler: SAMPLER_NAME_VALUES = InputField(
         default="euler",
         description=FieldDescriptions.scheduler,
         ui_type=UIType.Scheduler,
@@ -284,7 +296,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
     )
 
     @field_validator("cfg_scale")
-    def ge_one(cls, v: Union[List[float], float]) -> Union[List[float], float]:
+    def ge_one(cls, v):
         """validate that all cfg_scale values are >= 1"""
         if isinstance(v, list):
             for i in v:
@@ -295,18 +307,34 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 raise ValueError("cfg_scale must be greater than 1")
         return v
 
+    # TODO: pass this an emitter method or something? or a session for dispatching?
+    def dispatch_progress(
+        self,
+        context: InvocationContext,
+        source_node_id: str,
+        intermediate_state: PipelineIntermediateState,
+        base_model: BaseModelType,
+    ) -> None:
+        stable_diffusion_step_callback(
+            context=context,
+            intermediate_state=intermediate_state,
+            node=self.model_dump(),
+            source_node_id=source_node_id,
+            base_model=base_model,
+        )
+
     def get_conditioning_data(
         self,
         context: InvocationContext,
-        scheduler: Scheduler,
-        unet: UNet2DConditionModel,
-        seed: int,
+        scheduler,
+        unet,
+        seed,
     ) -> ConditioningData:
-        positive_cond_data = context.conditioning.load(self.positive_conditioning.conditioning_name)
+        positive_cond_data = context.services.latents.get(self.positive_conditioning.conditioning_name)
         c = positive_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
         extra_conditioning_info = c.extra_conditioning
 
-        negative_cond_data = context.conditioning.load(self.negative_conditioning.conditioning_name)
+        negative_cond_data = context.services.latents.get(self.negative_conditioning.conditioning_name)
         uc = negative_cond_data.conditionings[0].to(device=unet.device, dtype=unet.dtype)
 
         conditioning_data = ConditioningData(
@@ -323,7 +351,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
             ),
         )
 
-        conditioning_data = conditioning_data.add_scheduler_args_if_applicable(  # FIXME
+        conditioning_data = conditioning_data.add_scheduler_args_if_applicable(
             scheduler,
             # for ddim scheduler
             eta=0.0,  # ddim_eta
@@ -335,8 +363,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
     def create_pipeline(
         self,
-        unet: UNet2DConditionModel,
-        scheduler: Scheduler,
+        unet,
+        scheduler,
     ) -> StableDiffusionGeneratorPipeline:
         # TODO:
         # configure_model_padding(
@@ -347,10 +375,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         class FakeVae:
             class FakeVaeConfig:
-                def __init__(self) -> None:
+                def __init__(self):
                     self.block_out_channels = [0]
 
-            def __init__(self) -> None:
+            def __init__(self):
                 self.config = FakeVae.FakeVaeConfig()
 
         return StableDiffusionGeneratorPipeline(
@@ -367,11 +395,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
     def prep_control_data(
         self,
         context: InvocationContext,
-        control_input: Optional[Union[ControlField, List[ControlField]]],
+        control_input: Union[ControlField, List[ControlField]],
         latents_shape: List[int],
         exit_stack: ExitStack,
         do_classifier_free_guidance: bool = True,
-    ) -> Optional[List[ControlNetData]]:
+    ) -> List[ControlNetData]:
         # Assuming fixed dimensional scaling of LATENT_SCALE_FACTOR.
         control_height_resize = latents_shape[2] * LATENT_SCALE_FACTOR
         control_width_resize = latents_shape[3] * LATENT_SCALE_FACTOR
@@ -393,11 +421,18 @@ class DenoiseLatentsInvocation(BaseInvocation):
         #        and if weight is None, populate with default 1.0?
         controlnet_data = []
         for control_info in control_list:
-            control_model = exit_stack.enter_context(context.models.load(key=control_info.control_model.key))
+            control_model = exit_stack.enter_context(
+                context.services.model_manager.get_model(
+                    model_name=control_info.control_model.model_name,
+                    model_type=ModelType.ControlNet,
+                    base_model=control_info.control_model.base_model,
+                    context=context,
+                )
+            )
 
             # control_models.append(control_model)
             control_image_field = control_info.image
-            input_image = context.images.get_pil(control_image_field.image_name)
+            input_image = context.services.images.get_pil_image(control_image_field.image_name)
             # self.image.image_type, self.image.image_name
             # FIXME: still need to test with different widths, heights, devices, dtypes
             #        and add in batch_size, num_images_per_prompt?
@@ -455,17 +490,27 @@ class DenoiseLatentsInvocation(BaseInvocation):
         conditioning_data.ip_adapter_conditioning = []
         for single_ip_adapter in ip_adapter:
             ip_adapter_model: Union[IPAdapter, IPAdapterPlus] = exit_stack.enter_context(
-                context.models.load(key=single_ip_adapter.ip_adapter_model.key)
+                context.services.model_manager.get_model(
+                    model_name=single_ip_adapter.ip_adapter_model.model_name,
+                    model_type=ModelType.IPAdapter,
+                    base_model=single_ip_adapter.ip_adapter_model.base_model,
+                    context=context,
+                )
             )
 
-            image_encoder_model_info = context.models.load(key=single_ip_adapter.image_encoder_model.key)
+            image_encoder_model_info = context.services.model_manager.get_model(
+                model_name=single_ip_adapter.image_encoder_model.model_name,
+                model_type=ModelType.CLIPVision,
+                base_model=single_ip_adapter.image_encoder_model.base_model,
+                context=context,
+            )
 
             # `single_ip_adapter.image` could be a list or a single ImageField. Normalize to a list here.
-            single_ipa_image_fields = single_ip_adapter.image
-            if not isinstance(single_ipa_image_fields, list):
-                single_ipa_image_fields = [single_ipa_image_fields]
+            single_ipa_images = single_ip_adapter.image
+            if not isinstance(single_ipa_images, list):
+                single_ipa_images = [single_ipa_images]
 
-            single_ipa_images = [context.images.get_pil(image.image_name) for image in single_ipa_image_fields]
+            single_ipa_images = [context.services.images.get_pil_image(image.image_name) for image in single_ipa_images]
 
             # TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
             # models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
@@ -509,20 +554,26 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         t2i_adapter_data = []
         for t2i_adapter_field in t2i_adapter:
-            t2i_adapter_model_config = context.models.get_config(key=t2i_adapter_field.t2i_adapter_model.key)
-            t2i_adapter_loaded_model = context.models.load(key=t2i_adapter_field.t2i_adapter_model.key)
-            image = context.images.get_pil(t2i_adapter_field.image.image_name)
+            t2i_adapter_model_info = context.services.model_manager.get_model(
+                model_name=t2i_adapter_field.t2i_adapter_model.model_name,
+                model_type=ModelType.T2IAdapter,
+                base_model=t2i_adapter_field.t2i_adapter_model.base_model,
+                context=context,
+            )
+            image = context.services.images.get_pil_image(t2i_adapter_field.image.image_name)
 
             # The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
-            if t2i_adapter_model_config.base == BaseModelType.StableDiffusion1:
+            if t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusion1:
                 max_unet_downscale = 8
-            elif t2i_adapter_model_config.base == BaseModelType.StableDiffusionXL:
+            elif t2i_adapter_field.t2i_adapter_model.base_model == BaseModelType.StableDiffusionXL:
                 max_unet_downscale = 4
             else:
-                raise ValueError(f"Unexpected T2I-Adapter base model type: '{t2i_adapter_model_config.base}'.")
+                raise ValueError(
+                    f"Unexpected T2I-Adapter base model type: '{t2i_adapter_field.t2i_adapter_model.base_model}'."
+                )
 
             t2i_adapter_model: T2IAdapter
-            with t2i_adapter_loaded_model as t2i_adapter_model:
+            with t2i_adapter_model_info as t2i_adapter_model:
                 total_downscale_factor = t2i_adapter_model.total_downscale_factor
 
                 # Resize the T2I-Adapter input image.
@@ -542,7 +593,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
                     do_classifier_free_guidance=False,
                     width=t2i_input_width,
                     height=t2i_input_height,
-                    num_channels=t2i_adapter_model.config["in_channels"],  # mypy treats this as a FrozenDict
+                    num_channels=t2i_adapter_model.config.in_channels,
                     device=t2i_adapter_model.device,
                     dtype=t2i_adapter_model.dtype,
                     resize_mode=t2i_adapter_field.resize_mode,
@@ -567,15 +618,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
     # original idea by https://github.com/AmericanPresidentJimmyCarter
     # TODO: research more for second order schedulers timesteps
-    def init_scheduler(
-        self,
-        scheduler: Union[Scheduler, ConfigMixin],
-        device: torch.device,
-        steps: int,
-        denoising_start: float,
-        denoising_end: float,
-    ) -> Tuple[int, List[int], int]:
-        assert isinstance(scheduler, ConfigMixin)
+    def init_scheduler(self, scheduler, device, steps, denoising_start, denoising_end):
         if scheduler.config.get("cpu_only", False):
             scheduler.set_timesteps(steps, device="cpu")
             timesteps = scheduler.timesteps.to(device=device)
@@ -587,11 +630,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
         _timesteps = timesteps[:: scheduler.order]
 
         # get start timestep index
-        t_start_val = int(round(scheduler.config["num_train_timesteps"] * (1 - denoising_start)))
+        t_start_val = int(round(scheduler.config.num_train_timesteps * (1 - denoising_start)))
         t_start_idx = len(list(filter(lambda ts: ts >= t_start_val, _timesteps)))
 
         # get end timestep index
-        t_end_val = int(round(scheduler.config["num_train_timesteps"] * (1 - denoising_end)))
+        t_end_val = int(round(scheduler.config.num_train_timesteps * (1 - denoising_end)))
         t_end_idx = len(list(filter(lambda ts: ts >= t_end_val, _timesteps[t_start_idx:])))
 
         # apply order to indexes
@@ -604,16 +647,14 @@ class DenoiseLatentsInvocation(BaseInvocation):
 
         return num_inference_steps, timesteps, init_timestep
 
-    def prep_inpaint_mask(
-        self, context: InvocationContext, latents: torch.Tensor
-    ) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]:
+    def prep_inpaint_mask(self, context, latents):
         if self.denoise_mask is None:
             return None, None
 
-        mask = context.tensors.load(self.denoise_mask.mask_name)
+        mask = context.services.latents.get(self.denoise_mask.mask_name)
         mask = tv_resize(mask, latents.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
         if self.denoise_mask.masked_latents_name is not None:
-            masked_latents = context.tensors.load(self.denoise_mask.masked_latents_name)
+            masked_latents = context.services.latents.get(self.denoise_mask.masked_latents_name)
         else:
             masked_latents = None
 
@@ -625,11 +666,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
             seed = None
             noise = None
             if self.noise is not None:
-                noise = context.tensors.load(self.noise.latents_name)
+                noise = context.services.latents.get(self.noise.latents_name)
                 seed = self.noise.seed
 
             if self.latents is not None:
-                latents = context.tensors.load(self.latents.latents_name)
+                latents = context.services.latents.get(self.latents.latents_name)
                 if seed is None:
                     seed = self.latents.seed
 
@@ -655,30 +696,35 @@ class DenoiseLatentsInvocation(BaseInvocation):
                 do_classifier_free_guidance=True,
             )
 
-            # get the unet's config so that we can pass the base to dispatch_progress()
-            unet_config = context.models.get_config(self.unet.unet.key)
+            # Get the source node id (we are invoking the prepared node)
+            graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+            source_node_id = graph_execution_state.prepared_source_mapping[self.id]
 
-            def step_callback(state: PipelineIntermediateState) -> None:
-                context.util.sd_step_callback(state, unet_config.base)
+            def step_callback(state: PipelineIntermediateState):
+                self.dispatch_progress(context, source_node_id, state, self.unet.unet.base_model)
 
-            def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
+            def _lora_loader():
                 for lora in self.unet.loras:
-                    lora_info = context.models.load(**lora.model_dump(exclude={"weight"}))
-                    yield (lora_info.model, lora.weight)
+                    lora_info = context.services.model_manager.get_model(
+                        **lora.model_dump(exclude={"weight"}),
+                        context=context,
+                    )
+                    yield (lora_info.context.model, lora.weight)
                     del lora_info
                 return
 
-            unet_info = context.models.load(**self.unet.unet.model_dump())
-            assert isinstance(unet_info.model, UNet2DConditionModel)
+            unet_info = context.services.model_manager.get_model(
+                **self.unet.unet.model_dump(),
+                context=context,
+            )
             with (
                 ExitStack() as exit_stack,
-                ModelPatcher.apply_freeu(unet_info.model, self.unet.freeu_config),
-                set_seamless(unet_info.model, self.unet.seamless_axes),  # FIXME
+                ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config),
+                set_seamless(unet_info.context.model, self.unet.seamless_axes),
                 unet_info as unet,
                 # Apply the LoRA after unet has been moved to its target device for faster patching.
                 ModelPatcher.apply_lora_unet(unet, _lora_loader()),
             ):
-                assert isinstance(unet, UNet2DConditionModel)
                 latents = latents.to(device=unet.device, dtype=unet.dtype)
                 if noise is not None:
                     noise = noise.to(device=unet.device, dtype=unet.dtype)
@@ -746,8 +792,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
             if choose_torch_device() == torch.device("mps"):
                 mps.empty_cache()
 
-            name = context.tensors.save(tensor=result_latents)
-        return LatentsOutput.build(latents_name=name, latents=result_latents, seed=seed)
+            name = f"{context.graph_execution_state_id}__{self.id}"
+            context.services.latents.save(name, result_latents)
+        return build_latents_output(latents_name=name, latents=result_latents, seed=seed)
 
 
 @invocation(
@@ -755,9 +802,9 @@ class DenoiseLatentsInvocation(BaseInvocation):
     title="Latents to Image",
     tags=["latents", "image", "vae", "l2i"],
     category="latents",
-    version="1.2.1",
+    version="1.2.0",
 )
-class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
+class LatentsToImageInvocation(BaseInvocation, WithMetadata):
     """Generates an image from latents."""
 
     latents: LatentsField = InputField(
@@ -773,12 +820,14 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        latents = context.tensors.load(self.latents.latents_name)
+        latents = context.services.latents.get(self.latents.latents_name)
 
-        vae_info = context.models.load(**self.vae.vae.model_dump())
+        vae_info = context.services.model_manager.get_model(
+            **self.vae.vae.model_dump(),
+            context=context,
+        )
 
-        with set_seamless(vae_info.model, self.vae.seamless_axes), vae_info as vae:
-            assert isinstance(vae, torch.nn.Module)
+        with set_seamless(vae_info.context.model, self.vae.seamless_axes), vae_info as vae:
             latents = latents.to(vae.device)
             if self.fp32:
                 vae.to(dtype=torch.float32)
@@ -805,7 +854,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
                 vae.to(dtype=torch.float16)
                 latents = latents.half()
 
-            if self.tiled or context.config.get().tiled_decode:
+            if self.tiled or context.services.configuration.tiled_decode:
                 vae.enable_tiling()
             else:
                 vae.disable_tiling()
@@ -829,9 +878,22 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         if choose_torch_device() == torch.device("mps"):
             mps.empty_cache()
 
-        image_dto = context.images.save(image=image)
+        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,
+        )
 
-        return ImageOutput.build(image_dto)
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )
 
 
 LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
@@ -842,7 +904,7 @@ LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic",
     title="Resize Latents",
     tags=["latents", "resize"],
     category="latents",
-    version="1.0.1",
+    version="1.0.0",
 )
 class ResizeLatentsInvocation(BaseInvocation):
     """Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
@@ -865,7 +927,7 @@ class ResizeLatentsInvocation(BaseInvocation):
     antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias)
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.tensors.load(self.latents.latents_name)
+        latents = context.services.latents.get(self.latents.latents_name)
 
         # TODO:
         device = choose_torch_device()
@@ -883,8 +945,10 @@ class ResizeLatentsInvocation(BaseInvocation):
         if device == torch.device("mps"):
             mps.empty_cache()
 
-        name = context.tensors.save(tensor=resized_latents)
-        return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
+        name = f"{context.graph_execution_state_id}__{self.id}"
+        # context.services.latents.set(name, resized_latents)
+        context.services.latents.save(name, resized_latents)
+        return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
 
 
 @invocation(
@@ -892,7 +956,7 @@ class ResizeLatentsInvocation(BaseInvocation):
     title="Scale Latents",
     tags=["latents", "resize"],
     category="latents",
-    version="1.0.1",
+    version="1.0.0",
 )
 class ScaleLatentsInvocation(BaseInvocation):
     """Scales latents by a given factor."""
@@ -906,7 +970,7 @@ class ScaleLatentsInvocation(BaseInvocation):
     antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias)
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.tensors.load(self.latents.latents_name)
+        latents = context.services.latents.get(self.latents.latents_name)
 
         # TODO:
         device = choose_torch_device()
@@ -925,8 +989,10 @@ class ScaleLatentsInvocation(BaseInvocation):
         if device == torch.device("mps"):
             mps.empty_cache()
 
-        name = context.tensors.save(tensor=resized_latents)
-        return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
+        name = f"{context.graph_execution_state_id}__{self.id}"
+        # context.services.latents.set(name, resized_latents)
+        context.services.latents.save(name, resized_latents)
+        return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
 
 
 @invocation(
@@ -934,7 +1000,7 @@ class ScaleLatentsInvocation(BaseInvocation):
     title="Image to Latents",
     tags=["latents", "image", "vae", "i2l"],
     category="latents",
-    version="1.0.1",
+    version="1.0.0",
 )
 class ImageToLatentsInvocation(BaseInvocation):
     """Encodes an image into latents."""
@@ -950,9 +1016,8 @@ class ImageToLatentsInvocation(BaseInvocation):
     fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
 
     @staticmethod
-    def vae_encode(vae_info: LoadedModel, upcast: bool, tiled: bool, image_tensor: torch.Tensor) -> torch.Tensor:
+    def vae_encode(vae_info, upcast, tiled, image_tensor):
         with vae_info as vae:
-            assert isinstance(vae, torch.nn.Module)
             orig_dtype = vae.dtype
             if upcast:
                 vae.to(dtype=torch.float32)
@@ -996,9 +1061,12 @@ class ImageToLatentsInvocation(BaseInvocation):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        image = context.images.get_pil(self.image.image_name)
+        image = context.services.images.get_pil_image(self.image.image_name)
 
-        vae_info = context.models.load(**self.vae.vae.model_dump())
+        vae_info = context.services.model_manager.get_model(
+            **self.vae.vae.model_dump(),
+            context=context,
+        )
 
         image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
         if image_tensor.dim() == 3:
@@ -1006,26 +1074,22 @@ class ImageToLatentsInvocation(BaseInvocation):
 
         latents = self.vae_encode(vae_info, self.fp32, self.tiled, image_tensor)
 
+        name = f"{context.graph_execution_state_id}__{self.id}"
         latents = latents.to("cpu")
-        name = context.tensors.save(tensor=latents)
-        return LatentsOutput.build(latents_name=name, latents=latents, seed=None)
+        context.services.latents.save(name, latents)
+        return build_latents_output(latents_name=name, latents=latents, seed=None)
 
     @singledispatchmethod
     @staticmethod
     def _encode_to_tensor(vae: AutoencoderKL, image_tensor: torch.FloatTensor) -> torch.FloatTensor:
-        assert isinstance(vae, torch.nn.Module)
         image_tensor_dist = vae.encode(image_tensor).latent_dist
-        latents: torch.Tensor = image_tensor_dist.sample().to(
-            dtype=vae.dtype
-        )  # FIXME: uses torch.randn. make reproducible!
+        latents = image_tensor_dist.sample().to(dtype=vae.dtype)  # FIXME: uses torch.randn. make reproducible!
         return latents
 
     @_encode_to_tensor.register
     @staticmethod
     def _(vae: AutoencoderTiny, image_tensor: torch.FloatTensor) -> torch.FloatTensor:
-        assert isinstance(vae, torch.nn.Module)
-        latents: torch.FloatTensor = vae.encode(image_tensor).latents
-        return latents
+        return vae.encode(image_tensor).latents
 
 
 @invocation(
@@ -1033,7 +1097,7 @@ class ImageToLatentsInvocation(BaseInvocation):
     title="Blend Latents",
     tags=["latents", "blend"],
     category="latents",
-    version="1.0.1",
+    version="1.0.0",
 )
 class BlendLatentsInvocation(BaseInvocation):
     """Blend two latents using a given alpha. Latents must have same size."""
@@ -1049,8 +1113,8 @@ class BlendLatentsInvocation(BaseInvocation):
     alpha: float = InputField(default=0.5, description=FieldDescriptions.blend_alpha)
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents_a = context.tensors.load(self.latents_a.latents_name)
-        latents_b = context.tensors.load(self.latents_b.latents_name)
+        latents_a = context.services.latents.get(self.latents_a.latents_name)
+        latents_b = context.services.latents.get(self.latents_b.latents_name)
 
         if latents_a.shape != latents_b.shape:
             raise Exception("Latents to blend must be the same size.")
@@ -1058,12 +1122,7 @@ class BlendLatentsInvocation(BaseInvocation):
         # TODO:
         device = choose_torch_device()
 
-        def slerp(
-            t: Union[float, npt.NDArray[Any]],  # FIXME: maybe use np.float32 here?
-            v0: Union[torch.Tensor, npt.NDArray[Any]],
-            v1: Union[torch.Tensor, npt.NDArray[Any]],
-            DOT_THRESHOLD: float = 0.9995,
-        ) -> Union[torch.Tensor, npt.NDArray[Any]]:
+        def slerp(t, v0, v1, DOT_THRESHOLD=0.9995):
             """
             Spherical linear interpolation
             Args:
@@ -1096,16 +1155,12 @@ class BlendLatentsInvocation(BaseInvocation):
                 v2 = s0 * v0 + s1 * v1
 
             if inputs_are_torch:
-                v2_torch: torch.Tensor = torch.from_numpy(v2).to(device)
-                return v2_torch
-            else:
-                assert isinstance(v2, np.ndarray)
-                return v2
+                v2 = torch.from_numpy(v2).to(device)
+
+            return v2
 
         # blend
-        bl = slerp(self.alpha, latents_a, latents_b)
-        assert isinstance(bl, torch.Tensor)
-        blended_latents: torch.Tensor = bl  # for type checking convenience
+        blended_latents = slerp(self.alpha, latents_a, latents_b)
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         blended_latents = blended_latents.to("cpu")
@@ -1113,8 +1168,10 @@ class BlendLatentsInvocation(BaseInvocation):
         if device == torch.device("mps"):
             mps.empty_cache()
 
-        name = context.tensors.save(tensor=blended_latents)
-        return LatentsOutput.build(latents_name=name, latents=blended_latents)
+        name = f"{context.graph_execution_state_id}__{self.id}"
+        # context.services.latents.set(name, resized_latents)
+        context.services.latents.save(name, blended_latents)
+        return build_latents_output(latents_name=name, latents=blended_latents)
 
 
 # The Crop Latents node was copied from @skunkworxdark's implementation here:
@@ -1124,7 +1181,7 @@ class BlendLatentsInvocation(BaseInvocation):
     title="Crop Latents",
     tags=["latents", "crop"],
     category="latents",
-    version="1.0.1",
+    version="1.0.0",
 )
 # TODO(ryand): Named `CropLatentsCoreInvocation` to prevent a conflict with custom node `CropLatentsInvocation`.
 # Currently, if the class names conflict then 'GET /openapi.json' fails.
@@ -1159,7 +1216,7 @@ class CropLatentsCoreInvocation(BaseInvocation):
     )
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.tensors.load(self.latents.latents_name)
+        latents = context.services.latents.get(self.latents.latents_name)
 
         x1 = self.x // LATENT_SCALE_FACTOR
         y1 = self.y // LATENT_SCALE_FACTOR
@@ -1168,9 +1225,10 @@ class CropLatentsCoreInvocation(BaseInvocation):
 
         cropped_latents = latents[..., y1:y2, x1:x2]
 
-        name = context.tensors.save(tensor=cropped_latents)
+        name = f"{context.graph_execution_state_id}__{self.id}"
+        context.services.latents.save(name, cropped_latents)
 
-        return LatentsOutput.build(latents_name=name, latents=cropped_latents)
+        return build_latents_output(latents_name=name, latents=cropped_latents)
 
 
 @invocation_output("ideal_size_output")
@@ -1198,16 +1256,15 @@ class IdealSizeInvocation(BaseInvocation):
         description="Amount to multiply the model's dimensions by when calculating the ideal size (may result in initial generation artifacts if too large)",
     )
 
-    def trim_to_multiple_of(self, *args: int, multiple_of: int = LATENT_SCALE_FACTOR) -> Tuple[int, ...]:
+    def trim_to_multiple_of(self, *args, multiple_of=LATENT_SCALE_FACTOR):
         return tuple((x - x % multiple_of) for x in args)
 
     def invoke(self, context: InvocationContext) -> IdealSizeOutput:
-        unet_config = context.models.get_config(**self.unet.unet.model_dump())
         aspect = self.width / self.height
-        dimension: float = 512
-        if unet_config.base == BaseModelType.StableDiffusion2:
+        dimension = 512
+        if self.unet.unet.base_model == BaseModelType.StableDiffusion2:
             dimension = 768
-        elif unet_config.base == BaseModelType.StableDiffusionXL:
+        elif self.unet.unet.base_model == BaseModelType.StableDiffusionXL:
             dimension = 1024
         dimension = dimension * self.multiplier
         min_dimension = math.floor(dimension * 0.5)

invokeai/app/invocations/math.py

@@ -5,11 +5,10 @@ from typing import Literal
 import numpy as np
 from pydantic import ValidationInfo, field_validator
 
-from invokeai.app.invocations.fields import FieldDescriptions, InputField
 from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.shared.fields import FieldDescriptions
 
-from .baseinvocation import BaseInvocation, invocation
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
 
 
 @invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.0")

invokeai/app/invocations/metadata.py

@@ -5,22 +5,20 @@ from pydantic import BaseModel, ConfigDict, Field
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    InputField,
+    InvocationContext,
+    MetadataField,
+    OutputField,
+    UIType,
     invocation,
     invocation_output,
 )
 from invokeai.app.invocations.controlnet_image_processors import ControlField
-from invokeai.app.invocations.fields import (
-    FieldDescriptions,
-    ImageField,
-    InputField,
-    MetadataField,
-    OutputField,
-    UIType,
-)
 from invokeai.app.invocations.ip_adapter import IPAdapterModelField
 from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
+from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField
-from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.shared.fields import FieldDescriptions
 
 from ...version import __version__
 

invokeai/app/invocations/model.py

@@ -1,24 +1,31 @@
 import copy
 from typing import List, Optional
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField
-from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.app.shared.models import FreeUConfig
 
-from ...backend.model_manager import SubModelType
+from ...backend.model_management import BaseModelType, ModelType, SubModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
     invocation,
     invocation_output,
 )
 
 
 class ModelInfo(BaseModel):
-    key: str = Field(description="Key of model as returned by ModelRecordServiceBase.get_model()")
-    submodel_type: Optional[SubModelType] = Field(default=None, description="Info to load submodel")
+    model_name: str = Field(description="Info to load submodel")
+    base_model: BaseModelType = Field(description="Base model")
+    model_type: ModelType = Field(description="Info to load submodel")
+    submodel: Optional[SubModelType] = Field(default=None, description="Info to load submodel")
+
+    model_config = ConfigDict(protected_namespaces=())
 
 
 class LoraInfo(ModelInfo):
@@ -48,7 +55,7 @@ class VaeField(BaseModel):
 
 @invocation_output("unet_output")
 class UNetOutput(BaseInvocationOutput):
-    """Base class for invocations that output a UNet field."""
+    """Base class for invocations that output a UNet field"""
 
     unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
 
@@ -77,13 +84,20 @@ class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput):
 class MainModelField(BaseModel):
     """Main model field"""
 
-    key: str = Field(description="Model key")
+    model_name: str = Field(description="Name of the model")
+    base_model: BaseModelType = Field(description="Base model")
+    model_type: ModelType = Field(description="Model Type")
+
+    model_config = ConfigDict(protected_namespaces=())
 
 
 class LoRAModelField(BaseModel):
     """LoRA model field"""
 
-    key: str = Field(description="LoRA model key")
+    model_name: str = Field(description="Name of the LoRA model")
+    base_model: BaseModelType = Field(description="Base model")
+
+    model_config = ConfigDict(protected_namespaces=())
 
 
 @invocation(
@@ -91,7 +105,7 @@ class LoRAModelField(BaseModel):
     title="Main Model",
     tags=["model"],
     category="model",
-    version="1.0.1",
+    version="1.0.0",
 )
 class MainModelLoaderInvocation(BaseInvocation):
     """Loads a main model, outputting its submodels."""
@@ -100,40 +114,85 @@ class MainModelLoaderInvocation(BaseInvocation):
     # TODO: precision?
 
     def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
-        key = self.model.key
+        base_model = self.model.base_model
+        model_name = self.model.model_name
+        model_type = ModelType.Main
 
         # TODO: not found exceptions
-        if not context.models.exists(key):
-            raise Exception(f"Unknown model {key}")
+        if not context.services.model_manager.model_exists(
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+        ):
+            raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
+
+        """
+        if not context.services.model_manager.model_exists(
+            model_name=self.model_name,
+            model_type=SDModelType.Diffusers,
+            submodel=SDModelType.Tokenizer,
+        ):
+            raise Exception(
+                f"Failed to find tokenizer submodel in {self.model_name}! Check if model corrupted"
+            )
+
+        if not context.services.model_manager.model_exists(
+            model_name=self.model_name,
+            model_type=SDModelType.Diffusers,
+            submodel=SDModelType.TextEncoder,
+        ):
+            raise Exception(
+                f"Failed to find text_encoder submodel in {self.model_name}! Check if model corrupted"
+            )
+
+        if not context.services.model_manager.model_exists(
+            model_name=self.model_name,
+            model_type=SDModelType.Diffusers,
+            submodel=SDModelType.UNet,
+        ):
+            raise Exception(
+                f"Failed to find unet submodel from {self.model_name}! Check if model corrupted"
+            )
+        """
 
         return ModelLoaderOutput(
             unet=UNetField(
                 unet=ModelInfo(
-                    key=key,
-                    submodel_type=SubModelType.UNet,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.UNet,
                 ),
                 scheduler=ModelInfo(
-                    key=key,
-                    submodel_type=SubModelType.Scheduler,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Scheduler,
                 ),
                 loras=[],
             ),
             clip=ClipField(
                 tokenizer=ModelInfo(
-                    key=key,
-                    submodel_type=SubModelType.Tokenizer,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Tokenizer,
                 ),
                 text_encoder=ModelInfo(
-                    key=key,
-                    submodel_type=SubModelType.TextEncoder,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.TextEncoder,
                 ),
                 loras=[],
                 skipped_layers=0,
             ),
             vae=VaeField(
                 vae=ModelInfo(
-                    key=key,
-                    submodel_type=SubModelType.Vae,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Vae,
                 ),
             ),
         )
@@ -147,7 +206,7 @@ class LoraLoaderOutput(BaseInvocationOutput):
     clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
 
 
-@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.1")
+@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.0")
 class LoraLoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
 
@@ -170,16 +229,21 @@ class LoraLoaderInvocation(BaseInvocation):
         if self.lora is None:
             raise Exception("No LoRA provided")
 
-        lora_key = self.lora.key
+        base_model = self.lora.base_model
+        lora_name = self.lora.model_name
 
-        if not context.models.exists(lora_key):
-            raise Exception(f"Unkown lora: {lora_key}!")
+        if not context.services.model_manager.model_exists(
+            base_model=base_model,
+            model_name=lora_name,
+            model_type=ModelType.Lora,
+        ):
+            raise Exception(f"Unkown lora name: {lora_name}!")
 
-        if self.unet is not None and any(lora.key == lora_key for lora in self.unet.loras):
-            raise Exception(f'Lora "{lora_key}" already applied to unet')
+        if self.unet is not None and any(lora.model_name == lora_name for lora in self.unet.loras):
+            raise Exception(f'Lora "{lora_name}" already applied to unet')
 
-        if self.clip is not None and any(lora.key == lora_key for lora in self.clip.loras):
-            raise Exception(f'Lora "{lora_key}" already applied to clip')
+        if self.clip is not None and any(lora.model_name == lora_name for lora in self.clip.loras):
+            raise Exception(f'Lora "{lora_name}" already applied to clip')
 
         output = LoraLoaderOutput()
 
@@ -187,8 +251,10 @@ class LoraLoaderInvocation(BaseInvocation):
             output.unet = copy.deepcopy(self.unet)
             output.unet.loras.append(
                 LoraInfo(
-                    key=lora_key,
-                    submodel_type=None,
+                    base_model=base_model,
+                    model_name=lora_name,
+                    model_type=ModelType.Lora,
+                    submodel=None,
                     weight=self.weight,
                 )
             )
@@ -197,8 +263,10 @@ class LoraLoaderInvocation(BaseInvocation):
             output.clip = copy.deepcopy(self.clip)
             output.clip.loras.append(
                 LoraInfo(
-                    key=lora_key,
-                    submodel_type=None,
+                    base_model=base_model,
+                    model_name=lora_name,
+                    model_type=ModelType.Lora,
+                    submodel=None,
                     weight=self.weight,
                 )
             )
@@ -220,7 +288,7 @@ class SDXLLoraLoaderOutput(BaseInvocationOutput):
     title="SDXL LoRA",
     tags=["lora", "model"],
     category="model",
-    version="1.0.1",
+    version="1.0.0",
 )
 class SDXLLoraLoaderInvocation(BaseInvocation):
     """Apply selected lora to unet and text_encoder."""
@@ -250,19 +318,24 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
         if self.lora is None:
             raise Exception("No LoRA provided")
 
-        lora_key = self.lora.key
+        base_model = self.lora.base_model
+        lora_name = self.lora.model_name
 
-        if not context.models.exists(lora_key):
-            raise Exception(f"Unknown lora: {lora_key}!")
+        if not context.services.model_manager.model_exists(
+            base_model=base_model,
+            model_name=lora_name,
+            model_type=ModelType.Lora,
+        ):
+            raise Exception(f"Unknown lora name: {lora_name}!")
 
-        if self.unet is not None and any(lora.key == lora_key for lora in self.unet.loras):
-            raise Exception(f'Lora "{lora_key}" already applied to unet')
+        if self.unet is not None and any(lora.model_name == lora_name for lora in self.unet.loras):
+            raise Exception(f'Lora "{lora_name}" already applied to unet')
 
-        if self.clip is not None and any(lora.key == lora_key for lora in self.clip.loras):
-            raise Exception(f'Lora "{lora_key}" already applied to clip')
+        if self.clip is not None and any(lora.model_name == lora_name for lora in self.clip.loras):
+            raise Exception(f'Lora "{lora_name}" already applied to clip')
 
-        if self.clip2 is not None and any(lora.key == lora_key for lora in self.clip2.loras):
-            raise Exception(f'Lora "{lora_key}" already applied to clip2')
+        if self.clip2 is not None and any(lora.model_name == lora_name for lora in self.clip2.loras):
+            raise Exception(f'Lora "{lora_name}" already applied to clip2')
 
         output = SDXLLoraLoaderOutput()
 
@@ -270,8 +343,10 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
             output.unet = copy.deepcopy(self.unet)
             output.unet.loras.append(
                 LoraInfo(
-                    key=lora_key,
-                    submodel_type=None,
+                    base_model=base_model,
+                    model_name=lora_name,
+                    model_type=ModelType.Lora,
+                    submodel=None,
                     weight=self.weight,
                 )
             )
@@ -280,8 +355,10 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
             output.clip = copy.deepcopy(self.clip)
             output.clip.loras.append(
                 LoraInfo(
-                    key=lora_key,
-                    submodel_type=None,
+                    base_model=base_model,
+                    model_name=lora_name,
+                    model_type=ModelType.Lora,
+                    submodel=None,
                     weight=self.weight,
                 )
             )
@@ -290,8 +367,10 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
             output.clip2 = copy.deepcopy(self.clip2)
             output.clip2.loras.append(
                 LoraInfo(
-                    key=lora_key,
-                    submodel_type=None,
+                    base_model=base_model,
+                    model_name=lora_name,
+                    model_type=ModelType.Lora,
+                    submodel=None,
                     weight=self.weight,
                 )
             )
@@ -302,10 +381,13 @@ class SDXLLoraLoaderInvocation(BaseInvocation):
 class VAEModelField(BaseModel):
     """Vae model field"""
 
-    key: str = Field(description="Model's key")
+    model_name: str = Field(description="Name of the model")
+    base_model: BaseModelType = Field(description="Base model")
+
+    model_config = ConfigDict(protected_namespaces=())
 
 
-@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.1")
+@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.0")
 class VaeLoaderInvocation(BaseInvocation):
     """Loads a VAE model, outputting a VaeLoaderOutput"""
 
@@ -316,12 +398,25 @@ class VaeLoaderInvocation(BaseInvocation):
     )
 
     def invoke(self, context: InvocationContext) -> VAEOutput:
-        key = self.vae_model.key
+        base_model = self.vae_model.base_model
+        model_name = self.vae_model.model_name
+        model_type = ModelType.Vae
 
-        if not context.models.exists(key):
-            raise Exception(f"Unkown vae: {key}!")
-
-        return VAEOutput(vae=VaeField(vae=ModelInfo(key=key)))
+        if not context.services.model_manager.model_exists(
+            base_model=base_model,
+            model_name=model_name,
+            model_type=model_type,
+        ):
+            raise Exception(f"Unkown vae name: {model_name}!")
+        return VAEOutput(
+            vae=VaeField(
+                vae=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                )
+            )
+        )
 
 
 @invocation_output("seamless_output")

invokeai/app/invocations/noise.py

@@ -4,15 +4,17 @@
 import torch
 from pydantic import field_validator
 
-from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
-from invokeai.app.invocations.fields import FieldDescriptions, InputField, LatentsField, OutputField
-from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.invocations.latent import LatentsField
+from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.app.util.misc import SEED_MAX
 
 from ...backend.util.devices import choose_torch_device, torch_dtype
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    InputField,
+    InvocationContext,
+    OutputField,
     invocation,
     invocation_output,
 )
@@ -67,13 +69,13 @@ class NoiseOutput(BaseInvocationOutput):
     width: int = OutputField(description=FieldDescriptions.width)
     height: int = OutputField(description=FieldDescriptions.height)
 
-    @classmethod
-    def build(cls, latents_name: str, latents: torch.Tensor, seed: int) -> "NoiseOutput":
-        return cls(
-            noise=LatentsField(latents_name=latents_name, seed=seed),
-            width=latents.size()[3] * LATENT_SCALE_FACTOR,
-            height=latents.size()[2] * LATENT_SCALE_FACTOR,
-        )
+
+def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int):
+    return NoiseOutput(
+        noise=LatentsField(latents_name=latents_name, seed=seed),
+        width=latents.size()[3] * 8,
+        height=latents.size()[2] * 8,
+    )
 
 
 @invocation(
@@ -94,13 +96,13 @@ class NoiseInvocation(BaseInvocation):
     )
     width: int = InputField(
         default=512,
-        multiple_of=LATENT_SCALE_FACTOR,
+        multiple_of=8,
         gt=0,
         description=FieldDescriptions.width,
     )
     height: int = InputField(
         default=512,
-        multiple_of=LATENT_SCALE_FACTOR,
+        multiple_of=8,
         gt=0,
         description=FieldDescriptions.height,
     )
@@ -122,5 +124,6 @@ class NoiseInvocation(BaseInvocation):
             seed=self.seed,
             use_cpu=self.use_cpu,
         )
-        name = context.tensors.save(tensor=noise)
-        return NoiseOutput.build(latents_name=name, latents=noise, seed=self.seed)
+        name = f"{context.graph_execution_state_id}__{self.id}"
+        context.services.latents.save(name, noise)
+        return build_noise_output(latents_name=name, latents=noise, seed=self.seed)

invokeai/app/invocations/onnx.py

@@ -0,0 +1,508 @@
+# Copyright (c) 2023 Borisov Sergey (https://github.com/StAlKeR7779)
+
+import inspect
+
+# from contextlib import ExitStack
+from typing import List, Literal, Union
+
+import numpy as np
+import torch
+from diffusers.image_processor import VaeImageProcessor
+from pydantic import BaseModel, ConfigDict, Field, field_validator
+from tqdm import tqdm
+
+from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
+from invokeai.app.shared.fields import FieldDescriptions
+from invokeai.app.util.step_callback import stable_diffusion_step_callback
+from invokeai.backend import BaseModelType, ModelType, SubModelType
+
+from ...backend.model_management import ONNXModelPatcher
+from ...backend.stable_diffusion import PipelineIntermediateState
+from ...backend.util import choose_torch_device
+from ..util.ti_utils import extract_ti_triggers_from_prompt
+from .baseinvocation import (
+    BaseInvocation,
+    BaseInvocationOutput,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
+    UIComponent,
+    UIType,
+    WithMetadata,
+    invocation,
+    invocation_output,
+)
+from .controlnet_image_processors import ControlField
+from .latent import SAMPLER_NAME_VALUES, LatentsField, LatentsOutput, build_latents_output, get_scheduler
+from .model import ClipField, ModelInfo, UNetField, VaeField
+
+ORT_TO_NP_TYPE = {
+    "tensor(bool)": np.bool_,
+    "tensor(int8)": np.int8,
+    "tensor(uint8)": np.uint8,
+    "tensor(int16)": np.int16,
+    "tensor(uint16)": np.uint16,
+    "tensor(int32)": np.int32,
+    "tensor(uint32)": np.uint32,
+    "tensor(int64)": np.int64,
+    "tensor(uint64)": np.uint64,
+    "tensor(float16)": np.float16,
+    "tensor(float)": np.float32,
+    "tensor(double)": np.float64,
+}
+
+PRECISION_VALUES = Literal[tuple(ORT_TO_NP_TYPE.keys())]
+
+
+@invocation("prompt_onnx", title="ONNX Prompt (Raw)", tags=["prompt", "onnx"], category="conditioning", version="1.0.0")
+class ONNXPromptInvocation(BaseInvocation):
+    prompt: str = InputField(default="", description=FieldDescriptions.raw_prompt, ui_component=UIComponent.Textarea)
+    clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
+
+    def invoke(self, context: InvocationContext) -> ConditioningOutput:
+        tokenizer_info = context.services.model_manager.get_model(
+            **self.clip.tokenizer.model_dump(),
+        )
+        text_encoder_info = context.services.model_manager.get_model(
+            **self.clip.text_encoder.model_dump(),
+        )
+        with tokenizer_info as orig_tokenizer, text_encoder_info as text_encoder:  # , ExitStack() as stack:
+            loras = [
+                (
+                    context.services.model_manager.get_model(**lora.model_dump(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:
+                    ti_list.append(
+                        (
+                            name,
+                            context.services.model_manager.get_model(
+                                model_name=name,
+                                base_model=self.clip.text_encoder.base_model,
+                                model_type=ModelType.TextualInversion,
+                            ).context.model,
+                        )
+                    )
+                except Exception:
+                    # print(e)
+                    # import traceback
+                    # print(traceback.format_exc())
+                    print(f'Warn: trigger: "{trigger}" not found')
+            if loras or ti_list:
+                text_encoder.release_session()
+            with (
+                ONNXModelPatcher.apply_lora_text_encoder(text_encoder, loras),
+                ONNXModelPatcher.apply_ti(orig_tokenizer, text_encoder, ti_list) as (tokenizer, ti_manager),
+            ):
+                text_encoder.create_session()
+
+                # copy from
+                # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L153
+                text_inputs = tokenizer(
+                    self.prompt,
+                    padding="max_length",
+                    max_length=tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="np",
+                )
+                text_input_ids = text_inputs.input_ids
+                """
+                untruncated_ids = tokenizer(prompt, padding="max_length", return_tensors="np").input_ids
+
+                if not np.array_equal(text_input_ids, untruncated_ids):
+                    removed_text = self.tokenizer.batch_decode(
+                        untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                    )
+                    logger.warning(
+                        "The following part of your input was truncated because CLIP can only handle sequences up to"
+                        f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                    )
+                """
+
+                prompt_embeds = text_encoder(input_ids=text_input_ids.astype(np.int32))[0]
+
+        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+
+        # TODO: hacky but works ;D maybe rename latents somehow?
+        context.services.latents.save(conditioning_name, (prompt_embeds, None))
+
+        return ConditioningOutput(
+            conditioning=ConditioningField(
+                conditioning_name=conditioning_name,
+            ),
+        )
+
+
+# Text to image
+@invocation(
+    "t2l_onnx",
+    title="ONNX Text to Latents",
+    tags=["latents", "inference", "txt2img", "onnx"],
+    category="latents",
+    version="1.0.0",
+)
+class ONNXTextToLatentsInvocation(BaseInvocation):
+    """Generates latents from conditionings."""
+
+    positive_conditioning: ConditioningField = InputField(
+        description=FieldDescriptions.positive_cond,
+        input=Input.Connection,
+    )
+    negative_conditioning: ConditioningField = InputField(
+        description=FieldDescriptions.negative_cond,
+        input=Input.Connection,
+    )
+    noise: LatentsField = InputField(
+        description=FieldDescriptions.noise,
+        input=Input.Connection,
+    )
+    steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
+    cfg_scale: Union[float, List[float]] = InputField(
+        default=7.5,
+        ge=1,
+        description=FieldDescriptions.cfg_scale,
+    )
+    scheduler: SAMPLER_NAME_VALUES = InputField(
+        default="euler", description=FieldDescriptions.scheduler, input=Input.Direct, ui_type=UIType.Scheduler
+    )
+    precision: PRECISION_VALUES = InputField(default="tensor(float16)", description=FieldDescriptions.precision)
+    unet: UNetField = InputField(
+        description=FieldDescriptions.unet,
+        input=Input.Connection,
+    )
+    control: Union[ControlField, list[ControlField]] = InputField(
+        default=None,
+        description=FieldDescriptions.control,
+    )
+    # seamless:   bool = InputField(default=False, description="Whether or not to generate an image that can tile without seams", )
+    # seamless_axes: str = InputField(default="", description="The axes to tile the image on, 'x' and/or 'y'")
+
+    @field_validator("cfg_scale")
+    def ge_one(cls, v):
+        """validate that all cfg_scale values are >= 1"""
+        if isinstance(v, list):
+            for i in v:
+                if i < 1:
+                    raise ValueError("cfg_scale must be greater than 1")
+        else:
+            if v < 1:
+                raise ValueError("cfg_scale must be greater than 1")
+        return v
+
+    # based on
+    # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
+    def invoke(self, context: InvocationContext) -> LatentsOutput:
+        c, _ = context.services.latents.get(self.positive_conditioning.conditioning_name)
+        uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
+        graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
+        source_node_id = graph_execution_state.prepared_source_mapping[self.id]
+        if isinstance(c, torch.Tensor):
+            c = c.cpu().numpy()
+        if isinstance(uc, torch.Tensor):
+            uc = uc.cpu().numpy()
+        device = torch.device(choose_torch_device())
+        prompt_embeds = np.concatenate([uc, c])
+
+        latents = context.services.latents.get(self.noise.latents_name)
+        if isinstance(latents, torch.Tensor):
+            latents = latents.cpu().numpy()
+
+        # TODO: better execution device handling
+        latents = latents.astype(ORT_TO_NP_TYPE[self.precision])
+
+        # get the initial random noise unless the user supplied it
+        do_classifier_free_guidance = True
+        # latents_dtype = prompt_embeds.dtype
+        # latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)
+        # if latents.shape != latents_shape:
+        #    raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+
+        scheduler = get_scheduler(
+            context=context,
+            scheduler_info=self.unet.scheduler,
+            scheduler_name=self.scheduler,
+            seed=0,  # TODO: refactor this node
+        )
+
+        def torch2numpy(latent: torch.Tensor):
+            return latent.cpu().numpy()
+
+        def numpy2torch(latent, device):
+            return torch.from_numpy(latent).to(device)
+
+        def dispatch_progress(
+            self, context: InvocationContext, source_node_id: str, intermediate_state: PipelineIntermediateState
+        ) -> None:
+            stable_diffusion_step_callback(
+                context=context,
+                intermediate_state=intermediate_state,
+                node=self.model_dump(),
+                source_node_id=source_node_id,
+            )
+
+        scheduler.set_timesteps(self.steps)
+        latents = latents * np.float64(scheduler.init_noise_sigma)
+
+        extra_step_kwargs = {}
+        if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
+            extra_step_kwargs.update(
+                eta=0.0,
+            )
+
+        unet_info = context.services.model_manager.get_model(**self.unet.unet.model_dump())
+
+        with unet_info as unet:  # , ExitStack() as stack:
+            # loras = [(stack.enter_context(context.services.model_manager.get_model(**lora.dict(exclude={"weight"}))), lora.weight) for lora in self.unet.loras]
+            loras = [
+                (
+                    context.services.model_manager.get_model(**lora.model_dump(exclude={"weight"})).context.model,
+                    lora.weight,
+                )
+                for lora in self.unet.loras
+            ]
+
+            if loras:
+                unet.release_session()
+            with ONNXModelPatcher.apply_lora_unet(unet, loras):
+                # TODO:
+                _, _, h, w = latents.shape
+                unet.create_session(h, w)
+
+                timestep_dtype = next(
+                    (input.type for input in unet.session.get_inputs() if input.name == "timestep"), "tensor(float16)"
+                )
+                timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]
+                for i in tqdm(range(len(scheduler.timesteps))):
+                    t = scheduler.timesteps[i]
+                    # expand the latents if we are doing classifier free guidance
+                    latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
+                    latent_model_input = scheduler.scale_model_input(numpy2torch(latent_model_input, device), t)
+                    latent_model_input = latent_model_input.cpu().numpy()
+
+                    # predict the noise residual
+                    timestep = np.array([t], dtype=timestep_dtype)
+                    noise_pred = unet(sample=latent_model_input, timestep=timestep, encoder_hidden_states=prompt_embeds)
+                    noise_pred = noise_pred[0]
+
+                    # perform guidance
+                    if do_classifier_free_guidance:
+                        noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
+                        noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
+
+                    # compute the previous noisy sample x_t -> x_t-1
+                    scheduler_output = scheduler.step(
+                        numpy2torch(noise_pred, device), t, numpy2torch(latents, device), **extra_step_kwargs
+                    )
+                    latents = torch2numpy(scheduler_output.prev_sample)
+
+                    state = PipelineIntermediateState(
+                        run_id="test", step=i, timestep=timestep, latents=scheduler_output.prev_sample
+                    )
+                    dispatch_progress(self, context=context, source_node_id=source_node_id, intermediate_state=state)
+
+                    # call the callback, if provided
+                    # if callback is not None and i % callback_steps == 0:
+                    #    callback(i, t, latents)
+
+        torch.cuda.empty_cache()
+
+        name = f"{context.graph_execution_state_id}__{self.id}"
+        context.services.latents.save(name, latents)
+        return build_latents_output(latents_name=name, latents=torch.from_numpy(latents))
+
+
+# Latent to image
+@invocation(
+    "l2i_onnx",
+    title="ONNX Latents to Image",
+    tags=["latents", "image", "vae", "onnx"],
+    category="image",
+    version="1.2.0",
+)
+class ONNXLatentsToImageInvocation(BaseInvocation, WithMetadata):
+    """Generates an image from latents."""
+
+    latents: LatentsField = InputField(
+        description=FieldDescriptions.denoised_latents,
+        input=Input.Connection,
+    )
+    vae: VaeField = InputField(
+        description=FieldDescriptions.vae,
+        input=Input.Connection,
+    )
+    # tiled: bool = InputField(default=False, description="Decode latents by overlaping tiles(less memory consumption)")
+
+    def invoke(self, context: InvocationContext) -> ImageOutput:
+        latents = context.services.latents.get(self.latents.latents_name)
+
+        if self.vae.vae.submodel != SubModelType.VaeDecoder:
+            raise Exception(f"Expected vae_decoder, found: {self.vae.vae.model_type}")
+
+        vae_info = context.services.model_manager.get_model(
+            **self.vae.vae.model_dump(),
+        )
+
+        # clear memory as vae decode can request a lot
+        torch.cuda.empty_cache()
+
+        with vae_info as vae:
+            vae.create_session()
+
+            # copied from
+            # https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L427
+            latents = 1 / 0.18215 * latents
+            # image = self.vae_decoder(latent_sample=latents)[0]
+            # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
+            image = np.concatenate([vae(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])])
+
+            image = np.clip(image / 2 + 0.5, 0, 1)
+            image = image.transpose((0, 2, 3, 1))
+            image = VaeImageProcessor.numpy_to_pil(image)[0]
+
+        torch.cuda.empty_cache()
+
+        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,
+        )
+
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )
+
+
+@invocation_output("model_loader_output_onnx")
+class ONNXModelLoaderOutput(BaseInvocationOutput):
+    """Model loader output"""
+
+    unet: UNetField = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
+    clip: ClipField = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
+    vae_decoder: VaeField = OutputField(default=None, description=FieldDescriptions.vae, title="VAE Decoder")
+    vae_encoder: VaeField = OutputField(default=None, description=FieldDescriptions.vae, title="VAE Encoder")
+
+
+class OnnxModelField(BaseModel):
+    """Onnx model field"""
+
+    model_name: str = Field(description="Name of the model")
+    base_model: BaseModelType = Field(description="Base model")
+    model_type: ModelType = Field(description="Model Type")
+
+    model_config = ConfigDict(protected_namespaces=())
+
+
+@invocation("onnx_model_loader", title="ONNX Main Model", tags=["onnx", "model"], category="model", version="1.0.0")
+class OnnxModelLoaderInvocation(BaseInvocation):
+    """Loads a main model, outputting its submodels."""
+
+    model: OnnxModelField = InputField(
+        description=FieldDescriptions.onnx_main_model, input=Input.Direct, ui_type=UIType.ONNXModel
+    )
+
+    def invoke(self, context: InvocationContext) -> ONNXModelLoaderOutput:
+        base_model = self.model.base_model
+        model_name = self.model.model_name
+        model_type = ModelType.ONNX
+
+        # TODO: not found exceptions
+        if not context.services.model_manager.model_exists(
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+        ):
+            raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
+
+        """
+        if not context.services.model_manager.model_exists(
+            model_name=self.model_name,
+            model_type=SDModelType.Diffusers,
+            submodel=SDModelType.Tokenizer,
+        ):
+            raise Exception(
+                f"Failed to find tokenizer submodel in {self.model_name}! Check if model corrupted"
+            )
+
+        if not context.services.model_manager.model_exists(
+            model_name=self.model_name,
+            model_type=SDModelType.Diffusers,
+            submodel=SDModelType.TextEncoder,
+        ):
+            raise Exception(
+                f"Failed to find text_encoder submodel in {self.model_name}! Check if model corrupted"
+            )
+
+        if not context.services.model_manager.model_exists(
+            model_name=self.model_name,
+            model_type=SDModelType.Diffusers,
+            submodel=SDModelType.UNet,
+        ):
+            raise Exception(
+                f"Failed to find unet submodel from {self.model_name}! Check if model corrupted"
+            )
+        """
+
+        return ONNXModelLoaderOutput(
+            unet=UNetField(
+                unet=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.UNet,
+                ),
+                scheduler=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Scheduler,
+                ),
+                loras=[],
+            ),
+            clip=ClipField(
+                tokenizer=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Tokenizer,
+                ),
+                text_encoder=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.TextEncoder,
+                ),
+                loras=[],
+                skipped_layers=0,
+            ),
+            vae_decoder=VaeField(
+                vae=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.VaeDecoder,
+                ),
+            ),
+            vae_encoder=VaeField(
+                vae=ModelInfo(
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.VaeEncoder,
+                ),
+            ),
+        )

invokeai/app/invocations/param_easing.py

@@ -40,10 +40,8 @@ from easing_functions import (
 from matplotlib.ticker import MaxNLocator
 
 from invokeai.app.invocations.primitives import FloatCollectionOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
 
-from .baseinvocation import BaseInvocation, invocation
-from .fields import InputField
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
 
 
 @invocation(
@@ -111,7 +109,7 @@ EASING_FUNCTION_KEYS = Literal[tuple(EASING_FUNCTIONS_MAP.keys())]
     title="Step Param Easing",
     tags=["step", "easing"],
     category="step",
-    version="1.0.1",
+    version="1.0.0",
 )
 class StepParamEasingInvocation(BaseInvocation):
     """Experimental per-step parameter easing for denoising steps"""
@@ -150,19 +148,19 @@ class StepParamEasingInvocation(BaseInvocation):
         postlist = list(num_poststeps * [self.post_end_value])
 
         if log_diagnostics:
-            context.logger.debug("start_step: " + str(start_step))
-            context.logger.debug("end_step: " + str(end_step))
-            context.logger.debug("num_easing_steps: " + str(num_easing_steps))
-            context.logger.debug("num_presteps: " + str(num_presteps))
-            context.logger.debug("num_poststeps: " + str(num_poststeps))
-            context.logger.debug("prelist size: " + str(len(prelist)))
-            context.logger.debug("postlist size: " + str(len(postlist)))
-            context.logger.debug("prelist: " + str(prelist))
-            context.logger.debug("postlist: " + str(postlist))
+            context.services.logger.debug("start_step: " + str(start_step))
+            context.services.logger.debug("end_step: " + str(end_step))
+            context.services.logger.debug("num_easing_steps: " + str(num_easing_steps))
+            context.services.logger.debug("num_presteps: " + str(num_presteps))
+            context.services.logger.debug("num_poststeps: " + str(num_poststeps))
+            context.services.logger.debug("prelist size: " + str(len(prelist)))
+            context.services.logger.debug("postlist size: " + str(len(postlist)))
+            context.services.logger.debug("prelist: " + str(prelist))
+            context.services.logger.debug("postlist: " + str(postlist))
 
         easing_class = EASING_FUNCTIONS_MAP[self.easing]
         if log_diagnostics:
-            context.logger.debug("easing class: " + str(easing_class))
+            context.services.logger.debug("easing class: " + str(easing_class))
         easing_list = []
         if self.mirror:  # "expected" mirroring
             # if number of steps is even, squeeze duration down to (number_of_steps)/2
@@ -173,7 +171,7 @@ class StepParamEasingInvocation(BaseInvocation):
 
             base_easing_duration = int(np.ceil(num_easing_steps / 2.0))
             if log_diagnostics:
-                context.logger.debug("base easing duration: " + str(base_easing_duration))
+                context.services.logger.debug("base easing duration: " + str(base_easing_duration))
             even_num_steps = num_easing_steps % 2 == 0  # even number of steps
             easing_function = easing_class(
                 start=self.start_value,
@@ -185,14 +183,14 @@ class StepParamEasingInvocation(BaseInvocation):
                 easing_val = easing_function.ease(step_index)
                 base_easing_vals.append(easing_val)
                 if log_diagnostics:
-                    context.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
+                    context.services.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(easing_val))
             if even_num_steps:
                 mirror_easing_vals = list(reversed(base_easing_vals))
             else:
                 mirror_easing_vals = list(reversed(base_easing_vals[0:-1]))
             if log_diagnostics:
-                context.logger.debug("base easing vals: " + str(base_easing_vals))
-                context.logger.debug("mirror easing vals: " + str(mirror_easing_vals))
+                context.services.logger.debug("base easing vals: " + str(base_easing_vals))
+                context.services.logger.debug("mirror easing vals: " + str(mirror_easing_vals))
             easing_list = base_easing_vals + mirror_easing_vals
 
         # FIXME: add alt_mirror option (alternative to default or mirror), or remove entirely
@@ -227,12 +225,12 @@ class StepParamEasingInvocation(BaseInvocation):
                 step_val = easing_function.ease(step_index)
                 easing_list.append(step_val)
                 if log_diagnostics:
-                    context.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
+                    context.services.logger.debug("step_index: " + str(step_index) + ", easing_val: " + str(step_val))
 
         if log_diagnostics:
-            context.logger.debug("prelist size: " + str(len(prelist)))
-            context.logger.debug("easing_list size: " + str(len(easing_list)))
-            context.logger.debug("postlist size: " + str(len(postlist)))
+            context.services.logger.debug("prelist size: " + str(len(prelist)))
+            context.services.logger.debug("easing_list size: " + str(len(easing_list)))
+            context.services.logger.debug("postlist size: " + str(len(postlist)))
 
         param_list = prelist + easing_list + postlist
 

invokeai/app/invocations/primitives.py

@@ -1,28 +1,20 @@
 # Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
 
-from typing import Optional
+from typing import Optional, Tuple
 
 import torch
+from pydantic import BaseModel, Field
 
-from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
-from invokeai.app.invocations.fields import (
-    ColorField,
-    ConditioningField,
-    DenoiseMaskField,
-    FieldDescriptions,
-    ImageField,
-    Input,
-    InputField,
-    LatentsField,
-    OutputField,
-    UIComponent,
-)
-from invokeai.app.services.images.images_common import ImageDTO
-from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.shared.fields import FieldDescriptions
 
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
+    UIComponent,
     invocation,
     invocation_output,
 )
@@ -229,6 +221,18 @@ class StringCollectionInvocation(BaseInvocation):
 # region Image
 
 
+class ImageField(BaseModel):
+    """An image primitive field"""
+
+    image_name: str = Field(description="The name of the image")
+
+
+class BoardField(BaseModel):
+    """A board primitive field"""
+
+    board_id: str = Field(description="The id of the board")
+
+
 @invocation_output("image_output")
 class ImageOutput(BaseInvocationOutput):
     """Base class for nodes that output a single image"""
@@ -237,14 +241,6 @@ class ImageOutput(BaseInvocationOutput):
     width: int = OutputField(description="The width of the image in pixels")
     height: int = OutputField(description="The height of the image in pixels")
 
-    @classmethod
-    def build(cls, image_dto: ImageDTO) -> "ImageOutput":
-        return cls(
-            image=ImageField(image_name=image_dto.image_name),
-            width=image_dto.width,
-            height=image_dto.height,
-        )
-
 
 @invocation_output("image_collection_output")
 class ImageCollectionOutput(BaseInvocationOutput):
@@ -255,14 +251,16 @@ class ImageCollectionOutput(BaseInvocationOutput):
     )
 
 
-@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.1")
-class ImageInvocation(BaseInvocation):
+@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.0")
+class ImageInvocation(
+    BaseInvocation,
+):
     """An image primitive value"""
 
     image: ImageField = InputField(description="The image to load")
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name)
+        image = context.services.images.get_pil_image(self.image.image_name)
 
         return ImageOutput(
             image=ImageField(image_name=self.image.image_name),
@@ -292,40 +290,42 @@ class ImageCollectionInvocation(BaseInvocation):
 # region DenoiseMask
 
 
+class DenoiseMaskField(BaseModel):
+    """An inpaint mask field"""
+
+    mask_name: str = Field(description="The name of the mask image")
+    masked_latents_name: Optional[str] = Field(default=None, description="The name of the masked image latents")
+
+
 @invocation_output("denoise_mask_output")
 class DenoiseMaskOutput(BaseInvocationOutput):
     """Base class for nodes that output a single image"""
 
     denoise_mask: DenoiseMaskField = OutputField(description="Mask for denoise model run")
 
-    @classmethod
-    def build(cls, mask_name: str, masked_latents_name: Optional[str] = None) -> "DenoiseMaskOutput":
-        return cls(
-            denoise_mask=DenoiseMaskField(mask_name=mask_name, masked_latents_name=masked_latents_name),
-        )
-
 
 # endregion
 
 # region Latents
 
 
+class LatentsField(BaseModel):
+    """A latents tensor primitive field"""
+
+    latents_name: str = Field(description="The name of the latents")
+    seed: Optional[int] = Field(default=None, description="Seed used to generate this latents")
+
+
 @invocation_output("latents_output")
 class LatentsOutput(BaseInvocationOutput):
     """Base class for nodes that output a single latents tensor"""
 
-    latents: LatentsField = OutputField(description=FieldDescriptions.latents)
+    latents: LatentsField = OutputField(
+        description=FieldDescriptions.latents,
+    )
     width: int = OutputField(description=FieldDescriptions.width)
     height: int = OutputField(description=FieldDescriptions.height)
 
-    @classmethod
-    def build(cls, latents_name: str, latents: torch.Tensor, seed: Optional[int] = None) -> "LatentsOutput":
-        return cls(
-            latents=LatentsField(latents_name=latents_name, seed=seed),
-            width=latents.size()[3] * LATENT_SCALE_FACTOR,
-            height=latents.size()[2] * LATENT_SCALE_FACTOR,
-        )
-
 
 @invocation_output("latents_collection_output")
 class LatentsCollectionOutput(BaseInvocationOutput):
@@ -337,7 +337,7 @@ class LatentsCollectionOutput(BaseInvocationOutput):
 
 
 @invocation(
-    "latents", title="Latents Primitive", tags=["primitives", "latents"], category="primitives", version="1.0.1"
+    "latents", title="Latents Primitive", tags=["primitives", "latents"], category="primitives", version="1.0.0"
 )
 class LatentsInvocation(BaseInvocation):
     """A latents tensor primitive value"""
@@ -345,9 +345,9 @@ class LatentsInvocation(BaseInvocation):
     latents: LatentsField = InputField(description="The latents tensor", input=Input.Connection)
 
     def invoke(self, context: InvocationContext) -> LatentsOutput:
-        latents = context.tensors.load(self.latents.latents_name)
+        latents = context.services.latents.get(self.latents.latents_name)
 
-        return LatentsOutput.build(self.latents.latents_name, latents)
+        return build_latents_output(self.latents.latents_name, latents)
 
 
 @invocation(
@@ -368,11 +368,31 @@ class LatentsCollectionInvocation(BaseInvocation):
         return LatentsCollectionOutput(collection=self.collection)
 
 
+def build_latents_output(latents_name: str, latents: torch.Tensor, seed: Optional[int] = None):
+    return LatentsOutput(
+        latents=LatentsField(latents_name=latents_name, seed=seed),
+        width=latents.size()[3] * 8,
+        height=latents.size()[2] * 8,
+    )
+
+
 # endregion
 
 # region Color
 
 
+class ColorField(BaseModel):
+    """A color primitive field"""
+
+    r: int = Field(ge=0, le=255, description="The red component")
+    g: int = Field(ge=0, le=255, description="The green component")
+    b: int = Field(ge=0, le=255, description="The blue component")
+    a: int = Field(ge=0, le=255, description="The alpha component")
+
+    def tuple(self) -> Tuple[int, int, int, int]:
+        return (self.r, self.g, self.b, self.a)
+
+
 @invocation_output("color_output")
 class ColorOutput(BaseInvocationOutput):
     """Base class for nodes that output a single color"""
@@ -404,16 +424,18 @@ class ColorInvocation(BaseInvocation):
 # region Conditioning
 
 
+class ConditioningField(BaseModel):
+    """A conditioning tensor primitive value"""
+
+    conditioning_name: str = Field(description="The name of conditioning tensor")
+
+
 @invocation_output("conditioning_output")
 class ConditioningOutput(BaseInvocationOutput):
     """Base class for nodes that output a single conditioning tensor"""
 
     conditioning: ConditioningField = OutputField(description=FieldDescriptions.cond)
 
-    @classmethod
-    def build(cls, conditioning_name: str) -> "ConditioningOutput":
-        return cls(conditioning=ConditioningField(conditioning_name=conditioning_name))
-
 
 @invocation_output("conditioning_collection_output")
 class ConditioningCollectionOutput(BaseInvocationOutput):

invokeai/app/invocations/prompt.py

@@ -6,10 +6,8 @@ from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPrompt
 from pydantic import field_validator
 
 from invokeai.app.invocations.primitives import StringCollectionOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
 
-from .baseinvocation import BaseInvocation, invocation
-from .fields import InputField, UIComponent
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, UIComponent, invocation
 
 
 @invocation(

invokeai/app/invocations/sdxl.py

@@ -1,10 +1,14 @@
-from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, UIType
-from invokeai.app.services.shared.invocation_context import InvocationContext
-from invokeai.backend.model_manager import SubModelType
+from invokeai.app.shared.fields import FieldDescriptions
 
+from ...backend.model_management import ModelType, SubModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
+    UIType,
     invocation,
     invocation_output,
 )
@@ -30,7 +34,7 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
     vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
 
 
-@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.1")
+@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.0")
 class SDXLModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl base model, outputting its submodels."""
 
@@ -40,52 +44,72 @@ class SDXLModelLoaderInvocation(BaseInvocation):
     # TODO: precision?
 
     def invoke(self, context: InvocationContext) -> SDXLModelLoaderOutput:
-        model_key = self.model.key
+        base_model = self.model.base_model
+        model_name = self.model.model_name
+        model_type = ModelType.Main
 
         # TODO: not found exceptions
-        if not context.models.exists(model_key):
-            raise Exception(f"Unknown model: {model_key}")
+        if not context.services.model_manager.model_exists(
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+        ):
+            raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
 
         return SDXLModelLoaderOutput(
             unet=UNetField(
                 unet=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.UNet,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.UNet,
                 ),
                 scheduler=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Scheduler,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Scheduler,
                 ),
                 loras=[],
             ),
             clip=ClipField(
                 tokenizer=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Tokenizer,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Tokenizer,
                 ),
                 text_encoder=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.TextEncoder,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.TextEncoder,
                 ),
                 loras=[],
                 skipped_layers=0,
             ),
             clip2=ClipField(
                 tokenizer=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Tokenizer2,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Tokenizer2,
                 ),
                 text_encoder=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.TextEncoder2,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.TextEncoder2,
                 ),
                 loras=[],
                 skipped_layers=0,
             ),
             vae=VaeField(
                 vae=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Vae,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Vae,
                 ),
             ),
         )
@@ -96,7 +120,7 @@ class SDXLModelLoaderInvocation(BaseInvocation):
     title="SDXL Refiner Model",
     tags=["model", "sdxl", "refiner"],
     category="model",
-    version="1.0.1",
+    version="1.0.0",
 )
 class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     """Loads an sdxl refiner model, outputting its submodels."""
@@ -109,40 +133,56 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
     # TODO: precision?
 
     def invoke(self, context: InvocationContext) -> SDXLRefinerModelLoaderOutput:
-        model_key = self.model.key
+        base_model = self.model.base_model
+        model_name = self.model.model_name
+        model_type = ModelType.Main
 
         # TODO: not found exceptions
-        if not context.models.exists(model_key):
-            raise Exception(f"Unknown model: {model_key}")
+        if not context.services.model_manager.model_exists(
+            model_name=model_name,
+            base_model=base_model,
+            model_type=model_type,
+        ):
+            raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
 
         return SDXLRefinerModelLoaderOutput(
             unet=UNetField(
                 unet=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.UNet,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.UNet,
                 ),
                 scheduler=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Scheduler,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Scheduler,
                 ),
                 loras=[],
             ),
             clip2=ClipField(
                 tokenizer=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Tokenizer2,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Tokenizer2,
                 ),
                 text_encoder=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.TextEncoder2,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.TextEncoder2,
                 ),
                 loras=[],
                 skipped_layers=0,
             ),
             vae=VaeField(
                 vae=ModelInfo(
-                    key=model_key,
-                    submodel_type=SubModelType.Vae,
+                    model_name=model_name,
+                    base_model=base_model,
+                    model_type=model_type,
+                    submodel=SubModelType.Vae,
                 ),
             ),
         )

invokeai/app/invocations/strings.py

@@ -2,15 +2,16 @@
 
 import re
 
-from invokeai.app.services.shared.invocation_context import InvocationContext
-
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    InputField,
+    InvocationContext,
+    OutputField,
+    UIComponent,
     invocation,
     invocation_output,
 )
-from .fields import InputField, OutputField, UIComponent
 from .primitives import StringOutput
 
 

invokeai/app/invocations/t2i_adapter.py

@@ -1,21 +1,29 @@
 from typing import Union
 
-from pydantic import BaseModel, Field, field_validator, model_validator
+from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator
 
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
     invocation,
     invocation_output,
 )
 from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
-from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField, OutputField
+from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
-from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.shared.fields import FieldDescriptions
+from invokeai.backend.model_management.models.base import BaseModelType
 
 
 class T2IAdapterModelField(BaseModel):
-    key: str = Field(description="Model record key for the T2I-Adapter model")
+    model_name: str = Field(description="Name of the T2I-Adapter model")
+    base_model: BaseModelType = Field(description="Base model")
+
+    model_config = ConfigDict(protected_namespaces=())
 
 
 class T2IAdapterField(BaseModel):

invokeai/app/invocations/tiles.py

@@ -8,12 +8,16 @@ from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
     Classification,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
+    WithMetadata,
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.fields import ImageField, Input, InputField, OutputField, WithBoard, WithMetadata
-from invokeai.app.invocations.primitives import ImageOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.invocations.primitives import ImageField, ImageOutput
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.backend.tiles.tiles import (
     calc_tiles_even_split,
     calc_tiles_min_overlap,
@@ -232,7 +236,7 @@ BLEND_MODES = Literal["Linear", "Seam"]
     version="1.1.0",
     classification=Classification.Beta,
 )
-class MergeTilesToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
+class MergeTilesToImageInvocation(BaseInvocation, WithMetadata):
     """Merge multiple tile images into a single image."""
 
     # Inputs
@@ -264,7 +268,7 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         # existed in memory at an earlier point in the graph.
         tile_np_images: list[np.ndarray] = []
         for image in images:
-            pil_image = context.images.get_pil(image.image_name)
+            pil_image = context.services.images.get_pil_image(image.image_name)
             pil_image = pil_image.convert("RGB")
             tile_np_images.append(np.array(pil_image))
 
@@ -287,5 +291,18 @@ class MergeTilesToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
         # Convert into a PIL image and save
         pil_image = Image.fromarray(np_image)
 
-        image_dto = context.images.save(image=pil_image)
-        return ImageOutput.build(image_dto)
+        image_dto = context.services.images.create(
+            image=pil_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,
+        )
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )

invokeai/app/invocations/upscale.py

@@ -8,15 +8,13 @@ import torch
 from PIL import Image
 from pydantic import ConfigDict
 
-from invokeai.app.invocations.fields import ImageField
-from invokeai.app.invocations.primitives import ImageOutput
-from invokeai.app.services.shared.invocation_context import InvocationContext
+from invokeai.app.invocations.primitives import ImageField, ImageOutput
+from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
 from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
 from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
 from invokeai.backend.util.devices import choose_torch_device
 
-from .baseinvocation import BaseInvocation, invocation
-from .fields import InputField, WithBoard, WithMetadata
+from .baseinvocation import BaseInvocation, InputField, InvocationContext, WithMetadata, invocation
 
 # TODO: Populate this from disk?
 # TODO: Use model manager to load?
@@ -31,8 +29,8 @@ if choose_torch_device() == torch.device("mps"):
     from torch import mps
 
 
-@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.1")
-class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
+@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.0")
+class ESRGANInvocation(BaseInvocation, WithMetadata):
     """Upscales an image using RealESRGAN."""
 
     image: ImageField = InputField(description="The input image")
@@ -44,8 +42,8 @@ class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
     model_config = ConfigDict(protected_namespaces=())
 
     def invoke(self, context: InvocationContext) -> ImageOutput:
-        image = context.images.get_pil(self.image.image_name)
-        models_path = context.config.get().models_path
+        image = context.services.images.get_pil_image(self.image.image_name)
+        models_path = context.services.configuration.models_path
 
         rrdbnet_model = None
         netscale = None
@@ -89,7 +87,7 @@ class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
             netscale = 2
         else:
             msg = f"Invalid RealESRGAN model: {self.model_name}"
-            context.logger.error(msg)
+            context.services.logger.error(msg)
             raise ValueError(msg)
 
         esrgan_model_path = Path(f"core/upscaling/realesrgan/{self.model_name}")
@@ -112,6 +110,19 @@ class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
         if choose_torch_device() == torch.device("mps"):
             mps.empty_cache()
 
-        image_dto = context.images.save(image=pil_image)
+        image_dto = context.services.images.create(
+            image=pil_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,
+        )
 
-        return ImageOutput.build(image_dto)
+        return ImageOutput(
+            image=ImageField(image_name=image_dto.image_name),
+            width=image_dto.width,
+            height=image_dto.height,
+        )

invokeai/app/services/config/config_base.py

@@ -27,11 +27,11 @@ class InvokeAISettings(BaseSettings):
     """Runtime configuration settings in which default values are read from an omegaconf .yaml file."""
 
     initconf: ClassVar[Optional[DictConfig]] = None
-    argparse_groups: ClassVar[Dict[str, Any]] = {}
+    argparse_groups: ClassVar[Dict] = {}
 
     model_config = SettingsConfigDict(env_file_encoding="utf-8", arbitrary_types_allowed=True, case_sensitive=True)
 
-    def parse_args(self, argv: Optional[List[str]] = sys.argv[1:]) -> None:
+    def parse_args(self, argv: Optional[list] = sys.argv[1:]):
         """Call to parse command-line arguments."""
         parser = self.get_parser()
         opt, unknown_opts = parser.parse_known_args(argv)
@@ -68,7 +68,7 @@ class InvokeAISettings(BaseSettings):
         return OmegaConf.to_yaml(conf)
 
     @classmethod
-    def add_parser_arguments(cls, parser: ArgumentParser) -> None:
+    def add_parser_arguments(cls, parser):
         """Dynamically create arguments for a settings parser."""
         if "type" in get_type_hints(cls):
             settings_stanza = get_args(get_type_hints(cls)["type"])[0]
@@ -117,8 +117,7 @@ class InvokeAISettings(BaseSettings):
         """Return the category of a setting."""
         hints = get_type_hints(cls)
         if command_field in hints:
-            result: str = get_args(hints[command_field])[0]
-            return result
+            return get_args(hints[command_field])[0]
         else:
             return "Uncategorized"
 
@@ -159,7 +158,7 @@ class InvokeAISettings(BaseSettings):
         ]
 
     @classmethod
-    def add_field_argument(cls, command_parser, name: str, field, default_override=None) -> None:
+    def add_field_argument(cls, command_parser, name: str, field, default_override=None):
         """Add the argparse arguments for a setting parser."""
         field_type = get_type_hints(cls).get(name)
         default = (

invokeai/app/services/config/config_common.py

@@ -21,7 +21,7 @@ class PagingArgumentParser(argparse.ArgumentParser):
     It also supports reading defaults from an init file.
     """
 
-    def print_help(self, file=None) -> None:
+    def print_help(self, file=None):
         text = self.format_help()
         pydoc.pager(text)
 

invokeai/app/services/config/config_default.py

@@ -173,7 +173,7 @@ from __future__ import annotations
 
 import os
 from pathlib import Path
-from typing import Any, ClassVar, Dict, List, Literal, Optional
+from typing import Any, ClassVar, Dict, List, Literal, Optional, Union
 
 from omegaconf import DictConfig, OmegaConf
 from pydantic import Field
@@ -185,9 +185,7 @@ from .config_base import InvokeAISettings
 INIT_FILE = Path("invokeai.yaml")
 DB_FILE = Path("invokeai.db")
 LEGACY_INIT_FILE = Path("invokeai.init")
-DEFAULT_RAM_CACHE = 10.0
-DEFAULT_VRAM_CACHE = 0.25
-DEFAULT_CONVERT_CACHE = 20.0
+DEFAULT_MAX_VRAM = 0.5
 
 
 class Categories(object):
@@ -239,7 +237,6 @@ class InvokeAIAppConfig(InvokeAISettings):
     autoimport_dir      : Path = Field(default=Path('autoimport'), description='Path to a directory of models files to be imported on startup.', json_schema_extra=Categories.Paths)
     conf_path           : Path = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Paths)
     models_dir          : Path = Field(default=Path('models'), description='Path to the models directory', json_schema_extra=Categories.Paths)
-    convert_cache_dir   : Path = Field(default=Path('models/.cache'), description='Path to the converted models cache directory', json_schema_extra=Categories.Paths)
     legacy_conf_dir     : Path = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files', json_schema_extra=Categories.Paths)
     db_dir              : Path = Field(default=Path('databases'), description='Path to InvokeAI databases directory', json_schema_extra=Categories.Paths)
     outdir              : Path = Field(default=Path('outputs'), description='Default folder for output images', json_schema_extra=Categories.Paths)
@@ -263,10 +260,8 @@ class InvokeAIAppConfig(InvokeAISettings):
     version             : bool = Field(default=False, description="Show InvokeAI version and exit", json_schema_extra=Categories.Other)
 
     # CACHE
-    ram                 : float = Field(default=DEFAULT_RAM_CACHE, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
-    vram                : float = Field(default=DEFAULT_VRAM_CACHE, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
-    convert_cache       : float = Field(default=DEFAULT_CONVERT_CACHE, ge=0, description="Maximum size of on-disk converted models cache (GB)", json_schema_extra=Categories.ModelCache)
-
+    ram                 : float = Field(default=7.5, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
+    vram                : float = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number, GB)", json_schema_extra=Categories.ModelCache, )
     lazy_offload        : bool = Field(default=True, description="Keep models in VRAM until their space is needed", json_schema_extra=Categories.ModelCache, )
     log_memory_usage    : bool = Field(default=False, description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.", json_schema_extra=Categories.ModelCache)
 
@@ -409,11 +404,6 @@ class InvokeAIAppConfig(InvokeAISettings):
         """Path to the models directory."""
         return self._resolve(self.models_dir)
 
-    @property
-    def models_convert_cache_path(self) -> Path:
-        """Path to the converted cache models directory."""
-        return self._resolve(self.convert_cache_dir)
-
     @property
     def custom_nodes_path(self) -> Path:
         """Path to the custom nodes directory."""
@@ -443,20 +433,15 @@ class InvokeAIAppConfig(InvokeAISettings):
         return True
 
     @property
-    def ram_cache_size(self) -> float:
-        """Return the ram cache size using the legacy or modern setting (GB)."""
+    def ram_cache_size(self) -> Union[Literal["auto"], float]:
+        """Return the ram cache size using the legacy or modern setting."""
         return self.max_cache_size or self.ram
 
     @property
-    def vram_cache_size(self) -> float:
-        """Return the vram cache size using the legacy or modern setting (GB)."""
+    def vram_cache_size(self) -> Union[Literal["auto"], float]:
+        """Return the vram cache size using the legacy or modern setting."""
         return self.max_vram_cache_size or self.vram
 
-    @property
-    def convert_cache_size(self) -> float:
-        """Return the convert cache size on disk (GB)."""
-        return self.convert_cache
-
     @property
     def use_cpu(self) -> bool:
         """Return true if the device is set to CPU or the always_use_cpu flag is set."""

invokeai/app/services/download/download_base.py

@@ -260,16 +260,3 @@ class DownloadQueueServiceBase(ABC):
     def join(self) -> None:
         """Wait until all jobs are off the queue."""
         pass
-
-    @abstractmethod
-    def wait_for_job(self, job: DownloadJob, timeout: int = 0) -> DownloadJob:
-        """Wait until the indicated download job has reached a terminal state.
-
-        This will block until the indicated install job has completed,
-        been cancelled, or errored out.
-
-        :param job: The job to wait on.
-        :param timeout: Wait up to indicated number of seconds. Raise a TimeoutError if
-        the job hasn't completed within the indicated time.
-        """
-        pass

invokeai/app/services/download/download_default.py

@@ -4,11 +4,10 @@
 import os
 import re
 import threading
-import time
 import traceback
 from pathlib import Path
 from queue import Empty, PriorityQueue
-from typing import Any, Dict, List, Optional, Set
+from typing import Any, Dict, List, Optional
 
 import requests
 from pydantic.networks import AnyHttpUrl
@@ -49,12 +48,11 @@ class DownloadQueueService(DownloadQueueServiceBase):
         :param max_parallel_dl: Number of simultaneous downloads allowed [5].
         :param requests_session: Optional requests.sessions.Session object, for unit tests.
         """
-        self._jobs: Dict[int, DownloadJob] = {}
+        self._jobs = {}
         self._next_job_id = 0
-        self._queue: PriorityQueue[DownloadJob] = PriorityQueue()
+        self._queue = PriorityQueue()
         self._stop_event = threading.Event()
-        self._job_completed_event = threading.Event()
-        self._worker_pool: Set[threading.Thread] = set()
+        self._worker_pool = set()
         self._lock = threading.Lock()
         self._logger = InvokeAILogger.get_logger("DownloadQueueService")
         self._event_bus = event_bus
@@ -190,16 +188,6 @@ class DownloadQueueService(DownloadQueueServiceBase):
             if not job.in_terminal_state:
                 self.cancel_job(job)
 
-    def wait_for_job(self, job: DownloadJob, timeout: int = 0) -> DownloadJob:
-        """Block until the indicated job has reached terminal state, or when timeout limit reached."""
-        start = time.time()
-        while not job.in_terminal_state:
-            if self._job_completed_event.wait(timeout=0.25):  # in case we miss an event
-                self._job_completed_event.clear()
-            if timeout > 0 and time.time() - start > timeout:
-                raise TimeoutError("Timeout exceeded")
-        return job
-
     def _start_workers(self, max_workers: int) -> None:
         """Start the requested number of worker threads."""
         self._stop_event.clear()
@@ -235,7 +223,6 @@ class DownloadQueueService(DownloadQueueServiceBase):
 
             finally:
                 job.job_ended = get_iso_timestamp()
-                self._job_completed_event.set()  # signal a change to terminal state
                 self._queue.task_done()
         self._logger.debug(f"Download queue worker thread {threading.current_thread().name} exiting.")
 
@@ -420,11 +407,11 @@ class DownloadQueueService(DownloadQueueServiceBase):
 
 # Example on_progress event handler to display a TQDM status bar
 # Activate with:
-#   download_service.download(DownloadJob('http://foo.bar/baz', '/tmp', on_progress=TqdmProgress().update))
+#   download_service.download('http://foo.bar/baz', '/tmp', on_progress=TqdmProgress().job_update
 class TqdmProgress(object):
     """TQDM-based progress bar object to use in on_progress handlers."""
 
-    _bars: Dict[int, tqdm]  # type: ignore
+    _bars: Dict[int, tqdm]  # the tqdm object
     _last: Dict[int, int]  # last bytes downloaded
 
     def __init__(self) -> None:  # noqa D107

invokeai/app/services/events/events_base.py

@@ -11,7 +11,8 @@ from invokeai.app.services.session_queue.session_queue_common import (
     SessionQueueStatus,
 )
 from invokeai.app.util.misc import get_timestamp
-from invokeai.backend.model_manager import AnyModelConfig
+from invokeai.backend.model_management.model_manager import ModelInfo
+from invokeai.backend.model_management.models.base import BaseModelType, ModelType, SubModelType
 
 
 class EventServiceBase:
@@ -54,7 +55,7 @@ class EventServiceBase:
         queue_item_id: int,
         queue_batch_id: str,
         graph_execution_state_id: str,
-        node_id: str,
+        node: dict,
         source_node_id: str,
         progress_image: Optional[ProgressImage],
         step: int,
@@ -69,7 +70,7 @@ class EventServiceBase:
                 "queue_item_id": queue_item_id,
                 "queue_batch_id": queue_batch_id,
                 "graph_execution_state_id": graph_execution_state_id,
-                "node_id": node_id,
+                "node_id": node.get("id"),
                 "source_node_id": source_node_id,
                 "progress_image": progress_image.model_dump() if progress_image is not None else None,
                 "step": step,
@@ -170,7 +171,10 @@ class EventServiceBase:
         queue_item_id: int,
         queue_batch_id: str,
         graph_execution_state_id: str,
-        model_config: AnyModelConfig,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        submodel: SubModelType,
     ) -> None:
         """Emitted when a model is requested"""
         self.__emit_queue_event(
@@ -180,7 +184,10 @@ class EventServiceBase:
                 "queue_item_id": queue_item_id,
                 "queue_batch_id": queue_batch_id,
                 "graph_execution_state_id": graph_execution_state_id,
-                "model_config": model_config.model_dump(),
+                "model_name": model_name,
+                "base_model": base_model,
+                "model_type": model_type,
+                "submodel": submodel,
             },
         )
 
@@ -190,7 +197,11 @@ class EventServiceBase:
         queue_item_id: int,
         queue_batch_id: str,
         graph_execution_state_id: str,
-        model_config: AnyModelConfig,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        submodel: SubModelType,
+        model_info: ModelInfo,
     ) -> None:
         """Emitted when a model is correctly loaded (returns model info)"""
         self.__emit_queue_event(
@@ -200,7 +211,13 @@ class EventServiceBase:
                 "queue_item_id": queue_item_id,
                 "queue_batch_id": queue_batch_id,
                 "graph_execution_state_id": graph_execution_state_id,
-                "model_config": model_config.model_dump(),
+                "model_name": model_name,
+                "base_model": base_model,
+                "model_type": model_type,
+                "submodel": submodel,
+                "hash": model_info.hash,
+                "location": str(model_info.location),
+                "precision": str(model_info.precision),
             },
         )
 

invokeai/app/services/image_files/image_files_base.py

@@ -4,7 +4,7 @@ from typing import Optional
 
 from PIL.Image import Image as PILImageType
 
-from invokeai.app.invocations.fields import MetadataField
+from invokeai.app.invocations.baseinvocation import MetadataField
 from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 
 

invokeai/app/services/image_files/image_files_disk.py

@@ -7,7 +7,7 @@ from PIL import Image, PngImagePlugin
 from PIL.Image import Image as PILImageType
 from send2trash import send2trash
 
-from invokeai.app.invocations.fields import MetadataField
+from invokeai.app.invocations.baseinvocation import MetadataField
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
 from invokeai.app.util.thumbnails import get_thumbnail_name, make_thumbnail

invokeai/app/services/image_records/image_records_base.py

@@ -2,7 +2,7 @@ from abc import ABC, abstractmethod
 from datetime import datetime
 from typing import Optional
 
-from invokeai.app.invocations.fields import MetadataField
+from invokeai.app.invocations.metadata import MetadataField
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 
 from .image_records_common import ImageCategory, ImageRecord, ImageRecordChanges, ResourceOrigin

invokeai/app/services/image_records/image_records_sqlite.py

@@ -3,7 +3,7 @@ import threading
 from datetime import datetime
 from typing import Optional, Union, cast
 
-from invokeai.app.invocations.fields import MetadataField, MetadataFieldValidator
+from invokeai.app.invocations.baseinvocation import MetadataField, MetadataFieldValidator
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
 

invokeai/app/services/images/images_base.py

@@ -3,7 +3,7 @@ from typing import Callable, Optional
 
 from PIL.Image import Image as PILImageType
 
-from invokeai.app.invocations.fields import MetadataField
+from invokeai.app.invocations.baseinvocation import MetadataField
 from invokeai.app.services.image_records.image_records_common import (
     ImageCategory,
     ImageRecord,

invokeai/app/services/images/images_default.py

@@ -2,7 +2,7 @@ from typing import Optional
 
 from PIL.Image import Image as PILImageType
 
-from invokeai.app.invocations.fields import MetadataField
+from invokeai.app.invocations.baseinvocation import MetadataField
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.shared.pagination import OffsetPaginatedResults
 from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID

invokeai/app/services/invocation_cache/invocation_cache_memory.py

@@ -37,8 +37,7 @@ class MemoryInvocationCache(InvocationCacheBase):
         if self._max_cache_size == 0:
             return
         self._invoker.services.images.on_deleted(self._delete_by_match)
-        self._invoker.services.tensors.on_deleted(self._delete_by_match)
-        self._invoker.services.conditioning.on_deleted(self._delete_by_match)
+        self._invoker.services.latents.on_deleted(self._delete_by_match)
 
     def get(self, key: Union[int, str]) -> Optional[BaseInvocationOutput]:
         with self._lock:

invokeai/app/services/invocation_processor/invocation_processor_default.py

@@ -5,11 +5,11 @@ from threading import BoundedSemaphore, Event, Thread
 from typing import Optional
 
 import invokeai.backend.util.logging as logger
+from invokeai.app.invocations.baseinvocation import InvocationContext
 from invokeai.app.services.invocation_queue.invocation_queue_common import InvocationQueueItem
 from invokeai.app.services.invocation_stats.invocation_stats_common import (
     GESStatsNotFoundError,
 )
-from invokeai.app.services.shared.invocation_context import InvocationContextData, build_invocation_context
 from invokeai.app.util.profiler import Profiler
 
 from ..invoker import Invoker
@@ -24,10 +24,8 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
     __threadLimit: BoundedSemaphore
 
     def start(self, invoker: Invoker) -> None:
-        # LS - this will probably break
-        # but the idea is to enable multithreading up to the number of available
-        # GPUs. Nodes will block on model loading if no GPU is free.
-        self.__threadLimit = BoundedSemaphore(invoker.services.model_manager.gpu_count)
+        # if we do want multithreading at some point, we could make this configurable
+        self.__threadLimit = BoundedSemaphore(1)
         self.__invoker = invoker
         self.__stop_event = Event()
         self.__invoker_thread = Thread(
@@ -133,20 +131,16 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
                         # which handles a few things:
                         # - nodes that require a value, but get it only from a connection
                         # - referencing the invocation cache instead of executing the node
-                        context_data = InvocationContextData(
-                            invocation=invocation,
-                            session_id=graph_id,
-                            workflow=queue_item.workflow,
-                            source_node_id=source_node_id,
-                            queue_id=queue_item.session_queue_id,
-                            queue_item_id=queue_item.session_queue_item_id,
-                            batch_id=queue_item.session_queue_batch_id,
+                        outputs = invocation.invoke_internal(
+                            InvocationContext(
+                                services=self.__invoker.services,
+                                graph_execution_state_id=graph_execution_state.id,
+                                queue_item_id=queue_item.session_queue_item_id,
+                                queue_id=queue_item.session_queue_id,
+                                queue_batch_id=queue_item.session_queue_batch_id,
+                                workflow=queue_item.workflow,
+                            )
                         )
-                        context = build_invocation_context(
-                            services=self.__invoker.services,
-                            context_data=context_data,
-                        )
-                        outputs = invocation.invoke_internal(context=context, services=self.__invoker.services)
 
                         # Check queue to see if this is canceled, and skip if so
                         if self.__invoker.services.queue.is_canceled(graph_execution_state.id):

invokeai/app/services/invocation_services.py

@@ -3,15 +3,9 @@ from __future__ import annotations
 
 from typing import TYPE_CHECKING
 
-from invokeai.app.services.object_serializer.object_serializer_base import ObjectSerializerBase
-
 if TYPE_CHECKING:
     from logging import Logger
 
-    import torch
-
-    from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
-
     from .board_image_records.board_image_records_base import BoardImageRecordStorageBase
     from .board_images.board_images_base import BoardImagesServiceABC
     from .board_records.board_records_base import BoardRecordStorageBase
@@ -27,7 +21,10 @@ if TYPE_CHECKING:
     from .invocation_queue.invocation_queue_base import InvocationQueueABC
     from .invocation_stats.invocation_stats_base import InvocationStatsServiceBase
     from .item_storage.item_storage_base import ItemStorageABC
+    from .latents_storage.latents_storage_base import LatentsStorageBase
+    from .model_install import ModelInstallServiceBase
     from .model_manager.model_manager_base import ModelManagerServiceBase
+    from .model_records import ModelRecordServiceBase
     from .names.names_base import NameServiceBase
     from .session_processor.session_processor_base import SessionProcessorBase
     from .session_queue.session_queue_base import SessionQueueBase
@@ -39,6 +36,33 @@ if TYPE_CHECKING:
 class InvocationServices:
     """Services that can be used by invocations"""
 
+    # TODO: Just forward-declared everything due to circular dependencies. Fix structure.
+    board_images: "BoardImagesServiceABC"
+    board_image_record_storage: "BoardImageRecordStorageBase"
+    boards: "BoardServiceABC"
+    board_records: "BoardRecordStorageBase"
+    configuration: "InvokeAIAppConfig"
+    events: "EventServiceBase"
+    graph_execution_manager: "ItemStorageABC[GraphExecutionState]"
+    images: "ImageServiceABC"
+    image_records: "ImageRecordStorageBase"
+    image_files: "ImageFileStorageBase"
+    latents: "LatentsStorageBase"
+    logger: "Logger"
+    model_manager: "ModelManagerServiceBase"
+    model_records: "ModelRecordServiceBase"
+    download_queue: "DownloadQueueServiceBase"
+    model_install: "ModelInstallServiceBase"
+    processor: "InvocationProcessorABC"
+    performance_statistics: "InvocationStatsServiceBase"
+    queue: "InvocationQueueABC"
+    session_queue: "SessionQueueBase"
+    session_processor: "SessionProcessorBase"
+    invocation_cache: "InvocationCacheBase"
+    names: "NameServiceBase"
+    urls: "UrlServiceBase"
+    workflow_records: "WorkflowRecordsStorageBase"
+
     def __init__(
         self,
         board_images: "BoardImagesServiceABC",
@@ -51,9 +75,12 @@ class InvocationServices:
         images: "ImageServiceABC",
         image_files: "ImageFileStorageBase",
         image_records: "ImageRecordStorageBase",
+        latents: "LatentsStorageBase",
         logger: "Logger",
         model_manager: "ModelManagerServiceBase",
+        model_records: "ModelRecordServiceBase",
         download_queue: "DownloadQueueServiceBase",
+        model_install: "ModelInstallServiceBase",
         processor: "InvocationProcessorABC",
         performance_statistics: "InvocationStatsServiceBase",
         queue: "InvocationQueueABC",
@@ -63,8 +90,6 @@ class InvocationServices:
         names: "NameServiceBase",
         urls: "UrlServiceBase",
         workflow_records: "WorkflowRecordsStorageBase",
-        tensors: "ObjectSerializerBase[torch.Tensor]",
-        conditioning: "ObjectSerializerBase[ConditioningFieldData]",
     ):
         self.board_images = board_images
         self.board_image_records = board_image_records
@@ -76,9 +101,12 @@ class InvocationServices:
         self.images = images
         self.image_files = image_files
         self.image_records = image_records
+        self.latents = latents
         self.logger = logger
         self.model_manager = model_manager
+        self.model_records = model_records
         self.download_queue = download_queue
+        self.model_install = model_install
         self.processor = processor
         self.performance_statistics = performance_statistics
         self.queue = queue
@@ -88,5 +116,3 @@ class InvocationServices:
         self.names = names
         self.urls = urls
         self.workflow_records = workflow_records
-        self.tensors = tensors
-        self.conditioning = conditioning

invokeai/app/services/invocation_stats/invocation_stats_base.py

@@ -29,8 +29,8 @@ writes to the system log is stored in InvocationServices.performance_statistics.
 """
 
 from abc import ABC, abstractmethod
+from contextlib import AbstractContextManager
 from pathlib import Path
-from typing import Iterator
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation
 from invokeai.app.services.invocation_stats.invocation_stats_common import InvocationStatsSummary
@@ -40,17 +40,18 @@ class InvocationStatsServiceBase(ABC):
     "Abstract base class for recording node memory/time performance statistics"
 
     @abstractmethod
-    def __init__(self) -> None:
+    def __init__(self):
         """
         Initialize the InvocationStatsService and reset counters to zero
         """
+        pass
 
     @abstractmethod
     def collect_stats(
         self,
         invocation: BaseInvocation,
         graph_execution_state_id: str,
-    ) -> Iterator[None]:
+    ) -> AbstractContextManager:
         """
         Return a context object that will capture the statistics on the execution
         of invocaation. Use with: to place around the part of the code that executes the invocation.
@@ -60,7 +61,7 @@ class InvocationStatsServiceBase(ABC):
         pass
 
     @abstractmethod
-    def reset_stats(self, graph_execution_state_id: str) -> None:
+    def reset_stats(self, graph_execution_state_id: str):
         """
         Reset all statistics for the indicated graph.
         :param graph_execution_state_id: The id of the session whose stats to reset.
@@ -69,7 +70,7 @@ class InvocationStatsServiceBase(ABC):
         pass
 
     @abstractmethod
-    def log_stats(self, graph_execution_state_id: str) -> None:
+    def log_stats(self, graph_execution_state_id: str):
         """
         Write out the accumulated statistics to the log or somewhere else.
         :param graph_execution_state_id: The id of the session whose stats to log.

invokeai/app/services/invocation_stats/invocation_stats_default.py

@@ -2,7 +2,6 @@ import json
 import time
 from contextlib import contextmanager
 from pathlib import Path
-from typing import Iterator
 
 import psutil
 import torch
@@ -11,7 +10,7 @@ import invokeai.backend.util.logging as logger
 from invokeai.app.invocations.baseinvocation import BaseInvocation
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.item_storage.item_storage_common import ItemNotFoundError
-from invokeai.backend.model_manager.load.model_cache import CacheStats
+from invokeai.backend.model_management.model_cache import CacheStats
 
 from .invocation_stats_base import InvocationStatsServiceBase
 from .invocation_stats_common import (
@@ -42,10 +41,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
         self._invoker = invoker
 
     @contextmanager
-    def collect_stats(self, invocation: BaseInvocation, graph_execution_state_id: str) -> Iterator[None]:
-        # This is to handle case of the model manager not being initialized, which happens
-        # during some tests.
-        services = self._invoker.services
+    def collect_stats(self, invocation: BaseInvocation, graph_execution_state_id: str):
         if not self._stats.get(graph_execution_state_id):
             # First time we're seeing this graph_execution_state_id.
             self._stats[graph_execution_state_id] = GraphExecutionStats()
@@ -59,9 +55,8 @@ class InvocationStatsService(InvocationStatsServiceBase):
         start_ram = psutil.Process().memory_info().rss
         if torch.cuda.is_available():
             torch.cuda.reset_peak_memory_stats()
-
-        assert services.model_manager.load is not None
-        services.model_manager.load.ram_cache.stats = self._cache_stats[graph_execution_state_id]
+        if self._invoker.services.model_manager:
+            self._invoker.services.model_manager.collect_cache_stats(self._cache_stats[graph_execution_state_id])
 
         try:
             # Let the invocation run.
@@ -78,7 +73,7 @@ class InvocationStatsService(InvocationStatsServiceBase):
             )
             self._stats[graph_execution_state_id].add_node_execution_stats(node_stats)
 
-    def _prune_stale_stats(self) -> None:
+    def _prune_stale_stats(self):
         """Check all graphs being tracked and prune any that have completed/errored.
 
         This shouldn't be necessary, but we don't have totally robust upstream handling of graph completions/errors, so

invokeai/app/services/item_storage/item_storage_base.py

@@ -30,7 +30,7 @@ class ItemStorageABC(ABC, Generic[T]):
     @abstractmethod
     def set(self, item: T) -> None:
         """
-        Sets the item.
+        Sets the item. The id will be extracted based on id_field.
         :param item: the item to set
         """
         pass

invokeai/app/services/latents_storage/latents_storage_base.py

@@ -0,0 +1,45 @@
+# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
+
+from abc import ABC, abstractmethod
+from typing import Callable
+
+import torch
+
+
+class LatentsStorageBase(ABC):
+    """Responsible for storing and retrieving latents."""
+
+    _on_changed_callbacks: list[Callable[[torch.Tensor], None]]
+    _on_deleted_callbacks: list[Callable[[str], None]]
+
+    def __init__(self) -> None:
+        self._on_changed_callbacks = []
+        self._on_deleted_callbacks = []
+
+    @abstractmethod
+    def get(self, name: str) -> torch.Tensor:
+        pass
+
+    @abstractmethod
+    def save(self, name: str, data: torch.Tensor) -> None:
+        pass
+
+    @abstractmethod
+    def delete(self, name: str) -> None:
+        pass
+
+    def on_changed(self, on_changed: Callable[[torch.Tensor], None]) -> None:
+        """Register a callback for when an item is changed"""
+        self._on_changed_callbacks.append(on_changed)
+
+    def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
+        """Register a callback for when an item is deleted"""
+        self._on_deleted_callbacks.append(on_deleted)
+
+    def _on_changed(self, item: torch.Tensor) -> None:
+        for callback in self._on_changed_callbacks:
+            callback(item)
+
+    def _on_deleted(self, item_id: str) -> None:
+        for callback in self._on_deleted_callbacks:
+            callback(item_id)

invokeai/app/services/latents_storage/latents_storage_disk.py

@@ -0,0 +1,58 @@
+# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
+
+from pathlib import Path
+from typing import Union
+
+import torch
+
+from invokeai.app.services.invoker import Invoker
+
+from .latents_storage_base import LatentsStorageBase
+
+
+class DiskLatentsStorage(LatentsStorageBase):
+    """Stores latents in a folder on disk without caching"""
+
+    __output_folder: Path
+
+    def __init__(self, output_folder: Union[str, Path]):
+        self.__output_folder = output_folder if isinstance(output_folder, Path) else Path(output_folder)
+        self.__output_folder.mkdir(parents=True, exist_ok=True)
+
+    def start(self, invoker: Invoker) -> None:
+        self._invoker = invoker
+        self._delete_all_latents()
+
+    def get(self, name: str) -> torch.Tensor:
+        latent_path = self.get_path(name)
+        return torch.load(latent_path)
+
+    def save(self, name: str, data: torch.Tensor) -> None:
+        self.__output_folder.mkdir(parents=True, exist_ok=True)
+        latent_path = self.get_path(name)
+        torch.save(data, latent_path)
+
+    def delete(self, name: str) -> None:
+        latent_path = self.get_path(name)
+        latent_path.unlink()
+
+    def get_path(self, name: str) -> Path:
+        return self.__output_folder / name
+
+    def _delete_all_latents(self) -> None:
+        """
+        Deletes all latents from disk.
+        Must be called after we have access to `self._invoker` (e.g. in `start()`).
+        """
+        deleted_latents_count = 0
+        freed_space = 0
+        for latents_file in Path(self.__output_folder).glob("*"):
+            if latents_file.is_file():
+                freed_space += latents_file.stat().st_size
+                deleted_latents_count += 1
+                latents_file.unlink()
+        if deleted_latents_count > 0:
+            freed_space_in_mb = round(freed_space / 1024 / 1024, 2)
+            self._invoker.services.logger.info(
+                f"Deleted {deleted_latents_count} latents files (freed {freed_space_in_mb}MB)"
+            )

invokeai/app/services/latents_storage/latents_storage_forward_cache.py

@@ -0,0 +1,68 @@
+# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
+
+from queue import Queue
+from typing import Dict, Optional
+
+import torch
+
+from invokeai.app.services.invoker import Invoker
+
+from .latents_storage_base import LatentsStorageBase
+
+
+class ForwardCacheLatentsStorage(LatentsStorageBase):
+    """Caches the latest N latents in memory, writing-thorugh to and reading from underlying storage"""
+
+    __cache: Dict[str, torch.Tensor]
+    __cache_ids: Queue
+    __max_cache_size: int
+    __underlying_storage: LatentsStorageBase
+
+    def __init__(self, underlying_storage: LatentsStorageBase, max_cache_size: int = 20):
+        super().__init__()
+        self.__underlying_storage = underlying_storage
+        self.__cache = {}
+        self.__cache_ids = Queue()
+        self.__max_cache_size = max_cache_size
+
+    def start(self, invoker: Invoker) -> None:
+        self._invoker = invoker
+        start_op = getattr(self.__underlying_storage, "start", None)
+        if callable(start_op):
+            start_op(invoker)
+
+    def stop(self, invoker: Invoker) -> None:
+        self._invoker = invoker
+        stop_op = getattr(self.__underlying_storage, "stop", None)
+        if callable(stop_op):
+            stop_op(invoker)
+
+    def get(self, name: str) -> torch.Tensor:
+        cache_item = self.__get_cache(name)
+        if cache_item is not None:
+            return cache_item
+
+        latent = self.__underlying_storage.get(name)
+        self.__set_cache(name, latent)
+        return latent
+
+    def save(self, name: str, data: torch.Tensor) -> None:
+        self.__underlying_storage.save(name, data)
+        self.__set_cache(name, data)
+        self._on_changed(data)
+
+    def delete(self, name: str) -> None:
+        self.__underlying_storage.delete(name)
+        if name in self.__cache:
+            del self.__cache[name]
+        self._on_deleted(name)
+
+    def __get_cache(self, name: str) -> Optional[torch.Tensor]:
+        return None if name not in self.__cache else self.__cache[name]
+
+    def __set_cache(self, name: str, data: torch.Tensor):
+        if name not in self.__cache:
+            self.__cache[name] = data
+            self.__cache_ids.put(name)
+            if self.__cache_ids.qsize() > self.__max_cache_size:
+                self.__cache.pop(self.__cache_ids.get())

invokeai/app/services/model_install/model_install_base.py

@@ -14,13 +14,11 @@ from typing_extensions import Annotated
 
 from invokeai.app.services.config import InvokeAIAppConfig
 from invokeai.app.services.download import DownloadJob, DownloadQueueServiceBase
-from invokeai.app.services.events.events_base import EventServiceBase
+from invokeai.app.services.events import EventServiceBase
 from invokeai.app.services.invoker import Invoker
 from invokeai.app.services.model_records import ModelRecordServiceBase
 from invokeai.backend.model_manager import AnyModelConfig, ModelRepoVariant
-from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
-
-from ..model_metadata import ModelMetadataStoreBase
+from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata, ModelMetadataStore
 
 
 class InstallStatus(str, Enum):
@@ -129,8 +127,8 @@ class HFModelSource(StringLikeSource):
     def __str__(self) -> str:
         """Return string version of repoid when string rep needed."""
         base: str = self.repo_id
-        base += f":{self.variant or ''}"
         base += f":{self.subfolder}" if self.subfolder else ""
+        base += f" ({self.variant})" if self.variant else ""
         return base
 
 
@@ -245,7 +243,7 @@ class ModelInstallServiceBase(ABC):
         app_config: InvokeAIAppConfig,
         record_store: ModelRecordServiceBase,
         download_queue: DownloadQueueServiceBase,
-        metadata_store: ModelMetadataStoreBase,
+        metadata_store: ModelMetadataStore,
         event_bus: Optional["EventServiceBase"] = None,
     ):
         """
@@ -326,43 +324,6 @@ class ModelInstallServiceBase(ABC):
         :returns id: The string ID of the registered model.
         """
 
-    @abstractmethod
-    def heuristic_import(
-        self,
-        source: str,
-        config: Optional[Dict[str, Any]] = None,
-        access_token: Optional[str] = None,
-    ) -> ModelInstallJob:
-        r"""Install the indicated model using heuristics to interpret user intentions.
-
-        :param source: String source
-        :param config: Optional dict. Any fields in this dict
-         will override corresponding autoassigned probe fields in the
-         model's config record as described in `import_model()`.
-        :param access_token: Optional access token for remote sources.
-
-        The source can be:
-        1. A local file path in posix() format (`/foo/bar` or `C:\foo\bar`)
-        2. An http or https URL (`https://foo.bar/foo`)
-        3. A HuggingFace repo_id (`foo/bar`, `foo/bar:fp16`, `foo/bar:fp16:vae`)
-
-        We extend the HuggingFace repo_id syntax to include the variant and the
-        subfolder or path. The following are acceptable alternatives:
-            stabilityai/stable-diffusion-v4
-            stabilityai/stable-diffusion-v4:fp16
-            stabilityai/stable-diffusion-v4:fp16:vae
-            stabilityai/stable-diffusion-v4::/checkpoints/sd4.safetensors
-            stabilityai/stable-diffusion-v4:onnx:vae
-
-        Because a local file path can look like a huggingface repo_id, the logic
-        first checks whether the path exists on disk, and if not, it is treated as
-        a parseable huggingface repo.
-
-        The previous support for recursing into a local folder and loading all model-like files
-        has been removed.
-        """
-        pass
-
     @abstractmethod
     def import_model(
         self,
@@ -424,18 +385,6 @@ class ModelInstallServiceBase(ABC):
     def cancel_job(self, job: ModelInstallJob) -> None:
         """Cancel the indicated job."""
 
-    @abstractmethod
-    def wait_for_job(self, job: ModelInstallJob, timeout: int = 0) -> ModelInstallJob:
-        """Wait for the indicated job to reach a terminal state.
-
-        This will block until the indicated install job has completed,
-        been cancelled, or errored out.
-
-        :param job: The job to wait on.
-        :param timeout: Wait up to indicated number of seconds. Raise a TimeoutError if
-        the job hasn't completed within the indicated time.
-        """
-
     @abstractmethod
     def wait_for_installs(self, timeout: int = 0) -> List[ModelInstallJob]:
         """
@@ -445,8 +394,7 @@ class ModelInstallServiceBase(ABC):
         completed, been cancelled, or errored out.
 
         :param timeout: Wait up to indicated number of seconds. Raise an Exception('timeout') if
-        installs do not complete within the indicated time. A timeout of zero (the default)
-        will block indefinitely until the installs complete.
+        installs do not complete within the indicated time.
         """
 
     @abstractmethod
@@ -462,22 +410,3 @@ class ModelInstallServiceBase(ABC):
     @abstractmethod
     def sync_to_config(self) -> None:
         """Synchronize models on disk to those in the model record database."""
-
-    @abstractmethod
-    def download_and_cache(self, source: Union[str, AnyHttpUrl], access_token: Optional[str] = None) -> Path:
-        """
-        Download the model file located at source to the models cache and return its Path.
-
-        :param source: A Url or a string that can be converted into one.
-        :param access_token: Optional access token to access restricted resources.
-
-        The model file will be downloaded into the system-wide model cache
-        (`models/.cache`) if it isn't already there. Note that the model cache
-        is periodically cleared of infrequently-used entries when the model
-        converter runs.
-
-        Note that this doesn't automaticallly install or register the model, but is
-        intended for use by nodes that need access to models that aren't directly
-        supported by InvokeAI. The downloading process takes advantage of the download queue
-        to avoid interrupting other operations.
-        """

invokeai/app/services/model_install/model_install_default.py

@@ -17,10 +17,10 @@ from pydantic.networks import AnyHttpUrl
 from requests import Session
 
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.app.services.download import DownloadJob, DownloadQueueServiceBase, TqdmProgress
+from invokeai.app.services.download import DownloadJob, DownloadQueueServiceBase
 from invokeai.app.services.events.events_base import EventServiceBase
 from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.model_records import DuplicateModelException, ModelRecordServiceBase
+from invokeai.app.services.model_records import DuplicateModelException, ModelRecordServiceBase, ModelRecordServiceSQL
 from invokeai.backend.model_manager.config import (
     AnyModelConfig,
     BaseModelType,
@@ -33,6 +33,7 @@ from invokeai.backend.model_manager.metadata import (
     AnyModelRepoMetadata,
     CivitaiMetadataFetch,
     HuggingFaceMetadataFetch,
+    ModelMetadataStore,
     ModelMetadataWithFiles,
     RemoteModelFile,
 )
@@ -49,7 +50,6 @@ from .model_install_base import (
     ModelInstallJob,
     ModelInstallServiceBase,
     ModelSource,
-    StringLikeSource,
     URLModelSource,
 )
 
@@ -64,6 +64,7 @@ class ModelInstallService(ModelInstallServiceBase):
         app_config: InvokeAIAppConfig,
         record_store: ModelRecordServiceBase,
         download_queue: DownloadQueueServiceBase,
+        metadata_store: Optional[ModelMetadataStore] = None,
         event_bus: Optional[EventServiceBase] = None,
         session: Optional[Session] = None,
     ):
@@ -85,13 +86,19 @@ class ModelInstallService(ModelInstallServiceBase):
         self._lock = threading.Lock()
         self._stop_event = threading.Event()
         self._downloads_changed_event = threading.Event()
-        self._install_completed_event = threading.Event()
         self._download_queue = download_queue
         self._download_cache: Dict[AnyHttpUrl, ModelInstallJob] = {}
         self._running = False
         self._session = session
         self._next_job_id = 0
-        self._metadata_store = record_store.metadata_store  # for convenience
+        # There may not necessarily be a metadata store initialized
+        # so we create one and initialize it with the same sql database
+        # used by the record store service.
+        if metadata_store:
+            self._metadata_store = metadata_store
+        else:
+            assert isinstance(record_store, ModelRecordServiceSQL)
+            self._metadata_store = ModelMetadataStore(record_store.db)
 
     @property
     def app_config(self) -> InvokeAIAppConfig:  # noqa D102
@@ -138,7 +145,7 @@ class ModelInstallService(ModelInstallServiceBase):
     ) -> str:  # noqa D102
         model_path = Path(model_path)
         config = config or {}
-        if not config.get("source"):
+        if config.get("source") is None:
             config["source"] = model_path.resolve().as_posix()
         return self._register(model_path, config)
 
@@ -149,14 +156,12 @@ class ModelInstallService(ModelInstallServiceBase):
     ) -> str:  # noqa D102
         model_path = Path(model_path)
         config = config or {}
-        if not config.get("source"):
+        if config.get("source") is None:
             config["source"] = model_path.resolve().as_posix()
 
         info: AnyModelConfig = self._probe_model(Path(model_path), config)
-        old_hash = info.current_hash
-        dest_path = (
-            self.app_config.models_path / info.base.value / info.type.value / (config.get("name") or model_path.name)
-        )
+        old_hash = info.original_hash
+        dest_path = self.app_config.models_path / info.base.value / info.type.value / model_path.name
         try:
             new_path = self._copy_model(model_path, dest_path)
         except FileExistsError as excp:
@@ -172,40 +177,7 @@ class ModelInstallService(ModelInstallServiceBase):
             info,
         )
 
-    def heuristic_import(
-        self,
-        source: str,
-        config: Optional[Dict[str, Any]] = None,
-        access_token: Optional[str] = None,
-    ) -> 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))
-        elif match := re.match(hf_repoid_re, source):
-            source_obj = HFModelSource(
-                repo_id=match.group(1),
-                variant=match.group(2) if match.group(2) else None,  # pass None rather than ''
-                subfolder=Path(match.group(3)) if match.group(3) else None,
-                access_token=access_token,
-            )
-        elif re.match(r"^https?://[^/]+", source):
-            source_obj = URLModelSource(
-                url=AnyHttpUrl(source),
-                access_token=access_token,
-            )
-        else:
-            raise ValueError(f"Unsupported model source: '{source}'")
-        return self.import_model(source_obj, config)
-
     def import_model(self, source: ModelSource, config: Optional[Dict[str, Any]] = None) -> ModelInstallJob:  # noqa D102
-        similar_jobs = [x for x in self.list_jobs() if x.source == source and not x.in_terminal_state]
-        if similar_jobs:
-            self._logger.warning(f"There is already an active install job for {source}. Not enqueuing.")
-            return similar_jobs[0]
-
         if isinstance(source, LocalModelSource):
             install_job = self._import_local_model(source, config)
             self._install_queue.put(install_job)  # synchronously install
@@ -235,25 +207,14 @@ class ModelInstallService(ModelInstallServiceBase):
         assert isinstance(jobs[0], ModelInstallJob)
         return jobs[0]
 
-    def wait_for_job(self, job: ModelInstallJob, timeout: int = 0) -> ModelInstallJob:
-        """Block until the indicated job has reached terminal state, or when timeout limit reached."""
-        start = time.time()
-        while not job.in_terminal_state:
-            if self._install_completed_event.wait(timeout=5):  # in case we miss an event
-                self._install_completed_event.clear()
-            if timeout > 0 and time.time() - start > timeout:
-                raise TimeoutError("Timeout exceeded")
-        return job
-
-    # TODO: Better name? Maybe wait_for_jobs()? Maybe too easily confused with above
     def wait_for_installs(self, timeout: int = 0) -> List[ModelInstallJob]:  # noqa D102
         """Block until all installation jobs are done."""
         start = time.time()
         while len(self._download_cache) > 0:
-            if self._downloads_changed_event.wait(timeout=0.25):  # in case we miss an event
+            if self._downloads_changed_event.wait(timeout=5):  # in case we miss an event
                 self._downloads_changed_event.clear()
             if timeout > 0 and time.time() - start > timeout:
-                raise TimeoutError("Timeout exceeded")
+                raise Exception("Timeout exceeded")
         self._install_queue.join()
         return self._install_jobs
 
@@ -307,38 +268,6 @@ class ModelInstallService(ModelInstallServiceBase):
             path.unlink()
         self.unregister(key)
 
-    def download_and_cache(
-        self,
-        source: Union[str, AnyHttpUrl],
-        access_token: Optional[str] = None,
-        timeout: int = 0,
-    ) -> Path:
-        """Download the model file located at source to the models cache and return its Path."""
-        model_hash = sha256(str(source).encode("utf-8")).hexdigest()[0:32]
-        model_path = self._app_config.models_convert_cache_path / model_hash
-
-        # We expect the cache directory to contain one and only one downloaded file.
-        # We don't know the file's name in advance, as it is set by the download
-        # content-disposition header.
-        if model_path.exists():
-            contents = [x for x in model_path.iterdir() if x.is_file()]
-            if len(contents) > 0:
-                return contents[0]
-
-        model_path.mkdir(parents=True, exist_ok=True)
-        job = self._download_queue.download(
-            source=AnyHttpUrl(str(source)),
-            dest=model_path,
-            access_token=access_token,
-            on_progress=TqdmProgress().update,
-        )
-        self._download_queue.wait_for_job(job, timeout)
-        if job.complete:
-            assert job.download_path is not None
-            return job.download_path
-        else:
-            raise Exception(job.error)
-
     # --------------------------------------------------------------------------------------------
     # Internal functions that manage the installer threads
     # --------------------------------------------------------------------------------------------
@@ -371,7 +300,6 @@ class ModelInstallService(ModelInstallServiceBase):
                     job.total_bytes = self._stat_size(job.local_path)
                     job.bytes = job.total_bytes
                     self._signal_job_running(job)
-                    job.config_in["source"] = str(job.source)
                     if job.inplace:
                         key = self.register_path(job.local_path, job.config_in)
                     else:
@@ -402,7 +330,6 @@ class ModelInstallService(ModelInstallServiceBase):
                 # if this is an install of a remote file, then clean up the temporary directory
                 if job._install_tmpdir is not None:
                     rmtree(job._install_tmpdir)
-                self._install_completed_event.set()
                 self._install_queue.task_done()
 
         self._logger.info("Install thread exiting")
@@ -562,10 +489,10 @@ class ModelInstallService(ModelInstallServiceBase):
         return id
 
     @staticmethod
-    def _guess_variant() -> Optional[ModelRepoVariant]:
+    def _guess_variant() -> ModelRepoVariant:
         """Guess the best HuggingFace variant type to download."""
         precision = choose_precision(choose_torch_device())
-        return ModelRepoVariant.FP16 if precision == "float16" else None
+        return ModelRepoVariant.FP16 if precision == "float16" else ModelRepoVariant.DEFAULT
 
     def _import_local_model(self, source: LocalModelSource, config: Optional[Dict[str, Any]]) -> ModelInstallJob:
         return ModelInstallJob(
@@ -590,7 +517,7 @@ class ModelInstallService(ModelInstallServiceBase):
         if not source.access_token:
             self._logger.info("No HuggingFace access token present; some models may not be downloadable.")
 
-        metadata = HuggingFaceMetadataFetch(self._session).from_id(source.repo_id, source.variant)
+        metadata = HuggingFaceMetadataFetch(self._session).from_id(source.repo_id)
         assert isinstance(metadata, ModelMetadataWithFiles)
         remote_files = metadata.download_urls(
             variant=source.variant or self._guess_variant(),
@@ -638,8 +565,6 @@ class ModelInstallService(ModelInstallServiceBase):
         # TODO: Replace with tempfile.tmpdir() when multithreading is cleaned up.
         # Currently the tmpdir isn't automatically removed at exit because it is
         # being held in a daemon thread.
-        if len(remote_files) == 0:
-            raise ValueError(f"{source}: No downloadable files found")
         tmpdir = Path(
             mkdtemp(
                 dir=self._app_config.models_path,
@@ -655,16 +580,6 @@ class ModelInstallService(ModelInstallServiceBase):
             bytes=0,
             total_bytes=0,
         )
-        # In the event that there is a subfolder specified in the source,
-        # we need to remove it from the destination path in order to avoid
-        # creating unwanted subfolders
-        if hasattr(source, "subfolder") and source.subfolder:
-            root = Path(remote_files[0].path.parts[0])
-            subfolder = root / source.subfolder
-        else:
-            root = Path(".")
-            subfolder = Path(".")
-
         # we remember the path up to the top of the tmpdir so that it may be
         # removed safely at the end of the install process.
         install_job._install_tmpdir = tmpdir
@@ -674,7 +589,7 @@ class ModelInstallService(ModelInstallServiceBase):
         self._logger.debug(f"remote_files={remote_files}")
         for model_file in remote_files:
             url = model_file.url
-            path = root / model_file.path.relative_to(subfolder)
+            path = model_file.path
             self._logger.info(f"Downloading {url} => {path}")
             install_job.total_bytes += model_file.size
             assert hasattr(source, "access_token")

invokeai/app/services/model_load/__init__.py

@@ -1,6 +0,0 @@
-"""Initialization file for model load service module."""
-
-from .model_load_base import ModelLoadServiceBase
-from .model_load_default import ModelLoadService
-
-__all__ = ["ModelLoadServiceBase", "ModelLoadService"]

invokeai/app/services/model_load/model_load_base.py

@@ -1,45 +0,0 @@
-# Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Team
-"""Base class for model loader."""
-
-from abc import ABC, abstractmethod
-from typing import Optional
-
-from invokeai.app.services.shared.invocation_context import InvocationContextData
-from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
-from invokeai.backend.model_manager.load import LoadedModel
-from invokeai.backend.model_manager.load.convert_cache import ModelConvertCacheBase
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
-
-
-class ModelLoadServiceBase(ABC):
-    """Wrapper around AnyModelLoader."""
-
-    @abstractmethod
-    def load_model(
-        self,
-        model_config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-        context_data: Optional[InvocationContextData] = None,
-    ) -> LoadedModel:
-        """
-        Given a model's configuration, load it and return the LoadedModel object.
-
-        :param model_config: Model configuration record (as returned by ModelRecordBase.get_model())
-        :param submodel: For main (pipeline models), the submodel to fetch.
-        :param context_data: Invocation context data used for event reporting
-        """
-
-    @property
-    @abstractmethod
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
-        """Return the RAM cache used by this loader."""
-
-    @property
-    @abstractmethod
-    def convert_cache(self) -> ModelConvertCacheBase:
-        """Return the checkpoint convert cache used by this loader."""
-
-    @property
-    @abstractmethod
-    def gpu_count(self) -> int:
-        """Return the number of GPUs we are configured to use."""

invokeai/app/services/model_load/model_load_default.py

@@ -1,122 +0,0 @@
-# Copyright (c) 2024 Lincoln D. Stein and the InvokeAI Team
-"""Implementation of model loader service."""
-
-from typing import Optional, Type
-
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.app.services.invocation_processor.invocation_processor_common import CanceledException
-from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.shared.invocation_context import InvocationContextData
-from invokeai.backend.model_manager import AnyModel, AnyModelConfig, SubModelType
-from invokeai.backend.model_manager.load import (
-    LoadedModel,
-    ModelLoaderRegistry,
-    ModelLoaderRegistryBase,
-)
-from invokeai.backend.model_manager.load.convert_cache import ModelConvertCacheBase
-from invokeai.backend.model_manager.load.model_cache.model_cache_base import ModelCacheBase
-from invokeai.backend.util.logging import InvokeAILogger
-
-from .model_load_base import ModelLoadServiceBase
-
-
-class ModelLoadService(ModelLoadServiceBase):
-    """Wrapper around ModelLoaderRegistry."""
-
-    def __init__(
-        self,
-        app_config: InvokeAIAppConfig,
-        ram_cache: ModelCacheBase[AnyModel],
-        convert_cache: ModelConvertCacheBase,
-        registry: Optional[Type[ModelLoaderRegistryBase]] = ModelLoaderRegistry,
-    ):
-        """Initialize the model load service."""
-        logger = InvokeAILogger.get_logger(self.__class__.__name__)
-        logger.setLevel(app_config.log_level.upper())
-        self._logger = logger
-        self._app_config = app_config
-        self._ram_cache = ram_cache
-        self._convert_cache = convert_cache
-        self._registry = registry
-
-    def start(self, invoker: Invoker) -> None:
-        """Start the service."""
-        self._invoker = invoker
-
-    @property
-    def ram_cache(self) -> ModelCacheBase[AnyModel]:
-        """Return the RAM cache used by this loader."""
-        return self._ram_cache
-
-    @property
-    def gpu_count(self) -> int:
-        """Return the number of GPUs available for our uses."""
-        return len(self._ram_cache.execution_devices)
-
-    @property
-    def convert_cache(self) -> ModelConvertCacheBase:
-        """Return the checkpoint convert cache used by this loader."""
-        return self._convert_cache
-
-    def load_model(
-        self,
-        model_config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-        context_data: Optional[InvocationContextData] = None,
-    ) -> LoadedModel:
-        """
-        Given a model's configuration, load it and return the LoadedModel object.
-
-        :param model_config: Model configuration record (as returned by ModelRecordBase.get_model())
-        :param submodel: For main (pipeline models), the submodel to fetch.
-        :param context: Invocation context used for event reporting
-        """
-        if context_data:
-            self._emit_load_event(
-                context_data=context_data,
-                model_config=model_config,
-            )
-
-        implementation, model_config, submodel_type = self._registry.get_implementation(model_config, submodel_type)  # type: ignore
-        loaded_model: LoadedModel = implementation(
-            app_config=self._app_config,
-            logger=self._logger,
-            ram_cache=self._ram_cache,
-            convert_cache=self._convert_cache,
-        ).load_model(model_config, submodel_type)
-
-        if context_data:
-            self._emit_load_event(
-                context_data=context_data,
-                model_config=model_config,
-                loaded=True,
-            )
-        return loaded_model
-
-    def _emit_load_event(
-        self,
-        context_data: InvocationContextData,
-        model_config: AnyModelConfig,
-        loaded: Optional[bool] = False,
-    ) -> None:
-        if not self._invoker:
-            return
-        if self._invoker.services.queue.is_canceled(context_data.session_id):
-            raise CanceledException()
-
-        if not loaded:
-            self._invoker.services.events.emit_model_load_started(
-                queue_id=context_data.queue_id,
-                queue_item_id=context_data.queue_item_id,
-                queue_batch_id=context_data.batch_id,
-                graph_execution_state_id=context_data.session_id,
-                model_config=model_config,
-            )
-        else:
-            self._invoker.services.events.emit_model_load_completed(
-                queue_id=context_data.queue_id,
-                queue_item_id=context_data.queue_item_id,
-                queue_batch_id=context_data.batch_id,
-                graph_execution_state_id=context_data.session_id,
-                model_config=model_config,
-            )

invokeai/app/services/model_manager/__init__.py

@@ -1,17 +1 @@
-"""Initialization file for model manager service."""
-
-from invokeai.backend.model_manager import AnyModel, AnyModelConfig, BaseModelType, ModelType, SubModelType
-from invokeai.backend.model_manager.load import LoadedModel
-
-from .model_manager_default import ModelManagerService, ModelManagerServiceBase
-
-__all__ = [
-    "ModelManagerServiceBase",
-    "ModelManagerService",
-    "AnyModel",
-    "AnyModelConfig",
-    "BaseModelType",
-    "ModelType",
-    "SubModelType",
-    "LoadedModel",
-]
+from .model_manager_default import ModelManagerService  # noqa F401

invokeai/app/services/model_manager/model_manager_base.py

@@ -1,105 +1,286 @@
 # Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
 
+from __future__ import annotations
+
 from abc import ABC, abstractmethod
-from typing import Optional
+from logging import Logger
+from pathlib import Path
+from typing import TYPE_CHECKING, Callable, List, Literal, Optional, Tuple, Union
 
-from typing_extensions import Self
+from pydantic import Field
 
-from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.shared.invocation_context import InvocationContextData
-from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelType, SubModelType
-from invokeai.backend.model_manager.load.load_base import LoadedModel
+from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.backend.model_management import (
+    AddModelResult,
+    BaseModelType,
+    MergeInterpolationMethod,
+    ModelInfo,
+    ModelType,
+    SchedulerPredictionType,
+    SubModelType,
+)
+from invokeai.backend.model_management.model_cache import CacheStats
 
-from ..config import InvokeAIAppConfig
-from ..download import DownloadQueueServiceBase
-from ..events.events_base import EventServiceBase
-from ..model_install import ModelInstallServiceBase
-from ..model_load import ModelLoadServiceBase
-from ..model_records import ModelRecordServiceBase
-from ..shared.sqlite.sqlite_database import SqliteDatabase
+if TYPE_CHECKING:
+    from invokeai.app.invocations.baseinvocation import BaseInvocation, InvocationContext
 
 
 class ModelManagerServiceBase(ABC):
-    """Abstract base class for the model manager service."""
+    """Responsible for managing models on disk and in memory"""
 
-    # attributes:
-    # store: ModelRecordServiceBase = Field(description="An instance of the model record configuration service.")
-    # install: ModelInstallServiceBase = Field(description="An instance of the model install service.")
-    # load: ModelLoadServiceBase = Field(description="An instance of the model load service.")
-
-    @classmethod
     @abstractmethod
-    def build_model_manager(
-        cls,
-        app_config: InvokeAIAppConfig,
-        db: SqliteDatabase,
-        download_queue: DownloadQueueServiceBase,
-        events: EventServiceBase,
-    ) -> Self:
+    def __init__(
+        self,
+        config: InvokeAIAppConfig,
+        logger: Logger,
+    ):
         """
-        Construct the model manager service instance.
-
-        Use it rather than the __init__ constructor. This class
-        method simplifies the construction considerably.
+        Initialize with the path to the models.yaml config file.
+        Optional parameters are the torch device type, precision, max_models,
+        and sequential_offload boolean. Note that the default device
+        type and precision are set up for a CUDA system running at half precision.
         """
         pass
 
-    @property
     @abstractmethod
-    def store(self) -> ModelRecordServiceBase:
-        """Return the ModelRecordServiceBase used to store and retrieve configuration records."""
-        pass
-
-    @property
-    @abstractmethod
-    def load(self) -> ModelLoadServiceBase:
-        """Return the ModelLoadServiceBase used to load models from their configuration records."""
-        pass
-
-    @property
-    @abstractmethod
-    def install(self) -> ModelInstallServiceBase:
-        """Return the ModelInstallServiceBase used to download and manipulate model files."""
-        pass
-
-    @abstractmethod
-    def start(self, invoker: Invoker) -> None:
-        pass
-
-    @abstractmethod
-    def stop(self, invoker: Invoker) -> None:
-        pass
-
-    @abstractmethod
-    def load_model_by_config(
-        self,
-        model_config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-        context_data: Optional[InvocationContextData] = None,
-    ) -> LoadedModel:
-        pass
-
-    @abstractmethod
-    def load_model_by_key(
-        self,
-        key: str,
-        submodel_type: Optional[SubModelType] = None,
-        context_data: Optional[InvocationContextData] = None,
-    ) -> LoadedModel:
-        pass
-
-    @abstractmethod
-    def load_model_by_attr(
+    def get_model(
         self,
         model_name: str,
         base_model: BaseModelType,
         model_type: ModelType,
         submodel: Optional[SubModelType] = None,
-        context_data: Optional[InvocationContextData] = None,
-    ) -> LoadedModel:
+        node: Optional[BaseInvocation] = None,
+        context: Optional[InvocationContext] = None,
+    ) -> ModelInfo:
+        """Retrieve the indicated model with name and type.
+        submodel can be used to get a part (such as the vae)
+        of a diffusers pipeline."""
         pass
 
     @property
     @abstractmethod
-    def gpu_count(self) -> int:
-        """Return the number of GPUs we are configured to use."""
+    def logger(self):
+        pass
+
+    @abstractmethod
+    def model_exists(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+    ) -> bool:
+        pass
+
+    @abstractmethod
+    def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
+        """
+        Given a model name returns a dict-like (OmegaConf) object describing it.
+        Uses the exact format as the omegaconf stanza.
+        """
+        pass
+
+    @abstractmethod
+    def list_models(self, base_model: Optional[BaseModelType] = None, model_type: Optional[ModelType] = None) -> dict:
+        """
+        Return a dict of models in the format:
+        { model_type1:
+          { model_name1: {'status': 'active'|'cached'|'not loaded',
+                         'model_name' : name,
+                         'model_type' : SDModelType,
+                         'description': description,
+                         'format': 'folder'|'safetensors'|'ckpt'
+                         },
+            model_name2: { etc }
+          },
+          model_type2:
+            { model_name_n: etc
+        }
+        """
+        pass
+
+    @abstractmethod
+    def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
+        """
+        Return information about the model using the same format as list_models()
+        """
+        pass
+
+    @abstractmethod
+    def model_names(self) -> List[Tuple[str, BaseModelType, ModelType]]:
+        """
+        Returns a list of all the model names known.
+        """
+        pass
+
+    @abstractmethod
+    def add_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        model_attributes: dict,
+        clobber: bool = False,
+    ) -> AddModelResult:
+        """
+        Update the named model with a dictionary of attributes. Will fail with an
+        assertion error if the name already exists. Pass clobber=True to overwrite.
+        On a successful update, the config will be changed in memory. Will fail
+        with an assertion error if provided attributes are incorrect or
+        the model name is missing. Call commit() to write changes to disk.
+        """
+        pass
+
+    @abstractmethod
+    def update_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        model_attributes: dict,
+    ) -> AddModelResult:
+        """
+        Update the named model with a dictionary of attributes. Will fail with a
+        ModelNotFoundException if the name does not already exist.
+
+        On a successful update, the config will be changed in memory. Will fail
+        with an assertion error if provided attributes are incorrect or
+        the model name is missing. Call commit() to write changes to disk.
+        """
+        pass
+
+    @abstractmethod
+    def del_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+    ):
+        """
+        Delete the named model from configuration. If delete_files is true,
+        then the underlying weight file or diffusers directory will be deleted
+        as well. Call commit() to write to disk.
+        """
+        pass
+
+    @abstractmethod
+    def rename_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        new_name: str,
+    ):
+        """
+        Rename the indicated model.
+        """
+        pass
+
+    @abstractmethod
+    def list_checkpoint_configs(self) -> List[Path]:
+        """
+        List the checkpoint config paths from ROOT/configs/stable-diffusion.
+        """
+        pass
+
+    @abstractmethod
+    def convert_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: Literal[ModelType.Main, ModelType.Vae],
+    ) -> AddModelResult:
+        """
+        Convert a checkpoint file into a diffusers folder, deleting the cached
+        version and deleting the original checkpoint file if it is in the models
+        directory.
+        :param model_name: Name of the model to convert
+        :param base_model: Base model type
+        :param model_type: Type of model ['vae' or 'main']
+
+        This will raise a ValueError unless the model is not a checkpoint. It will
+        also raise a ValueError in the event that there is a similarly-named diffusers
+        directory already in place.
+        """
+        pass
+
+    @abstractmethod
+    def heuristic_import(
+        self,
+        items_to_import: set[str],
+        prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
+    ) -> dict[str, AddModelResult]:
+        """Import a list of paths, repo_ids or URLs. Returns the set of
+        successfully imported items.
+        :param items_to_import: Set of strings corresponding to models to be imported.
+        :param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
+
+        The prediction type helper is necessary to distinguish between
+        models based on Stable Diffusion 2 Base (requiring
+        SchedulerPredictionType.Epsilson) and Stable Diffusion 768
+        (requiring SchedulerPredictionType.VPrediction). It is
+        generally impossible to do this programmatically, so the
+        prediction_type_helper usually asks the user to choose.
+
+        The result is a set of successfully installed models. Each element
+        of the set is a dict corresponding to the newly-created OmegaConf stanza for
+        that model.
+        """
+        pass
+
+    @abstractmethod
+    def merge_models(
+        self,
+        model_names: List[str] = Field(
+            default=None, min_length=2, max_length=3, description="List of model names to merge"
+        ),
+        base_model: Union[BaseModelType, str] = Field(
+            default=None, description="Base model shared by all models to be merged"
+        ),
+        merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
+        alpha: Optional[float] = 0.5,
+        interp: Optional[MergeInterpolationMethod] = None,
+        force: Optional[bool] = False,
+        merge_dest_directory: Optional[Path] = None,
+    ) -> AddModelResult:
+        """
+        Merge two to three diffusrs pipeline models and save as a new model.
+        :param model_names: List of 2-3 models to merge
+        :param base_model: Base model to use for all models
+        :param merged_model_name: Name of destination merged model
+        :param alpha: Alpha strength to apply to 2d and 3d model
+        :param interp: Interpolation method. None (default)
+        :param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
+        """
+        pass
+
+    @abstractmethod
+    def search_for_models(self, directory: Path) -> List[Path]:
+        """
+        Return list of all models found in the designated directory.
+        """
+        pass
+
+    @abstractmethod
+    def sync_to_config(self):
+        """
+        Re-read models.yaml, rescan the models directory, and reimport models
+        in the autoimport directories. Call after making changes outside the
+        model manager API.
+        """
+        pass
+
+    @abstractmethod
+    def collect_cache_stats(self, cache_stats: CacheStats):
+        """
+        Reset model cache statistics for graph with graph_id.
+        """
+        pass
+
+    @abstractmethod
+    def commit(self, conf_file: Optional[Path] = None) -> None:
+        """
+        Write current configuration out to the indicated file.
+        If no conf_file is provided, then replaces the
+        original file/database used to initialize the object.
+        """
+        pass

invokeai/app/services/model_manager/model_manager_default.py

@@ -1,154 +1,413 @@
 # Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Team
-"""Implementation of ModelManagerServiceBase."""
 
-from typing import Optional
+from __future__ import annotations
 
-from typing_extensions import Self
+from logging import Logger
+from pathlib import Path
+from typing import TYPE_CHECKING, Callable, List, Literal, Optional, Tuple, Union
 
-from invokeai.app.services.invoker import Invoker
-from invokeai.app.services.shared.invocation_context import InvocationContextData
-from invokeai.backend.model_manager import AnyModelConfig, BaseModelType, LoadedModel, ModelType, SubModelType
-from invokeai.backend.model_manager.load import ModelCache, ModelConvertCache, ModelLoaderRegistry
-from invokeai.backend.util.logging import InvokeAILogger
+import torch
+from pydantic import Field
+
+from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.app.services.invocation_processor.invocation_processor_common import CanceledException
+from invokeai.backend.model_management import (
+    AddModelResult,
+    BaseModelType,
+    MergeInterpolationMethod,
+    ModelInfo,
+    ModelManager,
+    ModelMerger,
+    ModelNotFoundException,
+    ModelType,
+    SchedulerPredictionType,
+    SubModelType,
+)
+from invokeai.backend.model_management.model_cache import CacheStats
+from invokeai.backend.model_management.model_search import FindModels
+from invokeai.backend.util import choose_precision, choose_torch_device
 
-from ..config import InvokeAIAppConfig
-from ..download import DownloadQueueServiceBase
-from ..events.events_base import EventServiceBase
-from ..model_install import ModelInstallService, ModelInstallServiceBase
-from ..model_load import ModelLoadService, ModelLoadServiceBase
-from ..model_records import ModelRecordServiceBase, UnknownModelException
 from .model_manager_base import ModelManagerServiceBase
 
+if TYPE_CHECKING:
+    from invokeai.app.invocations.baseinvocation import InvocationContext
 
+
+# simple implementation
 class ModelManagerService(ModelManagerServiceBase):
-    """
-    The ModelManagerService handles various aspects of model installation, maintenance and loading.
-
-    It bundles three distinct services:
-    model_manager.store   -- Routines to manage the database of model configuration records.
-    model_manager.install -- Routines to install, move and delete models.
-    model_manager.load    -- Routines to load models into memory.
-    """
+    """Responsible for managing models on disk and in memory"""
 
     def __init__(
         self,
-        store: ModelRecordServiceBase,
-        install: ModelInstallServiceBase,
-        load: ModelLoadServiceBase,
+        config: InvokeAIAppConfig,
+        logger: Logger,
     ):
-        self._store = store
-        self._install = install
-        self._load = load
+        """
+        Initialize with the path to the models.yaml config file.
+        Optional parameters are the torch device type, precision, max_models,
+        and sequential_offload boolean. Note that the default device
+        type and precision are set up for a CUDA system running at half precision.
+        """
+        if config.model_conf_path and config.model_conf_path.exists():
+            config_file = config.model_conf_path
+        else:
+            config_file = config.root_dir / "configs/models.yaml"
 
-    @property
-    def store(self) -> ModelRecordServiceBase:
-        return self._store
+        logger.debug(f"Config file={config_file}")
 
-    @property
-    def install(self) -> ModelInstallServiceBase:
-        return self._install
+        device = torch.device(choose_torch_device())
+        device_name = torch.cuda.get_device_name() if device == torch.device("cuda") else ""
+        logger.info(f"GPU device = {device} {device_name}")
 
-    @property
-    def load(self) -> ModelLoadServiceBase:
-        return self._load
+        precision = config.precision
+        if precision == "auto":
+            precision = choose_precision(device)
+        dtype = torch.float32 if precision == "float32" else torch.float16
 
-    def start(self, invoker: Invoker) -> None:
-        for service in [self._store, self._install, self._load]:
-            if hasattr(service, "start"):
-                service.start(invoker)
+        # this is transitional backward compatibility
+        # support for the deprecated `max_loaded_models`
+        # configuration value. If present, then the
+        # cache size is set to 2.5 GB times
+        # the number of max_loaded_models. Otherwise
+        # use new `ram_cache_size` config setting
+        max_cache_size = config.ram_cache_size
 
-    def stop(self, invoker: Invoker) -> None:
-        for service in [self._store, self._install, self._load]:
-            if hasattr(service, "stop"):
-                service.stop(invoker)
+        logger.debug(f"Maximum RAM cache size: {max_cache_size} GiB")
 
-    def load_model_by_config(
-        self,
-        model_config: AnyModelConfig,
-        submodel_type: Optional[SubModelType] = None,
-        context_data: Optional[InvocationContextData] = None,
-    ) -> LoadedModel:
-        return self.load.load_model(model_config, submodel_type, context_data)
+        sequential_offload = config.sequential_guidance
 
-    def load_model_by_key(
-        self,
-        key: str,
-        submodel_type: Optional[SubModelType] = None,
-        context_data: Optional[InvocationContextData] = None,
-    ) -> LoadedModel:
-        config = self.store.get_model(key)
-        return self.load.load_model(config, submodel_type, context_data)
+        self.mgr = ModelManager(
+            config=config_file,
+            device_type=device,
+            precision=dtype,
+            max_cache_size=max_cache_size,
+            sequential_offload=sequential_offload,
+            logger=logger,
+        )
+        logger.info("Model manager service initialized")
 
-    def load_model_by_attr(
+    def get_model(
         self,
         model_name: str,
         base_model: BaseModelType,
         model_type: ModelType,
         submodel: Optional[SubModelType] = None,
-        context_data: Optional[InvocationContextData] = None,
-    ) -> LoadedModel:
+        context: Optional[InvocationContext] = None,
+    ) -> ModelInfo:
         """
-        Given a model's attributes, search the database for it, and if found, load and return the LoadedModel object.
-
-        This is provided for API compatability with the get_model() method
-        in the original model manager. However, note that LoadedModel is
-        not the same as the original ModelInfo that ws returned.
-
-        :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
-        :param context: The invocation context.
-
-        Exceptions: UnknownModelException -- model with this key not known
-                    NotImplementedException -- a model loader was not provided at initialization time
-                    ValueError -- more than one model matches this combination
+        Retrieve the indicated model. submodel can be used to get a
+        part (such as the vae) of a diffusers mode.
         """
-        configs = self.store.search_by_attr(model_name, base_model, model_type)
-        if len(configs) == 0:
-            raise UnknownModelException(f"{base_model}/{model_type}/{model_name}: Unknown model")
-        elif len(configs) > 1:
-            raise ValueError(f"{base_model}/{model_type}/{model_name}: More than one model matches.")
+
+        # we can emit model loading events if we are executing with access to the invocation context
+        if context:
+            self._emit_load_event(
+                context=context,
+                model_name=model_name,
+                base_model=base_model,
+                model_type=model_type,
+                submodel=submodel,
+            )
+
+        model_info = self.mgr.get_model(
+            model_name,
+            base_model,
+            model_type,
+            submodel,
+        )
+
+        if context:
+            self._emit_load_event(
+                context=context,
+                model_name=model_name,
+                base_model=base_model,
+                model_type=model_type,
+                submodel=submodel,
+                model_info=model_info,
+            )
+
+        return model_info
+
+    def model_exists(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+    ) -> bool:
+        """
+        Given a model name, returns True if it is a valid
+        identifier.
+        """
+        return self.mgr.model_exists(
+            model_name,
+            base_model,
+            model_type,
+        )
+
+    def model_info(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> Union[dict, None]:
+        """
+        Given a model name returns a dict-like (OmegaConf) object describing it.
+        """
+        return self.mgr.model_info(model_name, base_model, model_type)
+
+    def model_names(self) -> List[Tuple[str, BaseModelType, ModelType]]:
+        """
+        Returns a list of all the model names known.
+        """
+        return self.mgr.model_names()
+
+    def list_models(
+        self, base_model: Optional[BaseModelType] = None, model_type: Optional[ModelType] = None
+    ) -> list[dict]:
+        """
+        Return a list of models.
+        """
+        return self.mgr.list_models(base_model, model_type)
+
+    def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> Union[dict, None]:
+        """
+        Return information about the model using the same format as list_models()
+        """
+        return self.mgr.list_model(model_name=model_name, base_model=base_model, model_type=model_type)
+
+    def add_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        model_attributes: dict,
+        clobber: bool = False,
+    ) -> AddModelResult:
+        """
+        Update the named model with a dictionary of attributes. Will fail with an
+        assertion error if the name already exists. Pass clobber=True to overwrite.
+        On a successful update, the config will be changed in memory. Will fail
+        with an assertion error if provided attributes are incorrect or
+        the model name is missing. Call commit() to write changes to disk.
+        """
+        self.logger.debug(f"add/update model {model_name}")
+        return self.mgr.add_model(model_name, base_model, model_type, model_attributes, clobber)
+
+    def update_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        model_attributes: dict,
+    ) -> AddModelResult:
+        """
+        Update the named model with a dictionary of attributes. Will fail with a
+        ModelNotFoundException exception if the name does not already exist.
+        On a successful update, the config will be changed in memory. Will fail
+        with an assertion error if provided attributes are incorrect or
+        the model name is missing. Call commit() to write changes to disk.
+        """
+        self.logger.debug(f"update model {model_name}")
+        if not self.model_exists(model_name, base_model, model_type):
+            raise ModelNotFoundException(f"Unknown model {model_name}")
+        return self.add_model(model_name, base_model, model_type, model_attributes, clobber=True)
+
+    def del_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+    ):
+        """
+        Delete the named model from configuration. If delete_files is true,
+        then the underlying weight file or diffusers directory will be deleted
+        as well.
+        """
+        self.logger.debug(f"delete model {model_name}")
+        self.mgr.del_model(model_name, base_model, model_type)
+        self.mgr.commit()
+
+    def convert_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: Literal[ModelType.Main, ModelType.Vae],
+        convert_dest_directory: Optional[Path] = Field(
+            default=None, description="Optional directory location for merged model"
+        ),
+    ) -> AddModelResult:
+        """
+        Convert a checkpoint file into a diffusers folder, deleting the cached
+        version and deleting the original checkpoint file if it is in the models
+        directory.
+        :param model_name: Name of the model to convert
+        :param base_model: Base model type
+        :param model_type: Type of model ['vae' or 'main']
+        :param convert_dest_directory: Save the converted model to the designated directory (`models/etc/etc` by default)
+
+        This will raise a ValueError unless the model is not a checkpoint. It will
+        also raise a ValueError in the event that there is a similarly-named diffusers
+        directory already in place.
+        """
+        self.logger.debug(f"convert model {model_name}")
+        return self.mgr.convert_model(model_name, base_model, model_type, convert_dest_directory)
+
+    def collect_cache_stats(self, cache_stats: CacheStats):
+        """
+        Reset model cache statistics for graph with graph_id.
+        """
+        self.mgr.cache.stats = cache_stats
+
+    def commit(self, conf_file: Optional[Path] = None):
+        """
+        Write current configuration out to the indicated file.
+        If no conf_file is provided, then replaces the
+        original file/database used to initialize the object.
+        """
+        return self.mgr.commit(conf_file)
+
+    def _emit_load_event(
+        self,
+        context: InvocationContext,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        submodel: Optional[SubModelType] = None,
+        model_info: Optional[ModelInfo] = None,
+    ):
+        if context.services.queue.is_canceled(context.graph_execution_state_id):
+            raise CanceledException()
+
+        if model_info:
+            context.services.events.emit_model_load_completed(
+                queue_id=context.queue_id,
+                queue_item_id=context.queue_item_id,
+                queue_batch_id=context.queue_batch_id,
+                graph_execution_state_id=context.graph_execution_state_id,
+                model_name=model_name,
+                base_model=base_model,
+                model_type=model_type,
+                submodel=submodel,
+                model_info=model_info,
+            )
         else:
-            return self.load.load_model(configs[0], submodel, context_data)
+            context.services.events.emit_model_load_started(
+                queue_id=context.queue_id,
+                queue_item_id=context.queue_item_id,
+                queue_batch_id=context.queue_batch_id,
+                graph_execution_state_id=context.graph_execution_state_id,
+                model_name=model_name,
+                base_model=base_model,
+                model_type=model_type,
+                submodel=submodel,
+            )
 
     @property
-    def gpu_count(self) -> int:
-        """Return the number of GPUs we are using."""
-        return self.load.gpu_count
+    def logger(self):
+        return self.mgr.logger
 
-    @classmethod
-    def build_model_manager(
-        cls,
-        app_config: InvokeAIAppConfig,
-        model_record_service: ModelRecordServiceBase,
-        download_queue: DownloadQueueServiceBase,
-        events: EventServiceBase,
-    ) -> Self:
+    def heuristic_import(
+        self,
+        items_to_import: set[str],
+        prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
+    ) -> dict[str, AddModelResult]:
+        """Import a list of paths, repo_ids or URLs. Returns the set of
+        successfully imported items.
+        :param items_to_import: Set of strings corresponding to models to be imported.
+        :param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
+
+        The prediction type helper is necessary to distinguish between
+        models based on Stable Diffusion 2 Base (requiring
+        SchedulerPredictionType.Epsilson) and Stable Diffusion 768
+        (requiring SchedulerPredictionType.VPrediction). It is
+        generally impossible to do this programmatically, so the
+        prediction_type_helper usually asks the user to choose.
+
+        The result is a set of successfully installed models. Each element
+        of the set is a dict corresponding to the newly-created OmegaConf stanza for
+        that model.
         """
-        Construct the model manager service instance.
+        return self.mgr.heuristic_import(items_to_import, prediction_type_helper)
 
-        For simplicity, use this class method rather than the __init__ constructor.
+    def merge_models(
+        self,
+        model_names: List[str] = Field(
+            default=None, min_length=2, max_length=3, description="List of model names to merge"
+        ),
+        base_model: Union[BaseModelType, str] = Field(
+            default=None, description="Base model shared by all models to be merged"
+        ),
+        merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
+        alpha: float = 0.5,
+        interp: Optional[MergeInterpolationMethod] = None,
+        force: bool = False,
+        merge_dest_directory: Optional[Path] = Field(
+            default=None, description="Optional directory location for merged model"
+        ),
+    ) -> AddModelResult:
         """
-        logger = InvokeAILogger.get_logger(cls.__name__)
-        logger.setLevel(app_config.log_level.upper())
+        Merge two to three diffusrs pipeline models and save as a new model.
+        :param model_names: List of 2-3 models to merge
+        :param base_model: Base model to use for all models
+        :param merged_model_name: Name of destination merged model
+        :param alpha: Alpha strength to apply to 2d and 3d model
+        :param interp: Interpolation method. None (default)
+        :param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
+        """
+        merger = ModelMerger(self.mgr)
+        try:
+            result = merger.merge_diffusion_models_and_save(
+                model_names=model_names,
+                base_model=base_model,
+                merged_model_name=merged_model_name,
+                alpha=alpha,
+                interp=interp,
+                force=force,
+                merge_dest_directory=merge_dest_directory,
+            )
+        except AssertionError as e:
+            raise ValueError(e)
+        return result
 
-        ram_cache = ModelCache(
-            max_cache_size=app_config.ram_cache_size, max_vram_cache_size=app_config.vram_cache_size, logger=logger
+    def search_for_models(self, directory: Path) -> List[Path]:
+        """
+        Return list of all models found in the designated directory.
+        """
+        search = FindModels([directory], self.logger)
+        return search.list_models()
+
+    def sync_to_config(self):
+        """
+        Re-read models.yaml, rescan the models directory, and reimport models
+        in the autoimport directories. Call after making changes outside the
+        model manager API.
+        """
+        return self.mgr.sync_to_config()
+
+    def list_checkpoint_configs(self) -> List[Path]:
+        """
+        List the checkpoint config paths from ROOT/configs/stable-diffusion.
+        """
+        config = self.mgr.app_config
+        conf_path = config.legacy_conf_path
+        root_path = config.root_path
+        return [(conf_path / x).relative_to(root_path) for x in conf_path.glob("**/*.yaml")]
+
+    def rename_model(
+        self,
+        model_name: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        new_name: Optional[str] = None,
+        new_base: Optional[BaseModelType] = None,
+    ):
+        """
+        Rename the indicated model. Can provide a new name and/or a new base.
+        :param model_name: Current name of the model
+        :param base_model: Current base of the model
+        :param model_type: Model type (can't be changed)
+        :param new_name: New name for the model
+        :param new_base: New base for the model
+        """
+        self.mgr.rename_model(
+            base_model=base_model,
+            model_type=model_type,
+            model_name=model_name,
+            new_name=new_name,
+            new_base=new_base,
         )
-        convert_cache = ModelConvertCache(
-            cache_path=app_config.models_convert_cache_path, max_size=app_config.convert_cache_size
-        )
-        loader = ModelLoadService(
-            app_config=app_config,
-            ram_cache=ram_cache,
-            convert_cache=convert_cache,
-            registry=ModelLoaderRegistry,
-        )
-        installer = ModelInstallService(
-            app_config=app_config,
-            record_store=model_record_service,
-            download_queue=download_queue,
-            event_bus=events,
-        )
-        return cls(store=model_record_service, install=installer, load=loader)

invokeai/app/services/model_metadata/__init__.py

@@ -1,9 +0,0 @@
-"""Init file for ModelMetadataStoreService module."""
-
-from .metadata_store_base import ModelMetadataStoreBase
-from .metadata_store_sql import ModelMetadataStoreSQL
-
-__all__ = [
-    "ModelMetadataStoreBase",
-    "ModelMetadataStoreSQL",
-]

invokeai/app/services/model_metadata/metadata_store_base.py

@@ -1,65 +0,0 @@
-# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Development Team
-"""
-Storage for Model Metadata
-"""
-
-from abc import ABC, abstractmethod
-from typing import List, Set, Tuple
-
-from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
-
-
-class ModelMetadataStoreBase(ABC):
-    """Store, search and fetch model metadata retrieved from remote repositories."""
-
-    @abstractmethod
-    def add_metadata(self, model_key: str, metadata: AnyModelRepoMetadata) -> None:
-        """
-        Add a block of repo metadata to a model record.
-
-        The model record config must already exist in the database with the
-        same key. Otherwise a FOREIGN KEY constraint exception will be raised.
-
-        :param model_key: Existing model key in the `model_config` table
-        :param metadata: ModelRepoMetadata object to store
-        """
-
-    @abstractmethod
-    def get_metadata(self, model_key: str) -> AnyModelRepoMetadata:
-        """Retrieve the ModelRepoMetadata corresponding to model key."""
-
-    @abstractmethod
-    def list_all_metadata(self) -> List[Tuple[str, AnyModelRepoMetadata]]:  # key, metadata
-        """Dump out all the metadata."""
-
-    @abstractmethod
-    def update_metadata(self, model_key: str, metadata: AnyModelRepoMetadata) -> AnyModelRepoMetadata:
-        """
-        Update metadata corresponding to the model with the indicated key.
-
-        :param model_key: Existing model key in the `model_config` table
-        :param metadata: ModelRepoMetadata object to update
-        """
-
-    @abstractmethod
-    def list_tags(self) -> Set[str]:
-        """Return all tags in the tags table."""
-
-    @abstractmethod
-    def search_by_tag(self, tags: Set[str]) -> Set[str]:
-        """Return the keys of models containing all of the listed tags."""
-
-    @abstractmethod
-    def search_by_author(self, author: str) -> Set[str]:
-        """Return the keys of models authored by the indicated author."""
-
-    @abstractmethod
-    def search_by_name(self, name: str) -> Set[str]:
-        """
-        Return the keys of models with the indicated name.
-
-        Note that this is the name of the model given to it by
-        the remote source. The user may have changed the local
-        name. The local name will be located in the model config
-        record object.
-        """

invokeai/app/services/model_metadata/metadata_store_sql.py

@@ -1,222 +0,0 @@
-# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Development Team
-"""
-SQL Storage for Model Metadata
-"""
-
-import sqlite3
-from typing import List, Optional, Set, Tuple
-
-from invokeai.app.services.shared.sqlite.sqlite_database import SqliteDatabase
-from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata, UnknownMetadataException
-from invokeai.backend.model_manager.metadata.fetch import ModelMetadataFetchBase
-
-from .metadata_store_base import ModelMetadataStoreBase
-
-
-class ModelMetadataStoreSQL(ModelMetadataStoreBase):
-    """Store, search and fetch model metadata retrieved from remote repositories."""
-
-    def __init__(self, db: SqliteDatabase):
-        """
-        Initialize a new object from preexisting sqlite3 connection and threading lock objects.
-
-        :param conn: sqlite3 connection object
-        :param lock: threading Lock object
-        """
-        super().__init__()
-        self._db = db
-        self._cursor = self._db.conn.cursor()
-
-    def add_metadata(self, model_key: str, metadata: AnyModelRepoMetadata) -> None:
-        """
-        Add a block of repo metadata to a model record.
-
-        The model record config must already exist in the database with the
-        same key. Otherwise a FOREIGN KEY constraint exception will be raised.
-
-        :param model_key: Existing model key in the `model_config` table
-        :param metadata: ModelRepoMetadata object to store
-        """
-        json_serialized = metadata.model_dump_json()
-        with self._db.lock:
-            try:
-                self._cursor.execute(
-                    """--sql
-                    INSERT INTO model_metadata(
-                       id,
-                       metadata
-                    )
-                    VALUES (?,?);
-                    """,
-                    (
-                        model_key,
-                        json_serialized,
-                    ),
-                )
-                self._update_tags(model_key, metadata.tags)
-                self._db.conn.commit()
-            except sqlite3.IntegrityError as excp:  # FOREIGN KEY error: the key was not in model_config table
-                self._db.conn.rollback()
-                raise UnknownMetadataException from excp
-            except sqlite3.Error as excp:
-                self._db.conn.rollback()
-                raise excp
-
-    def get_metadata(self, model_key: str) -> AnyModelRepoMetadata:
-        """Retrieve the ModelRepoMetadata corresponding to model key."""
-        with self._db.lock:
-            self._cursor.execute(
-                """--sql
-                SELECT metadata FROM model_metadata
-                WHERE id=?;
-                """,
-                (model_key,),
-            )
-            rows = self._cursor.fetchone()
-            if not rows:
-                raise UnknownMetadataException("model metadata not found")
-            return ModelMetadataFetchBase.from_json(rows[0])
-
-    def list_all_metadata(self) -> List[Tuple[str, AnyModelRepoMetadata]]:  # key, metadata
-        """Dump out all the metadata."""
-        with self._db.lock:
-            self._cursor.execute(
-                """--sql
-                SELECT id,metadata FROM model_metadata;
-                """,
-                (),
-            )
-            rows = self._cursor.fetchall()
-        return [(x[0], ModelMetadataFetchBase.from_json(x[1])) for x in rows]
-
-    def update_metadata(self, model_key: str, metadata: AnyModelRepoMetadata) -> AnyModelRepoMetadata:
-        """
-        Update metadata corresponding to the model with the indicated key.
-
-        :param model_key: Existing model key in the `model_config` table
-        :param metadata: ModelRepoMetadata object to update
-        """
-        json_serialized = metadata.model_dump_json()  # turn it into a json string.
-        with self._db.lock:
-            try:
-                self._cursor.execute(
-                    """--sql
-                    UPDATE model_metadata
-                    SET
-                        metadata=?
-                    WHERE id=?;
-                    """,
-                    (json_serialized, model_key),
-                )
-                if self._cursor.rowcount == 0:
-                    raise UnknownMetadataException("model metadata not found")
-                self._update_tags(model_key, metadata.tags)
-                self._db.conn.commit()
-            except sqlite3.Error as e:
-                self._db.conn.rollback()
-                raise e
-
-        return self.get_metadata(model_key)
-
-    def list_tags(self) -> Set[str]:
-        """Return all tags in the tags table."""
-        self._cursor.execute(
-            """--sql
-            select tag_text from tags;
-            """
-        )
-        return {x[0] for x in self._cursor.fetchall()}
-
-    def search_by_tag(self, tags: Set[str]) -> Set[str]:
-        """Return the keys of models containing all of the listed tags."""
-        with self._db.lock:
-            try:
-                matches: Optional[Set[str]] = None
-                for tag in tags:
-                    self._cursor.execute(
-                        """--sql
-                        SELECT a.model_id FROM model_tags AS a,
-                                                     tags AS b
-                        WHERE a.tag_id=b.tag_id
-                          AND b.tag_text=?;
-                        """,
-                        (tag,),
-                    )
-                    model_keys = {x[0] for x in self._cursor.fetchall()}
-                    if matches is None:
-                        matches = model_keys
-                    matches = matches.intersection(model_keys)
-            except sqlite3.Error as e:
-                raise e
-        return matches if matches else set()
-
-    def search_by_author(self, author: str) -> Set[str]:
-        """Return the keys of models authored by the indicated author."""
-        self._cursor.execute(
-            """--sql
-            SELECT id FROM model_metadata
-            WHERE author=?;
-            """,
-            (author,),
-        )
-        return {x[0] for x in self._cursor.fetchall()}
-
-    def search_by_name(self, name: str) -> Set[str]:
-        """
-        Return the keys of models with the indicated name.
-
-        Note that this is the name of the model given to it by
-        the remote source. The user may have changed the local
-        name. The local name will be located in the model config
-        record object.
-        """
-        self._cursor.execute(
-            """--sql
-            SELECT id FROM model_metadata
-            WHERE name=?;
-            """,
-            (name,),
-        )
-        return {x[0] for x in self._cursor.fetchall()}
-
-    def _update_tags(self, model_key: str, tags: Set[str]) -> None:
-        """Update tags for the model referenced by model_key."""
-        # remove previous tags from this model
-        self._cursor.execute(
-            """--sql
-            DELETE FROM model_tags
-            WHERE model_id=?;
-            """,
-            (model_key,),
-        )
-
-        for tag in tags:
-            self._cursor.execute(
-                """--sql
-                INSERT OR IGNORE INTO tags (
-                  tag_text
-                  )
-                VALUES (?);
-                """,
-                (tag,),
-            )
-            self._cursor.execute(
-                """--sql
-                SELECT tag_id
-                FROM tags
-                WHERE tag_text = ?
-                LIMIT 1;
-                """,
-                (tag,),
-            )
-            tag_id = self._cursor.fetchone()[0]
-            self._cursor.execute(
-                """--sql
-                INSERT OR IGNORE INTO model_tags (
-                   model_id,
-                   tag_id
-                  )
-                VALUES (?,?);
-                """,
-                (model_key, tag_id),
-            )

invokeai/app/services/model_records/model_records_base.py

@@ -11,15 +11,8 @@ from typing import Any, Dict, List, Optional, Set, Tuple, Union
 from pydantic import BaseModel, Field
 
 from invokeai.app.services.shared.pagination import PaginatedResults
-from invokeai.backend.model_manager import (
-    AnyModelConfig,
-    BaseModelType,
-    ModelFormat,
-    ModelType,
-)
-from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata
-
-from ..model_metadata import ModelMetadataStoreBase
+from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelFormat, ModelType
+from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata, ModelMetadataStore
 
 
 class DuplicateModelException(Exception):
@@ -111,7 +104,7 @@ class ModelRecordServiceBase(ABC):
 
     @property
     @abstractmethod
-    def metadata_store(self) -> ModelMetadataStoreBase:
+    def metadata_store(self) -> ModelMetadataStore:
         """Return a ModelMetadataStore initialized on the same database."""
         pass
 
@@ -153,7 +146,7 @@ class ModelRecordServiceBase(ABC):
     @abstractmethod
     def exists(self, key: str) -> bool:
         """
-        Return True if a model with the indicated key exists in the database.
+        Return True if a model with the indicated key exists in the databse.
 
         :param key: Unique key for the model to be deleted
         """

invokeai/app/services/model_records/model_records_sql.py

@@ -54,9 +54,8 @@ from invokeai.backend.model_manager.config import (
     ModelFormat,
     ModelType,
 )
-from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata, UnknownMetadataException
+from invokeai.backend.model_manager.metadata import AnyModelRepoMetadata, ModelMetadataStore, UnknownMetadataException
 
-from ..model_metadata import ModelMetadataStoreBase, ModelMetadataStoreSQL
 from ..shared.sqlite.sqlite_database import SqliteDatabase
 from .model_records_base import (
     DuplicateModelException,
@@ -70,16 +69,16 @@ from .model_records_base import (
 class ModelRecordServiceSQL(ModelRecordServiceBase):
     """Implementation of the ModelConfigStore ABC using a SQL database."""
 
-    def __init__(self, db: SqliteDatabase, metadata_store: ModelMetadataStoreBase):
+    def __init__(self, db: SqliteDatabase):
         """
         Initialize a new object from preexisting sqlite3 connection and threading lock objects.
 
-        :param db: Sqlite connection object
+        :param conn: sqlite3 connection object
+        :param lock: threading Lock object
         """
         super().__init__()
         self._db = db
-        self._cursor = db.conn.cursor()
-        self._metadata_store = metadata_store
+        self._cursor = self._db.conn.cursor()
 
     @property
     def db(self) -> SqliteDatabase:
@@ -159,7 +158,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
                 self._db.conn.rollback()
                 raise e
 
-    def update_model(self, key: str, config: Union[Dict[str, Any], AnyModelConfig]) -> AnyModelConfig:
+    def update_model(self, key: str, config: Union[dict, AnyModelConfig]) -> AnyModelConfig:
         """
         Update the model, returning the updated version.
 
@@ -200,7 +199,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         with self._db.lock:
             self._cursor.execute(
                 """--sql
-                SELECT config, strftime('%s',updated_at) FROM model_config
+                SELECT config FROM model_config
                 WHERE id=?;
                 """,
                 (key,),
@@ -208,7 +207,7 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
             rows = self._cursor.fetchone()
             if not rows:
                 raise UnknownModelException("model not found")
-            model = ModelConfigFactory.make_config(json.loads(rows[0]), timestamp=rows[1])
+            model = ModelConfigFactory.make_config(json.loads(rows[0]))
         return model
 
     def exists(self, key: str) -> bool:
@@ -266,14 +265,12 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         with self._db.lock:
             self._cursor.execute(
                 f"""--sql
-                select config, strftime('%s',updated_at) FROM model_config
+                select config FROM model_config
                 {where};
                 """,
                 tuple(bindings),
             )
-            results = [
-                ModelConfigFactory.make_config(json.loads(x[0]), timestamp=x[1]) for x in self._cursor.fetchall()
-            ]
+            results = [ModelConfigFactory.make_config(json.loads(x[0])) for x in self._cursor.fetchall()]
         return results
 
     def search_by_path(self, path: Union[str, Path]) -> List[AnyModelConfig]:
@@ -282,14 +279,12 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         with self._db.lock:
             self._cursor.execute(
                 """--sql
-                SELECT config, strftime('%s',updated_at) FROM model_config
+                SELECT config FROM model_config
                 WHERE path=?;
                 """,
                 (str(path),),
             )
-            results = [
-                ModelConfigFactory.make_config(json.loads(x[0]), timestamp=x[1]) for x in self._cursor.fetchall()
-            ]
+            results = [ModelConfigFactory.make_config(json.loads(x[0])) for x in self._cursor.fetchall()]
         return results
 
     def search_by_hash(self, hash: str) -> List[AnyModelConfig]:
@@ -298,20 +293,18 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
         with self._db.lock:
             self._cursor.execute(
                 """--sql
-                SELECT config, strftime('%s',updated_at) FROM model_config
+                SELECT config FROM model_config
                 WHERE original_hash=?;
                 """,
                 (hash,),
             )
-            results = [
-                ModelConfigFactory.make_config(json.loads(x[0]), timestamp=x[1]) for x in self._cursor.fetchall()
-            ]
+            results = [ModelConfigFactory.make_config(json.loads(x[0])) for x in self._cursor.fetchall()]
         return results
 
     @property
-    def metadata_store(self) -> ModelMetadataStoreBase:
+    def metadata_store(self) -> ModelMetadataStore:
         """Return a ModelMetadataStore initialized on the same database."""
-        return self._metadata_store
+        return ModelMetadataStore(self._db)
 
     def get_metadata(self, key: str) -> Optional[AnyModelRepoMetadata]:
         """
@@ -332,18 +325,18 @@ class ModelRecordServiceSQL(ModelRecordServiceBase):
 
         :param tags: Set of tags to search for. All tags must be present.
         """
-        store = ModelMetadataStoreSQL(self._db)
+        store = ModelMetadataStore(self._db)
         keys = store.search_by_tag(tags)
         return [self.get_model(x) for x in keys]
 
     def list_tags(self) -> Set[str]:
         """Return a unique set of all the model tags in the metadata database."""
-        store = ModelMetadataStoreSQL(self._db)
+        store = ModelMetadataStore(self._db)
         return store.list_tags()
 
     def list_all_metadata(self) -> List[Tuple[str, AnyModelRepoMetadata]]:
         """List metadata for all models that have it."""
-        store = ModelMetadataStoreSQL(self._db)
+        store = ModelMetadataStore(self._db)
         return store.list_all_metadata()
 
     def list_models(

invokeai/app/services/object_serializer/object_serializer_base.py

@@ -1,44 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Callable, Generic, TypeVar
-
-T = TypeVar("T")
-
-
-class ObjectSerializerBase(ABC, Generic[T]):
-    """Saves and loads arbitrary python objects."""
-
-    def __init__(self) -> None:
-        self._on_deleted_callbacks: list[Callable[[str], None]] = []
-
-    @abstractmethod
-    def load(self, name: str) -> T:
-        """
-        Loads the object.
-        :param name: The name of the object to load.
-        :raises ObjectNotFoundError: if the object is not found
-        """
-        pass
-
-    @abstractmethod
-    def save(self, obj: T) -> str:
-        """
-        Saves the object, returning its name.
-        :param obj: The object to save.
-        """
-        pass
-
-    @abstractmethod
-    def delete(self, name: str) -> None:
-        """
-        Deletes the object, if it exists.
-        :param name: The name of the object to delete.
-        """
-        pass
-
-    def on_deleted(self, on_deleted: Callable[[str], None]) -> None:
-        """Register a callback for when an object is deleted"""
-        self._on_deleted_callbacks.append(on_deleted)
-
-    def _on_deleted(self, name: str) -> None:
-        for callback in self._on_deleted_callbacks:
-            callback(name)

invokeai/app/services/object_serializer/object_serializer_common.py

@@ -1,5 +0,0 @@
-class ObjectNotFoundError(KeyError):
-    """Raised when an object is not found while loading"""
-
-    def __init__(self, name: str) -> None:
-        super().__init__(f"Object with name {name} not found")

invokeai/app/services/object_serializer/object_serializer_disk.py

@@ -1,85 +0,0 @@
-import tempfile
-import typing
-from dataclasses import dataclass
-from pathlib import Path
-from typing import TYPE_CHECKING, Optional, TypeVar
-
-import torch
-
-from invokeai.app.services.object_serializer.object_serializer_base import ObjectSerializerBase
-from invokeai.app.services.object_serializer.object_serializer_common import ObjectNotFoundError
-from invokeai.app.util.misc import uuid_string
-
-if TYPE_CHECKING:
-    from invokeai.app.services.invoker import Invoker
-
-
-T = TypeVar("T")
-
-
-@dataclass
-class DeleteAllResult:
-    deleted_count: int
-    freed_space_bytes: float
-
-
-class ObjectSerializerDisk(ObjectSerializerBase[T]):
-    """Disk-backed storage for arbitrary python objects. Serialization is handled by `torch.save` and `torch.load`.
-
-    :param output_dir: The folder where the serialized objects will be stored
-    :param ephemeral: If True, objects will be stored in a temporary directory inside the given output_dir and cleaned up on exit
-    """
-
-    def __init__(self, output_dir: Path, ephemeral: bool = False):
-        super().__init__()
-        self._ephemeral = ephemeral
-        self._base_output_dir = output_dir
-        self._base_output_dir.mkdir(parents=True, exist_ok=True)
-        # Must specify `ignore_cleanup_errors` to avoid fatal errors during cleanup on Windows
-        self._tempdir = (
-            tempfile.TemporaryDirectory(dir=self._base_output_dir, ignore_cleanup_errors=True) if ephemeral else None
-        )
-        self._output_dir = Path(self._tempdir.name) if self._tempdir else self._base_output_dir
-        self.__obj_class_name: Optional[str] = None
-
-    def load(self, name: str) -> T:
-        file_path = self._get_path(name)
-        try:
-            return torch.load(file_path)  # pyright: ignore [reportUnknownMemberType]
-        except FileNotFoundError as e:
-            raise ObjectNotFoundError(name) from e
-
-    def save(self, obj: T) -> str:
-        name = self._new_name()
-        file_path = self._get_path(name)
-        torch.save(obj, file_path)  # pyright: ignore [reportUnknownMemberType]
-        return name
-
-    def delete(self, name: str) -> None:
-        file_path = self._get_path(name)
-        file_path.unlink()
-
-    @property
-    def _obj_class_name(self) -> str:
-        if not self.__obj_class_name:
-            # `__orig_class__` is not available in the constructor for some technical, undoubtedly very pythonic reason
-            self.__obj_class_name = typing.get_args(self.__orig_class__)[0].__name__  # pyright: ignore [reportUnknownMemberType, reportAttributeAccessIssue]
-        return self.__obj_class_name
-
-    def _get_path(self, name: str) -> Path:
-        return self._output_dir / name
-
-    def _new_name(self) -> str:
-        return f"{self._obj_class_name}_{uuid_string()}"
-
-    def _tempdir_cleanup(self) -> None:
-        """Calls `cleanup` on the temporary directory, if it exists."""
-        if self._tempdir:
-            self._tempdir.cleanup()
-
-    def __del__(self) -> None:
-        # In case the service is not properly stopped, clean up the temporary directory when the class instance is GC'd.
-        self._tempdir_cleanup()
-
-    def stop(self, invoker: "Invoker") -> None:
-        self._tempdir_cleanup()

invokeai/app/services/object_serializer/object_serializer_forward_cache.py

@@ -1,65 +0,0 @@
-from queue import Queue
-from typing import TYPE_CHECKING, Optional, TypeVar
-
-from invokeai.app.services.object_serializer.object_serializer_base import ObjectSerializerBase
-
-T = TypeVar("T")
-
-if TYPE_CHECKING:
-    from invokeai.app.services.invoker import Invoker
-
-
-class ObjectSerializerForwardCache(ObjectSerializerBase[T]):
-    """
-    Provides a LRU cache for an instance of `ObjectSerializerBase`.
-    Saving an object to the cache always writes through to the underlying storage.
-    """
-
-    def __init__(self, underlying_storage: ObjectSerializerBase[T], max_cache_size: int = 20):
-        super().__init__()
-        self._underlying_storage = underlying_storage
-        self._cache: dict[str, T] = {}
-        self._cache_ids = Queue[str]()
-        self._max_cache_size = max_cache_size
-
-    def start(self, invoker: "Invoker") -> None:
-        self._invoker = invoker
-        start_op = getattr(self._underlying_storage, "start", None)
-        if callable(start_op):
-            start_op(invoker)
-
-    def stop(self, invoker: "Invoker") -> None:
-        self._invoker = invoker
-        stop_op = getattr(self._underlying_storage, "stop", None)
-        if callable(stop_op):
-            stop_op(invoker)
-
-    def load(self, name: str) -> T:
-        cache_item = self._get_cache(name)
-        if cache_item is not None:
-            return cache_item
-
-        obj = self._underlying_storage.load(name)
-        self._set_cache(name, obj)
-        return obj
-
-    def save(self, obj: T) -> str:
-        name = self._underlying_storage.save(obj)
-        self._set_cache(name, obj)
-        return name
-
-    def delete(self, name: str) -> None:
-        self._underlying_storage.delete(name)
-        if name in self._cache:
-            del self._cache[name]
-        self._on_deleted(name)
-
-    def _get_cache(self, name: str) -> Optional[T]:
-        return None if name not in self._cache else self._cache[name]
-
-    def _set_cache(self, name: str, data: T):
-        if name not in self._cache:
-            self._cache[name] = data
-            self._cache_ids.put(name)
-            if self._cache_ids.qsize() > self._max_cache_size:
-                self._cache.pop(self._cache_ids.get())

invokeai/app/services/shared/graph.py

@@ -13,11 +13,14 @@ from invokeai.app.invocations import *  # noqa: F401 F403
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    Input,
+    InputField,
+    InvocationContext,
+    OutputField,
+    UIType,
     invocation,
     invocation_output,
 )
-from invokeai.app.invocations.fields import Input, InputField, OutputField, UIType
-from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.misc import uuid_string
 
 # in 3.10 this would be "from types import NoneType"

invokeai/app/services/shared/invocation_context.py

@@ -1,471 +0,0 @@
-from dataclasses import dataclass
-from pathlib import Path
-from typing import TYPE_CHECKING, Optional
-
-from PIL.Image import Image
-from torch import Tensor
-
-from invokeai.app.invocations.constants import IMAGE_MODES
-from invokeai.app.invocations.fields import MetadataField, WithBoard, WithMetadata
-from invokeai.app.services.boards.boards_common import BoardDTO
-from invokeai.app.services.config.config_default import InvokeAIAppConfig
-from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.app.services.images.images_common import ImageDTO
-from invokeai.app.services.invocation_services import InvocationServices
-from invokeai.app.services.workflow_records.workflow_records_common import WorkflowWithoutID
-from invokeai.app.util.step_callback import stable_diffusion_step_callback
-from invokeai.backend.model_manager.config import AnyModelConfig, BaseModelType, ModelFormat, ModelType, SubModelType
-from invokeai.backend.model_manager.load.load_base import LoadedModel
-from invokeai.backend.model_manager.metadata.metadata_base import AnyModelRepoMetadata
-from invokeai.backend.stable_diffusion.diffusers_pipeline import PipelineIntermediateState
-from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningFieldData
-
-if TYPE_CHECKING:
-    from invokeai.app.invocations.baseinvocation import BaseInvocation
-
-"""
-The InvocationContext provides access to various services and data about the current invocation.
-
-We do not provide the invocation services directly, as their methods are both dangerous and
-inconvenient to use.
-
-For example:
-- The `images` service allows nodes to delete or unsafely modify existing images.
-- The `configuration` service allows nodes to change the app's config at runtime.
-- The `events` service allows nodes to emit arbitrary events.
-
-Wrapping these services provides a simpler and safer interface for nodes to use.
-
-When a node executes, a fresh `InvocationContext` is built for it, ensuring nodes cannot interfere
-with each other.
-
-Many of the wrappers have the same signature as the methods they wrap. This allows us to write
-user-facing docstrings and not need to go and update the internal services to match.
-
-Note: The docstrings are in weird places, but that's where they must be to get IDEs to see them.
-"""
-
-
-@dataclass
-class InvocationContextData:
-    invocation: "BaseInvocation"
-    """The invocation that is being executed."""
-    session_id: str
-    """The session that is being executed."""
-    queue_id: str
-    """The queue in which the session is being executed."""
-    source_node_id: str
-    """The ID of the node from which the currently executing invocation was prepared."""
-    queue_item_id: int
-    """The ID of the queue item that is being executed."""
-    batch_id: str
-    """The ID of the batch that is being executed."""
-    workflow: Optional[WorkflowWithoutID] = None
-    """The workflow associated with this queue item, if any."""
-
-
-class InvocationContextInterface:
-    def __init__(self, services: InvocationServices, context_data: InvocationContextData) -> None:
-        self._services = services
-        self._context_data = context_data
-
-
-class BoardsInterface(InvocationContextInterface):
-    def create(self, board_name: str) -> BoardDTO:
-        """
-        Creates a board.
-
-        :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.
-
-        :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.
-        """
-        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.
-
-        :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.
-
-        :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.
-
-        :param message: The message to log.
-        """
-        self._services.logger.debug(message)
-
-    def info(self, message: str) -> None:
-        """
-        Logs an info message.
-
-        :param message: The message to log.
-        """
-        self._services.logger.info(message)
-
-    def warning(self, message: str) -> None:
-        """
-        Logs a warning message.
-
-        :param message: The message to log.
-        """
-        self._services.logger.warning(message)
-
-    def error(self, message: str) -> None:
-        """
-        Logs an error message.
-
-        :param message: The message to log.
-        """
-        self._services.logger.error(message)
-
-
-class ImagesInterface(InvocationContextInterface):
-    def save(
-        self,
-        image: Image,
-        board_id: Optional[str] = None,
-        image_category: ImageCategory = ImageCategory.GENERAL,
-        metadata: Optional[MetadataField] = None,
-    ) -> ImageDTO:
-        """
-        Saves an image, returning its DTO.
-
-        If the current queue item has a workflow or metadata, it is automatically saved with the image.
-
-        :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!**
-        :param image_category: The category of the image. Only the GENERAL category is added \
-            to the gallery.
-        :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!**
-        """
-
-        # If `metadata` is provided directly, use that. Else, use the metadata provided by `WithMetadata`, falling back to None.
-        metadata_ = None
-        if metadata:
-            metadata_ = metadata
-        elif isinstance(self._context_data.invocation, WithMetadata):
-            metadata_ = self._context_data.invocation.metadata
-
-        # If `board_id` is provided directly, use that. Else, use the board provided by `WithBoard`, falling back to None.
-        board_id_ = None
-        if board_id:
-            board_id_ = board_id
-        elif isinstance(self._context_data.invocation, WithBoard) and self._context_data.invocation.board:
-            board_id_ = self._context_data.invocation.board.board_id
-
-        return self._services.images.create(
-            image=image,
-            is_intermediate=self._context_data.invocation.is_intermediate,
-            image_category=image_category,
-            board_id=board_id_,
-            metadata=metadata_,
-            image_origin=ResourceOrigin.INTERNAL,
-            workflow=self._context_data.workflow,
-            session_id=self._context_data.session_id,
-            node_id=self._context_data.invocation.id,
-        )
-
-    def get_pil(self, image_name: str, mode: IMAGE_MODES | None = None) -> Image:
-        """
-        Gets an 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:
-            try:
-                image = image.convert(mode)
-            except ValueError:
-                self._services.logger.warning(
-                    f"Could not convert image from {image.mode} to {mode}. Using original mode instead."
-                )
-        return image
-
-    def get_metadata(self, image_name: str) -> Optional[MetadataField]:
-        """
-        Gets an 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.
-
-        :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.
-
-        :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.
-
-        :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.
-
-        :param conditioning_context_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.
-
-        :param name: The name of the conditioning data to load.
-        """
-
-        return self._services.conditioning.load(name)
-
-
-class ModelsInterface(InvocationContextInterface):
-    def exists(self, key: str) -> bool:
-        """
-        Checks if a model exists.
-
-        :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.
-
-        :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
-        # the event payloads.
-
-        return self._services.model_manager.load_model_by_key(
-            key=key, submodel_type=submodel_type, context_data=self._context_data
-        )
-
-    def load_by_attrs(
-        self, model_name: str, base_model: BaseModelType, model_type: ModelType, submodel: Optional[SubModelType] = None
-    ) -> LoadedModel:
-        """
-        Loads a model by its 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.load_model_by_attr(
-            model_name=model_name,
-            base_model=base_model,
-            model_type=model_type,
-            submodel=submodel,
-            context_data=self._context_data,
-        )
-
-    def get_config(self, key: str) -> AnyModelConfig:
-        """
-        Gets a model's info, an dict-like object.
-
-        :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.
-
-        :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.
-
-        :param path: The path to search for.
-        """
-        return self._services.model_manager.store.search_by_path(path)
-
-    def search_by_attrs(
-        self,
-        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.
-
-        :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=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."""
-
-        return self._services.configuration.get_config()
-
-
-class UtilInterface(InvocationContextInterface):
-    def sd_step_callback(self, intermediate_state: PipelineIntermediateState, base_model: BaseModelType) -> None:
-        """
-        The step callback emits a progress event with the current step, the total number of
-        steps, a preview image, and some other internal metadata.
-
-        This should be called after each denoising step.
-
-        :param intermediate_state: The intermediate state of the diffusion pipeline.
-        :param base_model: The base model for the current denoising step.
-        """
-
-        # The step callback needs access to the events and the invocation queue services, but this
-        # represents a dangerous level of access.
-        #
-        # We wrap the step callback so that nodes do not have direct access to these services.
-
-        stable_diffusion_step_callback(
-            context_data=self._context_data,
-            intermediate_state=intermediate_state,
-            base_model=base_model,
-            invocation_queue=self._services.queue,
-            events=self._services.events,
-        )
-
-
-class InvocationContext:
-    """
-    The `InvocationContext` provides access to various services and data for the current invocation.
-    """
-
-    def __init__(
-        self,
-        images: ImagesInterface,
-        tensors: TensorsInterface,
-        conditioning: ConditioningInterface,
-        models: ModelsInterface,
-        logger: LoggerInterface,
-        config: ConfigInterface,
-        util: UtilInterface,
-        boards: BoardsInterface,
-        context_data: InvocationContextData,
-        services: InvocationServices,
-    ) -> None:
-        self.images = images
-        """Provides methods to save, get and update images and their metadata."""
-        self.tensors = tensors
-        """Provides methods to save and get tensors, including image, noise, masks, and masked images."""
-        self.conditioning = conditioning
-        """Provides methods to save and get conditioning data."""
-        self.models = models
-        """Provides methods to check if a model exists, get a model, and get a model's info."""
-        self.logger = logger
-        """Provides access to the app logger."""
-        self.config = config
-        """Provides access to the app's config."""
-        self.util = util
-        """Provides utility methods."""
-        self.boards = boards
-        """Provides methods to interact with boards."""
-        self._data = context_data
-        """Provides data about the current queue item and invocation. This is an internal API and may change without warning."""
-        self._services = services
-        """Provides access to the full application services. This is an internal API and may change without warning."""
-
-
-def build_invocation_context(
-    services: InvocationServices,
-    context_data: InvocationContextData,
-) -> InvocationContext:
-    """
-    Builds the invocation context for a specific invocation execution.
-
-    :param invocation_services: The invocation services to wrap.
-    :param invocation_context_data: The invocation context data.
-    """
-
-    logger = LoggerInterface(services=services, context_data=context_data)
-    images = ImagesInterface(services=services, context_data=context_data)
-    tensors = TensorsInterface(services=services, context_data=context_data)
-    models = ModelsInterface(services=services, context_data=context_data)
-    config = ConfigInterface(services=services, context_data=context_data)
-    util = UtilInterface(services=services, context_data=context_data)
-    conditioning = ConditioningInterface(services=services, context_data=context_data)
-    boards = BoardsInterface(services=services, context_data=context_data)
-
-    ctx = InvocationContext(
-        images=images,
-        logger=logger,
-        config=config,
-        tensors=tensors,
-        models=models,
-        context_data=context_data,
-        util=util,
-        conditioning=conditioning,
-        services=services,
-        boards=boards,
-    )
-
-    return ctx

invokeai/app/services/shared/sqlite/sqlite_util.py

@@ -8,7 +8,6 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_2 import
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_3 import build_migration_3
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_4 import build_migration_4
 from invokeai.app.services.shared.sqlite_migrator.migrations.migration_5 import build_migration_5
-from invokeai.app.services.shared.sqlite_migrator.migrations.migration_6 import build_migration_6
 from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
 
 
@@ -34,7 +33,6 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
     migrator.register_migration(build_migration_3(app_config=config, logger=logger))
     migrator.register_migration(build_migration_4())
     migrator.register_migration(build_migration_5())
-    migrator.register_migration(build_migration_6())
     migrator.run_migrations()
 
     return db

invokeai/app/services/shared/sqlite_migrator/migrations/migration_6.py

@@ -1,62 +0,0 @@
-import sqlite3
-
-from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
-
-
-class Migration6Callback:
-    def __call__(self, cursor: sqlite3.Cursor) -> None:
-        self._recreate_model_triggers(cursor)
-        self._delete_ip_adapters(cursor)
-
-    def _recreate_model_triggers(self, cursor: sqlite3.Cursor) -> None:
-        """
-        Adds the timestamp trigger to the model_config table.
-
-        This trigger was inadvertently dropped in earlier migration scripts.
-        """
-
-        cursor.execute(
-            """--sql
-            CREATE TRIGGER IF NOT EXISTS model_config_updated_at
-            AFTER UPDATE
-            ON model_config FOR EACH ROW
-            BEGIN
-                UPDATE model_config SET updated_at = STRFTIME('%Y-%m-%d %H:%M:%f', 'NOW')
-                    WHERE id = old.id;
-            END;
-            """
-        )
-
-    def _delete_ip_adapters(self, cursor: sqlite3.Cursor) -> None:
-        """
-        Delete all the IP adapters.
-
-        The model manager will automatically find and re-add them after the migration
-        is done. This allows the manager to add the correct image encoder to their
-        configuration records.
-        """
-
-        cursor.execute(
-            """--sql
-            DELETE FROM model_config
-              WHERE type='ip_adapter';
-            """
-        )
-
-
-def build_migration_6() -> Migration:
-    """
-    Build the migration from database version 5 to 6.
-
-    This migration does the following:
-    - Adds the model_config_updated_at trigger if it does not exist
-    - Delete all ip_adapter models so that the model prober can find and
-      update with the correct image processor model.
-    """
-    migration_6 = Migration(
-        from_version=5,
-        to_version=6,
-        callback=Migration6Callback(),
-    )
-
-    return migration_6

invokeai/app/shared/fields.py

@@ -0,0 +1,67 @@
+class FieldDescriptions:
+    denoising_start = "When to start denoising, expressed a percentage of total steps"
+    denoising_end = "When to stop denoising, expressed a percentage of total steps"
+    cfg_scale = "Classifier-Free Guidance scale"
+    cfg_rescale_multiplier = "Rescale multiplier for CFG guidance, used for models trained with zero-terminal SNR"
+    scheduler = "Scheduler to use during inference"
+    positive_cond = "Positive conditioning tensor"
+    negative_cond = "Negative conditioning tensor"
+    noise = "Noise tensor"
+    clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
+    unet = "UNet (scheduler, LoRAs)"
+    vae = "VAE"
+    cond = "Conditioning tensor"
+    controlnet_model = "ControlNet model to load"
+    vae_model = "VAE model to load"
+    lora_model = "LoRA model to load"
+    main_model = "Main model (UNet, VAE, CLIP) to load"
+    sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
+    sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
+    onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
+    lora_weight = "The weight at which the LoRA is applied to each model"
+    compel_prompt = "Prompt to be parsed by Compel to create a conditioning tensor"
+    raw_prompt = "Raw prompt text (no parsing)"
+    sdxl_aesthetic = "The aesthetic score to apply to the conditioning tensor"
+    skipped_layers = "Number of layers to skip in text encoder"
+    seed = "Seed for random number generation"
+    steps = "Number of steps to run"
+    width = "Width of output (px)"
+    height = "Height of output (px)"
+    control = "ControlNet(s) to apply"
+    ip_adapter = "IP-Adapter to apply"
+    t2i_adapter = "T2I-Adapter(s) to apply"
+    denoised_latents = "Denoised latents tensor"
+    latents = "Latents tensor"
+    strength = "Strength of denoising (proportional to steps)"
+    metadata = "Optional metadata to be saved with the image"
+    metadata_collection = "Collection of Metadata"
+    metadata_item_polymorphic = "A single metadata item or collection of metadata items"
+    metadata_item_label = "Label for this metadata item"
+    metadata_item_value = "The value for this metadata item (may be any type)"
+    workflow = "Optional workflow to be saved with the image"
+    interp_mode = "Interpolation mode"
+    torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
+    fp32 = "Whether or not to use full float32 precision"
+    precision = "Precision to use"
+    tiled = "Processing using overlapping tiles (reduce memory consumption)"
+    detect_res = "Pixel resolution for detection"
+    image_res = "Pixel resolution for output image"
+    safe_mode = "Whether or not to use safe mode"
+    scribble_mode = "Whether or not to use scribble mode"
+    scale_factor = "The factor by which to scale"
+    blend_alpha = (
+        "Blending factor. 0.0 = use input A only, 1.0 = use input B only, 0.5 = 50% mix of input A and input B."
+    )
+    num_1 = "The first number"
+    num_2 = "The second number"
+    mask = "The mask to use for the operation"
+    board = "The board to save the image to"
+    image = "The image to process"
+    tile_size = "Tile size"
+    inclusive_low = "The inclusive low value"
+    exclusive_high = "The exclusive high value"
+    decimal_places = "The number of decimal places to round to"
+    freeu_s1 = 'Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
+    freeu_s2 = 'Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
+    freeu_b1 = "Scaling factor for stage 1 to amplify the contributions of backbone features."
+    freeu_b2 = "Scaling factor for stage 2 to amplify the contributions of backbone features."

invokeai/app/shared/models.py

@@ -1,6 +1,6 @@
 from pydantic import BaseModel, Field
 
-from invokeai.app.invocations.fields import FieldDescriptions
+from invokeai.app.shared.fields import FieldDescriptions
 
 
 class FreeUConfig(BaseModel):

invokeai/app/util/misc.py

@@ -5,7 +5,7 @@ import uuid
 import numpy as np
 
 
-def get_timestamp() -> int:
+def get_timestamp():
     return int(datetime.datetime.now(datetime.timezone.utc).timestamp())
 
 
@@ -20,16 +20,16 @@ def get_datetime_from_iso_timestamp(iso_timestamp: str) -> datetime.datetime:
 SEED_MAX = np.iinfo(np.uint32).max
 
 
-def get_random_seed() -> int:
+def get_random_seed():
     rng = np.random.default_rng(seed=None)
     return int(rng.integers(0, SEED_MAX))
 
 
-def uuid_string() -> str:
+def uuid_string():
     res = uuid.uuid4()
     return str(res)
 
 
-def is_optional(value: typing.Any) -> bool:
+def is_optional(value: typing.Any):
     """Checks if a value is typed as Optional. Note that Optional is sugar for Union[x, None]."""
     return typing.get_origin(value) is typing.Union and type(None) in typing.get_args(value)

invokeai/app/util/step_callback.py

@@ -1,18 +1,12 @@
-from typing import TYPE_CHECKING
-
 import torch
 from PIL import Image
 
 from invokeai.app.services.invocation_processor.invocation_processor_common import CanceledException, ProgressImage
-from invokeai.backend.model_manager.config import BaseModelType
 
+from ...backend.model_management.models import BaseModelType
 from ...backend.stable_diffusion import PipelineIntermediateState
 from ...backend.util.util import image_to_dataURL
-
-if TYPE_CHECKING:
-    from invokeai.app.services.events.events_base import EventServiceBase
-    from invokeai.app.services.invocation_queue.invocation_queue_base import InvocationQueueABC
-    from invokeai.app.services.shared.invocation_context import InvocationContextData
+from ..invocations.baseinvocation import InvocationContext
 
 
 def sample_to_lowres_estimated_image(samples, latent_rgb_factors, smooth_matrix=None):
@@ -31,13 +25,13 @@ def sample_to_lowres_estimated_image(samples, latent_rgb_factors, smooth_matrix=
 
 
 def stable_diffusion_step_callback(
-    context_data: "InvocationContextData",
+    context: InvocationContext,
     intermediate_state: PipelineIntermediateState,
+    node: dict,
+    source_node_id: str,
     base_model: BaseModelType,
-    invocation_queue: "InvocationQueueABC",
-    events: "EventServiceBase",
-) -> None:
-    if invocation_queue.is_canceled(context_data.session_id):
+):
+    if context.services.queue.is_canceled(context.graph_execution_state_id):
         raise CanceledException
 
     # Some schedulers report not only the noisy latents at the current timestep,
@@ -114,13 +108,13 @@ def stable_diffusion_step_callback(
 
     dataURL = image_to_dataURL(image, image_format="JPEG")
 
-    events.emit_generator_progress(
-        queue_id=context_data.queue_id,
-        queue_item_id=context_data.queue_item_id,
-        queue_batch_id=context_data.batch_id,
-        graph_execution_state_id=context_data.session_id,
-        node_id=context_data.invocation.id,
-        source_node_id=context_data.source_node_id,
+    context.services.events.emit_generator_progress(
+        queue_id=context.queue_id,
+        queue_item_id=context.queue_item_id,
+        queue_batch_id=context.queue_batch_id,
+        graph_execution_state_id=context.graph_execution_state_id,
+        node=node,
+        source_node_id=source_node_id,
         progress_image=ProgressImage(width=width, height=height, dataURL=dataURL),
         step=intermediate_state.step,
         order=intermediate_state.order,

invokeai/backend/__init__.py

@@ -1,3 +1,5 @@
 """
 Initialization file for invokeai.backend
 """
+from .model_management import BaseModelType, ModelCache, ModelInfo, ModelManager, ModelType, SubModelType  # noqa: F401
+from .model_management.models import SilenceWarnings  # noqa: F401

invokeai/backend/embeddings/__init__.py

@@ -1,4 +0,0 @@
-"""Initialization file for invokeai.backend.embeddings modules."""
-
-# from .model_patcher import ModelPatcher
-# __all__ = ["ModelPatcher"]

invokeai/backend/embeddings/embedding_base.py

@@ -1,12 +0,0 @@
-"""Base class for LoRA and Textual Inversion models.
-
-The EmbeddingRaw class is the base class of LoRAModelRaw and TextualInversionModelRaw,
-and is used for type checking of calls to the model patcher.
-
-The use of "Raw" here is a historical artifact, and carried forward in
-order to avoid confusion.
-"""
-
-
-class EmbeddingModelRaw:
-    """Base class for LoRA and Textual Inversion models."""

invokeai/backend/image_util/safety_checker.py

@@ -8,8 +8,8 @@ from PIL import Image
 
 import invokeai.backend.util.logging as logger
 from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend import SilenceWarnings
 from invokeai.backend.util.devices import choose_torch_device
-from invokeai.backend.util.silence_warnings import SilenceWarnings
 
 config = InvokeAIAppConfig.get_config()
 

invokeai/backend/install/install_helper.py

@@ -25,20 +25,18 @@ from invokeai.app.services.model_install import (
     ModelSource,
     URLModelSource,
 )
-from invokeai.app.services.model_metadata import ModelMetadataStoreSQL
 from invokeai.app.services.model_records import ModelRecordServiceBase, ModelRecordServiceSQL
 from invokeai.app.services.shared.sqlite.sqlite_util import init_db
 from invokeai.backend.model_manager import (
     BaseModelType,
     InvalidModelConfigException,
-    ModelRepoVariant,
     ModelType,
 )
 from invokeai.backend.model_manager.metadata import UnknownMetadataException
 from invokeai.backend.util.logging import InvokeAILogger
 
 # name of the starter models file
-INITIAL_MODELS = "INITIAL_MODELS.yaml"
+INITIAL_MODELS = "INITIAL_MODELS2.yaml"
 
 
 def initialize_record_store(app_config: InvokeAIAppConfig) -> ModelRecordServiceBase:
@@ -46,7 +44,7 @@ def initialize_record_store(app_config: InvokeAIAppConfig) -> ModelRecordService
     logger = InvokeAILogger.get_logger(config=app_config)
     image_files = DiskImageFileStorage(f"{app_config.output_path}/images")
     db = init_db(config=app_config, logger=logger, image_files=image_files)
-    obj: ModelRecordServiceBase = ModelRecordServiceSQL(db, ModelMetadataStoreSQL(db))
+    obj: ModelRecordServiceBase = ModelRecordServiceSQL(db)
     return obj
 
 
@@ -55,10 +53,12 @@ def initialize_installer(
 ) -> ModelInstallServiceBase:
     """Return an initialized ModelInstallService object."""
     record_store = initialize_record_store(app_config)
+    metadata_store = record_store.metadata_store
     download_queue = DownloadQueueService()
     installer = ModelInstallService(
         app_config=app_config,
         record_store=record_store,
+        metadata_store=metadata_store,
         download_queue=download_queue,
         event_bus=event_bus,
     )
@@ -98,13 +98,11 @@ class TqdmEventService(EventServiceBase):
         super().__init__()
         self._bars: Dict[str, tqdm] = {}
         self._last: Dict[str, int] = {}
-        self._logger = InvokeAILogger.get_logger(__name__)
 
     def dispatch(self, event_name: str, payload: Any) -> None:
         """Dispatch an event by appending it to self.events."""
-        data = payload["data"]
-        source = data["source"]
         if payload["event"] == "model_install_downloading":
+            data = payload["data"]
             dest = data["local_path"]
             total_bytes = data["total_bytes"]
             bytes = data["bytes"]
@@ -113,12 +111,6 @@ class TqdmEventService(EventServiceBase):
                 self._last[dest] = 0
             self._bars[dest].update(bytes - self._last[dest])
             self._last[dest] = bytes
-        elif payload["event"] == "model_install_completed":
-            self._logger.info(f"{source}: installed successfully.")
-        elif payload["event"] == "model_install_error":
-            self._logger.warning(f"{source}: installation failed with error {data['error']}")
-        elif payload["event"] == "model_install_cancelled":
-            self._logger.warning(f"{source}: installation cancelled")
 
 
 class InstallHelper(object):
@@ -233,19 +225,11 @@ class InstallHelper(object):
 
         if model_path.exists():  # local file on disk
             return LocalModelSource(path=model_path.absolute(), inplace=True)
-
-        # parsing huggingface repo ids
-        # we're going to do a little trick that allows for extended repo_ids of form "foo/bar:fp16"
-        variants = "|".join([x.lower() for x in ModelRepoVariant.__members__])
-        if match := re.match(f"^([^/]+/[^/]+?)(?::({variants}))?$", model_path_id_or_url):
-            repo_id = match.group(1)
-            repo_variant = ModelRepoVariant(match.group(2)) if match.group(2) else None
-            subfolder = Path(model_info.subfolder) if model_info.subfolder else None
+        if re.match(r"^[^/]+/[^/]+$", model_path_id_or_url):  # hugging face repo_id
             return HFModelSource(
-                repo_id=repo_id,
+                repo_id=model_path_id_or_url,
                 access_token=HfFolder.get_token(),
-                subfolder=subfolder,
-                variant=repo_variant,
+                subfolder=model_info.subfolder,
             )
         if re.match(r"^(http|https):", model_path_id_or_url):
             return URLModelSource(url=AnyHttpUrl(model_path_id_or_url))
@@ -286,14 +270,12 @@ class InstallHelper(object):
                 model_name=model_name,
             )
             if len(matches) > 1:
-                self._logger.error(
-                    "{model_to_remove} is ambiguous. Please use model_base/model_type/model_name (e.g. sd-1/main/my_model) to disambiguate"
-                )
+                print(f"{model} is ambiguous. Please use model_type:model_name (e.g. main:my_model) to disambiguate.")
             elif not matches:
-                self._logger.error(f"{model_to_remove}: unknown model")
+                print(f"{model}: unknown model")
             else:
                 for m in matches:
-                    self._logger.info(f"Deleting {m.type}:{m.name}")
+                    print(f"Deleting {m.type}:{m.name}")
                     installer.delete(m.key)
 
         installer.wait_for_installs()

invokeai/backend/install/invokeai_configure.py

@@ -18,30 +18,31 @@ from argparse import Namespace
 from enum import Enum
 from pathlib import Path
 from shutil import get_terminal_size
-from typing import Any, Optional, Set, Tuple, Type, get_args, get_type_hints
+from typing import Any, get_args, get_type_hints
 from urllib import request
 
 import npyscreen
+import omegaconf
 import psutil
 import torch
 import transformers
-from diffusers import AutoencoderKL, ModelMixin
+import yaml
+from diffusers import AutoencoderKL
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from huggingface_hub import HfFolder
 from huggingface_hub import login as hf_hub_login
-from omegaconf import DictConfig, OmegaConf
-from pydantic.error_wrappers import ValidationError
+from omegaconf import OmegaConf
+from pydantic import ValidationError
 from tqdm import tqdm
 from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 
 import invokeai.configs as configs
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.install.install_helper import InstallHelper, InstallSelections
 from invokeai.backend.install.legacy_arg_parsing import legacy_parser
-from invokeai.backend.model_manager import BaseModelType, ModelType
-from invokeai.backend.util import choose_precision, choose_torch_device
+from invokeai.backend.install.model_install_backend import InstallSelections, ModelInstall, hf_download_from_pretrained
+from invokeai.backend.model_management.model_probe import BaseModelType, ModelType
 from invokeai.backend.util.logging import InvokeAILogger
-from invokeai.frontend.install.model_install import addModelsForm
+from invokeai.frontend.install.model_install import addModelsForm, process_and_execute
 
 # TO DO - Move all the frontend code into invokeai.frontend.install
 from invokeai.frontend.install.widgets import (
@@ -60,7 +61,7 @@ warnings.filterwarnings("ignore")
 transformers.logging.set_verbosity_error()
 
 
-def get_literal_fields(field: str) -> Tuple[Any]:
+def get_literal_fields(field) -> list[Any]:
     return get_args(get_type_hints(InvokeAIAppConfig).get(field))
 
 
@@ -79,13 +80,12 @@ ATTENTION_SLICE_CHOICES = get_literal_fields("attention_slice_size")
 GENERATION_OPT_CHOICES = ["sequential_guidance", "force_tiled_decode", "lazy_offload"]
 GB = 1073741824  # GB in bytes
 HAS_CUDA = torch.cuda.is_available()
-_, MAX_VRAM = torch.cuda.mem_get_info() if HAS_CUDA else (0.0, 0.0)
+_, MAX_VRAM = torch.cuda.mem_get_info() if HAS_CUDA else (0, 0)
+
 
 MAX_VRAM /= GB
 MAX_RAM = psutil.virtual_memory().total / GB
 
-FORCE_FULL_PRECISION = False
-
 INIT_FILE_PREAMBLE = """# InvokeAI initialization file
 # This is the InvokeAI initialization file, which contains command-line default values.
 # Feel free to edit. If anything goes wrong, you can re-initialize this file by deleting
@@ -96,15 +96,13 @@ logger = InvokeAILogger.get_logger()
 
 
 class DummyWidgetValue(Enum):
-    """Dummy widget values."""
-
     zero = 0
     true = True
     false = False
 
 
 # --------------------------------------------
-def postscript(errors: Set[str]) -> None:
+def postscript(errors: None):
     if not any(errors):
         message = f"""
 ** INVOKEAI INSTALLATION SUCCESSFUL **
@@ -114,6 +112,9 @@ then run one of the following commands to start InvokeAI.
 Web UI:
    invokeai-web
 
+Command-line client:
+   invokeai
+
 If you installed using an installation script, run:
   {config.root_path}/invoke.{"bat" if sys.platform == "win32" else "sh"}
 
@@ -142,7 +143,7 @@ def yes_or_no(prompt: str, default_yes=True):
 
 
 # ---------------------------------------------
-def HfLogin(access_token) -> None:
+def HfLogin(access_token) -> str:
     """
     Helper for logging in to Huggingface
     The stdout capture is needed to hide the irrelevant "git credential helper" warning
@@ -161,7 +162,7 @@ def HfLogin(access_token) -> None:
 
 # -------------------------------------
 class ProgressBar:
-    def __init__(self, model_name: str = "file"):
+    def __init__(self, model_name="file"):
         self.pbar = None
         self.name = model_name
 
@@ -178,22 +179,6 @@ class ProgressBar:
         self.pbar.update(block_size)
 
 
-# ---------------------------------------------
-def hf_download_from_pretrained(model_class: Type[ModelMixin], model_name: str, destination: Path, **kwargs: Any):
-    filter = lambda x: "fp16 is not a valid" not in x.getMessage()  # noqa E731
-    logger.addFilter(filter)
-    try:
-        model = model_class.from_pretrained(
-            model_name,
-            resume_download=True,
-            **kwargs,
-        )
-        model.save_pretrained(destination, safe_serialization=True)
-    finally:
-        logger.removeFilter(filter)
-    return destination
-
-
 # ---------------------------------------------
 def download_with_progress_bar(model_url: str, model_dest: str, label: str = "the"):
     try:
@@ -264,7 +249,6 @@ def download_conversion_models():
 
 
 # ---------------------------------------------
-# TO DO: use the download queue here.
 def download_realesrgan():
     logger.info("Installing ESRGAN Upscaling models...")
     URLs = [
@@ -304,19 +288,18 @@ def download_lama():
 
 
 # ---------------------------------------------
-def download_support_models() -> None:
+def download_support_models():
     download_realesrgan()
     download_lama()
     download_conversion_models()
 
 
 # -------------------------------------
-def get_root(root: Optional[str] = None) -> str:
+def get_root(root: str = None) -> str:
     if root:
         return root
-    elif root := os.environ.get("INVOKEAI_ROOT"):
-        assert root is not None
-        return root
+    elif os.environ.get("INVOKEAI_ROOT"):
+        return os.environ.get("INVOKEAI_ROOT")
     else:
         return str(config.root_path)
 
@@ -407,7 +390,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
             begin_entry_at=3,
             max_height=2,
             relx=30,
-            max_width=80,
+            max_width=56,
             scroll_exit=True,
         )
         self.add_widget_intelligent(
@@ -472,25 +455,6 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
             max_width=110,
             scroll_exit=True,
         )
-        self.add_widget_intelligent(
-            npyscreen.TitleFixedText,
-            name="Model disk conversion cache size (GB). This is used to cache safetensors files that need to be converted to diffusers..",
-            begin_entry_at=0,
-            editable=False,
-            color="CONTROL",
-            scroll_exit=True,
-        )
-        self.nextrely -= 1
-        self.disk = self.add_widget_intelligent(
-            npyscreen.Slider,
-            value=clip(old_opts.convert_cache, range=(0, 100), step=0.5),
-            out_of=100,
-            lowest=0.0,
-            step=0.5,
-            relx=8,
-            scroll_exit=True,
-        )
-        self.nextrely += 1
         self.add_widget_intelligent(
             npyscreen.TitleFixedText,
             name="Model RAM cache size (GB). Make this at least large enough to hold a single full model (2GB for SD-1, 6GB for SDXL).",
@@ -531,14 +495,6 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
             )
         else:
             self.vram = DummyWidgetValue.zero
-
-        self.nextrely += 1
-        self.add_widget_intelligent(
-            npyscreen.FixedText,
-            value="Location of the database used to store model path and configuration information:",
-            editable=False,
-            color="CONTROL",
-        )
         self.nextrely += 1
         self.outdir = self.add_widget_intelligent(
             FileBox,
@@ -550,21 +506,19 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
             labelColor="GOOD",
             begin_entry_at=40,
             max_height=3,
-            max_width=127,
             scroll_exit=True,
         )
         self.autoimport_dirs = {}
         self.autoimport_dirs["autoimport_dir"] = self.add_widget_intelligent(
             FileBox,
-            name="Optional folder to scan for new checkpoints, ControlNets, LoRAs and TI models",
-            value=str(config.root_path / config.autoimport_dir) if config.autoimport_dir else "",
+            name="Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models",
+            value=str(config.root_path / config.autoimport_dir),
             select_dir=True,
             must_exist=False,
             use_two_lines=False,
             labelColor="GOOD",
             begin_entry_at=32,
             max_height=3,
-            max_width=127,
             scroll_exit=True,
         )
         self.nextrely += 1
@@ -601,10 +555,6 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
         self.attention_slice_label.hidden = not show
         self.attention_slice_size.hidden = not show
 
-    def show_hide_model_conf_override(self, value):
-        self.model_conf_override.hidden = value
-        self.model_conf_override.display()
-
     def on_ok(self):
         options = self.marshall_arguments()
         if self.validate_field_values(options):
@@ -634,21 +584,18 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
         else:
             return True
 
-    def marshall_arguments(self) -> Namespace:
+    def marshall_arguments(self):
         new_opts = Namespace()
 
         for attr in [
             "ram",
             "vram",
-            "convert_cache",
             "outdir",
         ]:
             if hasattr(self, attr):
                 setattr(new_opts, attr, getattr(self, attr).value)
 
         for attr in self.autoimport_dirs:
-            if not self.autoimport_dirs[attr].value:
-                continue
             directory = Path(self.autoimport_dirs[attr].value)
             if directory.is_relative_to(config.root_path):
                 directory = directory.relative_to(config.root_path)
@@ -663,18 +610,18 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
         generation_options = [GENERATION_OPT_CHOICES[x] for x in self.generation_options.value]
         for v in GENERATION_OPT_CHOICES:
             setattr(new_opts, v, v in generation_options)
+
         return new_opts
 
 
 class EditOptApplication(npyscreen.NPSAppManaged):
-    def __init__(self, program_opts: Namespace, invokeai_opts: InvokeAIAppConfig, install_helper: InstallHelper):
+    def __init__(self, program_opts: Namespace, invokeai_opts: Namespace):
         super().__init__()
         self.program_opts = program_opts
         self.invokeai_opts = invokeai_opts
         self.user_cancelled = False
         self.autoload_pending = True
-        self.install_helper = install_helper
-        self.install_selections = default_user_selections(program_opts, install_helper)
+        self.install_selections = default_user_selections(program_opts)
 
     def onStart(self):
         npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
@@ -693,6 +640,15 @@ class EditOptApplication(npyscreen.NPSAppManaged):
                 cycle_widgets=False,
             )
 
+    def new_opts(self):
+        return self.options.marshall_arguments()
+
+
+def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Namespace:
+    editApp = EditOptApplication(program_opts, invokeai_opts)
+    editApp.run()
+    return editApp.new_opts()
+
 
 def default_ramcache() -> float:
     """Run a heuristic for the default RAM cache based on installed RAM."""
@@ -704,19 +660,27 @@ def default_ramcache() -> float:
     )  # 2.1 is just large enough for sd 1.5 ;-)
 
 
-def default_startup_options(init_file: Path) -> InvokeAIAppConfig:
+def default_startup_options(init_file: Path) -> Namespace:
     opts = InvokeAIAppConfig.get_config()
-    opts.ram = default_ramcache()
-    opts.precision = "float32" if FORCE_FULL_PRECISION else choose_precision(torch.device(choose_torch_device()))
+    opts.ram = opts.ram or default_ramcache()
     return opts
 
 
-def default_user_selections(program_opts: Namespace, install_helper: InstallHelper) -> InstallSelections:
-    default_model = install_helper.default_model()
-    assert default_model is not None
-    default_models = [default_model] if program_opts.default_only else install_helper.recommended_models()
+def default_user_selections(program_opts: Namespace) -> InstallSelections:
+    try:
+        installer = ModelInstall(config)
+    except omegaconf.errors.ConfigKeyError:
+        logger.warning("Your models.yaml file is corrupt or out of date. Reinitializing")
+        initialize_rootdir(config.root_path, True)
+        installer = ModelInstall(config)
+
+    models = installer.all_models()
     return InstallSelections(
-        install_models=default_models if program_opts.yes_to_all else [],
+        install_models=[models[installer.default_model()].path or models[installer.default_model()].repo_id]
+        if program_opts.default_only
+        else [models[x].path or models[x].repo_id for x in installer.recommended_models()]
+        if program_opts.yes_to_all
+        else [],
     )
 
 
@@ -752,12 +716,22 @@ def initialize_rootdir(root: Path, yes_to_all: bool = False):
     path.mkdir(parents=True, exist_ok=True)
 
 
+def maybe_create_models_yaml(root: Path):
+    models_yaml = root / "configs" / "models.yaml"
+    if models_yaml.exists():
+        if OmegaConf.load(models_yaml).get("__metadata__"):  # up to date
+            return
+        else:
+            logger.info("Creating new models.yaml, original saved as models.yaml.orig")
+            models_yaml.rename(models_yaml.parent / "models.yaml.orig")
+
+    with open(models_yaml, "w") as yaml_file:
+        yaml_file.write(yaml.dump({"__metadata__": {"version": "3.0.0"}}))
+
+
 # -------------------------------------
-def run_console_ui(
-    program_opts: Namespace, initfile: Path, install_helper: InstallHelper
-) -> Tuple[Optional[Namespace], Optional[InstallSelections]]:
-    first_time = not (config.root_path / "invokeai.yaml").exists()
-    invokeai_opts = default_startup_options(initfile) if first_time else config
+def run_console_ui(program_opts: Namespace, initfile: Path = None) -> (Namespace, Namespace):
+    invokeai_opts = default_startup_options(initfile)
     invokeai_opts.root = program_opts.root
 
     if not set_min_terminal_size(MIN_COLS, MIN_LINES):
@@ -765,7 +739,13 @@ def run_console_ui(
             "Could not increase terminal size. Try running again with a larger window or smaller font size."
         )
 
-    editApp = EditOptApplication(program_opts, invokeai_opts, install_helper)
+    # the install-models application spawns a subprocess to install
+    # models, and will crash unless this is set before running.
+    import torch
+
+    torch.multiprocessing.set_start_method("spawn")
+
+    editApp = EditOptApplication(program_opts, invokeai_opts)
     editApp.run()
     if editApp.user_cancelled:
         return (None, None)
@@ -774,7 +754,7 @@ def run_console_ui(
 
 
 # -------------------------------------
-def write_opts(opts: InvokeAIAppConfig, init_file: Path) -> None:
+def write_opts(opts: Namespace, init_file: Path):
     """
     Update the invokeai.yaml file with values from current settings.
     """
@@ -782,7 +762,7 @@ def write_opts(opts: InvokeAIAppConfig, init_file: Path) -> None:
     new_config = InvokeAIAppConfig.get_config()
     new_config.root = config.root
 
-    for key, value in vars(opts).items():
+    for key, value in opts.__dict__.items():
         if hasattr(new_config, key):
             setattr(new_config, key, value)
 
@@ -799,7 +779,7 @@ def default_output_dir() -> Path:
 
 
 # -------------------------------------
-def write_default_options(program_opts: Namespace, initfile: Path) -> None:
+def write_default_options(program_opts: Namespace, initfile: Path):
     opt = default_startup_options(initfile)
     write_opts(opt, initfile)
 
@@ -809,11 +789,16 @@ def write_default_options(program_opts: Namespace, initfile: Path) -> None:
 # the legacy Args object in order to parse
 # the old init file and write out the new
 # yaml format.
-def migrate_init_file(legacy_format: Path) -> None:
+def migrate_init_file(legacy_format: Path):
     old = legacy_parser.parse_args([f"@{str(legacy_format)}"])
     new = InvokeAIAppConfig.get_config()
 
-    for attr in InvokeAIAppConfig.model_fields.keys():
+    fields = [
+        x
+        for x, y in InvokeAIAppConfig.model_fields.items()
+        if (y.json_schema_extra.get("category", None) if y.json_schema_extra else None) != "DEPRECATED"
+    ]
+    for attr in fields:
         if hasattr(old, attr):
             try:
                 setattr(new, attr, getattr(old, attr))
@@ -834,7 +819,7 @@ def migrate_init_file(legacy_format: Path) -> None:
 
 
 # -------------------------------------
-def migrate_models(root: Path) -> None:
+def migrate_models(root: Path):
     from invokeai.backend.install.migrate_to_3 import do_migrate
 
     do_migrate(root, root)
@@ -853,9 +838,7 @@ def migrate_if_needed(opt: Namespace, root: Path) -> bool:
         ):
             logger.info("** Migrating invokeai.init to invokeai.yaml")
             migrate_init_file(old_init_file)
-            omegaconf = OmegaConf.load(new_init_file)
-            assert isinstance(omegaconf, DictConfig)
-            config.parse_args(argv=[], conf=omegaconf)
+            config.parse_args(argv=[], conf=OmegaConf.load(new_init_file))
 
             if old_hub.exists():
                 migrate_models(config.root_path)
@@ -867,7 +850,6 @@ def migrate_if_needed(opt: Namespace, root: Path) -> bool:
 
 # -------------------------------------
 def main() -> None:
-    global FORCE_FULL_PRECISION  # FIXME
     parser = argparse.ArgumentParser(description="InvokeAI model downloader")
     parser.add_argument(
         "--skip-sd-weights",
@@ -919,6 +901,7 @@ def main() -> None:
         help="path to root of install directory",
     )
     opt = parser.parse_args()
+
     invoke_args = []
     if opt.root:
         invoke_args.extend(["--root", opt.root])
@@ -928,7 +911,6 @@ def main() -> None:
     logger = InvokeAILogger().get_logger(config=config)
 
     errors = set()
-    FORCE_FULL_PRECISION = opt.full_precision  # FIXME global
 
     try:
         # if we do a root migration/upgrade, then we are keeping previous
@@ -939,18 +921,14 @@ def main() -> None:
         # run this unconditionally in case new directories need to be added
         initialize_rootdir(config.root_path, opt.yes_to_all)
 
-        # this will initialize the models.yaml file if not present
-        install_helper = InstallHelper(config, logger)
-
-        models_to_download = default_user_selections(opt, install_helper)
+        models_to_download = default_user_selections(opt)
         new_init_file = config.root_path / "invokeai.yaml"
 
         if opt.yes_to_all:
             write_default_options(opt, new_init_file)
             init_options = Namespace(precision="float32" if opt.full_precision else "float16")
-
         else:
-            init_options, models_to_download = run_console_ui(opt, new_init_file, install_helper)
+            init_options, models_to_download = run_console_ui(opt, new_init_file)
             if init_options:
                 write_opts(init_options, new_init_file)
             else:
@@ -965,12 +943,10 @@ def main() -> None:
 
         if opt.skip_sd_weights:
             logger.warning("Skipping diffusion weights download per user request")
-
         elif models_to_download:
-            install_helper.add_or_delete(models_to_download)
+            process_and_execute(opt, models_to_download)
 
         postscript(errors=errors)
-
         if not opt.yes_to_all:
             input("Press any key to continue...")
     except WindowTooSmallException as e:

invokeai/backend/install/migrate_to_3.py

@@ -0,0 +1,591 @@
+"""
+Migrate the models directory and models.yaml file from an existing
+InvokeAI 2.3 installation to 3.0.0.
+"""
+
+import argparse
+import os
+import shutil
+import warnings
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Union
+
+import diffusers
+import transformers
+import yaml
+from diffusers import AutoencoderKL, StableDiffusionPipeline
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from omegaconf import DictConfig, OmegaConf
+from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer
+
+import invokeai.backend.util.logging as logger
+from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend.model_management import ModelManager
+from invokeai.backend.model_management.model_probe import BaseModelType, ModelProbe, ModelProbeInfo, ModelType
+
+warnings.filterwarnings("ignore")
+transformers.logging.set_verbosity_error()
+diffusers.logging.set_verbosity_error()
+
+
+# holder for paths that we will migrate
+@dataclass
+class ModelPaths:
+    models: Path
+    embeddings: Path
+    loras: Path
+    controlnets: Path
+
+
+class MigrateTo3(object):
+    def __init__(
+        self,
+        from_root: Path,
+        to_models: Path,
+        model_manager: ModelManager,
+        src_paths: ModelPaths,
+    ):
+        self.root_directory = from_root
+        self.dest_models = to_models
+        self.mgr = model_manager
+        self.src_paths = src_paths
+
+    @classmethod
+    def initialize_yaml(cls, yaml_file: Path):
+        with open(yaml_file, "w") as file:
+            file.write(yaml.dump({"__metadata__": {"version": "3.0.0"}}))
+
+    def create_directory_structure(self):
+        """
+        Create the basic directory structure for the models folder.
+        """
+        for model_base in [BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2]:
+            for model_type in [
+                ModelType.Main,
+                ModelType.Vae,
+                ModelType.Lora,
+                ModelType.ControlNet,
+                ModelType.TextualInversion,
+            ]:
+                path = self.dest_models / model_base.value / model_type.value
+                path.mkdir(parents=True, exist_ok=True)
+        path = self.dest_models / "core"
+        path.mkdir(parents=True, exist_ok=True)
+
+    @staticmethod
+    def copy_file(src: Path, dest: Path):
+        """
+        copy a single file with logging
+        """
+        if dest.exists():
+            logger.info(f"Skipping existing {str(dest)}")
+            return
+        logger.info(f"Copying {str(src)} to {str(dest)}")
+        try:
+            shutil.copy(src, dest)
+        except Exception as e:
+            logger.error(f"COPY FAILED: {str(e)}")
+
+    @staticmethod
+    def copy_dir(src: Path, dest: Path):
+        """
+        Recursively copy a directory with logging
+        """
+        if dest.exists():
+            logger.info(f"Skipping existing {str(dest)}")
+            return
+
+        logger.info(f"Copying {str(src)} to {str(dest)}")
+        try:
+            shutil.copytree(src, dest)
+        except Exception as e:
+            logger.error(f"COPY FAILED: {str(e)}")
+
+    def migrate_models(self, src_dir: Path):
+        """
+        Recursively walk through src directory, probe anything
+        that looks like a model, and copy the model into the
+        appropriate location within the destination models directory.
+        """
+        directories_scanned = set()
+        for root, dirs, files in os.walk(src_dir, followlinks=True):
+            for d in dirs:
+                try:
+                    model = Path(root, d)
+                    info = ModelProbe().heuristic_probe(model)
+                    if not info:
+                        continue
+                    dest = self._model_probe_to_path(info) / model.name
+                    self.copy_dir(model, dest)
+                    directories_scanned.add(model)
+                except Exception as e:
+                    logger.error(str(e))
+                except KeyboardInterrupt:
+                    raise
+            for f in files:
+                # don't copy raw learned_embeds.bin or pytorch_lora_weights.bin
+                # let them be copied as part of a tree copy operation
+                try:
+                    if f in {"learned_embeds.bin", "pytorch_lora_weights.bin"}:
+                        continue
+                    model = Path(root, f)
+                    if model.parent in directories_scanned:
+                        continue
+                    info = ModelProbe().heuristic_probe(model)
+                    if not info:
+                        continue
+                    dest = self._model_probe_to_path(info) / f
+                    self.copy_file(model, dest)
+                except Exception as e:
+                    logger.error(str(e))
+                except KeyboardInterrupt:
+                    raise
+
+    def migrate_support_models(self):
+        """
+        Copy the clipseg, upscaler, and restoration models to their new
+        locations.
+        """
+        dest_directory = self.dest_models
+        if (self.root_directory / "models/clipseg").exists():
+            self.copy_dir(self.root_directory / "models/clipseg", dest_directory / "core/misc/clipseg")
+        if (self.root_directory / "models/realesrgan").exists():
+            self.copy_dir(self.root_directory / "models/realesrgan", dest_directory / "core/upscaling/realesrgan")
+        for d in ["codeformer", "gfpgan"]:
+            path = self.root_directory / "models" / d
+            if path.exists():
+                self.copy_dir(path, dest_directory / f"core/face_restoration/{d}")
+
+    def migrate_tuning_models(self):
+        """
+        Migrate the embeddings, loras and controlnets directories to their new homes.
+        """
+        for src in [self.src_paths.embeddings, self.src_paths.loras, self.src_paths.controlnets]:
+            if not src:
+                continue
+            if src.is_dir():
+                logger.info(f"Scanning {src}")
+                self.migrate_models(src)
+            else:
+                logger.info(f"{src} directory not found; skipping")
+                continue
+
+    def migrate_conversion_models(self):
+        """
+        Migrate all the models that are needed by the ckpt_to_diffusers conversion
+        script.
+        """
+
+        dest_directory = self.dest_models
+        kwargs = {
+            "cache_dir": self.root_directory / "models/hub",
+            # local_files_only = True
+        }
+        try:
+            logger.info("Migrating core tokenizers and text encoders")
+            target_dir = dest_directory / "core" / "convert"
+
+            self._migrate_pretrained(
+                BertTokenizerFast, repo_id="bert-base-uncased", dest=target_dir / "bert-base-uncased", **kwargs
+            )
+
+            # sd-1
+            repo_id = "openai/clip-vit-large-patch14"
+            self._migrate_pretrained(
+                CLIPTokenizer, repo_id=repo_id, dest=target_dir / "clip-vit-large-patch14", **kwargs
+            )
+            self._migrate_pretrained(
+                CLIPTextModel, repo_id=repo_id, dest=target_dir / "clip-vit-large-patch14", force=True, **kwargs
+            )
+
+            # sd-2
+            repo_id = "stabilityai/stable-diffusion-2"
+            self._migrate_pretrained(
+                CLIPTokenizer,
+                repo_id=repo_id,
+                dest=target_dir / "stable-diffusion-2-clip" / "tokenizer",
+                **{"subfolder": "tokenizer", **kwargs},
+            )
+            self._migrate_pretrained(
+                CLIPTextModel,
+                repo_id=repo_id,
+                dest=target_dir / "stable-diffusion-2-clip" / "text_encoder",
+                **{"subfolder": "text_encoder", **kwargs},
+            )
+
+            # VAE
+            logger.info("Migrating stable diffusion VAE")
+            self._migrate_pretrained(
+                AutoencoderKL, repo_id="stabilityai/sd-vae-ft-mse", dest=target_dir / "sd-vae-ft-mse", **kwargs
+            )
+
+            # safety checking
+            logger.info("Migrating safety checker")
+            repo_id = "CompVis/stable-diffusion-safety-checker"
+            self._migrate_pretrained(
+                AutoFeatureExtractor, repo_id=repo_id, dest=target_dir / "stable-diffusion-safety-checker", **kwargs
+            )
+            self._migrate_pretrained(
+                StableDiffusionSafetyChecker,
+                repo_id=repo_id,
+                dest=target_dir / "stable-diffusion-safety-checker",
+                **kwargs,
+            )
+        except KeyboardInterrupt:
+            raise
+        except Exception as e:
+            logger.error(str(e))
+
+    def _model_probe_to_path(self, info: ModelProbeInfo) -> Path:
+        return Path(self.dest_models, info.base_type.value, info.model_type.value)
+
+    def _migrate_pretrained(self, model_class, repo_id: str, dest: Path, force: bool = False, **kwargs):
+        if dest.exists() and not force:
+            logger.info(f"Skipping existing {dest}")
+            return
+        model = model_class.from_pretrained(repo_id, **kwargs)
+        self._save_pretrained(model, dest, overwrite=force)
+
+    def _save_pretrained(self, model, dest: Path, overwrite: bool = False):
+        model_name = dest.name
+        if overwrite:
+            model.save_pretrained(dest, safe_serialization=True)
+        else:
+            download_path = dest.with_name(f"{model_name}.downloading")
+            model.save_pretrained(download_path, safe_serialization=True)
+            download_path.replace(dest)
+
+    def _download_vae(self, repo_id: str, subfolder: str = None) -> Path:
+        vae = AutoencoderKL.from_pretrained(repo_id, cache_dir=self.root_directory / "models/hub", subfolder=subfolder)
+        info = ModelProbe().heuristic_probe(vae)
+        _, model_name = repo_id.split("/")
+        dest = self._model_probe_to_path(info) / self.unique_name(model_name, info)
+        vae.save_pretrained(dest, safe_serialization=True)
+        return dest
+
+    def _vae_path(self, vae: Union[str, dict]) -> Path:
+        """
+        Convert 2.3 VAE stanza to a straight path.
+        """
+        vae_path = None
+
+        # First get a path
+        if isinstance(vae, str):
+            vae_path = vae
+
+        elif isinstance(vae, DictConfig):
+            if p := vae.get("path"):
+                vae_path = p
+            elif repo_id := vae.get("repo_id"):
+                if repo_id == "stabilityai/sd-vae-ft-mse":  # this guy is already downloaded
+                    vae_path = "models/core/convert/sd-vae-ft-mse"
+                    return vae_path
+                else:
+                    vae_path = self._download_vae(repo_id, vae.get("subfolder"))
+
+        assert vae_path is not None, "Couldn't find VAE for this model"
+
+        # if the VAE is in the old models directory, then we must move it into the new
+        # one. VAEs outside of this directory can stay where they are.
+        vae_path = Path(vae_path)
+        if vae_path.is_relative_to(self.src_paths.models):
+            info = ModelProbe().heuristic_probe(vae_path)
+            dest = self._model_probe_to_path(info) / vae_path.name
+            if not dest.exists():
+                if vae_path.is_dir():
+                    self.copy_dir(vae_path, dest)
+                else:
+                    self.copy_file(vae_path, dest)
+            vae_path = dest
+
+        if vae_path.is_relative_to(self.dest_models):
+            rel_path = vae_path.relative_to(self.dest_models)
+            return Path("models", rel_path)
+        else:
+            return vae_path
+
+    def migrate_repo_id(self, repo_id: str, model_name: str = None, **extra_config):
+        """
+        Migrate a locally-cached diffusers pipeline identified with a repo_id
+        """
+        dest_dir = self.dest_models
+
+        cache = self.root_directory / "models/hub"
+        kwargs = {
+            "cache_dir": cache,
+            "safety_checker": None,
+            # local_files_only = True,
+        }
+
+        owner, repo_name = repo_id.split("/")
+        model_name = model_name or repo_name
+        model = cache / "--".join(["models", owner, repo_name])
+
+        if len(list(model.glob("snapshots/**/model_index.json"))) == 0:
+            return
+        revisions = [x.name for x in model.glob("refs/*")]
+
+        # if an fp16 is available we use that
+        revision = "fp16" if len(revisions) > 1 and "fp16" in revisions else revisions[0]
+        pipeline = StableDiffusionPipeline.from_pretrained(repo_id, revision=revision, **kwargs)
+
+        info = ModelProbe().heuristic_probe(pipeline)
+        if not info:
+            return
+
+        if self.mgr.model_exists(model_name, info.base_type, info.model_type):
+            logger.warning(f"A model named {model_name} already exists at the destination. Skipping migration.")
+            return
+
+        dest = self._model_probe_to_path(info) / model_name
+        self._save_pretrained(pipeline, dest)
+
+        rel_path = Path("models", dest.relative_to(dest_dir))
+        self._add_model(model_name, info, rel_path, **extra_config)
+
+    def migrate_path(self, location: Path, model_name: str = None, **extra_config):
+        """
+        Migrate a model referred to using 'weights' or 'path'
+        """
+
+        # handle relative paths
+        dest_dir = self.dest_models
+        location = self.root_directory / location
+        model_name = model_name or location.stem
+
+        info = ModelProbe().heuristic_probe(location)
+        if not info:
+            return
+
+        if self.mgr.model_exists(model_name, info.base_type, info.model_type):
+            logger.warning(f"A model named {model_name} already exists at the destination. Skipping migration.")
+            return
+
+        # uh oh, weights is in the old models directory - move it into the new one
+        if Path(location).is_relative_to(self.src_paths.models):
+            dest = Path(dest_dir, info.base_type.value, info.model_type.value, location.name)
+            if location.is_dir():
+                self.copy_dir(location, dest)
+            else:
+                self.copy_file(location, dest)
+            location = Path("models", info.base_type.value, info.model_type.value, location.name)
+
+        self._add_model(model_name, info, location, **extra_config)
+
+    def _add_model(self, model_name: str, info: ModelProbeInfo, location: Path, **extra_config):
+        if info.model_type != ModelType.Main:
+            return
+
+        self.mgr.add_model(
+            model_name=model_name,
+            base_model=info.base_type,
+            model_type=info.model_type,
+            clobber=True,
+            model_attributes={
+                "path": str(location),
+                "description": f"A {info.base_type.value} {info.model_type.value} model",
+                "model_format": info.format,
+                "variant": info.variant_type.value,
+                **extra_config,
+            },
+        )
+
+    def migrate_defined_models(self):
+        """
+        Migrate models defined in models.yaml
+        """
+        # find any models referred to in old models.yaml
+        conf = OmegaConf.load(self.root_directory / "configs/models.yaml")
+
+        for model_name, stanza in conf.items():
+            try:
+                passthru_args = {}
+
+                if vae := stanza.get("vae"):
+                    try:
+                        passthru_args["vae"] = str(self._vae_path(vae))
+                    except Exception as e:
+                        logger.warning(f'Could not find a VAE matching "{vae}" for model "{model_name}"')
+                        logger.warning(str(e))
+
+                if config := stanza.get("config"):
+                    passthru_args["config"] = config
+
+                if description := stanza.get("description"):
+                    passthru_args["description"] = description
+
+                if repo_id := stanza.get("repo_id"):
+                    logger.info(f"Migrating diffusers model {model_name}")
+                    self.migrate_repo_id(repo_id, model_name, **passthru_args)
+
+                elif location := stanza.get("weights"):
+                    logger.info(f"Migrating checkpoint model {model_name}")
+                    self.migrate_path(Path(location), model_name, **passthru_args)
+
+                elif location := stanza.get("path"):
+                    logger.info(f"Migrating diffusers model {model_name}")
+                    self.migrate_path(Path(location), model_name, **passthru_args)
+
+            except KeyboardInterrupt:
+                raise
+            except Exception as e:
+                logger.error(str(e))
+
+    def migrate(self):
+        self.create_directory_structure()
+        # the configure script is doing this
+        self.migrate_support_models()
+        self.migrate_conversion_models()
+        self.migrate_tuning_models()
+        self.migrate_defined_models()
+
+
+def _parse_legacy_initfile(root: Path, initfile: Path) -> ModelPaths:
+    """
+    Returns tuple of (embedding_path, lora_path, controlnet_path)
+    """
+    parser = argparse.ArgumentParser(fromfile_prefix_chars="@")
+    parser.add_argument(
+        "--embedding_directory",
+        "--embedding_path",
+        type=Path,
+        dest="embedding_path",
+        default=Path("embeddings"),
+    )
+    parser.add_argument(
+        "--lora_directory",
+        dest="lora_path",
+        type=Path,
+        default=Path("loras"),
+    )
+    opt, _ = parser.parse_known_args([f"@{str(initfile)}"])
+    return ModelPaths(
+        models=root / "models",
+        embeddings=root / str(opt.embedding_path).strip('"'),
+        loras=root / str(opt.lora_path).strip('"'),
+        controlnets=root / "controlnets",
+    )
+
+
+def _parse_legacy_yamlfile(root: Path, initfile: Path) -> ModelPaths:
+    """
+    Returns tuple of (embedding_path, lora_path, controlnet_path)
+    """
+    # Don't use the config object because it is unforgiving of version updates
+    # Just use omegaconf directly
+    opt = OmegaConf.load(initfile)
+    paths = opt.InvokeAI.Paths
+    models = paths.get("models_dir", "models")
+    embeddings = paths.get("embedding_dir", "embeddings")
+    loras = paths.get("lora_dir", "loras")
+    controlnets = paths.get("controlnet_dir", "controlnets")
+    return ModelPaths(
+        models=root / models if models else None,
+        embeddings=root / embeddings if embeddings else None,
+        loras=root / loras if loras else None,
+        controlnets=root / controlnets if controlnets else None,
+    )
+
+
+def get_legacy_embeddings(root: Path) -> ModelPaths:
+    path = root / "invokeai.init"
+    if path.exists():
+        return _parse_legacy_initfile(root, path)
+    path = root / "invokeai.yaml"
+    if path.exists():
+        return _parse_legacy_yamlfile(root, path)
+
+
+def do_migrate(src_directory: Path, dest_directory: Path):
+    """
+    Migrate models from src to dest InvokeAI root directories
+    """
+    config_file = dest_directory / "configs" / "models.yaml.3"
+    dest_models = dest_directory / "models.3"
+
+    version_3 = (dest_directory / "models" / "core").exists()
+
+    # Here we create the destination models.yaml file.
+    # If we are writing into a version 3 directory and the
+    # file already exists, then we write into a copy of it to
+    # avoid deleting its previous customizations. Otherwise we
+    # create a new empty one.
+    if version_3:  # write into the dest directory
+        try:
+            shutil.copy(dest_directory / "configs" / "models.yaml", config_file)
+        except Exception:
+            MigrateTo3.initialize_yaml(config_file)
+        mgr = ModelManager(config_file)  # important to initialize BEFORE moving the models directory
+        (dest_directory / "models").replace(dest_models)
+    else:
+        MigrateTo3.initialize_yaml(config_file)
+        mgr = ModelManager(config_file)
+
+    paths = get_legacy_embeddings(src_directory)
+    migrator = MigrateTo3(from_root=src_directory, to_models=dest_models, model_manager=mgr, src_paths=paths)
+    migrator.migrate()
+    print("Migration successful.")
+
+    if not version_3:
+        (dest_directory / "models").replace(src_directory / "models.orig")
+        print(f"Original models directory moved to {dest_directory}/models.orig")
+
+    (dest_directory / "configs" / "models.yaml").replace(src_directory / "configs" / "models.yaml.orig")
+    print(f"Original models.yaml file moved to {dest_directory}/configs/models.yaml.orig")
+
+    config_file.replace(config_file.with_suffix(""))
+    dest_models.replace(dest_models.with_suffix(""))
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        prog="invokeai-migrate3",
+        description="""
+This will copy and convert the models directory and the configs/models.yaml from the InvokeAI 2.3 format
+'--from-directory' root to the InvokeAI 3.0 '--to-directory' root. These may be abbreviated '--from' and '--to'.a
+
+The old models directory and config file will be renamed 'models.orig' and 'models.yaml.orig' respectively.
+It is safe to provide the same directory for both arguments, but it is better to use the invokeai_configure
+script, which will perform a full upgrade in place.""",
+    )
+    parser.add_argument(
+        "--from-directory",
+        dest="src_root",
+        type=Path,
+        required=True,
+        help='Source InvokeAI 2.3 root directory (containing "invokeai.init" or "invokeai.yaml")',
+    )
+    parser.add_argument(
+        "--to-directory",
+        dest="dest_root",
+        type=Path,
+        required=True,
+        help='Destination InvokeAI 3.0 directory (containing "invokeai.yaml")',
+    )
+    args = parser.parse_args()
+    src_root = args.src_root
+    assert src_root.is_dir(), f"{src_root} is not a valid directory"
+    assert (src_root / "models").is_dir(), f"{src_root} does not contain a 'models' subdirectory"
+    assert (src_root / "models" / "hub").exists(), f"{src_root} does not contain a version 2.3 models directory"
+    assert (src_root / "invokeai.init").exists() or (
+        src_root / "invokeai.yaml"
+    ).exists(), f"{src_root} does not contain an InvokeAI init file."
+
+    dest_root = args.dest_root
+    assert dest_root.is_dir(), f"{dest_root} is not a valid directory"
+    config = InvokeAIAppConfig.get_config()
+    config.parse_args(["--root", str(dest_root)])
+
+    # TODO: revisit - don't rely on invokeai.yaml to exist yet!
+    dest_is_setup = (dest_root / "models/core").exists() and (dest_root / "databases").exists()
+    if not dest_is_setup:
+        from invokeai.backend.install.invokeai_configure import initialize_rootdir
+
+        initialize_rootdir(dest_root, True)
+
+    do_migrate(src_root, dest_root)
+
+
+if __name__ == "__main__":
+    main()

invokeai/backend/install/model_install_backend.py

@@ -0,0 +1,637 @@
+"""
+Utility (backend) functions used by model_install.py
+"""
+import os
+import re
+import shutil
+import warnings
+from dataclasses import dataclass, field
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from typing import Callable, Dict, List, Optional, Set, Union
+
+import requests
+import torch
+from diffusers import DiffusionPipeline
+from diffusers import logging as dlogging
+from huggingface_hub import HfApi, HfFolder, hf_hub_url
+from omegaconf import OmegaConf
+from tqdm import tqdm
+
+import invokeai.configs as configs
+from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.backend.model_management import AddModelResult, BaseModelType, ModelManager, ModelType, ModelVariantType
+from invokeai.backend.model_management.model_probe import ModelProbe, ModelProbeInfo, SchedulerPredictionType
+from invokeai.backend.util import download_with_resume
+from invokeai.backend.util.devices import choose_torch_device, torch_dtype
+
+from ..util.logging import InvokeAILogger
+
+warnings.filterwarnings("ignore")
+
+# --------------------------globals-----------------------
+config = InvokeAIAppConfig.get_config()
+logger = InvokeAILogger.get_logger(name="InvokeAI")
+
+# the initial "configs" dir is now bundled in the `invokeai.configs` package
+Dataset_path = Path(configs.__path__[0]) / "INITIAL_MODELS.yaml"
+
+Config_preamble = """
+# This file describes the alternative machine learning models
+# available to InvokeAI script.
+#
+# To add a new model, follow the examples below. Each
+# model requires a model config file, a weights file,
+# and the width and height of the images it
+# was trained on.
+"""
+
+LEGACY_CONFIGS = {
+    BaseModelType.StableDiffusion1: {
+        ModelVariantType.Normal: {
+            SchedulerPredictionType.Epsilon: "v1-inference.yaml",
+            SchedulerPredictionType.VPrediction: "v1-inference-v.yaml",
+        },
+        ModelVariantType.Inpaint: {
+            SchedulerPredictionType.Epsilon: "v1-inpainting-inference.yaml",
+            SchedulerPredictionType.VPrediction: "v1-inpainting-inference-v.yaml",
+        },
+    },
+    BaseModelType.StableDiffusion2: {
+        ModelVariantType.Normal: {
+            SchedulerPredictionType.Epsilon: "v2-inference.yaml",
+            SchedulerPredictionType.VPrediction: "v2-inference-v.yaml",
+        },
+        ModelVariantType.Inpaint: {
+            SchedulerPredictionType.Epsilon: "v2-inpainting-inference.yaml",
+            SchedulerPredictionType.VPrediction: "v2-inpainting-inference-v.yaml",
+        },
+    },
+    BaseModelType.StableDiffusionXL: {
+        ModelVariantType.Normal: "sd_xl_base.yaml",
+    },
+    BaseModelType.StableDiffusionXLRefiner: {
+        ModelVariantType.Normal: "sd_xl_refiner.yaml",
+    },
+}
+
+
+@dataclass
+class InstallSelections:
+    install_models: List[str] = field(default_factory=list)
+    remove_models: List[str] = field(default_factory=list)
+
+
+@dataclass
+class ModelLoadInfo:
+    name: str
+    model_type: ModelType
+    base_type: BaseModelType
+    path: Optional[Path] = None
+    repo_id: Optional[str] = None
+    subfolder: Optional[str] = None
+    description: str = ""
+    installed: bool = False
+    recommended: bool = False
+    default: bool = False
+    requires: Optional[List[str]] = field(default_factory=list)
+
+
+class ModelInstall(object):
+    def __init__(
+        self,
+        config: InvokeAIAppConfig,
+        prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
+        model_manager: Optional[ModelManager] = None,
+        access_token: Optional[str] = None,
+        civitai_api_key: Optional[str] = None,
+    ):
+        self.config = config
+        self.mgr = model_manager or ModelManager(config.model_conf_path)
+        self.datasets = OmegaConf.load(Dataset_path)
+        self.prediction_helper = prediction_type_helper
+        self.access_token = access_token or HfFolder.get_token()
+        self.civitai_api_key = civitai_api_key or config.civitai_api_key
+        self.reverse_paths = self._reverse_paths(self.datasets)
+
+    def all_models(self) -> Dict[str, ModelLoadInfo]:
+        """
+        Return dict of model_key=>ModelLoadInfo objects.
+        This method consolidates and simplifies the entries in both
+        models.yaml and INITIAL_MODELS.yaml so that they can
+        be treated uniformly. It also sorts the models alphabetically
+        by their name, to improve the display somewhat.
+        """
+        model_dict = {}
+
+        # first populate with the entries in INITIAL_MODELS.yaml
+        for key, value in self.datasets.items():
+            name, base, model_type = ModelManager.parse_key(key)
+            value["name"] = name
+            value["base_type"] = base
+            value["model_type"] = model_type
+            model_info = ModelLoadInfo(**value)
+            if model_info.subfolder and model_info.repo_id:
+                model_info.repo_id += f":{model_info.subfolder}"
+            model_dict[key] = model_info
+
+        # supplement with entries in models.yaml
+        installed_models = list(self.mgr.list_models())
+
+        for md in installed_models:
+            base = md["base_model"]
+            model_type = md["model_type"]
+            name = md["model_name"]
+            key = ModelManager.create_key(name, base, model_type)
+            if key in model_dict:
+                model_dict[key].installed = True
+            else:
+                model_dict[key] = ModelLoadInfo(
+                    name=name,
+                    base_type=base,
+                    model_type=model_type,
+                    path=value.get("path"),
+                    installed=True,
+                )
+        return {x: model_dict[x] for x in sorted(model_dict.keys(), key=lambda y: model_dict[y].name.lower())}
+
+    def _is_autoloaded(self, model_info: dict) -> bool:
+        path = model_info.get("path")
+        if not path:
+            return False
+        for autodir in ["autoimport_dir", "lora_dir", "embedding_dir", "controlnet_dir"]:
+            if autodir_path := getattr(self.config, autodir):
+                autodir_path = self.config.root_path / autodir_path
+                if Path(path).is_relative_to(autodir_path):
+                    return True
+        return False
+
+    def list_models(self, model_type):
+        installed = self.mgr.list_models(model_type=model_type)
+        print()
+        print(f"Installed models of type `{model_type}`:")
+        print(f"{'Model Key':50} Model Path")
+        for i in installed:
+            print(f"{'/'.join([i['base_model'],i['model_type'],i['model_name']]):50} {i['path']}")
+        print()
+
+    # logic here a little reversed to maintain backward compatibility
+    def starter_models(self, all_models: bool = False) -> Set[str]:
+        models = set()
+        for key, _value in self.datasets.items():
+            name, base, model_type = ModelManager.parse_key(key)
+            if all_models or model_type in [ModelType.Main, ModelType.Vae]:
+                models.add(key)
+        return models
+
+    def recommended_models(self) -> Set[str]:
+        starters = self.starter_models(all_models=True)
+        return {x for x in starters if self.datasets[x].get("recommended", False)}
+
+    def default_model(self) -> str:
+        starters = self.starter_models()
+        defaults = [x for x in starters if self.datasets[x].get("default", False)]
+        return defaults[0]
+
+    def install(self, selections: InstallSelections):
+        verbosity = dlogging.get_verbosity()  # quench NSFW nags
+        dlogging.set_verbosity_error()
+
+        job = 1
+        jobs = len(selections.remove_models) + len(selections.install_models)
+
+        # remove requested models
+        for key in selections.remove_models:
+            name, base, mtype = self.mgr.parse_key(key)
+            logger.info(f"Deleting {mtype} model {name} [{job}/{jobs}]")
+            try:
+                self.mgr.del_model(name, base, mtype)
+            except FileNotFoundError as e:
+                logger.warning(e)
+            job += 1
+
+        # add requested models
+        self._remove_installed(selections.install_models)
+        self._add_required_models(selections.install_models)
+        for path in selections.install_models:
+            logger.info(f"Installing {path} [{job}/{jobs}]")
+            try:
+                self.heuristic_import(path)
+            except (ValueError, KeyError) as e:
+                logger.error(str(e))
+            job += 1
+
+        dlogging.set_verbosity(verbosity)
+        self.mgr.commit()
+
+    def heuristic_import(
+        self,
+        model_path_id_or_url: Union[str, Path],
+        models_installed: Set[Path] = None,
+    ) -> Dict[str, AddModelResult]:
+        """
+        :param model_path_id_or_url: A Path to a local model to import, or a string representing its repo_id or URL
+        :param models_installed: Set of installed models, used for recursive invocation
+        Returns a set of dict objects corresponding to newly-created stanzas in models.yaml.
+        """
+
+        if not models_installed:
+            models_installed = {}
+
+        model_path_id_or_url = str(model_path_id_or_url).strip("\"' ")
+
+        # A little hack to allow nested routines to retrieve info on the requested ID
+        self.current_id = model_path_id_or_url
+        path = Path(model_path_id_or_url)
+
+        # fix relative paths
+        if path.exists() and not path.is_absolute():
+            path = path.absolute()  # make relative to current WD
+
+        # checkpoint file, or similar
+        if path.is_file():
+            models_installed.update({str(path): self._install_path(path)})
+
+        # folders style or similar
+        elif path.is_dir() and any(
+            (path / x).exists()
+            for x in {
+                "config.json",
+                "model_index.json",
+                "learned_embeds.bin",
+                "pytorch_lora_weights.bin",
+                "pytorch_lora_weights.safetensors",
+            }
+        ):
+            models_installed.update({str(model_path_id_or_url): self._install_path(path)})
+
+        # recursive scan
+        elif path.is_dir():
+            for child in path.iterdir():
+                self.heuristic_import(child, models_installed=models_installed)
+
+        # huggingface repo
+        elif len(str(model_path_id_or_url).split("/")) == 2:
+            models_installed.update({str(model_path_id_or_url): self._install_repo(str(model_path_id_or_url))})
+
+        # a URL
+        elif str(model_path_id_or_url).startswith(("http:", "https:", "ftp:")):
+            models_installed.update({str(model_path_id_or_url): self._install_url(model_path_id_or_url)})
+
+        else:
+            raise KeyError(f"{str(model_path_id_or_url)} is not recognized as a local path, repo ID or URL. Skipping")
+
+        return models_installed
+
+    def _remove_installed(self, model_list: List[str]):
+        all_models = self.all_models()
+        models_to_remove = []
+
+        for path in model_list:
+            key = self.reverse_paths.get(path)
+            if key and all_models[key].installed:
+                models_to_remove.append(path)
+
+        for path in models_to_remove:
+            logger.warning(f"{path} already installed. Skipping")
+            model_list.remove(path)
+
+    def _add_required_models(self, model_list: List[str]):
+        additional_models = []
+        all_models = self.all_models()
+        for path in model_list:
+            if not (key := self.reverse_paths.get(path)):
+                continue
+            for requirement in all_models[key].requires:
+                requirement_key = self.reverse_paths.get(requirement)
+                if not all_models[requirement_key].installed:
+                    additional_models.append(requirement)
+        model_list.extend(additional_models)
+
+    # install a model from a local path. The optional info parameter is there to prevent
+    # the model from being probed twice in the event that it has already been probed.
+    def _install_path(self, path: Path, info: ModelProbeInfo = None) -> AddModelResult:
+        info = info or ModelProbe().heuristic_probe(path, self.prediction_helper)
+        if not info:
+            logger.warning(f"Unable to parse format of {path}")
+            return None
+        model_name = path.stem if path.is_file() else path.name
+        if self.mgr.model_exists(model_name, info.base_type, info.model_type):
+            raise ValueError(f'A model named "{model_name}" is already installed.')
+        attributes = self._make_attributes(path, info)
+        return self.mgr.add_model(
+            model_name=model_name,
+            base_model=info.base_type,
+            model_type=info.model_type,
+            model_attributes=attributes,
+        )
+
+    def _install_url(self, url: str) -> AddModelResult:
+        with TemporaryDirectory(dir=self.config.models_path) as staging:
+            CIVITAI_RE = r".*civitai.com.*"
+            civit_url = re.match(CIVITAI_RE, url, re.IGNORECASE)
+            location = download_with_resume(
+                url, Path(staging), access_token=self.civitai_api_key if civit_url else None
+            )
+            if not location:
+                logger.error(f"Unable to download {url}. Skipping.")
+            info = ModelProbe().heuristic_probe(location, self.prediction_helper)
+            dest = self.config.models_path / info.base_type.value / info.model_type.value / location.name
+            dest.parent.mkdir(parents=True, exist_ok=True)
+            models_path = shutil.move(location, dest)
+
+        # staged version will be garbage-collected at this time
+        return self._install_path(Path(models_path), info)
+
+    def _install_repo(self, repo_id: str) -> AddModelResult:
+        # hack to recover models stored in subfolders --
+        # Required to get the "v2" model of monster-labs/control_v1p_sd15_qrcode_monster
+        subfolder = None
+        if match := re.match(r"^([^/]+/[^/]+):(\w+)$", repo_id):
+            repo_id = match.group(1)
+            subfolder = match.group(2)
+
+        hinfo = HfApi().model_info(repo_id)
+
+        # we try to figure out how to download this most economically
+        # list all the files in the repo
+        files = [x.rfilename for x in hinfo.siblings]
+        if subfolder:
+            files = [x for x in files if x.startswith(f"{subfolder}/")]
+        prefix = f"{subfolder}/" if subfolder else ""
+
+        location = None
+
+        with TemporaryDirectory(dir=self.config.models_path) as staging:
+            staging = Path(staging)
+            if f"{prefix}model_index.json" in files:
+                location = self._download_hf_pipeline(repo_id, staging, subfolder=subfolder)  # pipeline
+            elif f"{prefix}unet/model.onnx" in files:
+                location = self._download_hf_model(repo_id, files, staging)
+            else:
+                for suffix in ["safetensors", "bin"]:
+                    if f"{prefix}pytorch_lora_weights.{suffix}" in files:
+                        location = self._download_hf_model(
+                            repo_id, [f"pytorch_lora_weights.{suffix}"], staging, subfolder=subfolder
+                        )  # LoRA
+                        break
+                    elif (
+                        self.config.precision == "float16" and f"{prefix}diffusion_pytorch_model.fp16.{suffix}" in files
+                    ):  # vae, controlnet or some other standalone
+                        files = ["config.json", f"diffusion_pytorch_model.fp16.{suffix}"]
+                        location = self._download_hf_model(repo_id, files, staging, subfolder=subfolder)
+                        break
+                    elif f"{prefix}diffusion_pytorch_model.{suffix}" in files:
+                        files = ["config.json", f"diffusion_pytorch_model.{suffix}"]
+                        location = self._download_hf_model(repo_id, files, staging, subfolder=subfolder)
+                        break
+                    elif f"{prefix}learned_embeds.{suffix}" in files:
+                        location = self._download_hf_model(
+                            repo_id, [f"learned_embeds.{suffix}"], staging, subfolder=subfolder
+                        )
+                        break
+                    elif (
+                        f"{prefix}image_encoder.txt" in files and f"{prefix}ip_adapter.{suffix}" in files
+                    ):  # IP-Adapter
+                        files = ["image_encoder.txt", f"ip_adapter.{suffix}"]
+                        location = self._download_hf_model(repo_id, files, staging, subfolder=subfolder)
+                        break
+                    elif f"{prefix}model.{suffix}" in files and f"{prefix}config.json" in files:
+                        # This elif-condition is pretty fragile, but it is intended to handle CLIP Vision models hosted
+                        # by InvokeAI for use with IP-Adapters.
+                        files = ["config.json", f"model.{suffix}"]
+                        location = self._download_hf_model(repo_id, files, staging, subfolder=subfolder)
+                        break
+            if not location:
+                logger.warning(f"Could not determine type of repo {repo_id}. Skipping install.")
+                return {}
+
+            info = ModelProbe().heuristic_probe(location, self.prediction_helper)
+            if not info:
+                logger.warning(f"Could not probe {location}. Skipping install.")
+                return {}
+            dest = (
+                self.config.models_path
+                / info.base_type.value
+                / info.model_type.value
+                / self._get_model_name(repo_id, location)
+            )
+            if dest.exists():
+                shutil.rmtree(dest)
+            shutil.copytree(location, dest)
+            return self._install_path(dest, info)
+
+    def _get_model_name(self, path_name: str, location: Path) -> str:
+        """
+        Calculate a name for the model - primitive implementation.
+        """
+        if key := self.reverse_paths.get(path_name):
+            (name, base, mtype) = ModelManager.parse_key(key)
+            return name
+        elif location.is_dir():
+            return location.name
+        else:
+            return location.stem
+
+    def _make_attributes(self, path: Path, info: ModelProbeInfo) -> dict:
+        model_name = path.name if path.is_dir() else path.stem
+        description = f"{info.base_type.value} {info.model_type.value} model {model_name}"
+        if key := self.reverse_paths.get(self.current_id):
+            if key in self.datasets:
+                description = self.datasets[key].get("description") or description
+
+        rel_path = self.relative_to_root(path, self.config.models_path)
+
+        attributes = {
+            "path": str(rel_path),
+            "description": str(description),
+            "model_format": info.format,
+        }
+        legacy_conf = None
+        if info.model_type == ModelType.Main or info.model_type == ModelType.ONNX:
+            attributes.update(
+                {
+                    "variant": info.variant_type,
+                }
+            )
+            if info.format == "checkpoint":
+                try:
+                    possible_conf = path.with_suffix(".yaml")
+                    if possible_conf.exists():
+                        legacy_conf = str(self.relative_to_root(possible_conf))
+                    elif info.base_type in [BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2]:
+                        legacy_conf = Path(
+                            self.config.legacy_conf_dir,
+                            LEGACY_CONFIGS[info.base_type][info.variant_type][info.prediction_type],
+                        )
+                    else:
+                        legacy_conf = Path(
+                            self.config.legacy_conf_dir, LEGACY_CONFIGS[info.base_type][info.variant_type]
+                        )
+                except KeyError:
+                    legacy_conf = Path(self.config.legacy_conf_dir, "v1-inference.yaml")  # best guess
+
+        if info.model_type == ModelType.ControlNet and info.format == "checkpoint":
+            possible_conf = path.with_suffix(".yaml")
+            if possible_conf.exists():
+                legacy_conf = str(self.relative_to_root(possible_conf))
+            else:
+                legacy_conf = Path(
+                    self.config.root_path,
+                    "configs/controlnet",
+                    ("cldm_v15.yaml" if info.base_type == BaseModelType("sd-1") else "cldm_v21.yaml"),
+                )
+
+        if legacy_conf:
+            attributes.update({"config": str(legacy_conf)})
+        return attributes
+
+    def relative_to_root(self, path: Path, root: Optional[Path] = None) -> Path:
+        root = root or self.config.root_path
+        if path.is_relative_to(root):
+            return path.relative_to(root)
+        else:
+            return path
+
+    def _download_hf_pipeline(self, repo_id: str, staging: Path, subfolder: str = None) -> Path:
+        """
+        Retrieve a StableDiffusion model from cache or remote and then
+        does a save_pretrained() to the indicated staging area.
+        """
+        _, name = repo_id.split("/")
+        precision = torch_dtype(choose_torch_device())
+        variants = ["fp16", None] if precision == torch.float16 else [None, "fp16"]
+
+        model = None
+        for variant in variants:
+            try:
+                model = DiffusionPipeline.from_pretrained(
+                    repo_id,
+                    variant=variant,
+                    torch_dtype=precision,
+                    safety_checker=None,
+                    subfolder=subfolder,
+                )
+            except Exception as e:  # most errors are due to fp16 not being present. Fix this to catch other errors
+                if "fp16" not in str(e):
+                    print(e)
+
+            if model:
+                break
+
+        if not model:
+            logger.error(f"Diffusers model {repo_id} could not be downloaded. Skipping.")
+            return None
+        model.save_pretrained(staging / name, safe_serialization=True)
+        return staging / name
+
+    def _download_hf_model(self, repo_id: str, files: List[str], staging: Path, subfolder: None) -> Path:
+        _, name = repo_id.split("/")
+        location = staging / name
+        paths = []
+        for filename in files:
+            filePath = Path(filename)
+            p = hf_download_with_resume(
+                repo_id,
+                model_dir=location / filePath.parent,
+                model_name=filePath.name,
+                access_token=self.access_token,
+                subfolder=filePath.parent / subfolder if subfolder else filePath.parent,
+            )
+            if p:
+                paths.append(p)
+            else:
+                logger.warning(f"Could not download {filename} from {repo_id}.")
+
+        return location if len(paths) > 0 else None
+
+    @classmethod
+    def _reverse_paths(cls, datasets) -> dict:
+        """
+        Reverse mapping from repo_id/path to destination name.
+        """
+        return {v.get("path") or v.get("repo_id"): k for k, v in datasets.items()}
+
+
+# -------------------------------------
+def yes_or_no(prompt: str, default_yes=True):
+    default = "y" if default_yes else "n"
+    response = input(f"{prompt} [{default}] ") or default
+    if default_yes:
+        return response[0] not in ("n", "N")
+    else:
+        return response[0] in ("y", "Y")
+
+
+# ---------------------------------------------
+def hf_download_from_pretrained(model_class: object, model_name: str, destination: Path, **kwargs):
+    logger = InvokeAILogger.get_logger("InvokeAI")
+    logger.addFilter(lambda x: "fp16 is not a valid" not in x.getMessage())
+
+    model = model_class.from_pretrained(
+        model_name,
+        resume_download=True,
+        **kwargs,
+    )
+    model.save_pretrained(destination, safe_serialization=True)
+    return destination
+
+
+# ---------------------------------------------
+def hf_download_with_resume(
+    repo_id: str,
+    model_dir: str,
+    model_name: str,
+    model_dest: Path = None,
+    access_token: str = None,
+    subfolder: str = None,
+) -> Path:
+    model_dest = model_dest or Path(os.path.join(model_dir, model_name))
+    os.makedirs(model_dir, exist_ok=True)
+
+    url = hf_hub_url(repo_id, model_name, subfolder=subfolder)
+
+    header = {"Authorization": f"Bearer {access_token}"} if access_token else {}
+    open_mode = "wb"
+    exist_size = 0
+
+    if os.path.exists(model_dest):
+        exist_size = os.path.getsize(model_dest)
+        header["Range"] = f"bytes={exist_size}-"
+        open_mode = "ab"
+
+    resp = requests.get(url, headers=header, stream=True)
+    total = int(resp.headers.get("content-length", 0))
+
+    if resp.status_code == 416:  # "range not satisfiable", which means nothing to return
+        logger.info(f"{model_name}: complete file found. Skipping.")
+        return model_dest
+    elif resp.status_code == 404:
+        logger.warning("File not found")
+        return None
+    elif resp.status_code != 200:
+        logger.warning(f"{model_name}: {resp.reason}")
+    elif exist_size > 0:
+        logger.info(f"{model_name}: partial file found. Resuming...")
+    else:
+        logger.info(f"{model_name}: Downloading...")
+
+    try:
+        with (
+            open(model_dest, open_mode) as file,
+            tqdm(
+                desc=model_name,
+                initial=exist_size,
+                total=total + exist_size,
+                unit="iB",
+                unit_scale=True,
+                unit_divisor=1000,
+            ) as bar,
+        ):
+            for data in resp.iter_content(chunk_size=1024):
+                size = file.write(data)
+                bar.update(size)
+    except Exception as e:
+        logger.error(f"An error occurred while downloading {model_name}: {str(e)}")
+        return None
+    return model_dest

invokeai/backend/ip_adapter/ip_adapter.py

@@ -8,8 +8,8 @@ from PIL import Image
 from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
 
 from invokeai.backend.ip_adapter.ip_attention_weights import IPAttentionWeights
+from invokeai.backend.model_management.models.base import calc_model_size_by_data
 
-from ..raw_model import RawModel
 from .resampler import Resampler
 
 
@@ -92,7 +92,7 @@ class MLPProjModel(torch.nn.Module):
         return clip_extra_context_tokens
 
 
-class IPAdapter(RawModel):
+class IPAdapter:
     """IP-Adapter: https://arxiv.org/pdf/2308.06721.pdf"""
 
     def __init__(
@@ -124,9 +124,6 @@ class IPAdapter(RawModel):
         self.attn_weights.to(device=self.device, dtype=self.dtype)
 
     def calc_size(self):
-        # workaround for circular import
-        from invokeai.backend.model_manager.load.model_util import calc_model_size_by_data
-
         return calc_model_size_by_data(self._image_proj_model) + calc_model_size_by_data(self.attn_weights)
 
     def _init_image_proj_model(self, state_dict):

invokeai/backend/model_management/README.md

@@ -0,0 +1,27 @@
+# Model Cache
+
+## `glibc` Memory Allocator Fragmentation
+
+Python (and PyTorch) relies on the memory allocator from the C Standard Library (`libc`). On linux, with the GNU C Standard Library implementation (`glibc`), our memory access patterns have been observed to cause severe memory fragmentation. This fragmentation results in large amounts of memory that has been freed but can't be released back to the OS. Loading models from disk and moving them between CPU/CUDA seem to be the operations that contribute most to the fragmentation. This memory fragmentation issue can result in OOM crashes during frequent model switching, even if `max_cache_size` is set to a reasonable value (e.g. a OOM crash with `max_cache_size=16` on a system with 32GB of RAM).
+
+This problem may also exist on other OSes, and other `libc` implementations. But, at the time of writing, it has only been investigated on linux with `glibc`.
+
+To better understand how the `glibc` memory allocator works, see these references:
+- Basics: https://www.gnu.org/software/libc/manual/html_node/The-GNU-Allocator.html
+- Details: https://sourceware.org/glibc/wiki/MallocInternals
+
+Note the differences between memory allocated as chunks in an arena vs. memory allocated with `mmap`. Under `glibc`'s default configuration, most model tensors get allocated as chunks in an arena making them vulnerable to the problem of fragmentation.
+
+We can work around this memory fragmentation issue by setting the following env var:
+
+```bash
+# Force blocks >1MB to be allocated with `mmap` so that they are released to the system immediately when they are freed.
+MALLOC_MMAP_THRESHOLD_=1048576
+```
+
+See the following references for more information about the `malloc` tunable parameters:
+- https://www.gnu.org/software/libc/manual/html_node/Malloc-Tunable-Parameters.html
+- https://www.gnu.org/software/libc/manual/html_node/Memory-Allocation-Tunables.html
+- https://man7.org/linux/man-pages/man3/mallopt.3.html
+
+The model cache emits debug logs that provide visibility into the state of the `libc` memory allocator. See the `LibcUtil` class for more info on how these `libc` malloc stats are collected.

invokeai/backend/model_management/__init__.py

@@ -0,0 +1,20 @@
+# ruff: noqa: I001, F401
+"""
+Initialization file for invokeai.backend.model_management
+"""
+# This import must be first
+from .model_manager import AddModelResult, ModelInfo, ModelManager, SchedulerPredictionType
+from .lora import ModelPatcher, ONNXModelPatcher
+from .model_cache import ModelCache
+
+from .models import (
+    BaseModelType,
+    DuplicateModelException,
+    ModelNotFoundException,
+    ModelType,
+    ModelVariantType,
+    SubModelType,
+)
+
+# This import must be last
+from .model_merge import MergeInterpolationMethod, ModelMerger

invokeai/backend/model_management/convert_ckpt_to_diffusers.py

@@ -57,9 +57,10 @@ from transformers import (
 )
 
 from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.model_manager import BaseModelType, ModelVariantType
 from invokeai.backend.util.logging import InvokeAILogger
 
+from .models import BaseModelType, ModelVariantType
+
 try:
     from omegaconf import OmegaConf
     from omegaconf.dictconfig import DictConfig
@@ -1642,8 +1643,6 @@ def download_controlnet_from_original_ckpt(
     cross_attention_dim: Optional[bool] = None,
     scan_needed: bool = False,
 ) -> DiffusionPipeline:
-    from omegaconf import OmegaConf
-
     if from_safetensors:
         from safetensors import safe_open
 
@@ -1719,7 +1718,6 @@ def convert_ckpt_to_diffusers(
     """
     pipe = download_from_original_stable_diffusion_ckpt(checkpoint_path, **kwargs)
 
-    # TO DO: save correct repo variant
     pipe.save_pretrained(
         dump_path,
         safe_serialization=use_safetensors,
@@ -1738,5 +1736,4 @@ def convert_controlnet_to_diffusers(
     """
     pipe = download_controlnet_from_original_ckpt(checkpoint_path, **kwargs)
 
-    # TO DO: save correct repo variant
     pipe.save_pretrained(dump_path, safe_serialization=True)

invokeai/backend/model_management/detect_baked_in_vae.py

@@ -0,0 +1,31 @@
+# Copyright (c) 2024 Lincoln Stein and the InvokeAI Development Team
+"""
+This module exports the function has_baked_in_sdxl_vae().
+It returns True if an SDXL checkpoint model has the original SDXL 1.0 VAE,
+which doesn't work properly in fp16 mode.
+"""
+
+import hashlib
+from pathlib import Path
+
+from safetensors.torch import load_file
+
+SDXL_1_0_VAE_HASH = "bc40b16c3a0fa4625abdfc01c04ffc21bf3cefa6af6c7768ec61eb1f1ac0da51"
+
+
+def has_baked_in_sdxl_vae(checkpoint_path: Path) -> bool:
+    """Return true if the checkpoint contains a custom (non SDXL-1.0) VAE."""
+    hash = _vae_hash(checkpoint_path)
+    return hash != SDXL_1_0_VAE_HASH
+
+
+def _vae_hash(checkpoint_path: Path) -> str:
+    checkpoint = load_file(checkpoint_path, device="cpu")
+    vae_keys = [x for x in checkpoint.keys() if x.startswith("first_stage_model.")]
+    hash = hashlib.new("sha256")
+    for key in vae_keys:
+        value = checkpoint[key]
+        hash.update(bytes(key, "UTF-8"))
+        hash.update(bytes(str(value), "UTF-8"))
+
+    return hash.hexdigest()

invokeai/backend/model_management/lora.py

@@ -1,23 +1,21 @@
-# Copyright (c) 2024 Ryan Dick, Lincoln D. Stein, and the InvokeAI Development Team
-"""These classes implement model patching with LoRAs and Textual Inversions."""
 from __future__ import annotations
 
 import pickle
 from contextlib import contextmanager
-from typing import Any, Dict, Iterator, List, Optional, Tuple
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
-from diffusers import OnnxRuntimeModel, UNet2DConditionModel
+from compel.embeddings_provider import BaseTextualInversionManager
+from diffusers.models import UNet2DConditionModel
+from safetensors.torch import load_file
 from transformers import CLIPTextModel, CLIPTokenizer
 
 from invokeai.app.shared.models import FreeUConfig
-from invokeai.backend.model_manager import AnyModel
-from invokeai.backend.model_manager.load.optimizations import skip_torch_weight_init
-from invokeai.backend.onnx.onnx_runtime import IAIOnnxRuntimeModel
+from invokeai.backend.model_management.model_load_optimizations import skip_torch_weight_init
 
-from .lora import LoRAModelRaw
-from .textual_inversion import TextualInversionManager, TextualInversionModelRaw
+from .models.lora import LoRAModel
 
 """
 loras = [
@@ -64,8 +62,8 @@ class ModelPatcher:
     def apply_lora_unet(
         cls,
         unet: UNet2DConditionModel,
-        loras: Iterator[Tuple[LoRAModelRaw, float]],
-    ) -> None:
+        loras: List[Tuple[LoRAModel, float]],
+    ):
         with cls.apply_lora(unet, loras, "lora_unet_"):
             yield
 
@@ -74,8 +72,8 @@ class ModelPatcher:
     def apply_lora_text_encoder(
         cls,
         text_encoder: CLIPTextModel,
-        loras: Iterator[Tuple[LoRAModelRaw, float]],
-    ) -> None:
+        loras: List[Tuple[LoRAModel, float]],
+    ):
         with cls.apply_lora(text_encoder, loras, "lora_te_"):
             yield
 
@@ -84,8 +82,8 @@ class ModelPatcher:
     def apply_sdxl_lora_text_encoder(
         cls,
         text_encoder: CLIPTextModel,
-        loras: List[Tuple[LoRAModelRaw, float]],
-    ) -> None:
+        loras: List[Tuple[LoRAModel, float]],
+    ):
         with cls.apply_lora(text_encoder, loras, "lora_te1_"):
             yield
 
@@ -94,8 +92,8 @@ class ModelPatcher:
     def apply_sdxl_lora_text_encoder2(
         cls,
         text_encoder: CLIPTextModel,
-        loras: List[Tuple[LoRAModelRaw, float]],
-    ) -> None:
+        loras: List[Tuple[LoRAModel, float]],
+    ):
         with cls.apply_lora(text_encoder, loras, "lora_te2_"):
             yield
 
@@ -103,10 +101,10 @@ class ModelPatcher:
     @contextmanager
     def apply_lora(
         cls,
-        model: AnyModel,
-        loras: Iterator[Tuple[LoRAModelRaw, float]],
+        model: torch.nn.Module,
+        loras: List[Tuple[LoRAModel, float]],
         prefix: str,
-    ) -> None:
+    ):
         original_weights = {}
         try:
             with torch.no_grad():
@@ -123,7 +121,6 @@ class ModelPatcher:
                         # 2. From an API perspective, there's no reason that the `ModelPatcher` should be aware of the
                         #    intricacies of Stable Diffusion key resolution. It should just expect the input LoRA
                         #    weights to have valid keys.
-                        assert isinstance(model, torch.nn.Module)
                         module_key, module = cls._resolve_lora_key(model, layer_key, prefix)
 
                         # All of the LoRA weight calculations will be done on the same device as the module weight.
@@ -144,21 +141,17 @@ class ModelPatcher:
                         # TODO(ryand): Using torch.autocast(...) over explicit casting may offer a speed benefit on CUDA
                         # devices here. Experimentally, it was found to be very slow on CPU. More investigation needed.
                         layer_weight = layer.get_weight(module.weight) * (lora_weight * layer_scale)
-                        layer.to(device=torch.device("cpu"))
+                        layer.to(device="cpu")
 
-                        assert isinstance(layer_weight, torch.Tensor)  # mypy thinks layer_weight is a float|Any ??!
                         if module.weight.shape != layer_weight.shape:
                             # TODO: debug on lycoris
-                            assert hasattr(layer_weight, "reshape")
                             layer_weight = layer_weight.reshape(module.weight.shape)
 
-                        assert isinstance(layer_weight, torch.Tensor)  # mypy thinks layer_weight is a float|Any ??!
                         module.weight += layer_weight.to(dtype=dtype)
 
             yield  # wait for context manager exit
 
         finally:
-            assert hasattr(model, "get_submodule")  # mypy not picking up fact that torch.nn.Module has get_submodule()
             with torch.no_grad():
                 for module_key, weight in original_weights.items():
                     model.get_submodule(module_key).weight.copy_(weight)
@@ -169,8 +162,8 @@ class ModelPatcher:
         cls,
         tokenizer: CLIPTokenizer,
         text_encoder: CLIPTextModel,
-        ti_list: List[Tuple[str, TextualInversionModelRaw]],
-    ) -> Iterator[Tuple[CLIPTokenizer, TextualInversionManager]]:
+        ti_list: List[Tuple[str, Any]],
+    ) -> Tuple[CLIPTokenizer, TextualInversionManager]:
         init_tokens_count = None
         new_tokens_added = None
 
@@ -194,13 +187,13 @@ class ModelPatcher:
             ti_manager = TextualInversionManager(ti_tokenizer)
             init_tokens_count = text_encoder.resize_token_embeddings(None, pad_to_multiple_of).num_embeddings
 
-            def _get_trigger(ti_name: str, index: int) -> str:
+            def _get_trigger(ti_name, index):
                 trigger = ti_name
                 if index > 0:
                     trigger += f"-!pad-{i}"
                 return f"<{trigger}>"
 
-            def _get_ti_embedding(model_embeddings: torch.nn.Module, ti: TextualInversionModelRaw) -> torch.Tensor:
+            def _get_ti_embedding(model_embeddings, ti):
                 # for SDXL models, select the embedding that matches the text encoder's dimensions
                 if ti.embedding_2 is not None:
                     return (
@@ -228,7 +221,6 @@ class ModelPatcher:
             model_embeddings = text_encoder.get_input_embeddings()
 
             for ti_name, ti in ti_list:
-                assert isinstance(ti, TextualInversionModelRaw)
                 ti_embedding = _get_ti_embedding(text_encoder.get_input_embeddings(), ti)
 
                 ti_tokens = []
@@ -267,7 +259,7 @@ class ModelPatcher:
         cls,
         text_encoder: CLIPTextModel,
         clip_skip: int,
-    ) -> None:
+    ):
         skipped_layers = []
         try:
             for _i in range(clip_skip):
@@ -285,10 +277,9 @@ class ModelPatcher:
         cls,
         unet: UNet2DConditionModel,
         freeu_config: Optional[FreeUConfig] = None,
-    ) -> None:
+    ):
         did_apply_freeu = False
         try:
-            assert hasattr(unet, "enable_freeu")  # mypy doesn't pick up this attribute?
             if freeu_config is not None:
                 unet.enable_freeu(b1=freeu_config.b1, b2=freeu_config.b2, s1=freeu_config.s1, s2=freeu_config.s2)
                 did_apply_freeu = True
@@ -296,19 +287,109 @@ class ModelPatcher:
             yield
 
         finally:
-            assert hasattr(unet, "disable_freeu")  # mypy doesn't pick up this attribute?
             if did_apply_freeu:
                 unet.disable_freeu()
 
 
+class TextualInversionModel:
+    embedding: torch.Tensor  # [n, 768]|[n, 1280]
+    embedding_2: Optional[torch.Tensor] = None  # [n, 768]|[n, 1280]   - for SDXL models
+
+    @classmethod
+    def from_checkpoint(
+        cls,
+        file_path: Union[str, Path],
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        if not isinstance(file_path, Path):
+            file_path = Path(file_path)
+
+        result = cls()  # TODO:
+
+        if file_path.suffix == ".safetensors":
+            state_dict = load_file(file_path.absolute().as_posix(), device="cpu")
+        else:
+            state_dict = torch.load(file_path, map_location="cpu")
+
+        # both v1 and v2 format embeddings
+        # difference mostly in metadata
+        if "string_to_param" in state_dict:
+            if len(state_dict["string_to_param"]) > 1:
+                print(
+                    f'Warn: Embedding "{file_path.name}" contains multiple tokens, which is not supported. The first',
+                    " token will be used.",
+                )
+
+            result.embedding = next(iter(state_dict["string_to_param"].values()))
+
+        # v3 (easynegative)
+        elif "emb_params" in state_dict:
+            result.embedding = state_dict["emb_params"]
+
+        # v5(sdxl safetensors file)
+        elif "clip_g" in state_dict and "clip_l" in state_dict:
+            result.embedding = state_dict["clip_g"]
+            result.embedding_2 = state_dict["clip_l"]
+
+        # v4(diffusers bin files)
+        else:
+            result.embedding = next(iter(state_dict.values()))
+
+            if len(result.embedding.shape) == 1:
+                result.embedding = result.embedding.unsqueeze(0)
+
+            if not isinstance(result.embedding, torch.Tensor):
+                raise ValueError(f"Invalid embeddings file: {file_path.name}")
+
+        return result
+
+
+class TextualInversionManager(BaseTextualInversionManager):
+    pad_tokens: Dict[int, List[int]]
+    tokenizer: CLIPTokenizer
+
+    def __init__(self, tokenizer: CLIPTokenizer):
+        self.pad_tokens = {}
+        self.tokenizer = tokenizer
+
+    def expand_textual_inversion_token_ids_if_necessary(self, token_ids: list[int]) -> list[int]:
+        if len(self.pad_tokens) == 0:
+            return token_ids
+
+        if token_ids[0] == self.tokenizer.bos_token_id:
+            raise ValueError("token_ids must not start with bos_token_id")
+        if token_ids[-1] == self.tokenizer.eos_token_id:
+            raise ValueError("token_ids must not end with eos_token_id")
+
+        new_token_ids = []
+        for token_id in token_ids:
+            new_token_ids.append(token_id)
+            if token_id in self.pad_tokens:
+                new_token_ids.extend(self.pad_tokens[token_id])
+
+        # Do not exceed the max model input size
+        # The -2 here is compensating for compensate compel.embeddings_provider.get_token_ids(),
+        # which first removes and then adds back the start and end tokens.
+        max_length = list(self.tokenizer.max_model_input_sizes.values())[0] - 2
+        if len(new_token_ids) > max_length:
+            new_token_ids = new_token_ids[0:max_length]
+
+        return new_token_ids
+
+
 class ONNXModelPatcher:
+    from diffusers import OnnxRuntimeModel
+
+    from .models.base import IAIOnnxRuntimeModel
+
     @classmethod
     @contextmanager
     def apply_lora_unet(
         cls,
         unet: OnnxRuntimeModel,
-        loras: Iterator[Tuple[LoRAModelRaw, float]],
-    ) -> None:
+        loras: List[Tuple[LoRAModel, float]],
+    ):
         with cls.apply_lora(unet, loras, "lora_unet_"):
             yield
 
@@ -317,8 +398,8 @@ class ONNXModelPatcher:
     def apply_lora_text_encoder(
         cls,
         text_encoder: OnnxRuntimeModel,
-        loras: List[Tuple[LoRAModelRaw, float]],
-    ) -> None:
+        loras: List[Tuple[LoRAModel, float]],
+    ):
         with cls.apply_lora(text_encoder, loras, "lora_te_"):
             yield
 
@@ -329,9 +410,9 @@ class ONNXModelPatcher:
     def apply_lora(
         cls,
         model: IAIOnnxRuntimeModel,
-        loras: List[Tuple[LoRAModelRaw, float]],
+        loras: List[Tuple[LoRAModel, float]],
         prefix: str,
-    ) -> None:
+    ):
         from .models.base import IAIOnnxRuntimeModel
 
         if not isinstance(model, IAIOnnxRuntimeModel):
@@ -340,7 +421,7 @@ class ONNXModelPatcher:
         orig_weights = {}
 
         try:
-            blended_loras: Dict[str, torch.Tensor] = {}
+            blended_loras = {}
 
             for lora, lora_weight in loras:
                 for layer_key, layer in lora.layers.items():
@@ -351,7 +432,7 @@ class ONNXModelPatcher:
                     layer_key = layer_key.replace(prefix, "")
                     # TODO: rewrite to pass original tensor weight(required by ia3)
                     layer_weight = layer.get_weight(None).detach().cpu().numpy() * lora_weight
-                    if layer_key in blended_loras:
+                    if layer_key is blended_loras:
                         blended_loras[layer_key] += layer_weight
                     else:
                         blended_loras[layer_key] = layer_weight
@@ -418,7 +499,7 @@ class ONNXModelPatcher:
         tokenizer: CLIPTokenizer,
         text_encoder: IAIOnnxRuntimeModel,
         ti_list: List[Tuple[str, Any]],
-    ) -> Iterator[Tuple[CLIPTokenizer, TextualInversionManager]]:
+    ) -> Tuple[CLIPTokenizer, TextualInversionManager]:
         from .models.base import IAIOnnxRuntimeModel
 
         if not isinstance(text_encoder, IAIOnnxRuntimeModel):
@@ -436,7 +517,7 @@ class ONNXModelPatcher:
             ti_tokenizer = pickle.loads(pickle.dumps(tokenizer))
             ti_manager = TextualInversionManager(ti_tokenizer)
 
-            def _get_trigger(ti_name: str, index: int) -> str:
+            def _get_trigger(ti_name, index):
                 trigger = ti_name
                 if index > 0:
                     trigger += f"-!pad-{i}"

invokeai/backend/model_management/memory_snapshot.py

@@ -3,9 +3,8 @@ from typing import Optional
 
 import psutil
 import torch
-from typing_extensions import Self
 
-from ..util.libc_util import LibcUtil, Struct_mallinfo2
+from invokeai.backend.model_management.libc_util import LibcUtil, Struct_mallinfo2
 
 GB = 2**30  # 1 GB
 
@@ -28,7 +27,7 @@ class MemorySnapshot:
         self.malloc_info = malloc_info
 
     @classmethod
-    def capture(cls, run_garbage_collector: bool = True) -> Self:
+    def capture(cls, run_garbage_collector: bool = True):
         """Capture and return a MemorySnapshot.
 
         Note: This function has significant overhead, particularly if `run_garbage_collector == True`.
@@ -68,7 +67,7 @@ class MemorySnapshot:
 def get_pretty_snapshot_diff(snapshot_1: Optional[MemorySnapshot], snapshot_2: Optional[MemorySnapshot]) -> str:
     """Get a pretty string describing the difference between two `MemorySnapshot`s."""
 
-    def get_msg_line(prefix: str, val1: int, val2: int) -> str:
+    def get_msg_line(prefix: str, val1: int, val2: int):
         diff = val2 - val1
         return f"{prefix: <30} ({(diff/GB):+5.3f}): {(val1/GB):5.3f}GB -> {(val2/GB):5.3f}GB\n"
 

invokeai/backend/model_management/model_cache.py

@@ -0,0 +1,564 @@
+"""
+Manage a RAM cache of diffusion/transformer models for fast switching.
+They are moved between GPU VRAM and CPU RAM as necessary. If the cache
+grows larger than a preset maximum, then the least recently used
+model will be cleared and (re)loaded from disk when next needed.
+
+The cache returns context manager generators designed to load the
+model into the GPU within the context, and unload outside the
+context. Use like this:
+
+   cache = ModelCache(max_cache_size=7.5)
+   with cache.get_model('runwayml/stable-diffusion-1-5') as SD1,
+          cache.get_model('stabilityai/stable-diffusion-2') as SD2:
+       do_something_in_GPU(SD1,SD2)
+
+
+"""
+
+import gc
+import hashlib
+import math
+import os
+import sys
+import time
+from contextlib import suppress
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Dict, Optional, Type, Union, types
+
+import torch
+
+import invokeai.backend.util.logging as logger
+from invokeai.backend.model_management.memory_snapshot import MemorySnapshot, get_pretty_snapshot_diff
+from invokeai.backend.model_management.model_load_optimizations import skip_torch_weight_init
+
+from ..util.devices import choose_torch_device
+from .models import BaseModelType, ModelBase, ModelType, SubModelType
+
+if choose_torch_device() == torch.device("mps"):
+    from torch import mps
+
+# Maximum size of the cache, in gigs
+# Default is roughly enough to hold three fp16 diffusers models in RAM simultaneously
+DEFAULT_MAX_CACHE_SIZE = 6.0
+
+# amount of GPU memory to hold in reserve for use by generations (GB)
+DEFAULT_MAX_VRAM_CACHE_SIZE = 2.75
+
+# actual size of a gig
+GIG = 1073741824
+# Size of a MB in bytes.
+MB = 2**20
+
+
+@dataclass
+class CacheStats(object):
+    hits: int = 0  # cache hits
+    misses: int = 0  # cache misses
+    high_watermark: int = 0  # amount of cache used
+    in_cache: int = 0  # number of models in cache
+    cleared: int = 0  # number of models cleared to make space
+    cache_size: int = 0  # total size of cache
+    # {submodel_key => size}
+    loaded_model_sizes: Dict[str, int] = field(default_factory=dict)
+
+
+class ModelLocker(object):
+    "Forward declaration"
+
+    pass
+
+
+class ModelCache(object):
+    "Forward declaration"
+
+    pass
+
+
+class _CacheRecord:
+    size: int
+    model: Any
+    cache: ModelCache
+    _locks: int
+
+    def __init__(self, cache, model: Any, size: int):
+        self.size = size
+        self.model = model
+        self.cache = cache
+        self._locks = 0
+
+    def lock(self):
+        self._locks += 1
+
+    def unlock(self):
+        self._locks -= 1
+        assert self._locks >= 0
+
+    @property
+    def locked(self):
+        return self._locks > 0
+
+    @property
+    def loaded(self):
+        if self.model is not None and hasattr(self.model, "device"):
+            return self.model.device != self.cache.storage_device
+        else:
+            return False
+
+
+class ModelCache(object):
+    def __init__(
+        self,
+        max_cache_size: float = DEFAULT_MAX_CACHE_SIZE,
+        max_vram_cache_size: float = DEFAULT_MAX_VRAM_CACHE_SIZE,
+        execution_device: torch.device = torch.device("cuda"),
+        storage_device: torch.device = torch.device("cpu"),
+        precision: torch.dtype = torch.float16,
+        sequential_offload: bool = False,
+        lazy_offloading: bool = True,
+        sha_chunksize: int = 16777216,
+        logger: types.ModuleType = logger,
+        log_memory_usage: bool = False,
+    ):
+        """
+        :param max_cache_size: Maximum size of the RAM cache [6.0 GB]
+        :param execution_device: Torch device to load active model into [torch.device('cuda')]
+        :param storage_device: Torch device to save inactive model in [torch.device('cpu')]
+        :param precision: Precision for loaded models [torch.float16]
+        :param lazy_offloading: Keep model in VRAM until another model needs to be loaded
+        :param sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially
+        :param sha_chunksize: Chunksize to use when calculating sha256 model hash
+        :param log_memory_usage: If True, a memory snapshot will be captured before and after every model cache
+            operation, and the result will be logged (at debug level). There is a time cost to capturing the memory
+            snapshots, so it is recommended to disable this feature unless you are actively inspecting the model cache's
+            behaviour.
+        """
+        self.model_infos: Dict[str, ModelBase] = {}
+        # allow lazy offloading only when vram cache enabled
+        self.lazy_offloading = lazy_offloading and max_vram_cache_size > 0
+        self.precision: torch.dtype = precision
+        self.max_cache_size: float = max_cache_size
+        self.max_vram_cache_size: float = max_vram_cache_size
+        self.execution_device: torch.device = execution_device
+        self.storage_device: torch.device = storage_device
+        self.sha_chunksize = sha_chunksize
+        self.logger = logger
+        self._log_memory_usage = log_memory_usage
+
+        # used for stats collection
+        self.stats = None
+
+        self._cached_models = {}
+        self._cache_stack = []
+
+    def _capture_memory_snapshot(self) -> Optional[MemorySnapshot]:
+        if self._log_memory_usage:
+            return MemorySnapshot.capture()
+        return None
+
+    def get_key(
+        self,
+        model_path: str,
+        base_model: BaseModelType,
+        model_type: ModelType,
+        submodel_type: Optional[SubModelType] = None,
+    ):
+        key = f"{model_path}:{base_model}:{model_type}"
+        if submodel_type:
+            key += f":{submodel_type}"
+        return key
+
+    def _get_model_info(
+        self,
+        model_path: str,
+        model_class: Type[ModelBase],
+        base_model: BaseModelType,
+        model_type: ModelType,
+    ):
+        model_info_key = self.get_key(
+            model_path=model_path,
+            base_model=base_model,
+            model_type=model_type,
+            submodel_type=None,
+        )
+
+        if model_info_key not in self.model_infos:
+            self.model_infos[model_info_key] = model_class(
+                model_path,
+                base_model,
+                model_type,
+            )
+
+        return self.model_infos[model_info_key]
+
+    # TODO: args
+    def get_model(
+        self,
+        model_path: Union[str, Path],
+        model_class: Type[ModelBase],
+        base_model: BaseModelType,
+        model_type: ModelType,
+        submodel: Optional[SubModelType] = None,
+        gpu_load: bool = True,
+    ) -> Any:
+        if not isinstance(model_path, Path):
+            model_path = Path(model_path)
+
+        if not os.path.exists(model_path):
+            raise Exception(f"Model not found: {model_path}")
+
+        model_info = self._get_model_info(
+            model_path=model_path,
+            model_class=model_class,
+            base_model=base_model,
+            model_type=model_type,
+        )
+        key = self.get_key(
+            model_path=model_path,
+            base_model=base_model,
+            model_type=model_type,
+            submodel_type=submodel,
+        )
+        # TODO: lock for no copies on simultaneous calls?
+        cache_entry = self._cached_models.get(key, None)
+        if cache_entry is None:
+            self.logger.info(
+                f"Loading model {model_path}, type"
+                f" {base_model.value}:{model_type.value}{':'+submodel.value if submodel else ''}"
+            )
+            if self.stats:
+                self.stats.misses += 1
+
+            self_reported_model_size_before_load = model_info.get_size(submodel)
+            # Remove old models from the cache to make room for the new model.
+            self._make_cache_room(self_reported_model_size_before_load)
+
+            # Load the model from disk and capture a memory snapshot before/after.
+            start_load_time = time.time()
+            snapshot_before = self._capture_memory_snapshot()
+            with skip_torch_weight_init():
+                model = model_info.get_model(child_type=submodel, torch_dtype=self.precision)
+            snapshot_after = self._capture_memory_snapshot()
+            end_load_time = time.time()
+
+            self_reported_model_size_after_load = model_info.get_size(submodel)
+
+            self.logger.debug(
+                f"Moved model '{key}' from disk to cpu in {(end_load_time-start_load_time):.2f}s.\n"
+                f"Self-reported size before/after load: {(self_reported_model_size_before_load/GIG):.3f}GB /"
+                f" {(self_reported_model_size_after_load/GIG):.3f}GB.\n"
+                f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
+            )
+
+            if abs(self_reported_model_size_after_load - self_reported_model_size_before_load) > 10 * MB:
+                self.logger.debug(
+                    f"Model '{key}' mis-reported its size before load. Self-reported size before/after load:"
+                    f" {(self_reported_model_size_before_load/GIG):.2f}GB /"
+                    f" {(self_reported_model_size_after_load/GIG):.2f}GB."
+                )
+
+            cache_entry = _CacheRecord(self, model, self_reported_model_size_after_load)
+            self._cached_models[key] = cache_entry
+        else:
+            if self.stats:
+                self.stats.hits += 1
+
+        if self.stats:
+            self.stats.cache_size = self.max_cache_size * GIG
+            self.stats.high_watermark = max(self.stats.high_watermark, self._cache_size())
+            self.stats.in_cache = len(self._cached_models)
+            self.stats.loaded_model_sizes[key] = max(
+                self.stats.loaded_model_sizes.get(key, 0), model_info.get_size(submodel)
+            )
+
+        with suppress(Exception):
+            self._cache_stack.remove(key)
+        self._cache_stack.append(key)
+
+        return self.ModelLocker(self, key, cache_entry.model, gpu_load, cache_entry.size)
+
+    def _move_model_to_device(self, key: str, target_device: torch.device):
+        cache_entry = self._cached_models[key]
+
+        source_device = cache_entry.model.device
+        # Note: We compare device types only so that 'cuda' == 'cuda:0'. This would need to be revised to support
+        # multi-GPU.
+        if torch.device(source_device).type == torch.device(target_device).type:
+            return
+
+        if target_device.type == "cuda":
+            vram_device = (
+                target_device if target_device.index is not None else torch.device(str(target_device), index=0)
+            )
+            free_mem, _ = torch.cuda.mem_get_info(torch.device(vram_device))
+            if cache_entry.size > free_mem:
+                raise torch.cuda.OutOfMemoryError
+
+        start_model_to_time = time.time()
+        snapshot_before = self._capture_memory_snapshot()
+        cache_entry.model.to(target_device)
+        snapshot_after = self._capture_memory_snapshot()
+        end_model_to_time = time.time()
+        self.logger.debug(
+            f"Moved model '{key}' from {source_device} to"
+            f" {target_device} in {(end_model_to_time-start_model_to_time):.2f}s.\n"
+            f"Estimated model size: {(cache_entry.size/GIG):.3f} GB.\n"
+            f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
+        )
+
+        if (
+            snapshot_before is not None
+            and snapshot_after is not None
+            and snapshot_before.vram is not None
+            and snapshot_after.vram is not None
+        ):
+            vram_change = abs(snapshot_before.vram - snapshot_after.vram)
+
+            # If the estimated model size does not match the change in VRAM, log a warning.
+            if not math.isclose(
+                vram_change,
+                cache_entry.size,
+                rel_tol=0.1,
+                abs_tol=10 * MB,
+            ):
+                self.logger.debug(
+                    f"Moving model '{key}' from {source_device} to"
+                    f" {target_device} caused an unexpected change in VRAM usage. The model's"
+                    " estimated size may be incorrect. Estimated model size:"
+                    f" {(cache_entry.size/GIG):.3f} GB.\n"
+                    f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
+                )
+
+    class ModelLocker(object):
+        def __init__(self, cache, key, model, gpu_load, size_needed):
+            """
+            :param cache: The model_cache object
+            :param key: The key of the model to lock in GPU
+            :param model: The model to lock
+            :param gpu_load: True if load into gpu
+            :param size_needed: Size of the model to load
+            """
+            self.gpu_load = gpu_load
+            self.cache = cache
+            self.key = key
+            self.model = model
+            self.size_needed = size_needed
+            self.cache_entry = self.cache._cached_models[self.key]
+
+        def __enter__(self) -> Any:
+            if not hasattr(self.model, "to"):
+                return self.model
+
+            # NOTE that the model has to have the to() method in order for this
+            # code to move it into GPU!
+            if self.gpu_load:
+                self.cache_entry.lock()
+
+                try:
+                    if self.cache.lazy_offloading:
+                        self.cache._offload_unlocked_models(self.size_needed)
+
+                    self.cache._move_model_to_device(self.key, self.cache.execution_device)
+
+                    self.cache.logger.debug(f"Locking {self.key} in {self.cache.execution_device}")
+                    self.cache._print_cuda_stats()
+
+                except torch.cuda.OutOfMemoryError:
+                    self.cache.logger.warning("Out of GPU memory encountered.")
+                    self.cache_entry.unlock()
+                    raise
+                except Exception:
+                    self.cache_entry.unlock()
+                    raise
+
+            # TODO: not fully understand
+            # in the event that the caller wants the model in RAM, we
+            # move it into CPU if it is in GPU and not locked
+            elif self.cache_entry.loaded and not self.cache_entry.locked:
+                self.cache._move_model_to_device(self.key, self.cache.storage_device)
+
+            return self.model
+
+        def __exit__(self, type, value, traceback):
+            if not hasattr(self.model, "to"):
+                return
+
+            self.cache_entry.unlock()
+            if not self.cache.lazy_offloading:
+                self.cache._offload_unlocked_models()
+                self.cache._print_cuda_stats()
+
+    # TODO: should it be called untrack_model?
+    def uncache_model(self, cache_id: str):
+        with suppress(ValueError):
+            self._cache_stack.remove(cache_id)
+        self._cached_models.pop(cache_id, None)
+
+    def model_hash(
+        self,
+        model_path: Union[str, Path],
+    ) -> str:
+        """
+        Given the HF repo id or path to a model on disk, returns a unique
+        hash. Works for legacy checkpoint files, HF models on disk, and HF repo IDs
+
+        :param model_path: Path to model file/directory on disk.
+        """
+        return self._local_model_hash(model_path)
+
+    def cache_size(self) -> float:
+        """Return the current size of the cache, in GB."""
+        return self._cache_size() / GIG
+
+    def _has_cuda(self) -> bool:
+        return self.execution_device.type == "cuda"
+
+    def _print_cuda_stats(self):
+        vram = "%4.2fG" % (torch.cuda.memory_allocated() / GIG)
+        ram = "%4.2fG" % self.cache_size()
+
+        cached_models = 0
+        loaded_models = 0
+        locked_models = 0
+        for model_info in self._cached_models.values():
+            cached_models += 1
+            if model_info.loaded:
+                loaded_models += 1
+            if model_info.locked:
+                locked_models += 1
+
+        self.logger.debug(
+            f"Current VRAM/RAM usage: {vram}/{ram}; cached_models/loaded_models/locked_models/ ="
+            f" {cached_models}/{loaded_models}/{locked_models}"
+        )
+
+    def _cache_size(self) -> int:
+        return sum([m.size for m in self._cached_models.values()])
+
+    def _make_cache_room(self, model_size):
+        # calculate how much memory this model will require
+        # multiplier = 2 if self.precision==torch.float32 else 1
+        bytes_needed = model_size
+        maximum_size = self.max_cache_size * GIG  # stored in GB, convert to bytes
+        current_size = self._cache_size()
+
+        if current_size + bytes_needed > maximum_size:
+            self.logger.debug(
+                f"Max cache size exceeded: {(current_size/GIG):.2f}/{self.max_cache_size:.2f} GB, need an additional"
+                f" {(bytes_needed/GIG):.2f} GB"
+            )
+
+        self.logger.debug(f"Before unloading: cached_models={len(self._cached_models)}")
+
+        pos = 0
+        models_cleared = 0
+        while current_size + bytes_needed > maximum_size and pos < len(self._cache_stack):
+            model_key = self._cache_stack[pos]
+            cache_entry = self._cached_models[model_key]
+
+            refs = sys.getrefcount(cache_entry.model)
+
+            # HACK: This is a workaround for a memory-management issue that we haven't tracked down yet. We are directly
+            # going against the advice in the Python docs by using `gc.get_referrers(...)` in this way:
+            # https://docs.python.org/3/library/gc.html#gc.get_referrers
+
+            # manualy clear local variable references of just finished function calls
+            # for some reason python don't want to collect it even by gc.collect() immidiately
+            if refs > 2:
+                while True:
+                    cleared = False
+                    for referrer in gc.get_referrers(cache_entry.model):
+                        if type(referrer).__name__ == "frame":
+                            # RuntimeError: cannot clear an executing frame
+                            with suppress(RuntimeError):
+                                referrer.clear()
+                                cleared = True
+                                # break
+
+                    # repeat if referrers changes(due to frame clear), else exit loop
+                    if cleared:
+                        gc.collect()
+                    else:
+                        break
+
+            device = cache_entry.model.device if hasattr(cache_entry.model, "device") else None
+            self.logger.debug(
+                f"Model: {model_key}, locks: {cache_entry._locks}, device: {device}, loaded: {cache_entry.loaded},"
+                f" refs: {refs}"
+            )
+
+            # Expected refs:
+            # 1 from cache_entry
+            # 1 from getrefcount function
+            # 1 from onnx runtime object
+            if not cache_entry.locked and refs <= (3 if "onnx" in model_key else 2):
+                self.logger.debug(
+                    f"Unloading model {model_key} to free {(model_size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
+                )
+                current_size -= cache_entry.size
+                models_cleared += 1
+                if self.stats:
+                    self.stats.cleared += 1
+                del self._cache_stack[pos]
+                del self._cached_models[model_key]
+                del cache_entry
+
+            else:
+                pos += 1
+
+        if models_cleared > 0:
+            # There would likely be some 'garbage' to be collected regardless of whether a model was cleared or not, but
+            # there is a significant time cost to calling `gc.collect()`, so we want to use it sparingly. (The time cost
+            # is high even if no garbage gets collected.)
+            #
+            # Calling gc.collect(...) when a model is cleared seems like a good middle-ground:
+            # - If models had to be cleared, it's a signal that we are close to our memory limit.
+            # - If models were cleared, there's a good chance that there's a significant amount of garbage to be
+            #   collected.
+            #
+            # Keep in mind that gc is only responsible for handling reference cycles. Most objects should be cleaned up
+            # immediately when their reference count hits 0.
+            gc.collect()
+
+        torch.cuda.empty_cache()
+        if choose_torch_device() == torch.device("mps"):
+            mps.empty_cache()
+
+        self.logger.debug(f"After unloading: cached_models={len(self._cached_models)}")
+
+    def _offload_unlocked_models(self, size_needed: int = 0):
+        reserved = self.max_vram_cache_size * GIG
+        vram_in_use = torch.cuda.memory_allocated()
+        self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB")
+        for model_key, cache_entry in sorted(self._cached_models.items(), key=lambda x: x[1].size):
+            if vram_in_use <= reserved:
+                break
+            if not cache_entry.locked and cache_entry.loaded:
+                self._move_model_to_device(model_key, self.storage_device)
+                vram_in_use = torch.cuda.memory_allocated()
+                self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB")
+
+        torch.cuda.empty_cache()
+        if choose_torch_device() == torch.device("mps"):
+            mps.empty_cache()
+
+    def _local_model_hash(self, model_path: Union[str, Path]) -> str:
+        sha = hashlib.sha256()
+        path = Path(model_path)
+
+        hashpath = path / "checksum.sha256"
+        if hashpath.exists() and path.stat().st_mtime <= hashpath.stat().st_mtime:
+            with open(hashpath) as f:
+                hash = f.read()
+            return hash
+
+        self.logger.debug(f"computing hash of model {path.name}")
+        for file in list(path.rglob("*.ckpt")) + list(path.rglob("*.safetensors")) + list(path.rglob("*.pth")):
+            with open(file, "rb") as f:
+                while chunk := f.read(self.sha_chunksize):
+                    sha.update(chunk)
+        hash = sha.hexdigest()
+        with open(hashpath, "w") as f:
+            f.write(hash)
+        return hash

invokeai/backend/model_management/model_load_optimizations.py

@@ -1,16 +1,16 @@
 from contextlib import contextmanager
-from typing import Any, Generator
 
 import torch
 
 
-def _no_op(*args: Any, **kwargs: Any) -> None:
+def _no_op(*args, **kwargs):
     pass
 
 
 @contextmanager
-def skip_torch_weight_init() -> Generator[None, None, None]:
-    """Monkey patch several of the common torch layers (torch.nn.Linear, torch.nn.Conv1d, etc.) to skip weight initialization.
+def skip_torch_weight_init():
+    """A context manager that monkey-patches several of the common torch layers (torch.nn.Linear, torch.nn.Conv1d, etc.)
+    to skip weight initialization.
 
     By default, `torch.nn.Linear` and `torch.nn.ConvNd` layers initialize their weights (according to a particular
     distribution) when __init__ is called. This weight initialization step can take a significant amount of time, and is
@@ -18,14 +18,13 @@ def skip_torch_weight_init() -> Generator[None, None, None]:
     monkey-patches common torch layers to skip the weight initialization step.
     """
     torch_modules = [torch.nn.Linear, torch.nn.modules.conv._ConvNd, torch.nn.Embedding]
-    saved_functions = [hasattr(m, "reset_parameters") and m.reset_parameters for m in torch_modules]
+    saved_functions = [m.reset_parameters for m in torch_modules]
 
     try:
         for torch_module in torch_modules:
-            assert hasattr(torch_module, "reset_parameters")
             torch_module.reset_parameters = _no_op
+
         yield None
     finally:
         for torch_module, saved_function in zip(torch_modules, saved_functions, strict=True):
-            assert hasattr(torch_module, "reset_parameters")
             torch_module.reset_parameters = saved_function